Development and validation of a prognostic immunoinflammatory index for patients with gastric cancer.

BACKGROUND: Studies have demonstrated the influence of immunity and inflammation on the development of tumors. Although single biomarkers of immunity and inflammation have been shown to be clinically predictive, the use of biomarkers integrating both to predict prognosis in patients with gastric cancer remains to be investigated. AIM: To investigate the prognostic and clinical significance of inflammatory biomarkers and lymphocytes in patients undergoing surgical treatment for gastric cancer. METHODS: Univariate COX regression analysis was performed to identify potential prognostic factors for patients with gastric cancer undergoing surgical treatment. Least absolute shrinkage and selection operator-COX (LASSO-COX) regression analysis was performed to integrate these factors and formulate a new prognostic immunoinflammatory index (PII). The correlation between PII and clinical characteristics was statistically analyzed. Nomograms incorporating the PII score were devised and validated based on the time-dependent area under the curve and decision curve analysis. RESULTS: Patients exhibiting elevated neutrophil-to-lymphocyte ratio, platelet-to-lymphocyte ratio, and systemic immune inflammatory index displayed inferior progression-free survival (PFS) and overall survival (OS). Conversely, low levels of CD3(+), CD3(+) CD8(+), CD4(+)CD8(+), and CD3(+)CD16(+)CD56(+) T lymphocytes were associated with improved PFS and OS, while high CD19(+) T lymphocyte levels were linked to worse PFS and OS. The PII score demonstrated associations with tumor characteristics (primary tumor site and tumor size), establishing itself as an independent prognostic factor for both PFS and OS. Time-dependent area under the curve and decision curve analysis affirmed the effectiveness of the PII-based nomogram as a robust prognostic predictive model. CONCLUSION: PII may be a reliable predictor of prognosis in patients with gastric cancer undergoing surgical treatment, and it offers insights into cancer-related immune-inflammatory responses, with potential significance in clinical practice.

Verteporfin fluorescence in antineoplastic-treated pancreatic cancer cells found concentrated in mitochondria.

BACKGROUND: Traditional treatments for pancreatic cancer (PC) are inadequate. Photodynamic therapy (PDT) is non-invasive, and proven safe to kill cancer cells, including PC. However, the mitochondrial concentration of the photosensitizer, such as verteporfin, is key. AIM: To investigate the distribution of fluorescence of verteporfin in PC cells treated with antitumor drugs, post-PDT. METHODS: Workable survival rates of PC cells (AsPC-1, BxPC-3) were determined with chemotherapy [doxorubicin (DOX) and gemcitabine (GEM)] and non-chemotherapy [sirolimus (SRL) and cetuximab (CTX)] drugs in vitro, with or without verteporfin, as measured via MTT, flow cytometry, and laser confocal microscopy. Reduced cell proliferation was associated with GEM that was more enduring compared with DOX. Confocal laser microscopy allowed observation of GEM- and verteporfin-treated PC cells co-stained with 4',6-diamidino-2-phenylindole and MitoTracker Green to differentiate living and dead cells and subcellular localization of verteporfin, respectively. RESULTS: Cell survival significantly dropped upon exposure to either chemotherapy drug, but not to SRL or CTX. Both cell lines responded similarly to GEM. The intensity of fluorescence was associated with the concentration of verteporfin. Additional experiments using GEM showed that survival rates of the PC cells treated with 10 µmol/L verteporfin (but not less) were significantly lower relative to nil verteporfin. Living and dead stained cells treated with GEM were distinguishable. After GEM treatment, verteporfin was observed primarily in the mitochondria. CONCLUSION: Verteporfin was observed in living cells. In GEM -treated human PC cells, verteporfin was particularly prevalent in the mitochondria. This study supports further study of PDT for the treatment of PC after neoadjuvant chemotherapy.

Combination of DCE-MRI and NME-DWI via Deep Neural Network for Predicting Breast Cancer Molecular Subtypes.

