Alzheimer’s disease (AD) is a central neurodegenerative disease characterized by progressive cognitive decline and memory impairment in clinical. Currently, there are no effective treatments for AD. In recent years, a variety of therapeutic approaches from different perspectives have been explored to treat AD. Although the drug therapies targeted at the clearance of amyloid β-protein (Aβ) had made a breakthrough in clinical trials, there were associated with adverse events. Neuroinflammation plays a crucial role in the onset and progression of AD. Continuous neuroinflammatory was considered to be the third major pathological feature of AD, which could promote the formation of extracellular amyloid plaques and intracellular neurofibrillary tangles. At the same time, these toxic substances could accelerate the development of neuroinflammation, form a vicious cycle, and exacerbate disease progression. Reducing neuroinflammation could break the feedback loop pattern between neuroinflammation, Aβ plaque deposition and Tau tangles, which might be an effective therapeutic strategy for treating AD. Traditional Chinese herbs such as Polygonum multiflorum and Curcuma were utilized in the treatment of AD due to their ability to mitigate neuroinflammation. Non-steroidal anti-inflammatory drugs such as ibuprofen and indomethacin had been shown to reduce the level of inflammasomes in the body, and taking these drugs was associated with a low incidence of AD. Biosynthetic nanomaterials loaded with oxytocin were demonstrated to have the capability to anti-inflammatory and penetrate the blood-brain barrier effectively, and they played an anti-inflammatory role via sustained-releasing oxytocin in the brain. Transplantation of mesenchymal stem cells could reduce neuroinflammation and inhibit the activation of microglia. The secretion of mesenchymal stem cells could not only improve neuroinflammation, but also exert a multi-target comprehensive therapeutic effect, making it potentially more suitable for the treatment of AD. Enhancing the level of TREM2 in microglial cells using gene editing technologies, or application of TREM2 antibodies such as Ab-T1, hT2AB could improve microglial cell function and reduce the level of neuroinflammation, which might be a potential treatment for AD. Probiotic therapy, fecal flora transplantation, antibiotic therapy, and dietary intervention could reshape the composition of the gut microbiota and alleviate neuroinflammation through the gut-brain axis. However, the drugs of sodium oligomannose remain controversial. Both exercise intervention and electromagnetic intervention had the potential to attenuate neuroinflammation, thereby delaying AD process. This article focuses on the role of drug therapy, gene therapy, stem cell therapy, gut microbiota therapy, exercise intervention, and brain stimulation in improving neuroinflammation in recent years, aiming to provide a novel insight for the treatment of AD by intervening neuroinflammation in the future.

This paper explored the specific mechanism of ginsenoside Rg_1 in regulating mitochondrial fusion through the neurogenic gene Notch homologous protein 1(Notch1) pathway to alleviate hypoxia/reoxygenation(H/R) injury in HL-1 cells. The relative viability of HL-1 cells after six hours of hypoxia and two hours of reoxygenation was detected by cell counting kit-8(CCK-8). The lactate dehydrogenase(LDH) activity in the cell supernatant was detected by the lactate substrate method. The content of adenosine triphosphate(ATP) was detected by the luciferin method. Fluorescence probes were used to detect intracellular reactive oxygen species(Cyto-ROS) levels and mitochondrial membrane potential(ΔΨ_m). Mito-Tracker and Actin were co-imaged to detect the number of mitochondria in cells. Fluorescence quantitative polymerase chain reaction and Western blot were used to detect the mRNA and protein expression levels of Notch1, mitochondrial fusion protein 2(Mfn2), and mitochondrial fusion protein 1(Mfn1). The results showed that compared with that of the control group, the cell activity of the model group decreased, and the LDH released into the cell culture supernatant increased. The level of Cyto-ROS increased, and the content of ATP decreased. Compared with that of the model group, the cell activity of the ginsenoside Rg_1 group increased, and the LDH released into the cell culture supernatant decreased. The level of Cyto-ROS decreased, and the ATP content increased. Ginsenoside Rg_1 elevated ΔΨ_m and increased mitochondrial quantity in HL-1 cells with H/R injury and had good protection for mitochondria. After H/R injury, the mRNA and protein expression levels of Notch1 and Mfn1 decreased, while the mRNA and protein expression levels of Mfn2 increased. Ginsenoside Rg_1 increased the mRNA and protein levels of Notch1 and Mfn1, and decreased the mRNA and protein levels of Mfn2. Silencing Notch1 inhibited the action of ginsenoside Rg_1, decreased the mRNA and protein levels of Notch1 and Mfn1, and increased the mRNA and protein levels of Mfn2. In summary, ginsenoside Rg_1 regulated mitochondrial fusion through the Notch1 pathway to alleviate H/R injury in HL-1 cells.
Ginsenosides/pharmacology* ; Receptor, Notch1/genetics* ; Signal Transduction/drug effects* ; Mice ; Animals ; Mitochondrial Dynamics/drug effects* ; Mitochondria/metabolism* ; Cell Line ; Reactive Oxygen Species/metabolism* ; Oxygen/metabolism* ; Cell Hypoxia/drug effects* ; Cell Survival/drug effects* ; Membrane Potential, Mitochondrial/drug effects* ; Humans

