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Objective To investigate risk factors for the development of post-transplant diabetes mellitus (PTDM) in patients undergoing orthotopic liver transplantation. Methods We included 293 patients who underwent orthotopic liver transplantation at The Affiliated Hospital of Qingdao University from July 1, 2017 to January 31, 2023. According to whether PTDM occurred, the patients were divided into PTDM group (n=66) and non-PTDM group (n=227). Significantly different clinical para-meters between the two groups were included in a multivariable logistic regression model to determine independent risk factors for PTDM. The impact of PTDM on the survival rate of the patients was analyzed using a Kaplan-Meier survival curve. Results The incidence rate of PTDM among the patients was 22.5%. The PTDM group and non-PTDM group differed significantly in age, the percentage of patients with a history of hypertension, preoperative fasting blood glucose level, preoperative serum total cholesterol level, preoperative serum low-density lipoprotein level, the percentage of patients treated with cyclosporine after surgery, and the percentage of patients treated with anti-rejection regimen C (cyclosporine alone or combined with mycophenolate or sirolimus) or D (mycophenolate or sirolimus alone) after surgery (t=-3.191,U=-5.668--2.559,χ2=3.922-5.689,P46 years, a history of hypertension, preoperative fasting blood glucose level >6.1 mmol/L, preoperative serum low-density lipoprotein level >3.12 mmol/L, postoperative use of anti-rejection regimen C (cyclosporine alone or combined with mycophenolate or sirolimus) were independent risk factors for the occurrence of PTDM (P46 years, a history of hypertension, preoperative fasting blood glucose level >6.1 mmol/L, preoperative serum low-density lipoprotein level >3.12 mmol/L, and postoperative anti-rejection treatment with cyclosporine alone or combined with mycophenolate or sirolimus are independent risk factors for adult PTDM.

BACKGROUND With the continuous accumulation of data resources around the world, the application of data visualization has become more urgent, and the methods of data visualization have also shown diversified characteristics. Data visualization is a scientific and technical research on the visual representation of data, which studies how to display relatively obscure data visually in a real and accurate way, to vividly and intuitively express the information and rules contained in the data. Literature is an important carrier of knowledge dissemination, with systematic characteristics, continuity, a large quantity and fast update, and is an important source of information. The visualization research of literature is of great significance to the dissemination of knowledge. At present, the visualization research of document data is mostly combined with the bibliometric analysis method, which visualizes the relationship between documents in the form of a scientific knowledge map. The application of this technology and method has played an important role in mining the information and rules contained in document data, but it is slightly insufficient in the spatial attribute mining of document data, which requires in-depth research in this field. OBJECTIVES To provide new solutions for expanding the application of literature data in the field of spatial visualization, thereby further enhancing the usable value of literature data. METHODS GIS technology was used to carry out the research on the spatial visualization method of document data, excavate the common content features and spatial features of document data, and gather the structured document data of a certain volume of spatial feature information to lay the data foundation for the visualization research. At the same time, the combination point between GIS technology and document data was studied, and the strategy and technical route for information mining and spatial visualization of document data were formed. Taking the data of more than 140 Re-Os isotope dating documents published since 2015 to 2020 as an example, the application test of this technical method was carried out. A Re-Os isotope data visualization service system with a five-tier architecture including layers of support, data, service, application and user has been developed, to realize the spatial visualization application of Re-Os isotope dating document data results. RESULTS The results show that: (1) The study area contains more than 140 ore producing areas that carried out Re-Os isotope dating research from 2015 to 2020, covering the eastern, central, and western regions of China. (2) There are 19 main minerals in the study area, mainly copper (29.0%), molybdenum (28.3%), tungsten (12.4%) and gold (11.0%). (3) There are 12 kinds of detection objects, and the proportion of molybdenite as the detection object is 80.7%, which indicates that molybdenite is still the preferred research object of the Re-Os isotope dating method, which is inseparable from the high Re/Os value of molybdenite. (4) The detection age falls in the lower age range of 0-200Ma, accounting for 62.1% of the ore-producing areas, which are mainly distributed in the northeast and southeast regions of China, indicating that the Re-Os isotope ages in the eastern region are concentrated in the Mesozoic era, which is consistent with the existence of large-scale Mesozoic mineralization events in the eastern region of China. The ore-producing area in the age range above 200Ma accounts for 37.9%, which is mainly distributed in the central and western regions of China, indicating that the Re-Os isotope ages in the central and western regions are concentrated before the Mesozoic era and have the characteristics of multiple mineralization. CONCLUSIONS The research shows that the document data visualization technology and method proposed in this paper can be used to excavate the spatial characteristics of the document data, expand the application of the document data in the field of spatial visualization, and solve the problems of how to analyze and extract the common information of the document data, locate the spatial feature information, and realize the spatial visualization technology. In terms of revealing important target areas, key research minerals and main application technologies, it can provide spatial visualization information, and provide reference for finding the hot spots and difficulties of current metallogenic chronology research.