BACKGROUND: To explore whether the combination of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) and nonmono-exponential (NME) model-based diffusion-weighted imaging (DWI) via deep neural network (DNN) can improve the prediction of breast cancer molecular subtypes compared to either imaging technique used alone. PATIENTS AND METHODS: This prospective study examined 480 breast cancers in 475 patients undergoing DCE-MRI and NME-DWI at 3.0 T. Breast cancers were classified as follows: human epidermal growth factor receptor 2 enriched (HER2-enriched), luminal A, luminal B (HER2-), luminal B (HER2+), and triple-negative subtypes. A total of 20% cases were withheld as an independent test dataset, and the remaining cases were used to train DNN with an 80% to 20% training-validation split and 5-fold cross-validation. The diagnostic accuracies of DNN in 5-way subtype classification between the DCE-MRI, NME-DWI, and their combined multiparametric-MRI datasets were compared using analysis of variance with least significant difference posthoc test. Areas under the receiver-operating characteristic curves were calculated to assess the performances of DNN in binary subtype classification between the 3 datasets. RESULTS: The 5-way classification accuracies of DNN on both DCE-MRI (0.71) and NME-DWI (0.64) were significantly lower (P < .05) than on multiparametric-MRI (0.76), while on DCE-MRI was significantly higher (P < .05) than on NME-DWI. The comparative results of binary classification between the 3 datasets were consistent with the 5-way classification. CONCLUSION: The combination of DCE-MRI and NME-DWI via DNN achieved a significant improvement in breast cancer molecular subtype prediction compared to either imaging technique used alone. Additionally, DCE-MRI outperformed NME-DWI in differentiating subtypes.

Antifungal activity of ruthenium (II) complex combined with fluconazole against drug-resistant Candida albicans in vitro and its anti-invasive infection in vivo.

With the abuse of antibiotics and azoles, drug-resistant Candida albicans infections have increased sharply and are spreading rapidly, thereby significantly reducing the antifungal efficacy of existing therapeutics. Several patients die of fungal infections every year. Therefore, there is an urgent requirement to develop new drugs. Accordingly, we synthesized a series of polypyridyl ruthenium (II) complexes having the formula [Ru (NN)2 (bpm)] (PF6)2 (N-N = 2,2'-bipyridine) (bpy, in Ru1), 1,10-phenanthroline (phen, in Ru2), 4,7-diphenyl-1,10-phenanthroline (DIP, in Ru3) (bpm = 2,2'-bipyrimidine) and studied their antifungal activities. Ru3 alone had no effect on the drug-resistant strains, but Ru3 combined with fluconazole (FLC) exhibited significant antifungal activity on drug-resistant strains. A high-dose combination of Ru3 and FLC exhibited direct fungicidal activity by promoting the accumulation of reactive oxygen species and damaging the cellular structure of C. albicans. Additionally, the combination of Ru3 and FLC demonstrated potent antifungal efficacy in vivo in a mouse model of invasive candidiasis. Moreover, the combination significantly improved the survival state of mice, restored their immune systems, and reduced renal injury. These findings could provide ideas for the development of ruthenium (II) complexes as novel antifungal agents for drug-resistant microbial stains.

Metal nutrition and transport in the process of symbiotic nitrogen fixation.

Symbiotic nitrogen fixation (SNF) facilitated by the interaction between legumes and rhizobia is a well-documented and eco-friendly alternative to chemical nitrogen fertilizers. Host plants obtain fixed nitrogen from rhizobia by providing carbon and mineral nutrients. These mineral nutrients, which are mostly in the form of metal ions, are implicated in various stages of the SNF process. This review describes the functional roles played by metal ions in nodule formation and nitrogen fixation and specifically addresses their transport mechanisms and associated transporters within root nodules. Future research directions and potential strategies for enhancing SNF efficiency are also discussed.

Three new diterpenoids isolated from Euphorbia nematocypha Hand.-Mazz and their anti-fungal activity.

Three new diterpenoids, named nematocynine A-C (1-3), together with twelve known compounds (4-15) were isolated from the aerial part of Euphorbia nematocypha Hand.-Mazz (Hereinafter referred to E. nematocypha). Their structures were elucidated by detailed spectroscopic analysis and comparison with literature data. In addition, all the compounds were tested for their anti-candida albicans activities used alone or in combination with fluconazole against sensitive strain and resistant strain in vitro. Wherein only compound 11 shows weak activity against candida albicans resistant strain (MIC50 = 128.15 µg/mL) when used alone. Compounds 1, 4, 7, 8, 9, 10, 12, 13 and 15 in combination with fluconazole showed potent anti-fungal activities (MIC50 = 15 ± 5 µg/mL, FICI = 0.05 ± 0.04) against the Candida albicans resistant strain SC5314-FR. The synergistic effects were weaker against the Candida albicans resistant strain SC5314-FR when the compounds 2, 3, 5 and 14 were combined with fluconazole (FICI = 0.16 ± 0.06).

MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study.

OBJECTIVES: To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS). METHODS: We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer-Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA). RESULTS: Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82-10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789-0.991) and an accuracy of 0.889 (95% CI 0.708-0.977). CONCLUSION: The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making.

Bibliometric analysis of artificial intelligence and optical coherence tomography images: research hotspots and frontiers.

AIM: To explore the latest application of artificial intelligence (AI) in optical coherence tomography (OCT) images, and to analyze the current research status of AI in OCT, and discuss the future research trend. METHODS: On June 1, 2023, a bibliometric analysis of the Web of Science Core Collection was performed in order to explore the utilization of AI in OCT imagery. Key parameters such as papers, countries/regions, citations, databases, organizations, keywords, journal names, and research hotspots were extracted and then visualized employing the VOSviewer and CiteSpace V bibliometric platforms. RESULTS: Fifty-five nations reported studies on AI biotechnology and its application in analyzing OCT images. The United States was the country with the largest number of published papers. Furthermore, 197 institutions worldwide provided published articles, where University of London had more publications than the rest. The reference clusters from the study could be divided into four categories: thickness and eyes, diabetic retinopathy (DR), images and segmentation, and OCT classification. CONCLUSION: The latest hot topics and future directions in this field are identified, and the dynamic evolution of AI-based OCT imaging are outlined. AI-based OCT imaging holds great potential for revolutionizing clinical care.

Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS.

Objective: We aimed to assess the feasibility and superiority of machine learning (ML) methods to predict the risk of Major Adverse Cardiovascular Events (MACEs) in chest pain patients with NSTE-ACS. Methods: Enrolled chest pain patients were from two centers, Beijing Anzhen Emergency Chest Pain Center Beijing Bo'ai Hospital, China Rehabilitation Research Center. Five classifiers were used to develop ML models. Accuracy, Precision, Recall, F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system. Ultimately, ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs. Results: According to learning metrics, ML models constructed by different classifiers were superior over HEART (History, ECG, Age, Risk factors, & Troponin) scoring system when predicting acute myocardial infarction (AMI) and all-cause death. However, according to ROC curves and AUC, ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI. Among the five ML algorithms, Linear support vector machine (SVC), Naïve Bayes and Logistic regression classifiers stood out with all Accuracy, Precision, Recall and F-Measure from 0.8 to 1.0 for predicting any event, AMI, revascularization and all-cause death ( vs. HEART ≤ 0.78), with AUC from 0.88 to 0.98 for predicting any event, AMI and revascularization ( vs. HEART ≤ 0.85). ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome (ACS), abnormal electrocardiogram (ECG), elevated hs-cTn I, sex and smoking were risk factors of MACEs. Conclusion: Compared with HEART risk scoring system, the superiority of ML method was demonstrated when employing Linear SVC classifier, Naïve Bayes and Logistic. ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.

Cyclometalated iridium(III) complexes combined with fluconazole: antifungal activity against resistant C. albicans.

Candida albicans (C. albicans) is a ubiquitous clinical fungal pathogen. In recent years, combination therapy, a potential treatment method to overcome C. albicans resistance, has gained traction. In this study, we synthesized a series of cyclometalated iridium(III) complexes with the formula [Ir(C-N)2(tpphz)](PF6) (C-N = 2-phenylpyridine (ppy, in Ir1), 2-(2-thienyl)pyridine (thpy, in Ir2), 2-(2,4-difluorophenyl) pyridine (dfppy, in Ir3), tpphz = tetrapyrido[3,2-a:2',3'-c:3'',2''-h:2''',3'''-j]phenazine) and polypyridyl ruthenium(II) complexes with the formula [Ru(N-N)2(tpphz)](PF6)2 (N-N = 2,2'-bipyridine (bpy, in Ru1), 1,10-phenanthroline (phen, in Ru2), 4,7-diphenyl-1,10-phenanthroline (DIP, in Ru3)), and investigated their antifungal activities against drug-resistant C. albicans and their combination with fluconazole (FLC). Of which, the combination of the lead iridium(III) complex Ir2 and FLC showed strong antifungal activity against drug-resistant C. albicans. Mechanism studies have shown that they can inhibit the formation of hyphae and biofilm, damage mitochondrial function and accumulate intracellular ROS. Therefore, iridium(III) complexes combined with FLC can be used as a promising treatment to exert anti-drug-resistant C. albicans activity, in order to improve the treatment efficiency of fungal infection.

