As the brain's resident immune cells, microglia perform crucial functions such as phagocytosis, neuronal network maintenance, and injury restoration by adopting various phenotypes. Dynamic imaging of these phenotypes is essential for accessing brain diseases and therapeutic responses. Although numerous probes are available for imaging pro-inflammatory microglia, no PET tracers have been developed specifically to visualize anti-inflammatory microglia. In this study, we present an ¹⁸F-labeled PET tracer (QTFT) that targets the P2Y₁₂, a receptor highly expressed on anti-inflammatory microglia. [¹⁸F]QTFT exhibited high binding affinity to the P2Y₁₂ (14.43 nmol/L) and superior blood-brain barrier permeability compared to other candidates. Micro-PET imaging in IL-4-induced neuroinflammation models showed higher [¹⁸F]QTFT uptake in lesions compared to the contralateral normal brain tissues. Importantly, this specific uptake could be blocked by QTFT or a P2Y₁₂ antagonist. Furthermore, [¹⁸F]QTFT visualized brain lesions in mouse models of epilepsy, glioma, and aging by targeting the aberrantly expressed P2Y₁₂ in anti-inflammatory microglia. In a pilot clinical study, [¹⁸F]QTFT successfully located epileptic foci, showing enhanced radioactive signals in a patient with epilepsy. Collectively, these studies suggest that [¹⁸F]QTFT could serve as a valuable diagnostic tool for imaging various brain disorders by targeting P2Y₁₂ overexpressed in anti-inflammatory microglia.

Objective:To investigate the arsenic metabolism pattern and possible influencing factors in the population in drinking-water-borne endemic arsenic poisoning (drinking-water-borne arsenic poisoning for short) areas.Methods:In December 2004, a cluster sampling method was used to select arsenic poisoning population (arsenic poisoning group) and healthy population (control group) in drinking-water-borne arsenic poisoning area of Bayannur City, Inner Mongolia Autonomous Region as the survey subjects. A questionnaire survey was conducted. Arsenic content in drinking water at home of survey subjects, the levels of urinary arsenic and its metabolites, including [trivalent arsenic (As Ⅲ), inorganic arsenic (iAs), monomethylarsenic acid (pentavalent, MMA V), dimethylarsenic acid (pentavalent, DMA V), total arsenic (tAs), percentage of inorganic arsenic (iAs%), percentage of monomethylarsenic acid (MMA%), percentage of dimethylarsenic acid (DMA%), primary methylation index (PMI), secondary methylation index (SMI)] were tested using high performance liquid chromatography-inductively coupled plasma mass spectrometry; nail arsenic and nail selenium levels were tested using atomic fluorescence spectrometer. The influencing factors of arsenic metabolism pattern were analyzed by multiple linear regression. Results:A total of 536 survey subjects were included, including 155 individuals in the arsenic poisoning group and 381 in the control group. The water arsenic level ranged from 0.0 to 825.7 μg/L. Compared with the control group, there was no significant difference in the distribution of gender, education level and dental fluorosis in the arsenic poisoning group ( P > 0.05), but there were significant differences in the distribution of age, marital status, smoking, drinking and water arsenic ( P < 0.05). Compared with the control group, the levels of urinary As Ⅲ, iAs, MMA V, DMA V, tAs, MMA%, MMA/DMA and nail arsenic in the arsenic poisoning group were higher ( P < 0.05), while the levels of urinary DMA%, SMI and nail selenium were lower ( P < 0.05); but there was no statistically significant difference in the levels of urinary iAs% and PMI ( P > 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary As Ⅲ (β = - 19.82, - 23.83, 0.61, 0.21, 7.26, 2.98, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary tAs (β = 3.18, 3.25, 1.31, 15.59, P < 0.05). Gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic were the influencing factors of urinary iAs (β = - 20.47, - 25.90, 0.64, 0.25, 7.87, 3.11, P < 0.05). Age, gender, education level, water arsenic and nail arsenic were the influencing factors of urinary MMA V (β = 0.52, - 17.07, - 21.84, 0.22, 2.77, P < 0.05). Age, depth of wells, water arsenic and nail arsenic were the influencing factors of urinary DMA V (β = 2.35, 2.47, 0.85, 9.22, P < 0.05). Conclusions:Compared with healthy individuals, there are differences in arsenic metabolism pattern among individuals with drinking-water-borne arsenic poisoning. Age, gender, education level, depth of wells, water arsenic, total number of wells and nail arsenic may be influencing factors of different arsenic metabolism patterns.

