Assessing the impact of incentive coordination effect on the equilibrium of agricultural water usage by China's South-to-North Water Diversion Middle Route Project.

In recent years, the sustainable development of agricultural water resources has received much attention. The mismatch between agricultural water distribution patterns, land resources, and socioeconomics threatens food production, especially in vast water-scarce plains. Therefore, monitoring agricultural water spatial equilibrium (AWRSE) is necessary. Based on equilibrium theory and information entropy, in this study, the AWRSE evaluation model is constructed from three aspects: agricultural water resources, land resources, and socioeconomics. In addition, the relationship between social factors with cropping pattern as the primary explanatory variable and AWRSE was examined in conjunction with the extended STIRPAT model and applied to the water-receiving area of the Middle Route of South-to-North Water Diversion Project (MR-SNWDP). The results show that compared with the pre-diversion period, the AWRSE of 75% of the water-receiving cities has been significantly improved by the MR-SNWTP water supply. The MK test z value of the overall regional AWRSE has changed from - 0.328 to - 2.65, and the AWRSE development has shifted from not significantly better to significantly better. The cropping pattern shows a positive response to this development, and this effect can be mitigated in the late stage of water transfer; when the proportion of food crop cultivation increases by 1%, the sub-regional AWRSE value will increase by 0.347%. The evaluation model demonstrates a broad range of inclusiveness and application potential; it provides novel insights for examining agroecological, social, and economic stability.

Understanding the Integrated Health Management System Policy in China From Multiple Perspectives: Systematic Review and Content Analysis.

BACKGROUND: The integrated health management system (IHMS), which unites all health care-related institutions under a health-centered organizational framework, is of great significance to China in promoting the hierarchical treatment system and improving the new health care reform. China's IHMS policy consists of multiple policies at different levels and at different times; however, there is a lack of comprehensive interpretation and analysis of these policies, which is not conducive to the further development of the IHMS in China. OBJECTIVE: This study aims to comprehensively analyze and understand the characteristics, development, and evolution of China's IHMS policy to inform the design and improvement of the system. METHODS: We followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to collect 152 policy documents. With the perspective of policy tools and policy orientation as the core, a comprehensive 6D framework including policy level, policy nature, release time, policy tools, stakeholders, and policy orientation was established by combining the content of policy texts. These dimensions were then analyzed using content analysis. RESULTS: First, we found that, regarding the coordination of policy tools and stakeholders, China's IHMS policy was more inclined to use environment-based policy tools (1089/1929, 56.45%), which suggests a need for further balance in the internal structure of policy tools. Attention to different actors varied, and the participation of physicians and residents needs further improvement (65/2019, 3.22% and 11/2019, 0.54%, respectively). Second, in terms of level differences, Shanghai's IHMS policy used fewer demand-based policy tools (43/483, 8.9%), whereas the national IHMS policy and those of other provinces and cities used fewer supply-based tools (61/357, 17.1% and 248/357, 69.5%, respectively). The national IHMS strategy placed more emphasis on the construction of smart health care (including digital health; 10/275, 3.6%), whereas Shanghai was a leader in the development of healthy community and healthy China (9/158, 5.7% and 4/158, 2.5%, respectively). Third, in terms of time evolution, the various policy tools showed an increasing and then decreasing trend from 2014 to 2021, with relatively more use of environment-based policy tools and less use of demand-based policy tools in the last 3 years. The growth of China's IHMS policy can be divided into 3 stages: the disease-centered period (2014-2017), the e-health technology development period (2017-2019), and the health-centered period (2018-2021). CONCLUSIONS: Policy makers should make several adjustments, such as coordinating policy tools and the uneven relationships among stakeholders; grasping key policy priorities in the context of local characteristics; and focusing on horizontal, multidimensional integration of health resources starting from the community. This study expands the objects of policy research and improves the framework for policy analysis. The findings provide some possible lessons for future policy formulation and optimization.

Comparing the influence of big data resources on medical knowledge recall for staff with and without medical collaboration platform.