It has been well documented that there is a two-way relationship between diabetes mellitus and periodontitis. Diabetes mellitus represents an established risk factor for chronic periodontitis. Conversely, chronic periodontitis adversely modulates serum glucose levels in diabetic patients. Activated immune and inflammatory responses are noted during diabetes and periodontitis, under the modulation of similar biological mediators. These activated responses result in increased activity of certain immune-inflammatory mediators including adipokines and microRNAs in diabetic patients with periodontal disease. Notably, certain microbes in the oral cavity were identified to be involved in the occurrence of diabetes and periodontitis. In other words, these immune-inflammatory mediators and microbes may potentially serve as biomarkers for risk assessment and therapy selection in diabetes and periodontitis. In this review, we briefly provide an updated overview on different potential biomarkers, providing novel diagnostic and therapeutic insights on periodontal complications and diabetes mellitus.

IntroductionFemale breast cancer has risen to be the most common malignancy worldwide, causing a huge disease burden for both patients and society. Both senescence and oxidative stress attach importance to cancer development and progression. However, the prognostic roles of senescence and oxidative stress remain obscure in breast cancer. In this present study, we attempted to establish a predictive model based on senescence-oxidative stress co-relation genes (SOSCRGs) and evaluate its clinical utility in multiple dimensions.MethodsSOSCRGs were identified via correlation analysis. Transcriptome data and clinical information of patients with breast invasive carcinoma (BRCA) were accessed from The Cancer Genome Atlas (TCGA) and GSE96058. SVM algorithm was employed to process subtype classification of patients with BRCA based on SOSCRGs. LASSO regression analysis was utilized to establish the predictive model based on SOSCRGs. Analyses of the predictive model with regards to efficacy evaluation, subgroup analysis, clinical association, immune infiltration, functional strength, mutation feature, and drug sensitivity were organized. Single-cell analysis was applied to decipher the expression pattern of key SOSCRGs in the tumor microenvironment. Additionally, qPCR was conducted to check the expression levels of key SOSCRGs in five different breast cancer cell lines.ResultsA total of 246 SOSCRGs were identified. Two breast cancer subtypes were determined based on SOSCRGs and subtype 1 showed an active immune landscape. A SOSCRGs-based predictive model was subsequently developed and the risk score was clarified as independent prognostic predictors in breast cancer. A novel nomogram was constructed and exhibited favorable predictive capability. We further ascertained that the infiltration levels of immune cells and expressions of immune checkpoints were significantly influenced by the risk score. The two risk groups were characterized by distinct functional strengths. Sugar metabolism and glycolysis were significantly upregulated in the high risk group. The low risk group was deciphered to harbor PIK3CA mutation-driven tumorigenesis, while TP53 mutation was dominant in the high risk group. The analysis further revealed a significantly positive correlation between risk score and TMB. Patients in the low risk group may also sensitively respond to several drug agents. Single-cell analysis dissected that ERRFI1, ETS1, NDRG1, and ZMAT3 were expressed in the tumor microenvironment. Moreover, the expression levels of the seven SOSCRGs in five different breast cancer cell lines were quantified and compared by qPCR respectively.ConclusionMultidimensional evaluations verified the clinical utility of the SOSCRGs-based predictive model to predict prognosis, aid clinical decision, and risk stratification for patients with breast cancer.