Lactobacillus murinus: A key factor in suppression of enterogenous Candida albicans infections in Compound Agrimony enteritis capsules-treated mice.

ETHNOPHARMACOLOGICAL RELEVANCE: Compound Agrimony (FuFangXianHeCao, FFXHC) Enteritis Capsules is an ethnomedicine that is derived from Yi Nationality Herbal Medicine for enteritis treatment. We found that FFXHC reduced the mortality outcomes in enterogenic Candida albicans infected mice models and increased the abundance of Lactobacillus murinus in the intestines. Lactobacillus murinus exhibited comparable therapeutic effects to those of FFXHC in enterogenic Candida albicans infected mice. This study provides novel perspectives into the pharmacological mechanisms of FFXHC. AIM OF THE STUDY: We investigated the mechanisms via which FFXHC inhibits C. albicans infections and its effects on L. murinus. MATERIALS AND METHODS: Enterogenous C. albicans infection mice models were established and various parameters, including survival rate, weight change, number of colonies, treatment effects on intestinal mucosa, microecology, and immune cytokines evaluated. Susceptibility of C. albicans to L. murinus was evaluated in vitro. RESULTS: Treatment with FFXHC reduced the number of colonies, improved the health status, enhanced the survival rates, increased the abundance of L. murinus, reduced damage to the intestinal mucosa, and elevated occludin as well as claudin-1 levels. Interestingly, TNF-α, IFN-γ, IL-10, IL-22, and IL-17A levels were increased while IL-1ß levels were suppressed in the intestinal mucosa without any change in peripheral blood cytokine levels. Moreover, FFXHC promoted L. murinus proliferation. This study also confirmed the incubation-dependent anti-C. albicans effects exerted by the metabolic supernatants of L. murinus. CONCLUSIONS: FFXHC effectively alleviated intestinal infections of C. albicans in mice and increased the abundance of L. murinus. Supplementation of L. murinus in food can achieve the effects that are comparable to those of FFXHC. Thus, L. murinus maybe essential in FFXHC-based treatment of intestinal C. albicans infections.

Establishment of the normal reference values of left ventricular function parameters evaluated by CZT SPECT stress gated myocardial perfusion imaging in low-likelihood of stable coronary artery disease / 中华核医学与分子影像杂志