BACKGROUND: This study aims to examine how big data resources affect the recall of prior medical knowledge by healthcare professionals, and how this differs in environments with and without remote consultation platforms. METHOD: This study investigated two distinct categories of medical institutions, namely 132 medical institutions with platforms, and 176 medical institutions without the platforms. Big data resources are categorized into two levels-medical institutional level and public level-and three types, namely data, technology, and services. The data are analyzed using SmartPLS2. RESULTS: (1) In both scenarios, shared big data resources at the public level have a significant direct impact on the recall of prior medical knowledge. However, there is a significant difference in the direct impact of big data resources at the institutional level in both scenarios. (2) In institutions with platforms, for the three big data resources (the medical big data assets and big data deployment technical capacity at the medical institutional level, and policies of medical big data at the public level) without direct impacts, there exist three indirect pathways. (3) In institutions without platforms, for the two big data resources (the service capability and big data technical capacity at the medical institutional level) without direct impacts, there exist three indirect pathways. CONCLUSIONS: The different interactions between big data, technology, and services, as well as between different levels of big data resources, affect the way clinical doctors recall relevant medical knowledge. These interaction patterns vary between institutions with and without platforms. This study provides a reference for governments and institutions to design big data environments for improving clinical capabilities.

Particle filter-based parameter estimation algorithm for prognostic risk assessment of progression in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is a malignant tumor that threatens human life and health. The development of a new NSCLC risk assessment model based on electronic medical records has great potential for reducing the risk of cancer recurrence. In this process, machine learning is a powerful method for automatically extracting risk factors and indicating impact weights for NSCLC deaths. However, when the number of samples reaches a certain value, it is difficult for machine learning to improve the prediction accuracy, and it is also challenging to use the characteristic data of subsequent patients effectively. Therefore, this study aimed to build a postoperative survival risk assessment model for patients with NSCLC that updates the model parameters and improves model accuracy based on new patient data. The model perspective was a combination of particle filtering and parameter estimation. To demonstrate the feasibility and further evaluate the performance of our approach, we performed an empirical analysis experiment. The study showed that our method achieved an overall accuracy of 92% and a recall of 71% for deceased patients. Compared with traditional machine learning models, the accuracy of the model estimated by particle filter parameters has been improved by 2%, and the recall rate for dead patients has been improved by 11%. Additionally, this study outcome shows that this method can better utilize subsequent patients' characteristic data, be more relevant to different patients, and help achieve precision medicine.

Deriving particulate backscattering coefficient at 400†nm from small-scale optically shallow waters using Landsat-8 data: a case study at Luhuitou Peninsula, Sanya.

The particulate backscattering coefficient (bbp) plays an important role in the growth of coral reefs by influencing the light field conditions. Small-scale optically shallow waters are commonly found in coastal fringing reefs, making it challenging to monitor the spatial and temporal patterns accurately using Aqua satellites with a low spatial resolution. In this study, six existing optimization-based algorithms for deriving bbp at 400 nm (bbp(400)) were evaluated with three simulated Landsat-8 (spatial resolution = 30 m) data sets and in situ data from the Luhuitou Peninsula, Sanya. The comparison results indicated that the HOPE (hyperspectral optimization process exemplar) (Fix-H-error or Fix-H-error-free) algorithm which sets an input value of the water depth alone outperformed other algorithms. However, the estimated bbp(400) from all the algorithms tended to be either overestimated and underestimated due to the improper the spectral shape value of the backscattering coefficient. The HOPE (Fix-H-error) algorithm estimated-bbp(400) from in situ reflectance also had a good correlation with the in situ total suspended particle concentrations data derived-bbp(400), with a correlation coefficient of 0.83. Therefore, the HOPE (Fix-H-error) algorithm was selected to estimate the bbp(400) from satellite-based Landsat-8 data of the Luhuitou Peninsula, Sanya. Time-series (2014-2021) results from these Landsat-8 images reveal the seasonal variation of bbp(400). The bbp(400) was low from May to September every year. From October to December or January, bbp(400) had an increasing trend, and then it decreased until May. Spatial analysis indicated that bbp(400) decreased with increasing water depth. The spatial and temporal patterns of bbp(400) were consistent with in situ observations reported in the literature. This study preliminarily showed the efficiency of an optimization-based algorithm in deriving bbp(400) in small-scale optically shallow water region using Landsat-8 data.

