ObjectiveThe rapid advancement of bioanalytical technologies has heightened the demand for high-throughput, label-free, and real-time cellular analysis. Electrochemical impedance spectroscopy (EIS) operating in the GHz frequency range (GHz-EIS) has emerged as a promising tool for characterizing cell suspensions due to its ability to rapidly and non-invasively capture the dielectric properties of cells and their microenvironment. Although GHz-EIS enables rapid and label-free detection of cell suspensions, significant challenges remain in interpreting GHz impedance data for complex samples, limiting the broader application of this technique in cellular research. To address these challenges, this study presents a novel method that integrates GHz-EIS with deep learning algorithms, aiming to improve the precision of cell suspension concentration identification and quantification. This method provides a more efficient and accurate solution for the analysis of GHz impedance data. MethodsThe proposed method comprises two key components: dielectric property dataset construction and backpropagation (BP) neural network modeling. Yeast cell suspensions at varying concentrations were prepared and separately introduced into a coaxial sensor for impedance measurement. The dielectric properties of these suspensions were extracted using a GHz-EIS dielectric property extraction method applied to the measured impedance data. A dielectric properties dataset incorporating concentration labels was subsequently established and divided into training and testing subsets. A BP neural network model employing specific activation functions (ReLU and Leaky ReLU) was then designed. The model was trained and tested using the constructed dataset, and optimal model parameters were obtained through this process. This BP neural network enables automated extraction and analytical processing of dielectric properties, facilitating precise recognition of cell suspension concentrations through data-driven training. ResultsThrough comparative analysis with conventional centrifugal methods, the recognized concentration values of cell suspensions showed high consistency, with relative errors consistently below 5%. Notably, high-concentration samples exhibited even smaller deviations, further validating the precision and reliability of the proposed methodology. To benchmark the recognition performance against different algorithms, two typical approaches—support vector machines (SVM) and K-nearest neighbor (KNN)—were selected for comparison. The proposed method demonstrated superior performance in quantifying cell concentrations. Specifically, the BP neural network achieved a mean absolute percentage error (MAPE) of 2.06% and an R² value of 0.997 across the entire concentration range, demonstrating both high predictive accuracy and excellent model fit. ConclusionThis study demonstrates that the proposed method enables accurate and rapid determination of unknown sample concentrations. By combining GHz-EIS with BP neural network algorithms, efficient identification of cell concentrations is achieved, laying the foundation for the development of a convenient online cell analysis platform and showing significant application prospects. Compared to typical recognition approaches, the proposed method exhibits superior capabilities in recognizing cell suspension concentrations. Furthermore, this methodology not only accelerates research in cell biology and precision medicine but also paves the way for future EIS biosensors capable of intelligent, adaptive analysis in dynamic biological research.

It has become an industry consensus that self-assembled nanoparticles (SAN) are formed by molecular recognition of chemical components in traditional Chinese medicine during the decoction process. The insoluble components in the decoction are mostly in the form of nanoparticles, which can improve the problem of poor water solubility. However, the transfer rate of these insoluble components in the decoction is still very low, which limits the efficacy of the drug. This study aimed to refine the traditional decoction self-assembly phenomenon. The self-assembled nanoparticles were constructed by micro-precipitation method (MP-SAN), and characterized by particle size, zeta potential, stability index and morphology. The formation of MP-SAN and alterations in related physicochemical properties were evaluated using modern spectroscopic and thermal analysis techniques. The quality value transmitting pattern of lignan components within the MP-SAN was assessed via high performance liquid chromatography (HPLC). The MP-SAN showed sphere-like structure with uniform morphology, particle size of (245.3 ± 3.2) nm, polydispersity index (PDI) of (0.13 ± 0.03), zeta potential of (-48.9 ± 5.9) mV and stability index (SI) of (86.05% ± 2.27%). Comprehensive analyses using ultraviolet visible spectroscopy, Fourier transform infrared spectroscopy, differential scanning calorimetry, and other techniques confirmed molecular recognition between the decoction and ethanol extraction, leading to electron rearrangement under the influence of non-covalent bonding. This resulted in the formation of nanoparticles possessing superior thermal stability. As determined by HPLC, the encapsulation rates of the index components in the MP-SAN were all greater than 75% (dehydrodiconiferyl alcohol: 77.00%; herpetolide A: 78.57%; herpetrione: 94.53%), and the transfer rates were all higher than 65% (dehydrodiconiferyl alcohol: 96.01%; herpetolide A: 67.86%; herpetrione: 65.55%), which were 1.34, 1.38 and 4.81 times compared with those of the traditional decoction. In summary, this study successfully constructed the MP-SAN based on micro-precipitation method to achieve high transfer rate and high encapsulation rate of insoluble components in docoction, which provides a pharmaceutics idea for the efficient utilization of pharmacodynamic substance basis of traditional Chinese medicine.

