Objective To establish an HPLC method to determine the concentration of inositol nicotinate（IN） in rat skin, and study the pharmacokinetic characteristics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats. Methods HPLC method was used to establish a simple and rapid analytical method for the determination of IN concentration in the skin of rats at different time points after administration. The established method was used to study the pharmacokinetics of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats, and the pharmacokinetic parameters were fitted with DAS software. Results The linearity of the analytical method was good in the concentration range of 0.25-20 μg/ml, the quantitative limit was 0.25 μg/ml, and the average recovery rate was 96.18%. The pharmacokinetic parameters of IN after transdermal administration of heparin sodium inositol nicotinate cream in rats were as follows: t_1/2 was （4.555±2.054） h, T_max was （6±0）h, C_max was （16.929±2.153）mg/L, AUC_0−t was （150.665±16.568） mg·h /L ,AUC_0−∞ was （161.074±23.917） mg·h /L, MRT_（0−t） was （9.044±0.618）h, MRT_{（0−∞）} was （10.444±1.91） h, CLz/F was （0.19±0.03） L/（h·kg）, and Vz/F was （1.19±0.437） L/（h·kg）. Conclusion IN could quickly penetrate the skin and accumulate in the skin for a long time, which was beneficial to the pharmacological action of drugs on the lesion site for a long time. The method is simple, rapid, specific and reproducible, which could be successfully applied to the pharmacokinetic study of IN after transdermal administration in rats.

Objective To reveal the scientific output and trends in pulmonary nodules/early-stage lung cancer prediction models. Methods Publications on predictive models of pulmonary nodules/early lung cancer between January 1, 2002 and June 3, 2023 were retrieved and extracted from CNKI, Wanfang, VIP and Web of Science database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze the hotspots and theme trends. Results A marked increase in the number of publications related to pulmonary nodules/early-stage lung cancer prediction models was observed. A total of 12581 authors from 2711 institutions in 64 countries/regions published 2139 documents in 566 academic journals in English. A total of 282 articles from 1256 authors were published in 176 journals in Chinese. The Chinese and English journals which published the most pulmonary nodules/early-stage lung cancer prediction model-related papers were Journal of Clinical Radiology and Frontiers in Oncology, respectively. Chest was the most frequently cited journal. China and the United States were the leading countries in the field of pulmonary nodules/early-stage lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that multi-omics, nomogram, machine learning and artificial intelligence were the current focus of research. Conclusion Over the last two decades, research on risk-prediction models for pulmonary nodules/early-stage lung cancer has attracted increasing attention. Prognosis, machine learning, artificial intelligence, nomogram, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of pulmonary nodules/early-stage lung cancer prediction models. More high-quality future studies should be conducted to validate the efficacy and safety of pulmonary nodules/early-stage lung cancer prediction models further and reduce the global burden of lung cancer.

Objective To explore the recognition capabilities of electronic nose combined with machine learning in identifying the breath odor map of benign and malignant pulmonary nodules and Traditional Chinese Medicine (TCM) syndrome elements. Methods The study design was a single-center observational study. General data and four diagnostic information were collected from 108 patients with pulmonary nodules admitted to the Department of Cardiothoracic Surgery of Hospital of Chengdu University of TCM from April 2023 to March 2024. The patients' TCM disease location and nature distribution characteristics were analyzed using the syndrome differentiation method. The Cyranose 320 electronic nose was used to collect the odor profiles of oral exhalation, and five machine learning algorithms including random forest (RF), K-nearest neighbor (KNN), logistic regression (LR), support vector machine (SVM), and eXtreme gradient boosting (XGBoost) were employed to identify the exhaled breath profiles of benign and malignant pulmonary nodules and different TCM syndromes. Results (1) The common disease locations in pulmonary nodules were ranked in descending order as liver, lung, and kidney; the common disease natures were ranked in descending order as Yin deficiency, phlegm, dampness, Qi stagnation, and blood deficiency. (2) The electronic nose combined with the RF algorithm had the best efficacy in identifying the exhaled breath profiles of benign and malignant pulmonary nodules, with an AUC of 0.91, accuracy of 86.36%, specificity of 75.00%, and sensitivity of 92.85%. (3) The electronic nose combined with RF, LR, or XGBoost algorithms could effectively identify the different TCM disease locations and natures of pulmonary nodules, with classification accuracy, specificity, and sensitivity generally exceeding 80.00%.Conclusion Electronic nose combined with machine learning not only has the potential capabilities to differentiate the benign and malignant pulmonary nodules, but also provides new technologies and methods for the objective diagnosis of TCM syndromes in pulmonary nodules.