To explore the best process quality of citrus and passion fruit to make compound juice, the pulp of citrus and passion fruit were used. After squeezing the juice and coarsely filtering, the raw juice of citrus and passion fruit was mixed in proportion, and pectinase was added as a clarifying agent for clarification. On the basis of single factor, three factors of clarification agent concentration, clarification temperature and clarification time were selected to design and analyze the optimal process conditions of the Box-Benhnken method in the response surface method. The optimal combination of processes selected through sensory evaluation was as follows: the mixing ratio of citrus and passion fruit raw juice was 2∶1; 0.19% of clarifying agent was added; the clarification temperature was 49 ℃; and the clarification time was 1.5 h. The compound juice of citrus and passion fruit is orange-yellow in color, rich fruit aroma, well-coordinated flavor and taste, and of good quality.

BACKGROUND A geological database is an important part of earth information science, which will provide reliable data support for geological research and application. Re-Os dating technology is widely applied in the study of ore deposit, mantle evolution, and marine environment. OBJECTIVES To construct a journal publication-based Re-Os dating database to integrate related research achievements, and improve the management and application of the technology. METHODS The technical route of GIS spatial database construction was adopted, and systematically studied database construction methods from multiple dimensions such as database construction ideas, data integration methods, and data database construction. RESULTS The database used publicly published Re-Os isotope dating documents as the data source and gathered more than 100 publicly published Re-Os dating documents from the last ten years. More than 35 journals were involved. The data sources were authoritative, extensive and representative. Through the structural transformation and spatial processing of unstructured fragmented document data, the database had the characteristics of authoritative data source, structured data content, and attributed spatial location. CONCLUSIONS The Re-Os dating database has a reliable data source, which can provide data support for the geological survey and research of mineral deposits.

Endogenous reference genes (ERGs) provide vital information regarding genetically modified organisms (GMOs). The successful detection of ERGs can identity GMOs and the source of genes, verify stability and reliability of the detection system, and calculate the level of genetically modified (GM) ingredients in mixtures. The reported ERGs in rice include sucrose-phosphate synthase (SPS), phospholipase D (PLD), RBE4 and rice root-specific GOS9 genes. Based on the characteristics of ERGs, a new ERG gene, phosphoenolpyruvate carboxylase (PEPC), was selected, and further compared with the four existing genes. A total of 18 rice varieties and 29 non-rice crops were used to verify the interspecies specificity, intraspecies consistency, sensitivity, stability and reliability of these five ERGs using qualitative and quantitative PCR. Qualitative detection indicated that SPS and PEPC displayed sufficient specificity, and the detection sensitivity was 0.05% and 0.005%, respectively. Although the specificity of both RBE4 and GOS9 were adequate, the amplicons were small and easily confused with primer dimers. Non-specific amplification of the PLD gene was present in maize and potato. Real-time quantitative PCR detection indicated that PLD, SPS and PEPC displayed good specificity, with R2 of the standard curve greater than 0.98, while the amplification efficiency ranged between 90% and 110%. Both the detection sensitivities of PLD and PEPC were five copies and that of SPS was ten copies. RBE4 showed typical amplification in maize, beet and Arabidopsis, while GOS9 was found in maize, tobacco and oats. PEPC exhibited excellent detection sensitivity and species specificity, which made it a potentially useful application in GM-rice supervision and administration. Additionally, SPS and PLD are also suitable for GM-rice detection. This study effectively established a foundation for GMO detection, which not only provides vital technical support for GMO identification, but also is of great significance for enhancing the comparability of detection results, and the standardization of ERG testing in GM-rice. Keywords: Endogenous reference gene, Rice, Genetically modified crop, Phosphoenolpyruvate carboxylase gene, Sucrose-phosphate synthase gene, Phospholipase D gene, Starch branching enzyme 4 gene, Rice root-specific GOS9 gene