Human exhaled breath has great application prospects,e.g.,monitoring pharmacokinetics,disease diagnosis,due to its advantages such as non-invasive and high-frequency sampling.Breath samples can be collected from the oral and nasal cavity.However,the oral and nasal environment affect the chemical composition of breath sample.Therefore,the investigation on the chemical composition of mouth-exhaled breath and nose-exhaled breath is crucial for selection of appropriate sampling strategy for individual studies.In this work,secondary electrospray ionization-high resolution mass spectrometry(SESI-HRMS)was applied to analysis of respiratory metabolomics in real time.A quantitative analysis approach was established for 9 kinds of volatile organic compounds(VOCs)e.g.2-butanone,2-pentanone,ethyl acetate,methyl methacrylate,toluene,styrene,mesitylene,isoprene and limonene.The limit of detection was 2.3?240.8 ng/m3.The intra-day(n=6)and inter-day(n=18)relative standard deviations were 0.6%?4.6%and 4.3%?12.2%,respectively.Nine healthy subjects were recruited to investigate the chemical composition of mouth-exhaled and nose-exhaled breath.The results showed the good performance in quantitative analysis of 9 VOCs in breath air.It was found that the number of unique component(m/z)detected in mouth-exhaled breath(167)was 2.2 times greater than that detected in nose-exhaled breath(76),which might result from the complex environment in oral cavity.The signal intensity of commun component(163)was significantly different between mouth-exhaled breath and nose-exhaled breath.Additionally,the elemental composition analysis showed that the proportion of polar compounds detected in nose-exhaled breath was higher than that in mouth-exhaled breath.This study demonstrated that there was significant differences in the chemical composition between mouth-exhaled and nose-exhaled breath,which provided a theoretical basis for selection of exhalation mode.

Objective To analyze the effects of deep learning image reconstruction(DLIR)and adaptive statistical iterative recon-struction V(ASIR-V)on the imaging quality of chest CT in patient with pulmonary nodules,and to evaluate the differences based on different image reconstruction techniques in the detection of efficiency of computer-aided diagnosis(CAD)for pulmonary nodules.Methods The image data of pulmonary nodules of eighty patients with chest CT screening were reconstructed with ASIR-V 80％,DLIR-low(DLIR-L),DLIR-medium(DLIR-M)and DLIR-high(DLIR-H)images,respectively.The objective image quality and sub-jective image quality of the four groups were compared and analyzed.Objective image quality includes CT value of region of interest(ROI),noise,signal-to-noise ratio(SNR),contrast-to-noise ratio(CNR)and image average gradient.The diagnostic efficacy of CAD in detecting pulmonary nodules of reconstructed images among four groups were further evaluated.Results There were no signifi-cant difference in CT value of ROI of reconstructed images among the four groups(P＞0.05).The noise,SNR and CNR of DLIR-H images were similar to those of ASIR-V 80％(P＞0.05),but significantly better than those of DLIR-L and DLIR-M(P＜0.05).The average gradient of DLIR-L,DLIR-M and DLIR-H images were significantly higher than those of ASIR-V 80％(P＜0.05).The subjective image quality scores of DLIR-L,DLIR-M and DLIR-H images were significantly higher than those of ASIR-V 80％(P＜0.05),and the subjective image quality score of DLIR-H image was the highest.CAD showed the highest true positive rate in DLIR-H images for detecting pulmonary nodules(P＜0.05),and CAD showed the highest false positives per capita in ASIR-V 80％images for detecting pulmonary nodules(P＜0.05).Conclusion The noise,SNR and CNR of DLIR-H images are similar to those of ASIR-V 80％,with the significantly higher image clarity and subjective image quality scores.DLIR-H has advantages in CAD detection of pulmonary nodules,which is an ideal image reconstruction technology for chest CT pulmonary nodule screening.