Assessment of atmospheric emissivity models for clear-sky conditions with reanalysis data.

Atmospheric longwave downward radiation (Ld) is one of the significant components of net radiation (Rn), and it drives several essential ecosystem processes. Ld can be estimated with simple empirical methods using atmospheric emissivity (εa) submodels. In this study, eight global models for εa were evaluated, and the best-performing model was calibrated on a global scale using a parametric instability analysis approach. The climatic data were obtained from a dynamically consistent scale resolution of basic atmospheric quantities and computed parameters known as NCEP/NCAR reanalysis (NNR) data. The performance model was evaluated with monthly average values from the NNR data. The Brutsaert equation demonstrated the best performance, and then it was calibrated. The seasonal global trend of the Brutsaert equation calibrated coefficient ranged between 1.2 and 1.4, and the K-means analysis identified five homogeneous zones (clusters) with similar behavior. Finally, the calibrated Brutsaert equation improved the Rn estimation, with an error reduction, at the worldwide scale, of 64%. Meanwhile, the error reduction for each cluster ranged from 18 to 77%. Hence, Brutsaert's equation coefficient should not be considered a constant value for use in εa estimation, nor in time or location.

Effects of multi-organ crosstalk on the physiology and pathology of adipose tissue.

In previous studies, adipocytes were found to play an important role in regulating whole-body nutrition and energy balance, and are also important in energy metabolism, hormone secretion, and immune regulation. Different adipocytes have different contributions to the body, with white adipocytes primarily storing energy and brown adipocytes producing heat. Recently discovered beige adipocytes, which have characteristics in between white and brown adipocytes, also have the potential to produce heat. Adipocytes interact with other cells in the microenvironment to promote blood vessel growth and immune and neural network interactions. Adipose tissue plays an important role in obesity, metabolic syndrome, and type 2 diabetes. Dysfunction in adipose tissue endocrine and immune regulation can cause and promote the occurrence and development of related diseases. Adipose tissue can also secrete multiple cytokines, which can interact with organs; however, previous studies have not comprehensively summarized the interaction between adipose tissue and other organs. This article reviews the effect of multi-organ crosstalk on the physiology and pathology of adipose tissue, including interactions between the central nervous system, heart, liver, skeletal muscle, and intestines, as well as the mechanisms of adipose tissue in the development of various diseases and its role in disease treatment. It emphasizes the importance of a deeper understanding of these mechanisms for the prevention and treatment of related diseases. Determining these mechanisms has enormous potential for identifying new targets for treating diabetes, metabolic disorders, and cardiovascular diseases.

Large Spontaneous Polarization Ferroelectric Property, Switchable Second-Harmonic Generation Responses, and Magnetism in an Fe-Based Compound.

Since the switchable spontaneous polarization of ferroelectric materials endows it with many useful properties such as a large pyroelectric coefficient, switchable spontaneous polarization, and semiconductor, it has a wide range of application prospects, and the research of high-performance molecular ferroelectric materials has become a hot spot. We obtained a 0D organic-inorganic hybrid ferroelectric [(CH3)3NCH2CH2CH3]2FeCl4 (1) with well-defined ferroelectric domains and excellent domain inversion and exhibited a relatively large spontaneous polarization (Ps = 9 µC/m-2) and a Curie temperature (Tc) of 394 K. Furthermore, compound 1 belongs to the non-centrosymmetrical space group Cmc21 and has a strong second-harmonic generation signal. Interestingly, we also performed magnetic tests on 1, which confirmed that it is a magnetic material. This work provides clues for exploring the application of high-performance molecular ferroelectric materials in future multifunctional smart devices.