OBJECTIVE: To explore the clinical significance of 26 circulating tumor DNA (ctDNA) associated with multiple myeloma (MM) in peripheral blood of new diagnosed patients. METHODS: We conducted a study to detect 26 ctDNA mutations in the peripheral blood of 31 newly diagnosed multiple myeloma (NDMM) patients. RESULTS: Among the 31 NDMM patients, the ctDNA detection rate was 93.55%, significantly higher than that of FISH and chromosome screening methods. The most frequently mutated genes in NDMM were ACTG1 and GNAS. Notably, ACTG1 mutations were exclusive to NDMM patients, furthermore, resulted from the missense mutation of the exon 4. ACTG1 was the gene most frequently co-mutated with others. All patients with ACTG1 mutations were surviving, and there was a positive correlation between ACTG1 mutation and the survival of patients. GNAS mutations were confined to exon 1. CONCLUSION The detection rate of ctDNA sequencing in peripheral blood of NDMM patients was higher than that in bone marrow. ACTG1 and GNAS genes have a guiding role in the prognosis of newly diagnosed patients.
Humans ; Multiple Myeloma/blood* ; Circulating Tumor DNA/genetics* ; Mutation ; Prognosis ; GTP-Binding Protein alpha Subunits, Gs/genetics* ; Chromogranins ; Male ; Female ; Middle Aged

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective Hypertriglyceridemic waist(HW),hypertriglyceridemic waist-to-height ratio(HWHtR),and waist-to-hip ratio(WHR)have been shown to be indicators of cardiometabolic risk factors.However,it is not clear which indicator is more suitable for children and adolescents.We aimed to investigate the relationship between HW,HWHtR,WHR,and cardiovascular risk factors clustering to determine the best screening tools for cardiometabolic risk in children and adolescents. Methods This was a national cross-sectional study.Anthropometric and biochemical variables were assessed in approximately 70,000 participants aged 6-18 years from seven provinces in China.Demographics,physical activity,dietary intake,and family history of chronic diseases were obtained through questionnaires.ANOVA,x2 and logistic regression analysis was conducted. Results A significant sex difference was observed for HWHtR and WHR,but not for HW phenotype.The risk of cardiometabolic health risk factor clustering with HW phenotype or the HWHtR phenotype was significantly higher than that with the non-HW or non-HWHtR phenotypes among children and adolescents(HW:OR = 12.22,95%CI:9.54-15.67;HWHtR:OR = 9.70,95%CI:6.93-13.58).Compared with the HW and HWHtR phenotypes,the association between risk of cardiometabolic health risk factors(CHRF)clustering and high WHR was much weaker and not significant(WHR:OR = 1.14,95%CI:0.97-1.34). Conclusion Compared with HWHtR and WHR,the HW phenotype is a more convenient indicator with higher applicability to screen children and adolescents for cardiovascular risk factors.