Objective:To establish the normal reference value of left ventricular function parameters by cadmium-zinc-tellurium (CZT) SPECT stress gated myocardial perfusion imaging (G-MPI) in low-likelihood of stable coronary artery disease (SCAD).Methods:From March 2022 to August 2022, 348 consecutive SCAD patients (146 males, 202 females, age (58±10) years) who underwent exercise or pharmacological stress G-MPI (CZT SPECT) in Beijing Anzhen Hospital, Capital Medical University were retrospectively recruited. Left ventricular end-diastolic volume (EDV), end-systolic volume (ESV), and left ventricular ejection fraction (LVEF) were acquired using quantitative gated SPECT (QGS) analysis. EDV and ESV were corrected by body surface area (BSA) to obtain EDV index (EDVI) and ESV index (ESVI), respectively. Independent-sample t test, one-way analysis of variance and Mann-Whitney U test were used for data analysis. The influences of EDV, ESV, EDVI, ESVI and LVEF were analyzed by multiple regressions for linear models. Results:There were 314 patients with low-likelihood of SCAD (128 males, 186 females, age (58±10) years) and 34 normal controls (18 males, 16 females, age (55±10) years). There were no significant differences of basic clinical characteristics and left ventricular function parameters in different genders between 2 groups ( z values: from -1.74 to -0.02, t values: from -1.16 to 1.17, all P>0.05). Using the 95% CI as the cut-off value for left ventricular function parameters in patients with a low-likelihood of SCAD, the upper limits of EDV, ESV, EDVI and ESVI in females and males were 84 and 111 ml, 30 and 44 ml, 47 and 54 ml/m 2, 17 and 21 ml/m 2, respectively, and the lower limit of LVEF in females and males were 58% and 55%, respectively. In the low-likelihood of SCAD group, the EDV ((58±13) vs (77±17) ml) and ESV ((16±7) vs (26±9) ml) of females were smaller than those of males ( t values: 10.65, 10.35, both P<0.001), while LVEF of females was higher than that of males ((72±7)% vs (67±6)%; t=-6.23, P<0.001). However, there were no significant differences in left ventricular function parameters among different age groups with the same gender ( F values: 0.12-2.19, all P>0.05). Based on multiple regression for linear models, the primary predictors of EDV, ESV and LVEF were gender and weight ( β values: from -0.380 to 0.358, all P<0.05). Conclusions:Normal reference values of left ventricular function parameters are established by CZT SPECT stress G-MPI in low-likelihood of SCAD patients. Left ventricular EDV and ESV of females are smaller than those of males, while LVEF of females is higher than that of males. The influence of gender on left ventricular function parameters should be considered in clinical practice.

Exploring the Feasibility of Machine Learning to Predict Risk Stratification Within 3 Months in Chest Pain Patients with Suspected NSTE-ACS / 生物医学与环境科学（英文）

OBJECTIVE@#We aimed to assess the feasibility and superiority of machine learning (ML) methods to predict the risk of Major Adverse Cardiovascular Events (MACEs) in chest pain patients with NSTE-ACS.@*METHODS@#Enrolled chest pain patients were from two centers, Beijing Anzhen Emergency Chest Pain Center Beijing Bo'ai Hospital, China Rehabilitation Research Center. Five classifiers were used to develop ML models. Accuracy, Precision, Recall, F-Measure and AUC were used to assess the model performance and prediction effect compared with HEART risk scoring system. Ultimately, ML model constructed by Naïve Bayes was employed to predict the occurrence of MACEs.@*RESULTS@#According to learning metrics, ML models constructed by different classifiers were superior over HEART (History, ECG, Age, Risk factors, & Troponin) scoring system when predicting acute myocardial infarction (AMI) and all-cause death. However, according to ROC curves and AUC, ML model constructed by different classifiers performed better than HEART scoring system only in prediction for AMI. Among the five ML algorithms, Linear support vector machine (SVC), Naïve Bayes and Logistic regression classifiers stood out with all Accuracy, Precision, Recall and F-Measure from 0.8 to 1.0 for predicting any event, AMI, revascularization and all-cause death ( vs. HEART ≤ 0.78), with AUC from 0.88 to 0.98 for predicting any event, AMI and revascularization ( vs. HEART ≤ 0.85). ML model developed by Naïve Bayes predicted that suspected acute coronary syndrome (ACS), abnormal electrocardiogram (ECG), elevated hs-cTn I, sex and smoking were risk factors of MACEs.@*CONCLUSION@#Compared with HEART risk scoring system, the superiority of ML method was demonstrated when employing Linear SVC classifier, Naïve Bayes and Logistic. ML method could be a promising method to predict MACEs in chest pain patients with NSTE-ACS.

Predictive value of left ventricular ejection fraction reserve assessed by SPECT G-MPI for major adverse cardiovascular event in patients with coronary artery disease / 中华心血管病杂志