Objective To construct a universal influenza mRNA vaccine and evaluate its immunogenicity.Methods The antigen sequence of hemagglutinin(HA),nucleoprotein(NP)and matrix protein 2 ectodomain(M2e)in influenza A/California/04/2009 was optimized.HA,NP and 3 tandem M2e(3M2e)were cloned into pcDNA3.1 vector,respectively.Then the mRNAs were synthesized by linearization,in vitro transcription,enzymatic capping and enzymatic tailing,and named as mRNA-HA,mRNA-NP and mRNA-3M2e,respectively.The protein expression of the 3 kinds of mRNAs in 293T cells was detected by immunofluorescence assay.Comb-mRNA vaccine was prepared by enveloped mRNA-HA,mRNA-NP and mRNA-3M2e with lipid nanoparticles,respectively,and the particle size and potential were identified.Twenty-eight 6-week-old female BALB/c mice(18～22 g)were randomly divided into LNP group(n=14)and Comb-mRNA group(n=14).Hemagglutination inhibition(HI)method and microneutralization(MN)test were used to evaluate the serum antibody titer induced by Comb-mRNA vaccines.The mice were infected by 5LD50 wild-type H1 N1 influenza virus to evaluate the protective efficacy.Results The mRNA-HA,mRNA-NP and mRNA-3M2e were successfully constructed,and the 3 mRNAs could be expressed in 293T cells.The average size of mRNA encapsulated by lipid nanoparticles was 119.53±6.5 nm,and the average potential was-8.23±1.3 mV.The geometric mean titer(GMT)of HI and MN in the Comb-mRNA group were 179.6 and 201.6,compared with the LNP group.The ratio of IFN-γ+CD4+/CD8+Tcells was increased.The Comb-mRNA group could provide protection against 5LD50 wild type influenza H1 N1 virus after 2 weeks of booster immunization.Conclusion Comb-mRNA,an influenza vaccine candidate,can induce immune responses and protect mice from influenza virus challenge.

Objective:To construct a predictive model to assess the risk of severe respiratory syncytial virus infection among children under five years in China, conduct preliminary validation of this model by using external data, and develop an individual risk assessment tool available for their parents.Methods:The admission after RSV infection was used as a marker of severe infection. Based on the evidence of RSV hospitalization-related risk factors and real-world data, such as the prevalence of various risk factors in children under five years old in China, a Monte Carlo-based individual RSV hospitalization risk prediction model for children under five years old was constructed. Taking Suzhou City as an example, the model was externally validated, and an interactive risk prediction tool (RSV HeaRT) was developed on the WeChat mini-program platform.Results:The estimation model showed that in children under five years old in China if the population did not have any risk factors for severe RSV infection, the RSV annual hospitalization rate was 2.2/1 000 (95% CI: 0.9/1 000-7.5/1 000). Based on this baseline hospitalization rate and the prevalence of related risk factors in Suzhou, the model predicted an RSV hospitalization rate of 8.0/1 000 (95% CI: 4.6/1 000-24.4/1 000) for children under five years old annually in Suzhou, which was close to the reported RSV hospitalization rate in literature (10/1 000-20/1 000). In the developed RSV HeaRT WeChat mini-program, target users (such as parents of children) could input basic information, disease history, and social environmental factors of the child into the mini-program, and the tool could provide real-time feedback on the following predicted results: First, the relative risk of hospitalization due to RSV infection in current children compared to general children; Second, the probability of hospitalization due to RSV infection within the next year; Third, the relative risk of adverse outcomes during hospitalization in the event of RSV infection. Conclusion:This study is based on real-world evidence related to RSV hospitalization risk and constructs an RSV hospitalization risk prediction model suitable for Chinese children based on the combination of the current prevalence of risk factors in children under five years old in China. The accuracy of the prediction model results has been preliminarily demonstrated. Based on this design, the RSV HeaRT developed can facilitate parents to evaluate the hospitalization risk of children.