Chronic myeloid leukemia (CML) is a malignant hematologic disorder caused by abnormal proliferation of hematopoietic stem cells. In recent years, while the application of tyrosine kinase inhibitors (TKIs) has significantly improved the prognosis of CML patients through in-depth exploration of pathogenesis of CML and advancements in targeted therapies, some patients still face challenges including drug resistance, disease relapse, and failure to achieve treatment-free remission. Imunotherapy, as a complementary or alternative strategy, holds significant potential for overcoming these limitations, and has gradually emerged as a critical research focus in CML treatment. This review aims to summarize the current research status and latest advances in immunotherapy for CML.

Objective To analyze the differences in distribution of traditional Chinese medicine (TCM) syndrome elements and salivary microbiota between the individuals with pulmonary nodules and those without, and to explore the potential correlation between the distribution of TCM syndrome elements and salivary microbiota in patients with pulmonary nodules. Methods We retrospectively recruited 173 patients with pulmonary nodules (PN) and 40 healthy controls (HC). The four diagnostic information was collected from all participants, and syndrome differentiation method was used to analyze the distribution of TCM syndrome elements in both groups. Saliva samples were obtained from the subjects for 16S rRNA high-throughput sequencing to obtain differential microbiota and to explore the correlation between TCM syndrome elements and salivary microbiota in the evolution of the pulmonary nodule disease. Results The study found that in the PN group, the primary TCM syndrome elements related to disease location were the lung and liver, and the primary TCM syndrome elements related to disease nature were yin deficiency and phlegm. In the HC group, the primary TCM syndrome elements related to disease location were the lung and spleen, and the primary TCM syndrome elements related to disease nature were dampness and qi deficiency. There were differences between the two groups in the distribution of TCM syndrome elements related to disease location (lung, liver, kidney, exterior, heart) and disease nature (yin deficiency, phlegm, qi stagnation, qi deficiency, dampness, blood deficiency, heat, blood stasis) (P<0.05). The species abundance of the salivary microbiota was higher in the PN group than that in the HC group (P<0.05), and there was significant difference in community composition between the two groups (P<0.05). Correlation analysis using multiple methods, including Mantel test network heatmap analysis and Spearman correlation analysis and so on, the results showed that in the PN group, Prevotella and Porphyromonas were positively correlated with disease location in the lung, and Porphyromonas and Granulicatella were positively correlated with disease nature in yin deficiency (P<0.05). Conclusion The study concludes that there are notable differences in the distribution of TCM syndrome elements and the species abundance and composition of salivary microbiota between the patients with pulmonary nodules and the healthy individuals. The distinct external syndrome manifestations in patients with pulmonary nodules, compared to healthy individuals, may be a cascade event triggered by changes in the salivary microbiota. The dual correlation of Porphyromonas with both disease location and nature suggests that changes in its abundance may serve as an objective indicator for the improvement of symptoms in patients with yin deficiency-type pulmonary nodules.

Objective To construct a "disease-syndrome combination" mathematical representation model for pulmonary nodules based on oral microbiome data, utilizing a multimodal data algorithm framework centered on dynamic systems theory. Furthermore, to compare predictive models under various algorithmic frameworks and validate the efficacy of the optimal model in predicting the presence of pulmonary nodules. Methods A total of 213 subjects were prospectively enrolled from July 2022 to March 2023 at the Hospital of Chengdu University of Traditional Chinese Medicine, Sichuan Cancer Hospital, and the Chengdu Integrated Traditional Chinese and Western Medicine Hospital. This cohort included 173 patients with pulmonary nodules and 40 healthy subjects. A novel multimodal data algorithm framework centered on dynamic systems theory, termed VAEGANTF (Variational Auto Encoder-Generative Adversarial Network-Transformer), was proposed. Subsequently, based on a multi-dimensional integrated dataset of “clinical features-syndrome elements-microorganisms”, all subjects were divided into training (70%) and testing (30%) sets for model construction and efficacy testing, respectively. Using pulmonary nodules as dependent variables, and combining candidate markers such as clinical features, lesion location, disease nature, and microbial genera, the independent variables were screened based on variable importance ranking after identifying and addressing multicollinearity. Missing values were then imputed, and data were standardized. Eight machine learning algorithms were then employed to construct pulmonary nodule risk prediction models: random forest, least absolute shrinkage and selection operator (LASSO) regression, support vector machine, multilayer perceptron, eXtreme Gradient Boosting (XGBoost), VAE-ViT (Vision Transformer), GAN-ViT, and VAEGANTF. K-fold cross-validation was used for model parameter tuning and optimization. The efficacy of the eight predictive models was evaluated using confusion matrices and receiver operating characteristic (ROC) curves, and the optimal model was selected. Finally, goodness-of-fit testing and decision curve analysis (DCA) were performed to evaluate the optimal model. Results There were no statistically significant differences between the two groups in demographic characteristics such as age and sex. The 213 subjects were randomly divided into training and testing sets (7 : 3), and prediction models were constructed using the eight machine learning algorithms. After excluding potential problems such as multicollinearity, a total of 301 clinical feature information, syndrome elements, and microbial genera markers were included for model construction. The area under the curve (AUC) values of the random forest, LASSO regression, support vector machine, multilayer perceptron, and VAE-ViT models did not reach 0.85, indicating poor efficacy. The AUC values of the XGBoost, GAN-ViT, and VAEGANTF models all reached above 0.85, with the VAEGANTF model exhibiting the highest AUC value (AUC=0.923). Goodness-of-fit testing indicated good calibration ability of the VAEGANTF model, and decision curve analysis showed a high degree of clinical benefit. The nomogram results showed that age, sex, heart, lung, Qixu, blood stasis, dampness, Porphyromonas genus, Granulicatella genus, Neisseria genus, Haemophilus genus, and Actinobacillus genus could be used as predictors. Conclusion The “disease-syndrome combination” risk prediction model for pulmonary nodules based on the VAEGANTF algorithm framework, which incorporates multi-dimensional data features of “clinical features-syndrome elements-microorganisms”, demonstrates better performance compared to other machine learning algorithms and has certain reference value for early non-invasive diagnosis of pulmonary nodules.