Objective: To evaluate the prognostic value of left ventricular ejection fraction (LVEF) reserve assessed by gated SPECT myocardial perfusion imaging (SPECT G-MPI) for major adverse cardiovascular event (MACE) in patients with coronary artery disease. Methods: This is a retrospective cohort study. From January 2017 to December 2019, patients with coronary artery disease and confirmed myocardial ischemia by stress and rest SPECT G-MPI, and underwent coronary angiography within 3 months were enrolled. The sum stress score (SSS) and sum resting score (SRS) were analyzed by the standard 17-segment model, and the sum difference score (SDS, SDS=SSS-SRS) was calculated. The LVEF at stress and rest were analyzed by 4DM software. The LVEF reserve (ΔLVEF) was calculated (ΔLVEF=stress LVEF-rest LVEF). The primary endpoint was MACE, which was obtained by reviewing the medical record system or by telephone follow-up once every twelve months. Patients were divided into MACE-free and MACE groups. Spearman correlation analysis was used to analyze the correlation between ΔLVEF and all MPI parameters. Cox regression analysis was used to analyze the independent factors of MACE, and the optimal SDS cutoff value for predicting MACE was determined by receiver operating characteristic curve (ROC). Kaplan-Meier survival curves were plotted to compare the difference in the incidence of MACE between different SDS groups and different ΔLVEF groups. Results: A total of 164 patients with coronary artery disease [120 male; age (58.6±10.7) years] were included. The average follow-up time was (26.5±10.4) months, and a total of 30 MACE were recorded during follow-up. Multivariate Cox regression analysis showed that SDS (HR=1.069, 95%CI: 1.005-1.137, P=0.035) and ΔLVEF (HR=0.935, 95%CI: 0.878-0.995, P=0.034) were independent predictors of MACE. According to ROC curve analysis, the optimal cut-off to predict MACE was a SDS of 5.5 with an area under the curve of 0.63 (P=0.022). Survival analysis showed that the incidence of MACE was significantly higher in the SDS≥5.5 group than in the SDS<5.5 group (27.6% vs. 13.2%, P=0.019), but the incidence of MACE was significantly lower in the ΔLVEF≥0 group than in theΔLVEF<0 group (11.0% vs. 25.6%, P=0.022). Conclusions: LVEF reserve (ΔLVEF) assessed by SPECT G-MPI serves as an independent protective factor for MACE, while SDS is an independent risk predictor in patients with coronary artery disease. SPECT G-MPI is valuable for risk stratification by assessing myocardial ischemia and LVEF.

Two central circadian oscillators OsPRR59 and OsPRR95 modulate magnesium homeostasis and carbon fixation in rice.

Photosynthesis, which provides oxygen and energy for all living organisms, is circadian regulated. Photosynthesis-associated metabolism must tightly coordinate with the circadian clock to maximize the efficiency of the light-energy capture and carbon fixation. However, the molecular basis for the interplay of photosynthesis and the circadian clock is not fully understood, particularly in crop plants. Here, we report two central oscillator genes of circadian clock, OsPRR95 and OsPRR59 in rice, which function as transcriptional repressors to negatively regulate the rhythmic expression of OsMGT3 encoding a chloroplast-localized Mg2+ transporter. OsMGT3-dependent rhythmic Mg fluctuations modulate carbon fixation and consequent sugar output in rice chloroplasts. Furthermore, sugar triggers the increase of superoxide, which may act as a feedback signal to positively regulate the expression of OsPRR95 and OsPRR59. Taken together, our results reveal a negative-feedback loop that strengthens the crosstalk between photosynthetic carbon fixation and the circadian clock, which may improve plan adaptation and performance in fluctuating environments.

Carbon-nitrogen trading in symbiotic nodules depends on magnesium import.

Symbiotic nitrogen fixation provides large amounts of nitrogen for global agricultural systems with little environmental or economic costs. The basis of symbiosis is the nutrient exchange occurring between legumes and rhizobia, but key regulators controlling nutrient exchange are largely unknown. Here, we reveal that magnesium (Mg), an important nutrient factor that preferentially accumulates in inner cortical cells of soybean nodules, shows the most positive correlation with nodule carbon (C) import and nitrogen (N) export. We further identified a pair of Mg transporter genes, GmMGT4 and GmMGT5, that are specifically expressed in the nodule cortex, modulating both nodule Mg import and C-N transport processes. The GmMGT4&5-dependent Mg import activates the activity of a plasmodesmata-located ß-1,3-glucanase GmBG2 and consequently keeps plasmodesmata permeable for C-N transport in nodule inner cortical cells. Our studies discovered an important regulating pathway for host plants fine-tuning nodule C-N trading to achieve optimal growth, which may be helpful for optimizing nutrient management for soybean production.

A reinforcement learning-based near-optimal hierarchical approach for motion control: Design and experiment.

