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.

An intensive breast array sensor was designed based on three-dimensional electrical impedance tomography in this work.Firstly,an electrical impedance sensor for detection of breast cancer was developed.The sensor adopted the integrated design of excitation electrode array and ground electrode to achieve structural simplification.It realized electric field densification through conical matrix and double-layer circumferentially arranged electrode array and improved the detection accuracy of target object through taper optimization.Secondly,the imaging system was designed,and the sensor was optimized by numerical simulation.The simulation results showed that halving the number of electrodes did not affect imaging accuracy of the sensor,but could improve the imaging speed.Finally,the performance of the sensor was verified by experiment.The signal-to-noise ratio and channel consistency of the system were at a good level.The sensor was used to reconstruct three-dimensional image of the experimental model with relative volume of the detection field of 0.4%.The image correlation coefficient of the single target imaging was above 0.6 and the position of the double target object could be clearly identified,and thus the visual detection of breast cancer was realized.

Programmed cell death 1 ligand 1 (PD-L1) is an important immunosuppressive molecule, which inhibits the function of T cells and other immune cells by binding to the receptor programmed cell death-1. The PD-L1 expression disorder plays an important role in the occurrence, development, and treatment of sepsis or other inflammatory diseases, and has become an important target for the treatment of these diseases. Mesenchymal stem cells (MSCs) are a kind of pluripotent stem cells with multiple differentiation potential. In recent years, MSCs have been found to have a strong immunosuppressive ability and are used to treat various inflammatory insults caused by hyperimmune diseases. Moreover, PD-L1 is deeply involved in the immunosuppressive events of MSCs and plays an important role in the treatment of various diseases. In this review, we will summarize the main regulatory mechanism of PD-L1 expression, and discuss various biological functions of PD-L1 in the immune regulation of MSCs.

ObjectiveThe detection of RNA single nucleotide polymorphism (SNP) is of great importance due to their association with protein expression related to various diseases and drug responses. At present, splintR ligase-assisted methods are important approaches for RNA direct detection, but its specificity will be limited when the fidelity of ligases is not ideal. The aim of this study was to create a method to improve the specificity of splintR ligase for RNA detection. MethodsIn this study, a dual-competitive-padlock-probe (DCPLP) assay without the need for additional enzymes or reactions is proposed to improve specificity of splintR ligase ligation. To verify the method, we employed dual competitive padlock probe-mediated rolling circle amplification (DCPLP-RCA) to genotype the CYP2C9 gene. ResultsThe specificity was well improved through the competition and strand displacement of dual padlock probe, with an 83.26% reduction in nonspecific signal. By detecting synthetic RNA samples, the method demonstrated a dynamic detection range of 10 pmol/L-1 nmol/L. Furthermore, clinical samples were applied to the method to evaluate its performance, and the genotyping results were consistent with those obtained using the qPCR method. ConclusionThis study has successfully established a highly specific direct RNA SNP detection method, and provided a novel avenue for accurate identification of various types of RNAs.