We explored the genetic basis of the promiscuous symbiosis of Sophora flavescens with diverse rhizobia. To determine the impact of Nod factors (NFs) on the symbiosis of S. flavescens, nodulation-related gene mutants of representative rhizobial strains were generated. Strains with mutations in common nodulation genes (nodC, nodM, and nodE) failed to nodulate S. flavescens, indicating that the promiscuous nodulation of this plant is strictly dependent on the basic NF structure. Mutations of the NF decoration genes nodH, nodS, nodZ, and noeI did not affect the nodulation of S. flavescens, but these mutations affected the nitrogen-fixation efficiency of nodules. Wild-type Bradyrhizobium diazoefficiens USDA110 cannot nodulate S. flavescens, but we obtained 14 Tn5 mutants of B. diazoefficiens that nodulated S. flavescens. This suggested that the mutations had disrupted a negative regulator that prevents nodulation of S. flavescens, leading to nonspecific nodulation. For Ensifer fredii CCBAU 45436 mutants, the minimal NF structure was sufficient for nodulation of soybean and S. flavescens. In summary, the mechanism of promiscuous symbiosis of S. flavescens with rhizobia might be related to its nonspecific recognition of NF structures, and the host specificity of rhizobia may also be controlled by currently unknown nodulation-related genes.

Objectives: Human papillomavirus is the necessary cause of cervical cancer, in particular the human papillomavirus-16/18 strains, which have been detected in ~70% of all cervical cancer cases worldwide. This study aims to assess whether other cofactors, which might be specific for the Chinese population, are involved in the development of cervical cancer. These findings may support the future direction of cervical cancer prevention. Study Design: Systematic literature review. Methods: The following databases were searched: MEDLINE, MEDLINE-IN-PROCESS, EMBASE, China National Knowledge Infrastructure, Wanfang Data and Chongqing VIP Information. The target population were adolescents or adults from mainland China. All observational studies irrespective of intervention or comparator reporting risk factors for cervical cancer were included. The Newcastle-Ottawa Scale was used to assess study quality. The impact of each outcome was reported in numerical terms. Results: A total of 2,676 articles were screened. A total of 21 articles met the inclusion criteria. All studies were case-controlled designs mostly conducted in hospitals of South-Eastern China. A total of 18 studies reported lifestyle behaviours as significant influencing factors in the development of cervical cancer. Sexual behaviour, gestational factors, screening history, disease history and socio-demographics status were reported as significant risk factors for cervical cancer development. Conclusion: This review provides an up-to-date insight of current cervical cancer risk factors in China. Due to the heterogeneity of the results, further evaluation is recommended to determine the association of these risk factors to the overall risk of cervical cancer.

Pancreatic progenitor cells (PPCs) are the primary source for all pancreatic cells, including beta-cells, and thus the proliferation and differentiation of PPCs into islet-like cell clusters (ICCs) opens an avenue to providing transplantable islets for diabetic patients. Meanwhile, mesenchymal stem cells (MSCs) can enhance the development and function of different cell types of interest, but their role on PPCs remains unknown. We aimed to explore the mechanism-of-action whereby MSCs induce the in vitro and in vivo PPC/ICC development by means of our established co-culture system of human PPCs with human fetal bone marrow-derived MSCs. We examined the effect of MSC-conditioned medium on PPC proliferation and survival. Meanwhile, we studied the effect of MSC co-culture enhanced PPC/ICC function in vitro and in vivo co-/transplantation. Furthermore, we identified IGF1 as a critical factor responsible for the MSC effects on PPC differentiation and proliferation via IGF1-PI3K/Akt and IGF1-MEK/ERK1/2, respectively. In conclusion, our data indicate that MSCs stimulated the differentiation and proliferation of human PPCs via IGF1 signaling, and more importantly, promoted the in vivo engraftment function of ICCs. Taken together, our protocol may provide a mechanism-driven basis for the proliferation and differentiation of PPCs into clinically transplantable islets.