Objective To explore the effect of Shugan Lipi decoction on inflammation and intestinal flora,Toll like receptor 4(TLR4),T cell immunoglobulin domain and mucin domain-3(Tim-3)in non-alcoholic steatohepatitis(NASH)rats.Methods The NASH model was established by feeding methionine and choline deficient diet for 4 weeks.SD rats were randomly divided into blank group(intragastric administration with 0.9％NaCl),model group(NASH model,intragastric administration with 0.9％NaCl),and experimental group(NASH model,intragastric administration with 6.18g·kg-1 Shugan Lipi decoction).Illumina sequencing by synthesis method was used to detect the 16S rRNA sequence of rat Intestinal microbiota.Western blot method was used to detect the expression levels of Tim-3 and TLR4 proteins.Enzyme-linked immunosorbent assay was used to detect tumor necrosis factor-α(TNF-α),interleukin(IL)-6 and IL-10 levels in each group of rats.Results After 4 weeks of medication,the relative abundance of Bacteroidetes in the blank,model and experimental groups were(47.96±10.52)％,(42.90±15.01)％and(57.15±10.99)％;the relative abundance of Firmicutes were(49.27±9.99)％,(53.06±11.47)％and(39.99±11.88)％;the relative expression levels of Tim-3 protein were 1.03±0.07,0.24±0.06 and 1.57±0.11;the relative expression levels of TLR4 protein were 1.04±0.11,3.23±0.33 and 0.94±0.27;the levels of TNF-α were(403.03±25.25),(576.87±60.29)and(385.16±37.67)pg·mL-1;the levels of IL-6 were(125.35±14.07),(189.75±34.30)and(113.71±18.35)pg·mL-1;the levels of IL-10 were(123.20±15.96),(66.71±11.94)and(119.54±10.57)pg·mL-1,respectively.The above indexes in the experimental group showed statistically significant differences compared with the model group(P＜0.01,P＜0.05).Conclusion Shugan Lipi decoction may regulate inflammatory cytokines by affecting intestinal flora and TLR4,Tim-3 protein expression,affect liver inflammatory response,and improve NASH.

Objective:To investigate the associations of onset age, diabetes duration, and glycated hemoglobin (HbA1c) levels with ischemic stroke risk in type 2 diabetes patients.Methods:The participants were from Comprehensive Research on the Prevention and Control of the Diabetes in Jiangsu Province. The study used data from baseline survey from December 2013 to January 2014 and follow-up until December 31, 2021. After excluding the participants who had been diagnosed with stroke at baseline survey and those with incomplete information on onset age, diabetes duration, and HbA1c level, a total of 17 576 type 2 diabetes patients were included. Cox proportional hazard model was used to calculate the hazard ratio ( HR) and 95% CI of onset age, diabetes duration, and HbA1c level for ischemic stroke. Results:During the median follow-up time of 8.02 years, 2 622 ischemic stroke cases were registered. Multivariate Cox proportional risk regression model showed that a 5-year increase in type 2 diabetes onset age was significantly associated with a 5% decreased risk for ischemic stroke ( HR=0.95, 95% CI: 0.92-0.99). A 5-year increase in diabetes duration was associated with a 5% increased risk for ischemic stroke ( HR=1.05, 95% CI: 1.02-1.10). Higher HbA1c (per 1 standard deviation increase: HR=1.17, 95% CI: 1.13-1.21) was associated with an increased risk for ischemic stroke. Conclusion:The earlier onset age of diabetes, longer diabetes duration, and high levels of HbA1c are associated with an increased risk for ischemic stroke in type 2 diabetes patients.

Endometriosis is a common chronic gynecological disease with endometrial cell implantation outside the uterus.Angiogenesis is a major pathophysiology in endometriosis.Our previous studies have demon-strated that the prodrug of epigallocatechin gallate(ProEGCG)exhibits superior anti-endometriotic and anti-angiogenic effects compared to epigallocatechin gallate(EGCG).However,their direct binding targets and underlying mechanisms for the differential effects remain unknown.In this study,we demonstrated that oral ProEGCG can be effective in preventing and treating endometriosis.Additionally,1D and 2D Proteome Integral Solubility Alteration assay-based chemical proteomics identified metadherin(MTDH)and PX domain containing serine/threonine kinase-like(PXK)as novel binding targets of EGCG and ProEGCG,respectively.Computational simulation and BioLayer interferometry were used to confirm their binding affinity.Our results showed that MTDH-EGCG inhibited protein kinase B(Akt)-mediated angiogenesis,while PXK-ProEGCG inhibited epidermal growth factor(EGF)-mediated angiogenesis via the EGF/hypoxia-inducible factor(HIF-1a)/vascular endothelial growth factor(VEGF)pathway.In vitro and in vivo knockdown assays and microvascular network imaging further confirmed the involvement of these signaling pathways.Moreover,our study demonstrated that ProEGCG has superior therapeutic effects than EGCG by targeting distinct signal transduction pathways and may act as a novel anti-angiogenic therapy for endometriosis.

