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.

OBJECTIVE: To investigate the effect of non-chemotherapy strategy of retinoic acid (ATRA) combined with arsenic trioxide (ATO) on the survival of patients with acute promyelocytic leukemia (APL). METHODS: The data of APL patients with complete information diagnosed in the hematology department of our hospital from June 2009 to November 2024 were retrospective analyzed. All patients in the non-CHT group received ATRA-ATO induction, consolidation and maintenance therapy. Patients in the CHT group received ATRA-ATO+chemotherapy induction therapy, followed by 3 cycles of ATRA-ATO+CHT consolidation therapy and 6-10 cycles of ATRA-ATO maintenance therapy. The primary endpoint was event-free survival (EFS). Secondary endpoints included overall survival (OS), remission rate, differentiation syndrome (DS) and safety. RESULTS: There were 182 patients with APL and 15 patients with early death (ED), accounting for 8.24%, which was related to age and risk stratification. There was no significant difference in remission rate between the non-CHT group and the CHT group (P =0.486). As of February 2025, the median follow-up time of patients was 39.5 months. The EFS of the non-CHT group was significantly better than that of the CHT group (P =0.038). There was no significant difference in OS between the two groups (P =0.442). Subgroup analysis showed that EFS in the non-CHT was longer in standard-risk patients (P =0.012). There was no significant difference in EFS (P =0.585) and OS (P =0.473) between the CHT and non-CHT groups in high-risk patients. The incidence of mild DS was 23.6% in the non-CHT group and 23.1% in the CHT group, respectively, with no statistically significant difference(P =0.937). Compared with CHT group, the incidence of serious adverse events was lower in the non-CHT group. CONCLUSION The non-chemotherapy regimen of ATRA combined with ATO is a feasible method to cure APL patients.
Humans ; Leukemia, Promyelocytic, Acute/drug therapy* ; Arsenic Trioxide/therapeutic use* ; Tretinoin/administration & dosage* ; Retrospective Studies ; Female ; Treatment Outcome ; Male ; Adult ; Antineoplastic Combined Chemotherapy Protocols/therapeutic use* ; Middle Aged ; Remission Induction

Progressive pulmonary fibrosis (PPF) is a progressive and lethal condition with few effective treatment options. Improvements in quality of life for patients with PPF remain limited even while receiving treatment with approved antifibrotic drugs. Traditional Chinese medicine (TCM) has the potential to improve cough, dyspnea and fatigue symptoms of patients with PPF. TCM treatments are typically diverse and individualized, requiring urgent development of efficient and precise design strategies to identify effective treatment options. We designed an innovative Bayesian adaptive two-stage trial, hoping to provide new ideas for the rapid evaluation of the effectiveness of TCM in PPF. An open-label, two-stage, adaptive Bayesian randomized controlled trial will be conducted in China. Based on Bayesian methods, the trial will employ response-adaptive randomization to allocate patients to study groups based on data collected over the course of the trial. The adaptive Bayesian trial design will employ a Bayesian hierarchical model with "stopping" and "continuation" criteria once a predetermined posterior probability of superiority or futility and a decision threshold are reached. The trial can be implemented more efficiently by sharing the master protocol and organizational management mechanisms of the sub-trial we have implemented. The primary patient-reported outcome is a change in the Leicester Cough Questionnaire score, reflecting an improvement in cough-specific quality of life. The adaptive Bayesian trial design may be a promising method to facilitate the rapid clinical evaluation of TCM effectiveness for PPF, and will provide an example for how to evaluate TCM effectiveness in rare and refractory diseases. However, due to the complexity of the trial implementation, sufficient simulation analysis by professional statistical analysts is required to construct a Bayesian response-adaptive randomization procedure for timely response. Moreover, detailed standard operating procedures need to be developed to ensure the feasibility of the trial implementation. Please cite this article as: Zhang C, Nie YS, Zhang CT, Yang HJ, Zhang HR, Xiao W, Cui GF, Li J, Li SJ, Huang QS, Yan SY. An adaptive Bayesian randomized controlled trial of traditional Chinese medicine in progressive pulmonary fibrosis: Rationale and study design. J Integr Med. 2025; 23(2): 138-145.
Female ; Humans ; Male ; Bayes Theorem ; Disease Progression ; Drugs, Chinese Herbal/therapeutic use* ; Medicine, Chinese Traditional/methods* ; Pulmonary Fibrosis/therapy* ; Quality of Life ; Randomized Controlled Trials as Topic ; Research Design ; Adaptive Clinical Trials as Topic