Objective:To construct a predictive model to assess the risk of severe respiratory syncytial virus infection among children under five years in China, conduct preliminary validation of this model by using external data, and develop an individual risk assessment tool available for their parents.Methods:The admission after RSV infection was used as a marker of severe infection. Based on the evidence of RSV hospitalization-related risk factors and real-world data, such as the prevalence of various risk factors in children under five years old in China, a Monte Carlo-based individual RSV hospitalization risk prediction model for children under five years old was constructed. Taking Suzhou City as an example, the model was externally validated, and an interactive risk prediction tool (RSV HeaRT) was developed on the WeChat mini-program platform.Results:The estimation model showed that in children under five years old in China if the population did not have any risk factors for severe RSV infection, the RSV annual hospitalization rate was 2.2/1 000 (95% CI: 0.9/1 000-7.5/1 000). Based on this baseline hospitalization rate and the prevalence of related risk factors in Suzhou, the model predicted an RSV hospitalization rate of 8.0/1 000 (95% CI: 4.6/1 000-24.4/1 000) for children under five years old annually in Suzhou, which was close to the reported RSV hospitalization rate in literature (10/1 000-20/1 000). In the developed RSV HeaRT WeChat mini-program, target users (such as parents of children) could input basic information, disease history, and social environmental factors of the child into the mini-program, and the tool could provide real-time feedback on the following predicted results: First, the relative risk of hospitalization due to RSV infection in current children compared to general children; Second, the probability of hospitalization due to RSV infection within the next year; Third, the relative risk of adverse outcomes during hospitalization in the event of RSV infection. Conclusion:This study is based on real-world evidence related to RSV hospitalization risk and constructs an RSV hospitalization risk prediction model suitable for Chinese children based on the combination of the current prevalence of risk factors in children under five years old in China. The accuracy of the prediction model results has been preliminarily demonstrated. Based on this design, the RSV HeaRT developed can facilitate parents to evaluate the hospitalization risk of children.

Objective:To investigate the relationship between the outcome of skin injury and urinary arsenic methylation metabolism levels in people exposed to arsenic through drinking water.Methods:Using cluster sampling method, permanent residents from drinking-water-borne endemic arsenic poisoning areas in Bayannur City, Inner Mongolia Autonomous Region were selected as survey subjects in 2004 (before water improvement). In 2017 (after water improvement), 74 survey subjects from 2004 were tracked and followed up. Urine samples were collected from survey subjects and high-performance liquid chromatography inductively coupled plasma mass spectrometry was used to detect the levels of arsenic methylation metabolites in urine. According to the "Diagnosis of Endemic Arsenic Poisoning" (WS/T 211-2015), the clinical grading (normal, suspicious, mild, moderate and severe) of skin injury of the survey subjects and the outcome of 2017 (improved, unchanged, aggravated) were assessed. A database was established and SPSS 25.0 software was used for statistical analysis.Results:The clinical grading ratios of skin injuries among survey subjects in 2004 and 2017 were compared, the differences were statistically significant (normal, suspicious, mild, moderate and severe: 38, 18, 4, 14 cases in 2004 and 27, 31, 3, 13 cases in 2017, χ 2 = 53.02, P < 0.001). Compared with 2004, in 2017, the levels of total arsenic (tAs), inorganic arsenic (iAs), monomethylarsenic (MMA), dimethylarsenic (DMA), percentage of inorganic arsenic (iAs%), and ratio of monomethylarsenic to dimethylarsenic (MMA/DMA) in the urine of survey subjects were low, and the differences were statistically significant ( Z = - 8.24, - 9.07, - 7.81, - 8.04, - 8.24, - 3.56, P < 0.001). The levels of dimethylarsenic percentage (DMA%), monomethylation rate (PMI) and dimethylation rate (SMI) were higher, and the differences were statistically significant ( Z = - 6.39, - 8.24, - 3.52, P < 0.001). In 2004, patients with different clinical grading of skin injuries had different outcomes in 2017 (χ 2 = 30.80, P < 0.001). There were statistically significant differences in tAs, iAs, MMA and DMA variation in urine among skin injury patients with different outcomes ( H = 10.62, 9.35, 8.80, 9.13, P < 0.05). Conclusions:Improving water can significantly reduce the levels of tAs, iAs, MMA, and DMA in the urine of arsenic exposed individuals. The outcome of skin injury in individuals exposed to arsenic through drinking water is related to the variation of urinary arsenic methylation metabolites tAs, iAs, MMA, and DMA.