Are associations of leisure-time physical activity with mortality attenuated by high levels of chronic ambient fine particulate matter (PM2.5) in older adults? A prospective cohort study.

BACKGROUND AND PURPOSE: Although leisure-time physical activity (PA) has established health benefits in older adults, it is equivocal if exercising in environments with high levels of PM2.5 concentrations is equally beneficial for them. To explore the independent and joint associations of ambient PM2.5 and PA with all-cause mortality among adults aged 60 or older and to assess the modifying effect of age (60-74 years vs. 75+ years) on the joint associations. METHODS: A prospective cohort study based on the MJ Cohort repeat examinations (2005-2016) and the Taiwan Air Quality Monitoring Network and death registry linkages (2005-2022). We included MJ Cohort participants aged 60 or more at baseline who attended the health check-ups at least twice (n = 21,760). Metabolic equivalent hours per week (MET-h/week) of leisure-time PA were computed. Multivariable adjusted associations were examined using time-varying Cox proportional hazard models. RESULTS: There were 3539 all-cause deaths over a mean follow-up of 12.81 (SD = 3.67) years. Ambient PM2.5 and physical inactivity are both independently associated with all-cause mortality. The joint associations of PA and PM2.5 concentrations with all-cause mortality differed in the young-old (60-74 years) and the older-old (75+ years) (P for interaction = 0.01); Higher levels of long-term PM2.5 exposures (≥25 µg/m3) had little influence on the associations between PA and mortality in the young-old (HR = 0.68 (0.56-0.83) and HR = 0.72 (0.59-0.88) for participants with 7.5-<15 and 15+ MET-h/week respectively) but eliminated associations between exposure and outcome in the older-old (HR = 0.91 (0.69-01.21) and HR = 1.02 (0.76-1.38) for participants with 7.5-<15 and 15+ MET-h/week). CONCLUSION: Long-term exposures to higher PM2.5 concentrations may eliminate the beneficial associations of PA with all-cause mortality among adults aged 75 and over.

Stellate ganglion instrumentation for pharmacological blockade, nerve recording, and stimulation in patients with ventricular arrhythmias: Preliminary experience.

BACKGROUND: Stellate ganglion blockade (SGB) can control ventricular arrhythmias (VAs), but outcomes are unclear. Percutaneous stellate ganglion (SG) recording and stimulation in humans has not been reported. OBJECTIVE: The purpose of this study was to assess the outcomes of SGB and the feasibility of SG stimulation and recording in humans with VAs. METHODS: Two patient cohorts were included-group 1: patients undergoing SGB for drug-refractory VAs. SGB was performed by injection of liposomal bupivacaine. Incidence of VAs at 24 and 72 hours and clinical outcomes were collected; group 2: patients undergoing SG stimulation and recording during VA ablation; a 2-F octapolar catheter was placed at the SG at the C7 level. Recording (30 kHz sampling, 0.5-2 kHz filter) and stimulation (up to 80 mA output, 50 Hz, 2 ms pulse width for 20-30 seconds) was performed. RESULTS: Group 1 included 25 patients [age 59.2 ± 12.8 years; 19 (76%) men] who underwent SGB for VAs. Nineteen patients (76.0%) were free of VA up to 72 hours postprocedure. However, 15 (60.0%) had VAs recurrence for a mean of 5.47 ± 4.52 days. Group 2 included 11 patients (mean age 63 ± 12.7 years; 82.7% men). SG stimulation caused consistent increases in systolic blood pressure. We recorded unequivocal signals with temporal association with arrhythmias in 4 of 11 patients. CONCLUSION: SGB provides short-term VA control, but has no benefit in the absence of definitive VA therapies. SG recording and stimulation is feasible and may have value to elicit VA and understand neural mechanisms of VA in the electrophysiology laboratory.