As a data-driven design method, model-free optimal control based on reinforcement learning provides an effective way to find optimal control strategies. The design of model-free optimal control is sensitive to system data because it relies on data rather than detailed dynamic models. A prerequisite for generating applicable data is that the system must be open-loop stable (with a stable equilibrium point), which restricts the data-based control design methods in actual control problems and leads to rare experimental studies or verification in the literature. To improve this situation and enrich its applications, we propose a pre-stabilized mechanism and apply it to the motion control of a mechanical system together with a reinforcement learning-based model-free optimal control method, which constitutes a so-called hierarchical control structure. We design two real-time control experiments on an underactuated system to verify its effectiveness. The control results show that the proposed hierarchical control is quite promising in controlling this mechanical system, even though it is open-loop unstable with unknown dynamics.

UPLC-MS/MS method for the determination of voriconazole plasma concentration from pediatric patients with hematologic tumors: An application toward personalized therapy.

Untreated invasive fungal infection is one of the important risk factors affecting the prognosis of pediatric patients with hematologic tumors. Voriconazole (VOR) is the first-line antifungal drug for the treatment of Aspergillus infections. In order to reduce the risk of adverse drug reactions while producing an ideal antifungal effect, therapeutic drug monitoring was performed to maintain the VOR plasma concentration in a range of 1,000-5,500 ng/ml. In the present study, a reliable, accurate, sensitive and quick ultra-high performance liquid chromatograph-tandem mass spectrometry (UPLC-MS/MS) method was developed for the determination of the VOR level. Protein precipitation was performed using acetonitrile, and then the chromatographic separation was carried out by UPLC using a C18 column with the gradient mobile phases comprising 0.1% methanoic acid in acetonitrile (A) and 0.1% methanoic acid in water (B). In the selective reaction monitor mode, the mass spectrometric detection was carried out using an TSQ Endura triple quadruple mass spectrometer. The performance of this UPLC-MS/MS method was validated as per the National Medical Products Administration for Bioanalytical Method Validation. Additionally, the plasma concentrations of VOR in pediatric patients with hematologic tumors were detected using this method, and the analyzed results were used for personalized therapy.

Effects of dietary citrus extract on growth performance, carcass characteristics and meat quality of pigs.

This study investigated the effects of citrus extract on growth, carcass and meat quality of Duroc × Landrace × Large White pigs. One hundred and eight pigs (54 barrows, 54 females) were assigned to one of three dietary treatments for 138 days. The dietary treatments were (1) basic diet; (2) basic diet supplemented with 75 mg/kg chlortetracycline; and (3) basic diet supplemented with citrus extract (0.25 ml/kg during 56-112 days of age and 0.20 ml/kg during 113-194 days of age). No significant differences among treatments were found for growth performance, carcass characteristics, meat quality and free amino acids (p > 0.05). Feeding citrus extract tended to increase intramuscular fat (p = 0.052). Citrus extract and chlortetracycline increased C15:0 concentration (p = 0.016) and superoxide dismutase activity (p = 0.004). The pigs that received chlortetracycline exhibited the lowest (p = 0.033) muscle malondialdehyde concentration. Overall, citrus extract ameliorated some meat quality indicators without adverse effects on pig growth or carcass performance.

Lactic acid in macrophage polarization: The significant role in inflammation and cancer.

Metabolite lactic acid has always been regarded as a metabolic by-product rather than a bioactive molecule. Recently, this view has changed since it was discovered that lactic acid can be used as a signal molecule and has novel signal transduction functions both intracellular and extracellular, which can regulate key functions in the immune system. In recent years, more and more evidence has shown that lactic acid is closely related to the metabolism and polarization of macrophages. During inflammation, lactic acid is a regulator of macrophage metabolism, and it can prevent excessive inflammatory responses; In malignant tumors, lactic acid produced by tumor tissues promotes the polarization of tumor-associated macrophages, which in turn promotes tumor progression. In this review, we examined the relationship between lactic acid and macrophage metabolism. We further discussed how lactic acid plays a role in maintaining the homeostasis of macrophages, as well as the biology of macrophage polarization and the M1/M2 imbalance in human diseases. Potential methods to target lactic acid in the treatment of inflammation and cancer will also be discussed so as to provide new strategies for the treatment of diseases.