Objective To analyze the factors affecting the prognosis of patients with knee osteoarthritis,and to construct a nomogram prediction model in conjunction with multi-dimensional clinical indicators.Methods The clinical data of 234 pa-tients with knee osteoarthritis who were treated in our hospital from January 2015 to June 2021 were retrospectively analyzed,including 126 males and 108 females;age more than 60 years old for 135 cases,age less than 60 years old for 99 cases.Lysholm knee function score was used to evaluate the prognosis of the patients,and the patients were divided into good progno-sis group for 155 patients and poor prognosis group for 79 patients according to the prognosis.The clinical data of the subjects in the experimental cohort were analyzed by single factor and multiple factors.The patients were divided into experimental co-hort and verification cohort,the results of the multiple factor analysis were visualized to obtain a nomogram prediction model,the receiver operating characteristic curve(ROC),calibration curve and decision curve were used to evaluate the model's dis-crimination,accuracy and clinical benefit rate.Results The results of multivariate analysis showed that smoking,pre-treatment K-L grades of Ⅲto Ⅳ,and high levels of interleukin 6(IL-6)and matrix metallo proteinase-3(MMP-3)were risk factors for the prognosis of patients with knee osteoarthritis.ROC test results showed that the area under the curve of the nomogram model in the experimental cohort and validation cohort was 0.806[95％CI(0.742,0.866)]and 0.786[(95％CI(0.678,0.893)],re-spectively.The results of the calibration curve showed that the Brier values of the experimental cohort and verification cohort were 0.151 points and 0.134 points,respectively.When the threshold probability value in the decision curve was set to 31％,the clinical benefit rates of the experimental cohort and validation cohort were 51％and 56％,respectively.Conclusion The prognostic model of patients with knee osteoarthritis constructed based on multi-dimensional clinical data has both theoretical and practical significance,and can provide a reference for taking targeted measures to improve the prognosis of patients.

Objective The aim of this study was to identify macrophage related genes(MRGs)in liver cancer and construct a prognostic risk prediction model for liver cancer.Methods The liver cancer scRNA-seq data from the GEO database were down-loaded to identify genes specifically expressed in macrophages as MRGs.The GO and KEGG functional enrichment analyses on MRGs were conducted.In the TCGA-LIHC dataset of the TCGA database,multiple random sampling single factor Cox regression for single-factor Cox regression,LASSO regression,and multivariate Cox regression analyses were employed to identify MRGs for liver cancer prognosis prediction,and a liver cancer prognostic prediction model was constructed.Results The results obtained 8 major cell types,including those containing macrophages through clustering using scRNA-seq data from the GEO database.The proportion of macrophages in the immune microenvironment of liver cancer was significantly higher than that of normal tissues(P=0.016),and genes such as SPP1,RNASE1,and MMP9 were highly expressed.Multiple metabolic pathways,including purine metabolism,citric acid cycle,and drug metabolism cytochrome P450 were activated in liver cancer-associated macrophages.This study identified 777 MRGs from liver cancer scRNA-seq(LogFC＞0.25,P＜0.05),which mainly involved in functions such as actin binding and regula-tion of immune receptor activity.Seven MRGs,including ATP1B3,ATP6V0B,HBEGF,KLF2,NR1H3,RAB10,and SPP1 were select-ed from the 169 stable prognostic genes(P＜0.05)for the construction of the prognosis model.The AUC values for the 1,3,and 5-year survival outcomes of the model in the TCGA liver cancer cohort were 0.791,0.791,and 0.751,respectively.In the validation ICGC cohort,they were 0.614,0.682,and 0.688,respectively,demonstrating good predictive performance.In liver cancer patients with high prognosis risk scores,the expression of macrophages M0,neutrophils,and regulatory T cells was higher(P＜0.05),and im-munosuppression and immune activation coexisted.Conclusion Liver cancer MRGs can serve as potential biomarkers for predicting the prognosis of liver cancer patients.These liver cancer MRGs are mainly associated with actin binding,immune receptor activity,and infiltration of various immune cells.