Identification of CD8+ T cell infiltration-related genes and their prognostic values in cervical cancer.

Cervical cancer is a female-specific cancer with relatively high morbidity and mortality. As known to all, immune cell infiltrations in the cancer microenvironment are closely related to the cancer diagnosis and prognosis. Here we revealed that the CD8+ T cell infiltration was significantly upregulated in cervical cancer versus normal cervix uteri samples. Through univariate and multivariate cox analyses, we discovered that the CD8+ T cell infiltration was the only independent beneficial factor for the prognosis of cervical cancer. To explore the genes associated with the CD8+ T cell infiltration in cervical cancer, we performed the WGCNA analysis on the differentially expressed genes (DEGs) of cervical cancer versus normal cervix uteri tissues. As a result, 231 DEGs were found to be associated with CD8+ T cell infiltration in cervical cancer. Subsequently, with the Cytoscape analysis, we identified 105 hub genes out of the 231 DEGs. To further explore the genes that might be responsible for the prognosis of cervical cancer, we performed a univariate cox analysis followed by a LASSO assay on the 105 hub genes and located four genes (IGSF6, TLR10, FCRL3, and IFI30) finally. The four genes could be applied to the prediction of the prognosis of cervical cancer, and relatively higher expression of these four genes predicted a better prognosis. These findings contributed to our understanding of the prognostic values of CD8+ T cell infiltration and its associated genes in cervical cancer and thus might benefit future immune-related therapies.

Impact of big data resources on clinicians' activation of prior medical knowledge.

Background: Activating prior medical knowledge in diagnosis and treatment is an important basis for clinicians to improve their care ability. However, it has not been systematically explained whether and how various big data resources affect the activation of prior knowledge in the big data environment faced by clinicians. Objective: The aim of this study is to contribute to a better understanding on how the activation of prior knowledge of clinicians is affected by a wide range of shared and private big data resources, to reveal the impact of big data resources on clinical competence and professional development of clinicians. Method: Through the comprehensive analysis of extant research results, big data resources are classified as big data itself, big data technology and big data services at the public and institutional levels. A survey was conducted on clinicians and IT personnel in Chinese hospitals. A total of 616 surveys are completed, involving 308 medical institutions. Each medical institution includes a clinician and an IT personnel. SmartPLS version 2.0 software package was used to test the direct impact of big data resources on the activation of prior knowledge. We further analyze their indirect impact of those big data resources without direct impact. Results: (1) Big data quality environment at the institutional level and the big data sharing environment at the public level directly affect activation of prior medical knowledge; (2) Big data service environment at the institutional level directly affects activation of prior medical knowledge; (3) Big data deployment environment at the institutional level and big data service environment at the public level have no direct impact on activation of prior knowledge of clinicians, but they have an indirect impact through big data quality environment and service environment at the institutional level and the big data sharing environment at the public level. Conclusions: Big data technology, big data itself and big data service at the public level and institutional level interact and influence each other to activate prior medical knowledge. This study highlights the implications of big data resources on improvement of clinicians' diagnosis and treatment ability.

Prognostic Value of HLA Class I in Patients with Hepatocellular Carcinoma.

BACKGROUND: Downregulation of HLA class I molecules is a major tumor escape mechanism from immune attack. However, its prognostic impact for patients with hepatocellular carcinoma is still unclear. This study aimed to investigate whether HLA class I has prognostic significance in patients with hepatocellular carcinoma. METHODS: A cohort of 132 patients with hepatocellular carcinoma was enrolled. HLA class I expression was detected by immunohistochemistry. Levels of HLA class I expression were dichotomized as low and high according to staining intensity or staining percentage of positive tumor cells, respectively. Association of HLA class I expression with clinical characteristics and survival was analyzed. RESULTS: None of the clinical characteristics, including gender, age, virus infection, cirrhosis, AFP, vascular invasion, tumor size and number, was significantly associated with staining percentage of HLA class I or staining intensity (p > 0.05). Low staining percentage of HLA class I was significantly associated with a worse survival (p = 0.011), which was further confirmed by Cox regression hazards model in multivariate analysis (HR 0.416, 95% CI 0.204 - 0.849, p = 0.016). Staining intensity of HLA class I was not significantly associated with survival (p > 0.05). CONCLUSIONS: Expression of HLA class I might be a significant prognostic factor in hepatocellular carcinoma, and downregulation of HLA class I was significantly associated with a worse survival in terms of expression percentage of HLA class I.