Objective To explore the medication rules of Professor HUANG Li for the treatment of recurrent angina pectoris after percutaneous coronary intervention(PCI)for coronary heart disease by data mining method.Methods The prescriptions for effective cases of recurrent angina pectoris after PCI for coronary heart disease treated by Professor HUANG Li in the outpatient department of China-Japan Friendship Hospital were collected.SPSS Statistics 26.0 software and SPSS Modeler 18.0 software were used for frequency statistics,analysis of the therapeutic actions,properties,flavors and meridian tropism of the prescribed herbs as well as association rule analysis,cluster analysis and factor analysis of the herbs.Results A total of 344 Chinese medicine prescriptions were obtained,involving 209 herbs,with a cumulative frequency of 5 874 times.The top 30 Chinese medicinals were named as the high-frequency Chinese medicines,and the herbs with the frequency over 100 times in descending order were Astragali Radix,Chuanxiong Rhizoma,Puerariae Lobatae Radix,Rhodiolae Crenulatae Radix et Rhizoma,Notoginseng Radix et Rhizoma,Poria,Dalbergiae Odoriferae Lignum,Atractylodis Macrocephalae Rhizoma,Curcumae Rhizoma,Sparganii Rhizoma,Dioscoreae Rhizoma,Citri Reticulatae Pericarpium,Pinelliae Rhizoma Praeparatum,Codonopsis Radix,and Glycyrrhizae Radix et Rhizoma.The high-frequency Chinese medicinals were mostly classified as blood-activating and stasis-resolving drugs and qi-replenishing drugs.The medicinal properties of the drugs were characterized by being warm,mild,or cold,the flavors were predominated by being sweet,pungent or bitter,and the medicinals usually had the meridian tropism of the spleen,lung and liver meridians.A total of 30 association rules were mined out,cluster analysis yielded 5 herbal groups,and factor analysis yielded 11 groups of common factors.Conclusion For the treatment of cardiovascular diseases,Professor HUANG Li follows the theory of qi,blood and water,and especially pays more attention to the ascending and descending of qi movement.For qi deficiency and blood stasis contribute to the basic pathogenesis of recurrent angina pectoris after PCI,the therapy of benefiting qi,activating blood and removing stasis is recommended.Moreover,the simultaneous regulation of five zang-organs and simultaneous use of the cold and warm herbs are performed,and the herbs of benefiting qi and invigorating spleen,resolving phlegm and inducing diuresis,tranquilizing mind,promoting qi and dissipating masses,and activating blood to eliminate stasis are used for adjuvant therapy.

Objective To achieve automatic segmentation of cell nuclei in gastrointestinal cancer pathological images by using a deep learning algorithm,so as to assist in the quantitative analysis of subsequent pathological images.Methods A total of 59 patients with gastrointestinal cancer treated in Ruijin Hospital,Shanghai Jiao Tong University School of Medicine from Jan 2022 to Feb 2022,were selected as the research objects.Python and LabelMe were used for data anonymization,image segmentation,and region of interest annotation of patients'pathological images.A total of 944 pathological images were included,and 9 703 nuclei were annotated.Then,a new semantic segmentation model based on deep learning was constructed.The model introduced deformable attention transformer(DAT)to realize automatic,accurate and efficient segmentation of pathological image nuclei.Finally,multiple segmentation evaluation criteria are used to evaluate the model's performance.Results The mean absolute error of the segmentation results of the model proposed in this paper was 0.112 6,and the dice coefficient(Dice)was 0.721 5.Its effect was significantly better than the U-net baseline model,and it was ahead of models such as ResU-net++,R2Unet and R2AttUnet.Moreover,the segmentation results were relatively stable with good generalization.Conclusion The segmentation model established in this study can accurately identify and segment the nuclei in the pathological images,with good robustness and generalization,which is helpful to play an auxiliary diagnostic role in practical applications.