This experiment aims to study the taste-masking effects of different kinds of corrigent used individually and in combination on ibuprofen oral solution, in order to optimize the taste-masking formulation. Firstly, a wide range of corrigent and the mass fractions were extensively screened using electronic tongue technology. Subsequently, a combination of sensory evaluation, analytic hierarchy process (AHP)-fuzzy mathematics evaluation, and Box-Behnken experimental design were employed to comprehensively assess the taste-masking effects of different combinations of corrigent on ibuprofen oral solution, optimize the taste-masking formulation, and validate the results. The study received ethical approval from the Review Committee of the Beijing University of Chinese Medicine (ethical code: 2024BZYLL0102). The results showed that corrigent fractions and types were screened separately through single-factor experiments. Subsequently, a Box-Behnken response surface design combined with AHP and fuzzy mathematics evaluation was used to fit a functional model: Z = 688.310 11 - 3 023.722 22X₁ - 11.477 00X₂ + 62.721 67X₃ + 14.600 00X₁X₂ - 179.666 67X₁X₃ - 3.152 00X₂X₃ + 4 031.111 11X₁² + 0.525 28X₂² + 9.772 00X₃². This model is stable and reliable, determining the optimal taste-masking formulation to be 3.9 g·L^-1 of stevioside, 100 g L^-1 of xylitol, and 30 g L^-1 of methyl-β-cyclodextrin. The comprehensive score of the verification test is 88.14, with a relative RSD of 0.39%, indicating the feasibility of this model. This study achieved the improvement of the taste of ibuprofen oral solution through a combination of objective and subjective methods. Three types of corrigent were identified for enhancing drug taste, leading to the selection of the optimal taste-masking formulation for ibuprofen oral solution. This significantly enhanced the taste of the original formulation, improved patient compliance, and offered a new approach to mitigating the undesirable taste of formulations.

Objective:To investigate the precipitating and aggravating factors in patients with fibromyalgia (FMS) compared to patients with rheumatoid arthritis (RA).Methods:This study was conducted from January 2015 to November 2021, using a cross-sectional survey research method, based on references to develop a patient-reported "onset and exacerbation triggers questionnaire", and surveyed patients with FMS and RA at the same time, and counted the types and proportions of onset and exacerbation triggers in the two groups of patients and used the chi-square test to make comparisons between the groups.Results:A total of 415 patients with FMS and 200 patients with RA participated the survey. 146 patients with FMS (35.2%) and 38 patients with RA (19.0%) reported morbidity triggers. Experiencing physical injury (71, 17.1%), wind-cold/cold-dampness (30 patients, 7.2%), mental stress (26, 6.2%), and exercise fatigue (10 patients, 2.4%) were the common morbidity triggers for FMS. More FMS patients reported to have experienced physical injuries and mental stress before the onset of the disease compared to RA patients [8.2%(17/200), χ2=5.41, P=0.020; 1.5%(3/200), χ2=6.82, P=0.009]. Exacerbation triggers were reported by 319 patients with FMS (76.9%) and 137 patients with RA (68.5%), in the order of weather changes (219 patients, 52.7%), physical labor (192 patients, 46.2%), mood swings (147 patients, 35.4%), sleep deprivation (145 patients, 34.9%), and mental stress (130 patients, 31.3%). The proportion of FMS patients with symptom exacerbation due to physical labor [46.2%(192/415)], mood swings[35.4%(147/415)], sleep deprivation[34.9%(145/415)], mental stress[31.3%(130/415)], and infection [9.3%(39/415)] was significantly higher than that of RA patients [35.0%(70/200), χ2=7.00, P=0.008; 19.5%(39/200), χ2=16.22, P<0.001; 13.5%(27/200), χ2=30.79, P<0.001; 17.5%(35/200), χ2=13.14, P<0.001; 3.0%(6/200), χ2=8.15, P=0.004). Conclusion:More than a third of FMS patients reported precipitating factors, and nearly four fifths FMS patients reported at least one aggravating trigger. FMS patients are likely to be more sensitive to environmental changes and perceived stress than RA patients.

Adsorption of single-stranded DNA(ssDNA)onto unmodified gold nanoparticles(AuNPs)has been utilized as a common strategy for colorimetric assays,enabling facile detection of target analytes.In this study,a label-free,sensitive AuNP-based colorimetric biosensor was developed for visual detection of kanamycin(Kana)using a diblock oligonucleotide probe(dP)that included a poly-adenine(polyA20)tail.In the presence of Kana,the aptamer on the MBs@double-stranded DNA(dsDNA)complex specifically bound to Kana,leading to dissociation of dPs from complementary aptamer strands.The dP sequences were anchored to the surface of AuNPs due to the strong affinity between polyA20 tail and AuNPs,thus stabilizing the colloidal AuNPs against aggregation when high concentration level salt(NaCl)was introduced.Compared with random sequence(RS)with the same number of bases,AuNPs colorimetric strategy with polyA20 tail sequence significantly improved the detection sensitivity of Kana by two orders of magnitude.The results showed that the colorimetric sensor had good colorimetric performance when the concentration of Kana ranged from 0.01 to 1 nmol/L,and the detection limit(S/N=3)was 0.01 nmol/L.This sensing system did not require large instruments and other signal amplification technique,and was suitable for visualization and real-time detection of large quantities of samples with high sensitivity and specificity.This study provided a good idea for visual detection of other antibiotic drugs and chemical pollutants affecting food safety.