The plateau zokor (Myospalax baileyi) and plateau pika (Ochotona curzoniae) are native species unique to the Qinghai-Tibetan Plateau with successful adaptation to the hypoxic environment. In this study, the number of red blood cells, hemoglobin concentration, mean hematocrit and mean volume of red blood cells were measured in plateau zokors and plateau pikas at different altitudes. Hemoglobin subtypes of two plateau animals were identified by mass spectrometry sequencing. The forward selection sites in two animals' hemoglobin subunits were analyzed by PAML4.8 program. Homologous modeling was used to analyze the effect of forward selection sites on the affinity of hemoglobin to oxygen. The adapting strategies of plateau zokors and plateau pikas to hypoxia at different altitudes were analyzed through comparing blood parameters between the two species. The results indicated that, with increasing altitudes, plateau zokors responded to hypoxia by increasing red blood cell count and decreasing red blood cell volume, while plateau pikas took the opposite strategies to plateau zokors. In erythrocytes of plateau pikas, both adult α₂β₂ and fetal α₂ε₂ hemoglobins were identified, while erythrocytes of plateau zokors only had adult α₂β₂ hemoglobin, however the affinities and the allosteric effects of the hemoglobin of plateau zokors were significantly higher than those of plateau pikas. Mechanistically, in the α and β subunits of hemoglobin of plateau zokors and pikas, the numbers and the sites of the positively selected amino acids as well as the side chain groups polarities and orientations of the amino acids differed significantly, which may result in the difference of the affinities to oxygen of hemoglobin between plateau zokors and pikas. In conclusion, the adaptive mechanisms to respond to hypoxia in blood properties of plateau zokors and plateau pikas are species-specific.
Animals ; Altitude ; Amino Acids ; Hemoglobins ; Hypoxia ; Lagomorpha

Metabolic reprogramming is a common phenomenon in cancer, with aerobic glycolysis being one of its important characteristics. Hypoxia-inducible factor-1α (HIF1Α) is thought to play an important role in aerobic glycolysis. Meanwhile, naringin is a natural flavanone glycoside derived from grapefruits and many other citrus fruits. In this work, we identified glycolytic genes related to HIF1Α by analyzing the colon cancer database. The analysis of extracellular acidification rate and cell function verified the regulatory effects of HIF1Α overexpression on glycolysis, and the proliferation and migration of colon cancer cells. Moreover, naringin was used as an inhibitor of colon cancer cells to illustrate its effect on HIF1Α function. The results showed that the HIF1Α and enolase 2 (ENO2) levels in colon cancer tissues were highly correlated, and their high expression indicated a poor prognosis for colon cancer patients. Mechanistically, HIF1Α directly binds to the DNA promoter region and upregulates the transcription of ENO2; ectopic expression of ENO2 increased aerobic glycolysis in colon cancer cells. Most importantly, we found that the appropriate concentration of naringin inhibited the transcriptional activity of HIF1Α, which in turn decreased aerobic glycolysis in colon cancer cells. Generally, naringin reduces glycolysis in colon cancer cells by reducing the transcriptional activity of HIF1Α and the proliferation and invasion of colon cancer cells. This study helps to elucidate the relationship between colon cancer progression and glucose metabolism, and demonstrates the efficacy of naringin in the treatment of colon cancer.
Glycolysis ; Colonic Neoplasms/metabolism* ; Humans ; Hypoxia-Inducible Factor 1, alpha Subunit/metabolism* ; Phosphopyruvate Hydratase/metabolism* ; Flavanones/pharmacology* ; Cell Line, Tumor ; Databases, Genetic ; Cell Proliferation/drug effects* ; Transfection ; Warburg Effect, Oncologic