Objective:To explore the pathogenic agents of acute respiratory infection (ARI) in children in Beijing.Methods:In the cross-sectional study, 3 groups of children from different departments were enrolled from Feb 6 th, 2023 (6 th week) to May 28 th (21 th week), 2023, including influenza-like case group from emergency department for nucleic acid testing of influenza virus (Flu) and human metapneumovirus (HMPV), the outpatient ARI group under nucleic acid testing for Flu, respiratory syncytial virus (RSV), adenovirus (ADV), and parainfluenza virus (PIV), and the inpatient ARI group under nucleic acid testing for Flu, RSV, HMPV, ADV, human bocavirus (HBoV), Rhinovirus (Rh), PIV, coronavirus (HCoV), Mycoplasma pneumoniae (Mp) and Chlamydia pneumonia (Cp). Results:There were 320 influenza-like cases enrolled, including 192 males and 128 females, aged 4.7 (3.6, 6.9) years, and 117 cases (36.6%) positive for Flu A, which contained similar proportion of pandemic H1N1 (H1N1) 47.0% (55/117) and H3N2 53.0% (62/117), and 13 cases for HMPV 4.1% (13/320). The rate of Flu reached its peak at the 10 th week, with H1N1 as the predominant one from the 6 th to 9 th week (10.0%-50.0%) and then H3N2 from the 10 th to 16 th week (15.0%-90.0%). HMPV was detected from the 15 th week 5.0% (1/20), and then reached to 30.0% (6/20) at the 20 th week. In the outpatient ARI group, 7 573 were enrolled, including 4 131 males and 3 442 females, aged 4.0 (2.1, 5.3) years, and the highest positive rate for RSV 32.9% (2 491/7 573), followed by Flu A 12.1% (915/7 573). The dominant one was Flu A in weeks 6-14 (23.2%-74.7%), then RSV in the 15 th week 24.8% (36/145). In the inpatient ARI group, 1 391 patients were enrolled, including 804 males and 587 females, aged 3.3 (0.4, 5.8) years, and the highest positive rate for Rh 18.7% (260/1 391), followed by RSV 12.4% (173/1 391), Flu A 10.2% (142/1 391, of which 116 cases (81.7%) were H1N1, and 26 cases (18.3%) were H3N2) and HMPV 3.1% (43/1 391). H1N1 was detected from the 7 th week 10% (6/60), to peak in the 11 th week 31.8% (21/66). H3N2 was detected from the 8 th week 1.5% (1/68), and then kept in low level. The proportion of H1N1 among Flu was 81.7% (116/142) in the inpatient ARI group. RSV was detected from 12 th week 1.3% (1/80), reaching 30.4% (35/115) at 19 th week. The positive rate of HMPV reached 12.1% (14/116) at 21 th week. Conclusions:In the spring of 2023, the first one in Beijing is the Flu epidemic, with H1N1 being the predominant one in the early stage and H3N2 in the later stage. Then, there is a postponed RSV epidemic and an increased HMPV detection. In addition, nucleic acid testing for outpatient children should be strengthened to provide early warning of epidemics.

Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.

Objective:To summarize the best evidence on case management of patients with home enteral nutrition.Methods:Relevant evidence on the case management of home enteral nutrition patients was retrieved by literature search,and the evidence was extracted and summarized for the literature that met the quality requirements.Result:A total of 10 literatures were included,including 1 guideline,3 expert consensus,2 industry standards,1 systematic review and 3 randomized controlled trials.By establishment of archives,policy management,establishment of multidisciplinary teams,overall evaluation of home enteral nutrition,as well as implementation management,a total of 33 home enteral nutrition case management was summarized from 6 aspects including health education and follow-up,etc.Conclusion:All the summarized relevant evidence about case filing and management of home enteral nutrition patients can be applied in clinical practice to promote the standardized management of home enteral nutrition.

Aim To study the main active components and potential mechanism of selenshenzhi prescription a-gainst esophageal cancer by network pharmacology and in vivo and in vitro experiments.Methods The com-mon target was extracted from TCMSP,OMIM and GeneCards databases,and the PPI network was con-structed using STRING database.DAVID database was used for GO and KEGG enrichment analysis,and a network was constructed based on STRING and DAVID database for in vivo and in vitro experimental verifica-tion.Results Prediction results showed that a total of 100 active ingredients and 749 related targets were ob-tained,and 168 common targets were obtained between selenoshenzhi recipe and esophageal cancer,which were involved in the PI3K-AKT signaling pathway and proteoglycan signaling pathways in cancer.Selenshenz-hi prescription was used to conduct preliminary verifi-cation of related targets for human esophageal cancer EC9706 based on in vitro experiments.The results showed that selenshenzhi prescription could significantly inhibit the proliferation of esophageal cancer cells and induce the apoptosis of EC9706 through the expression of Bax,Bcl-2,caspase-3 and other key apoptotic pro-teins.Lastly,the core target and pathway of selensh-enzhi prescription were preliminically verified based on in vivo animal experiments on nude mice with esopha-geal cancer.The results showed that selenshenzhi pre-scription could significantly inhibit tumor proliferation,promote tumor cell apoptosis,and induce tumor apop-tosis by regulating the expression of key proteins on PI3K/AKT signaling pathway.Conclusions Selensh-enzhi prescription can control the occurrence and de-velopment of esophageal cancer through the synergistic effect of multi-components,multi-targets and multi-pathways,and provide a theoretical basis for further clinical investigation of the mechanism of selenshenzhi prescription in the treatment of esophageal cancer in the future.