Mechanism of Impact of Big Data Resources on Medical Collaborative Networks From the Perspective of Transaction Efficiency of Medical Services: Survey Study.

BACKGROUND: The application of big data resources and the development of medical collaborative networks (MCNs) boost each other. However, MCNs are often assumed to be exogenous. How big data resources affect the emergence, development, and evolution of endogenous MCNs has not been well explained. OBJECTIVE: This study aimed to explore and understand the influence of the mechanism of a wide range of shared and private big data resources on the transaction efficiency of medical services to reveal the impact of big data resources on the emergence and development of endogenous MCNs. METHODS: This study was conducted by administering a survey questionnaire to information technology staff and medical staff from 132 medical institutions in China. Data from information technology staff and medical staff were integrated. Structural equation modeling was used to test the direct impact of big data resources on transaction efficiency of medical services. For those big data resources that had no direct impact, we analyzed their indirect impact. RESULTS: Sharing of diagnosis and treatment data (ß=.222; P=.03) and sharing of medical research data (ß=.289; P=.04) at the network level (as big data itself) positively directly affected the transaction efficiency of medical services. Network protection of the external link systems (ß=.271; P=.008) at the level of medical institutions (as big data technology) positively directly affected the transaction efficiency of medical services. Encryption security of web-based data (as big data technology) at the level of medical institutions, medical service capacity available for external use, real-time data of diagnosis and treatment services (as big data itself) at the level of medical institutions, and policies and regulations at the network level indirectly affected the transaction efficiency through network protection of the external link systems at the level of medical institutions. CONCLUSIONS: This study found that big data technology, big data itself, and policy at the network and organizational levels interact with, and influence, each other to form the transaction efficiency of medical services. On the basis of the theory of neoclassical economics, the study highlighted the implications of big data resources for the emergence and development of endogenous MCNs.

Thermally responsive hydrogel for atrial fibrillation related stroke prevention.

Atrial fibrillation induced stroke accounts for up to 15% of all strokes. These strokes are caused approximately 90% of the time by clot formation in the left atrial appendage (LAA). To prevent these clots, the most common approach is to administer blood thinners. However, contraindications prevent some people from being able to have blood thinners. Devices have been developed to seal the LAA to prevent clot formation in these patients. Current devices, such as the LARIAT® tie off the LAA theoretically preventing blood from entering the LAA. These have had limited clinical success mainly due to failure to completely close the LAA leaving holes and orifices for thrombi to form. To overcome this lack of complete closure, many surgeons use off-label approaches, classically filling the LAA filamentous coils, to cover these holes. Although this usually helps largely cover the holes, placement is challenging, the coils can migrate, the holes are not fully closed as there is space within and around the coils that don't fully mold to the LAA geometry. Furthermore, the coils can develop device related thrombi defeating their purpose. Therefore, these are not fully sufficient to complement the closure techniques in closing the LAA. To address limitation of the closure devices and coil sealing of remaining holes, we developed a thermally responsive hydrogel (Thermogel) that solidifies once injected into the LAA to uniformly and fully close off the LAA thus preventing clot formation and device related thrombi. This Thermogel consists of three portions: 1) a structural component composed of thiolated Pluronic F127 for gel to solid transition following injection, 2) Heparin for anticoagulation, and 3) Dopamine for adhesion to the surrounding endothelium in the turbulent flow encountered in cardiovascular applications. Here we have demonstrated that Thermogel, in conjunction with the LARIAT®, is capable of filling the defects in small and large animals through catheter injection. Thermogel was biocompatible and led to atrophy of the LAA at 5 weeks in a large animal model. Given the advantages of this Thermogel for sealing this defect and ability to be delivered through an endovascular approach, Thermogel presents a viable adjuvant to current occlusion-based treatments for sealing cardiovascular defects.