The line loss rate of power supply enterprises is an important economic and technical index, which directly reflects the lean level of marketing professional management of power supply enterprises. At present, the confusion of transformer-user archives in transformer area is still widespread, which is a key issue in line loss management in power supply enterprises. As the downlink communication channel, low-voltage power line carrier can realize cross-station reading, which brings great difficulties to the identification of transformer-user relation. Because the data of calculating the line loss rate of the transformer area depends on the electrical information acquisition system, the running state of the electrical information acquisition equipment is also one of the important factors affecting the line loss rate of the transformer area. This paper will focus on the fault diagnosis technology of electrical information acquisition equipment, and use it to carry out the identification of transformer-user transformers.

Intelligent acquisition terminal is an important medium for power users to collect electric energy data and load data. It realizes centralized collection of electricity information through local and remote communication technology. With the continuous development of communication technology, the acquisition terminal is becoming more and more intelligent and its functions are becoming more and more complex. The content of field equipment fault diagnosis analysis technology is getting higher and higher. This paper combines with the current intelligent acquisition terminal fault diagnosis mode to optimize the research, from the technical and management aspects, makes an analysis of the causes of acquisition anomalies and the lack of means, and puts forward reasonable efficiency improvement suggestions.

Knowledge of protein function is important for biological, medical and therapeutic studies, but many proteins are still unknown in function. There is a need for more improved functional prediction methods. Our SVM-Prot web-server employed a machine learning method for predicting protein functional families from protein sequences irrespective of similarity, which complemented those similarity-based and other methods in predicting diverse classes of proteins including the distantly-related proteins and homologous proteins of different functions. Since its publication in 2003, we made major improvements to SVM-Prot with (1) expanded coverage from 54 to 192 functional families, (2) more diverse protein descriptors protein representation, (3) improved predictive performances due to the use of more enriched training datasets and more variety of protein descriptors, (4) newly integrated BLAST analysis option for assessing proteins in the SVM-Prot predicted functional families that were similar in sequence to a query protein, and (5) newly added batch submission option for supporting the classification of multiple proteins. Moreover, 2 more machine learning approaches, K nearest neighbor and probabilistic neural networks, were added for facilitating collective assessment of protein functions by multiple methods. SVM-Prot can be accessed at http://bidd2.nus.edu.sg/cgi-bin/svmprot/svmprot.cgi.

Despite the rapid progress of organic solar cells based on non-fullerene acceptors, simultaneously achieving high power conversion efficiency and long-term stability for commercialization requires sustainable research effort. Here, we demonstrate stable devices by integrating a wide bandgap electron-donating polymer (namely PTzBI-dF) and two acceptors (namely L8BO and Y6) that feature similar structures yet different thermal and morphological properties. The organic solar cell based on PTzBI-dF:L8BO:Y6 could achieve a promising efficiency of 18.26% in the conventional device structure. In the inverted structure, excellent long-term thermal stability over 1400 h under 85 °C continuous heating is obtained. The improved performance can be ascribed to suppressed charge recombination along with appropriate charge transport. We find that the morphological features in terms of crystalline coherence length of fresh and aged films can be gradually regulated by the weight ratio of L8BO:Y6. Additionally, the occurrence of melting point decrease and reduced enthalpy in PTzBI-dF:L8BO:Y6 films could prohibit the amorphous phase to cluster, and consequently overcome the energetic traps accumulation aroused by thermal stress, which is a critical issue in high efficiency non-fullerene acceptors-based devices. This work provides insight into understanding non-fullerene acceptors-based organic solar cells for improved efficiency and stability.

Internet of things is a hot topic in the field of research, get a lot of attention, On behalf of the future development trend of the network, Internet of Things has a wide range of applications, because of the efficient and reliable information transmission in modern agriculture. In the greenhouse, the conditions of the Greenhouse determine the quality of crops, high yield and many other aspects. Research on Intelligent Agriculture Greenhouses based on Internet of Things, mainly Research on how to control the conditions of the greenhouses, So that the indoor conditions suitable for crop growth. In the pater, we study of Zigbee technology, Designed the solar power supply module, greenhouse hardware and software part, And the system was tested by experiment, The analysis of the experimental data shows that the system can provide good conditions for the growth of crops to achieve the high yield and high quality of crops.