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

Primary female genital system lymphoma(PFGSL)is a rare subtype of extranodal lymphoma and patients commonly present in the department of gynecology.At present,there is a lack of uniform standards for the treatment of PFGSL.Although the classification of lymphoid neoplasmas was updated by the World Health Organization classification of haematolymphoid tumors in 2016,PFGSL was still not elaborated in sufficient detail.Most cases of PFGSL are non-Hodgkin lymphoma,involving the ovary and cervix.In some cases,involvement of uterine corpus,vagina,and vulva is reported.In this article,we report two cases of non-Hodgkin lymphoma in the female genital system,one from the uterus and the other from the ovary.By presenting the diagnosis and treatment of the two cases and reviewing the literature,we aim to provide a reference for clinicians in recognizing and treating rare cases.
Female ; Humans ; Genital Neoplasms, Female/diagnosis* ; Lymphoma, Non-Hodgkin/diagnosis* ; Ovarian Neoplasms

With the in-depth study of molecular biology,non-small cell lung cancer(NSCLC)has opened the era of precision medicine based on mutation-based molecular targeting therapy.Epidermal growth factor receptor(EGFR)driver mutations are closely related to the progression of NSCLC,and EGFR-tyrosine kinase inhibitors(TKIs)developed based on this have achieved significant therapeutic effects,but acquired drug resistance is still one of the major factors limiting their long-term use.As resistance mechanisms are further investigated,in addition to secondary EGFR mutation,MET amplification,HER2 amplification,histologic transformation,etc.,receptor tyrosine kinase(RTK)fusion mutation have been shown to be a targetable mechanism of acquired resistance.Among the acquired RTK fusion mutations,rearranged during transfection(RET)fusion mutations are the accessible targets of our concern.As the RET molecule continues to be explored,drugs targeting RET fusions have been approved and marketed.There are different clinical strategies to deal with acquired RET fusion mutation mediating resistance to EGFR-TKIs treatment.In this review,the structure and function of RET,its relationship with EGFR-TKIs resistance,and treatment strategies are reviewed to further improve patient survival outcomes.

Objective To develop a health belief model(HBM)based adolescent alcohol-related cognition scale to measure adolescent alcohol-related cognition and test its reliability and validity.Methods The adolescents'alcohol-related cognitive scale was developed based on HBM model.By using purposive sampling,three general high schools in Qingpu District,Shanghai were selected.One-third of the classes from grades 10 and 11 in each school were randomly selected,and the students from these classes were surveyed as the research subjects.Exploratory factor analysis and confirmatory factor analysis were used to analyze its reliability(internal consistency reliability and combination reliability)and validity(structural validity,convergent validity,discriminative validity and criterion validity).Results A total of 970 questionnaires were collected,of which 948 were valid,with an effective rate of 97.7%.The adolescents'alcohol-related cognitive scale contained 22 items.Five common factors were extracted from exploratory factor analysis,including perceived susceptibility,perceived severity,perceived benefits,perceived obstacles,and self-efficacy.The cumulative variance contribution rate reached 83.89%.The results of confirmatory factor analysis confirmed the overall fit of the model.The average variance extracted value(AVE)of each dimension was greater than 0.5,and the convergent validity of the model was ideal.The AVE square root of each dimension of the scale was greater than its correlation coefficient,indicating good discrimination validity.Cronbach's α coefficient of the total volume table was 0.892,indicating good overall reliability.Conclusion The adolescents'alcohol-related cognitive scale developed in this study has good reliability and validity,which can be used to measure adolescents'alcohol-related perceptions.

Purpose/Significance To deeply analyze the research progress on the application of medical knowledge graph in the field of stroke,to discuss the problems of the development of stroke knowledge graph in China,and to put forward suggestions for the construc-tion of stroke knowledge graph.Method/Process By reviewing and analyzing the relevant literature,the application of medical knowledge graph in the field of stroke is sorted out and summarized.Result/Conclusion There are still many deficiencies in the development of stroke knowledge graph in China,and in the future,in-depth research can be carried out from four aspects,namely,expanding the ap-plication scope of knowledge graph,promoting the fusion of knowledge graph,developing more efficient algorithms,and upgrading to cog-nitive graph by joint artificial intelligence(AI).