This study was aimed at investigating the pathogenic and molecular characteristics of Klebsiella pneumoniae(KP)in fecal samples of diarrhea cases in a district of Beijing.Fecal samples from diarrhea cases in an outpatient department in a district of Beijing from 2018 to 2021 were collected,and used for isolation and culture of KP.The KP strains isolated strains were subjected to drug resistance phenotype testing and whole-genome sequencing.Multilocus sequence typing and whole-genome phyletic evolution analysis were performed on the sequencing results.The cases'epidemiological and clinical characteristics were analyzed.From 2018 to 2021,1 103 fecal samples were collected and detected.The total detection rate of KP was 10.43％(115/1 103),and the infection rate of KP mixed with other diarrhea-causing pathogens was 42.61％(49/115).The positivity rate was slightly high(12.47％,61/489)a-mong females and was highest in young adults 16-45 years of age.Small peaks were observed in January,April to May,and August to September.The gastrointestinal symptoms in cases were mainly nausea and watery stool,and the suspicious food was unknown.Ampicillin,tetracycline,and sulfafurazole were the top three antibiotics to which these 115 KP strains showed resistance,and 29 strains were resistant to multiple antibiotics.The strains were divided into 72 sequence types,among which ST23 was dominant.According to the phylogenetic tree,the strains were divided into four main branches,among which 14 ST23 strains had a very close genetic relationship with the highly virulent NTUH-K2044 reference strain.KP infection persisted in fecal samples from diarrhea cases in the district of Beijing.Women and young adults were particularly susceptible.The drug resistance of KP strains in this region was very serious,and the ST types were diverse.Moreover,the ST23 pathogenic strains were closely related to high virulence strains.

Objective:To construct a risk prediction model for the inter-hospital transfer of critically ill children using machine learning methods, identify key medical features affecting transfer outcomes, and improve the success rate of transfers.Methods:A prospective study was conducted on critically ill children admitted to the pediatric transfer center of Hunan Children's Hospital from January 2020 to January 2021. Medical data on critical care features and relevant data from the Pediatric Risk of Mortality (PRISMⅢ) scoring system were collected and processed. Three machine learning models, including logistic regression, decision tree, and Relief algorithm, were used to construct the risk prediction model. A back propagation neural network was employed to build a referral outcome prediction model to verify and analyze the selected medical features from the risk prediction model, exploring the key medical features influencing inter-hospital transfer risk.Results:Among the 549 transferred children included in the study, 222 were neonates (40.44%) and 327 were non-neonates (59.56%). There were 50 children in-hospital deaths, resulting in a mortality rate of 9.11%. After processing 151 critical care medical feature data points, each model selected the top 15 important features influencing transfer outcomes, with a total of 34 selected features. The decision tree model had an overlap of 72.7% with PRISMⅢ indicators, higher than logistic regression (36.4%) and Relief algorithm (27.3%). The training prediction accuracy of the decision tree model was 0.94, higher than the accuracy of 0.90 when including all features, indicating its clinical utility. Among the top 15 important features selected by the decision tree model, the impact on transfer outcomes was ranked as follows based on quantitative feature violin plots: base excess, total bilirubin, ionized calcium, total time, arterial oxygen pressure, blood parameters (including white blood cells, platelets, prothrombin time/activated partial thromboplastin time), carbon dioxide pressure, blood glucose, systolic blood pressure, heart rate, organ failure, lactate, capillary refill time, temperature, and cyanosis. Eight of these important features overlapped with PRISMⅢ indicators, including systolic blood pressure, heart rate, temperature, pupillary reflex, consciousness, acidosis, arterial oxygen pressure, carbon dioxide pressure, blood parameters, and blood glucose. The decision tree was used to select the top 15 medical features with high impact on the neonatal and non-neonatal datasets, respectively. A total of 19 features were selected, among which there were 8 differences and 11 overlap terms between the important features of the neonatal and non-neonatal.Conclusions:Machine learning models could serve as reliable tools for predicting the risk of inter-hospital transfer of critically ill children. The decision tree model exhibits superior performance and helps identify key medical features affecting inter-hospital transfer risk, thereby improving the success rate of inter-hospital transfers for critically ill children.