Objective To investigate the mechanism behind the protective effects of gastrodin against microglia-mediated inflammatory responses following hypoxic-ischemic brain damage(HIBD)in neonatal mice.Methods Thirty-six 10-day-old C57BL/6J mice were randomized into sham-operated group,HIBD(induced by ligation of the left common carotid artery followed by hypoxia for 40 min)group,and HIBD with gastrodin treatment groups(n=12).In gastrodin treatment group,100 mg/kg gastrodin was injected intraperitoneally 1 h before and at 2 and 12 h after hypoxia.After the treatments,the expressions of CCR5,AKT,p-AKT,and TNF-α and the co-expression of IBA1 and CCR5 in the corpus callosum of the mice were detected with Western blotting and immunofluorescence double staining.In a BV2 microglial cell model of oxygen-glucose deprivation(OGD),the effects of pretreatment with gastrodin and Maraviroc(an CCR5 antagonist)on protein expressions of CCR5,AKT,p-AKT,TNF-α and IL-1β were evaluated using Western blotting and immunofluorescence double staining.Results The neonatal mice with HIBD showed significantly increased expressions of CCR5 and TNF-α with lowered p-AKT expression in the brain tissues,and GAS treatment obviously reversed these changes.HIBD also significantly increased the co-expression of IBA1 and CCR5 in the corpus callosum of the mice,which was obviously lowered by gastrodin treatment.In BV2 cells,OGD significantly increased the expressions of CCR5,TNF-α,and IL-1β and decreased the expression of p-AKT,and these changes were inhibited by treatment with gastrodin,Maraviroc or their combination;the inhibitory effect of the combined treatment did not differ significantly from that of gastrodin or Maraviroc alone.Conclusion Gastrodin can produce neuroprotective effects in neonatal mice with HIBD by inhibiting inflammatory cytokine production and activate AKT phosphorylation via inhibiting CCR5.

Activation of nuclear factor erythroid 2-related factor 2(Nrf2)by Kelch-like ECH-associated protein 1(Keap1)alkylation plays a central role in anti-inflammatory therapy.However,activators of Nrf2 through alkylation of Keap1-Kelch domain have not been identified.Deoxynyboquinone(DNQ)is a natural small molecule discovered from marine actinomycetes.The current study was designed to investigate the anti-inflammatory effects and molecular mechanisms of DNQ via alkylation of Keap1.DNQ exhibited signif-icant anti-inflammatory properties both in vitro and in vivo.The pharmacophore responsible for the anti-inflammatory properties of DNQ was determined to be the α,β-unsaturated amides moieties by a chemical reaction between DNQ and N-acetylcysteine.DNQ exerted anti-inflammatory effects through activation of Nrf2/ARE pathway.Keap1 was demonstrated to be the direct target of DNQ and bound with DNQ through conjugate addition reaction involving alkylation.The specific alkylation site of DNQ on Keap1 for Nrf2 activation was elucidated with a synthesized probe in conjunction with liquid chromatography-tandem mass spectrometry.DNQ triggered the ubiquitination and subsequent degra-dation of Keap1 by alkylation of the cysteine residue 489(Cys489)on Keap1-Kelch domain,ultimately enabling the activation of Nrf2.Our findings revealed that DNQ exhibited potent anti-inflammatory capacity through α,β-unsaturated amides moieties active group which specifically activated Nrf2 signal pathway via alkylation/ubiquitination of Keap1-Kelch domain,suggesting the potential values of targeting Cys489 on Keap1-Kelch domain by DNQ-like small molecules in inflammatory therapies.

The analysis of nano-micro nuclear particles has attracted significant attention due to the crucial role of their elemental and isotopic characteristics in tracing the origins of particulate matter and assessing its potential risks to human health and the environment.However,challenges persist in obtaining accurate and consistent element profiles and ratios for small-sized nanoparticles due to their low level and the transient nature.In this study,a high-speed digital oscilloscope was integrated with multiple collector inductively coupled plasma mass spectrometry(MC-ICP-MS)to develop a high time-resolution"Event-triggered signal capture"(ETSC)system for single particle analysis.This innovative approach enabled the analysis of element/isotope within rare earth nanoparticles at ag-fg level.The ETSC accurately recorded the complete profile of single particle,event captured by the electron multiplier with nanosecond time resolution,allowing for high-sensitivity element analysis and high-precision isotope analysis of single particles.The results demonstrated that the ETSC system could achieve quantitative analysis of ag levels of ytterbium(Yb)in 50-nm rare earth-doped particles,with a detection limit as low as 38 ag for Yb.Moreover,the isotopic precision of single particle analysis for 173/171Yb could reach 0.047(standard deviation),and the standard error for isotopic analysis of multiple particles could achieve a level of 2‰-3‰(permil)for 173/171Yb.Finally,the capability of ETSC system to analyze environmental samples was demonstrated through the analysis of doped ytterbium oxide nanoparticles.All these findings demonstrated that the ETSC provided a unique method for elemental and isotopic analysis of single nuclear particles.