An integrated analysis of single-cell and bulk transcriptomics reveals EFNA1 as a novel prognostic biomarker for cervical cancer.

Cervical cancer is a serious threat to women's health and lives worldwide. The recovery and survival of cervical cancer can be improved by customizing therapy strategies based on individual-specific gene expression patterns. EFNA1 was reported to be dysregulated in many cancers and associated with their overall survivals, but its prognostic value in cervical cancer is still unclear. In this study, we performed analyses on the single-cell and bulk RNA sequencing data to study the role of EFNA1 in cervical cancer. EFNA1 was found to be significantly upregulated in cervical cancer tissue, especially the cancer cell subgroup within tumors, which was verified by immunohistochemistry. Through Cox regressions, we found that high EFNA1 expression is an independent risk factor for cervical cancer. Nomogram analysis indicated that EFNA1 could be a predicting factor for the survival probabilities of cervical cancer. Gene ontology and pathway analyses showed that EFNA1 was involved in many tumorigenesis pathways, protein, and virus productions. These findings suggested that EFNA1 could be a prognostic biomarker and potential therapeutic target for cervical cancer.

The ratio of urinary TREM-1/TREM-2 mRNA expression in chronic kidney disease and renal fibrosis.

BACKGROUND: The non-invasive identification of novel renal fibrosis biomarkers needs to be further studied. METHODS: We collected urine samples from 77 biopsy-proven CKD patients and 15 healthy controls. The expression of urinary TREM-1 and TREM-2 was measured and the correlation with renal function parameter and pathological indicators was performed. The receiver operating characteristic (ROC) curve for the diagnosis of renal fibrosis was calculated. The protein expression of TREM-1 and TREM-2 in kidney tissues was measured. RESULTS: The TREM-1/TREM-2 ratio was decreased in CKD patients and correlated with serum creatinine, estimated glomerular filtration rate and cystatin c. Further, the TREM-1/TREM-2 ratio was significantly decreased in moderate-severe fibrosis patients compared with none-mild renal fibrosis. TREM-1/TREM-2 ratio was correlated with the score of tubulointerstitial fibrosis (TIF) and the score of glomerular sclerosis. The ROC curve showed that the urinary TREM-1/TREM-2 ratio can diagnosemoderate-severe renal fibrosis at a cut-off value of 1.338 with a sensitivity of 86.4% and specificity of 81.8%. In human moderate-severe fibrosis kidney tissue, the protein expression of TREM-1 was lower and the TREM-2 was higher than none-mild fibrosis kidney tissue. CONCLUSION: Urinary TREM-1/TREM-2 ratio was a potential biomarker for the diagnosis of renal fibrosis in CKD patients.

Venous anatomy of the left ventricular summit: Therapeutic implications for ethanol infusion.

BACKGROUND: Venous ethanol ablation (VEA) is effective for treatment of left ventricular (LV) summit (LVS) arrhythmias. The LVS venous anatomy is poorly understood and has inconsistent nomenclature. OBJECTIVE: The purpose of this study was to delineate the LVS venous anatomy by selective venography and 3-dimensional (3D) mapping during VEA and by venous-phase coronary computed tomographic angiography (vCTA). METHODS: We analyzed (1) LVS venograms and 3D maps of 53 patients undergoing VEA; and (2) 3D reconstructions of 52 vCTAs, tracing LVS veins. RESULTS: Angiography identified the following LVS veins: (1) LV annular branch of the great cardiac vein (GCV) (19/53); (2) septal (rightward) branches of the anterior ventricular vein (AIV) (53/53); and (3) diagonal branches of the AIV (51/53). Collateral connections between LVS veins and outflow, conus, and retroaortic veins were common. VEA was delivered to target arrhythmias in 38 of 53 septal, 6 of 53 annular, and 2 of 53 diagonal veins. vCTA identified LVS veins (range 1-5) in a similar distribution. GCV-AIV transition could either form an angle close to the left main artery bifurcation (n = 16; 88° ± 13°) or cut diagonally (n = 36; 133°±12°) (P ≤.001). Twenty-one patients had LV annular vein. In 28 patients only septal LVS veins were visualized in vCTA, in 2 patients only diagonal veins and in 22 patients both septal and diagonal veins were seen. In 39 patients the LVS veins reached the outflow tracts and their vicinity. CONCLUSION: We provide a systematic atlas and nomenclature of LVS veins related to arrhythmogenic substrates. vCTA can be useful for noninvasive evaluation of LVS veins before ethanol ablation.

