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Objective: To observe the efficacy of different combinations of antihypertensive drugs in patients with nocturnal hypertension (NH), and provide reference for clinical work. Methods: A cross-sectional study was used to collect clinical data of NH patients diagnosed through ambulatory blood pressure monitoring during physical examinations at the First Affiliated Hospital of Hunan University of Medicine from January 2021 to December 2022. According to the different motheds of medication treatment for patients, NH patients were divided into single-drug group, dual-drug group and multi-drug group.Angiotensin converting enzyme inhibitor (ACEI)/ angiotensin Ⅱ receptor antagonist (ARB) or calcium antagonist was used in single-drug group, ACEI/ARB combined with calcium antagonist were used in double-drug group, and any three or four combinations of ACEI/ARB, calcium antagonists, β-blockers and diuretics were used in multi-drug group. The clinical data of the three groups of patients were analyzed and compared. Results: There were 829 patients with NH, including 294 cases (35.46%) in single-drug group, 400 cases (48.25%) in dual-drug group and 135 cases (16.29%) in multiple-drug group. After 6 months of drug treatment, the nocturnal systolic blood pressure (NSBP) and nocturnal diastolic blood pressure (NDBP) of patients in single-drug group, dual-drug group and multi-drug group showed significant differences compared to before treatment (P＜0.05)，among them, the NSBP and NDBP of patients in dual-drug group were significantly lower than those in single-drug group and multi-drug group, but there was no significant difference between the latter two groups (P＞0.05). The decreases in NSBP and NDBP in dual-drug group (21.75%, 20.06%) were greater than those in single-drug group (17.60%, 15.26%) and multi-drug group (18.23%, 16.51%, P＜0.05), respectively. Conclusion: All three motheds of medication treatment can effectively control nocturnal blood pressure, but ACEI/ARB combined with calcium antagonist are more effective in lowering nocturnal blood pressure.

Flooding is a critical factor that limits the establishment of a symbiosis between rice and arbuscular mycorrhizal fungi (AMF) in wetland ecosystems. The distribution of carbon resources in roots and the acclimation strategies of rice to flooding stress in the presence of AMF are poorly understood. We conducted a root box experiment, employing nylon sheets or nylon meshes to create separate fungal chambers that either prevented or allowed the roots and any molecules to pass through. We found that the mycorrhizal colonization rate and the expression of genes OsD14L and OsCERK1, which are involved in fungal perception during symbiosis, both increased in mycorrhizal rice roots following intermittent flooding compared to continuous flooding. Furthermore, AMF inoculation affected root morphological traits, facilitating both shallower and deeper soil exploration. Increased submergence intensity led to carbohydrate deprivation in roots, while high mycorrhizal colonization increased soil oxygen consumption and decreased the neutral lipid concentration in roots. However, mycorrhizal inoculation increased the rice photosynthesis rate and facilitated acclimation to submergence by mediating the expression of the genes OsCIPK15 and OsSUB1A to enhance rice shoot elongation and the sugar concentration in roots as a result of reduced competition for carbon between rice and AMF under different flooding conditions. [ABSTRACT FROM AUTHOR]

Efficiently obtaining leaf nitrogen content (LNC) in rice to monitor the nutritional health status is crucial in achieving precision fertilization on demand. Unmanned aerial vehicle (UAV)-based hyperspectral technology is an important tool for determining LNC. However, the intricate coupling between spectral information and nitrogen remains elusive. To address this, this study proposed an estimation method for LNC that integrates hybrid preferred features with deep learning modeling algorithms based on UAV hyperspectral imagery. The proposed approach leverages XGBoost, Pearson correlation coefficient (PCC), and a synergistic combination of both to identify the characteristic variables for LNC estimation. We then construct estimation models of LNC using statistical regression methods (partial least-squares regression (PLSR)) and machine learning algorithms (random forest (RF); deep neural networks (DNN)). The optimal model is utilized to map the spatial distribution of LNC at the field scale. The study was conducted at the National Agricultural Science and Technology Park, Guangzhou, located in Baiyun District of Guangdong, China. The results reveal that the combined PCC-XGBoost algorithm significantly enhances the accuracy of rice nitrogen inversion compared to the standalone screening approach. Notably, the model built with the DNN algorithm exhibits the highest predictive performance and demonstrates great potential in mapping the spatial distribution of LNC. This indicates the potential role of the proposed model in precision fertilization and the enhancement of nitrogen utilization efficiency in rice cultivation. The outcomes of this study offer a valuable reference for enhancing agricultural practices and sustainable crop management. [ABSTRACT FROM AUTHOR]

Manganese-based nanomaterials have attracted great interest in cancer nanotheranostics owing to their excellent physicochemical properties. Herein, we report α-phase manganese sulfide nanotheranostics (MnSN) as an MRI-guided chemodynamic therapy agent and cGAS-STING agonist for tumor immunotherapy. MnSN can degrade into manganese ions in response to the mildly acidic tumor microenvironment. This unique feature endows MnSN with the ability to produce sufficient hydroxyl radicals (˙OH) and achieve efficient chemodynamic therapy in vitro and vivo. More importantly, the released manganese ions successfully activate the cGAS-STING pathway, resulting in the significant immune effect and inhibiting the tumor metastasis. Besides, the pH-responsive degradation endows MnSN with activable T1 magnetic resonance imaging to monitor the treatment progress. This study demonstrates that MnSN holds great potential for the precise diagnosis and inhibition of tumor growth and metastasis. [ABSTRACT FROM AUTHOR]

[18F]tetrafluoroborate (TFB) has been introduced as the 18F-labeled PET imaging probe for the human sodium iodide symporter (NIS). Noninvasive NIS imaging using [18F]TFB has received much interest in recent years for evaluating various NIS-expressing tumors. Cancers are a global concern with enormous implications; therefore, improving diagnostic methods for accurate detection of cancer is extremely important. Our aim was to investigate the PET imaging capabilities of [18F]TFB in NIS-transfected lung cell line A549 and endogenous NIS-expressing tumor cells, such as thyroid cancer K1 and gastric cancer MKN45, and broaden its application in the medical field. Western blot and flow cytometry were used to assess the NIS expression level. Radioactivity counts of [18F]TFB, in vitro, in the three tumor cells were substantially higher than those in the KI inhibition group in the uptake experiment. In vivo PET imaging clearly delineated the three tumors based on the specific accumulation of [18F]TFB in a mouse model. Ex vivo biodistribution investigation showed high [18F]TFB absorption in the tumor location, which was consistent with the PET imaging results. These results support the use of NIS-transfected lung cell line A549 and NIS-expressing tumor cells MKN45 and K1, to investigate probing capabilities of [18F]TFB. We also demonstrate, for the first time, the feasibility of [18F]TFB in diagnosing stomach cancer. In conclusion, this study illustrates the promising future of [18F]TFB for tumor diagnosis and NIS reporter imaging. [ABSTRACT FROM AUTHOR]