Evaluation and simulation of spatial variability of soil property effects on deep percolation and nitrate leaching within a large-scale field in arid Northwest China.

Variability of soil properties within large-scale fields not only exists in the horizontal domain, but also in the vertical direction, causing spatial variability in yield. Three yield zones were delineated based on measured yield in 2017 and 2018 within a large field in northwest China. The Soil Water Heat Carbon Nitrogen Simulator (WHCNS) model was calibrated and used to simulate yield, nitrogen uptake (Nu), water use efficiency (WUE), fertilizer N (nitrogen) use efficiency (FNUE), deep percolation (DP), nitrate leaching (NL) and residual nitrate (RN) at each sampling point in different yield zones. Based on the simulations, there were significant differences in Nu, WUE, FNUE, DP, NL and RN in 0-100 cm and 100-160 cm soil layers among the three yield zones. DP, NL and RN in the layers were strongly determined by the interaction of zone and year (p < 0.05), thus yielding consistent patterns mainly determined by soil properties and meteorological factors. The modelled ranges of DP, NL, and RN (0-160 cm) were 25-119 mm, 15-94 kg ha-1, and 178-476 kg·ha-1 respectively, across the field. Soil texture in the maize main root zone (0-100 cm) has a great influence on yield and Nu, and in the 100-160 cm layer upon DP and NL. RN was abundant after harvest and should be taken into account to determine the nitrogen fertilization demand for the following crop. The study showed that the process of delineating zones can be based on historical yield, making it feasibly easier than mapping soil properties. In view of the fact that there were large losses of water and nitrogen with uniform irrigation and fertilization management, the effects of vertically variable soil properties should be considered in future precision agriculture research, to achieve higher economic benefits and utilization efficiency.

Optimize the spatial distribution of crop water consumption based on a cellular automata model: A case study of the middle Heihe River basin, China.

Globally, agriculture is by far the largest water consuming sector and in areas where water is scarce, the spatial optimization of crop water consumption used to improve irrigation benefits becomes critical for regional water management. The spatial heterogeneity of environmental parameters brings great challenge to spatial optimization. Therefore, cellular automaton (CA), crop suitability (CS), spatial distributed crop water consumption model and optimization model were integrated and applied on the middle reaches of Heihe River basin, northwest of China. The cellular automata based Water Consumption Optimization (CA-WCSO) model is not only a spatial dynamic optimization model for crop water consumption, but also a decision support tool that reflects the interaction between water consumption at field level and management regulations at regional level. Six optimization paths: i) forward progressive (FP), ii) forward interlacing (F-IL), iii) forward interpolation (F-IP), iv) reverse progressive (R-P), v) reverse interlacing (R-IL) and vi) reverse interpolation (R-IP) of crop water consumption for the baseline year and the planning year were applied on the study site. Results for baseline year (2015) demonstrate that the six optimization paths can slightly reduce the water consumption (>1.4%) but significantly improve the irrigation benefits of the region by 20.56%. Using CA-WCSO model, decision makers can modify model's constraints and select appropriate optimization path to get the optimized crop planting patterns and make future regional water allocation plans.