Objective To investigate the clinical effect of metal supported multi-sided versus ordinary ultra-fine drainage tube in the uniportal video-assisted thoracic surgery (VATS) lower pulmonary lobectomy. Methods From January 2021 to June 2022, the clinical data of patients who underwent uniportal VATS lower lobectomy in our hospital were retrospectively analyzed. According to the different types of ultra-fine drainage tubes used in the surgery, the patients were divided into an experimental group (using multi-sided hole 10F ultra-fine drainage tubes with metal support) and a control group (using ordinary 12F ultra-fine drainage tubes). The clinical data of the two groups were compared. Results A total of 190 patients were enrolled, including 108 males and 82 females. There were 90 patients in the experimental group aged 56.60±10.14 years; and 100 patients in the control group aged 57.07±11.04 years. The incidences of postoperative lung infection and pleural effusion in the experimental group were lower than those in the control group, with statistically significant differences (P<0.05). The postoperative visual analogue scale score, the need to adjust the chest drainage tube after the surgery, the need for chest puncture after the surgery, the time of postoperative chest tube removal, and the hospitalization cost were statistically different (P<0.05). There was no statistical difference in the length of postoperative hospital stay or the incidences of postoperative lung leakage, arrhythmia, and atelectasis complications (P>0.05). Conclusion Compared with the ordinary ultra-fine drainage tubes, multi-sided hole ultra-fine drainage tubes with metal support can reduce the incidences of lung infection and pleural effusion complications after the uniportal VATS lower lobectomy, reduce the pain and economic burden, which can be applied in the uniportal VATS lower lobectomy.

Drug testing involves many analytical instruments and test items, sample pretreatment is tedious, the industry's intelligence level remains low, making drug testing a labour-intensive job. However, in the era of Industry 4.0 intelligent manufacturing, intelligent transformation of the quality control (QC) laboratory has become the focus of industry. At the same time, driven by consistency evaluation of the quality and efficacy of generic drugs and the centralized procurement policies, pharmaceutical companies have intensified their competition, further stimulating the intrinsic demand for laboratory intelligence. Based on the current state and future trends of the pharmaceutical industry, this review discusses the development of a digital and automated QC laboratory. It points out the necessity of transitioning from the traditional centralized laboratory model to an intelligent, distributed quality control model to accommodate continuous manufacturing processes. At the same time, it also analyses the potential challenges in the implementation process and coping strategies, in order to provide relevant practitioners with ideas for building intelligent QC laboratories.

Objective To explore the changes in serum energy metabolites in patients with peripheral T-cell lymphoma,and investigate serum biomarkers for monitoring peripheral T-cell lymphoma from the perspective of energy metabolism.Methods Multiple/selected reaction monitoring(MRM/SRM)was used to detect the energy-related metabolites in the sera of 16 patients with newly diagnosed peripheral T-cell lymphoma admitted in the Hematology Medical Center of the Second Affiliated Hospital of Army Medical University from November 2020 to December 2021,as well as 10 recruited healthy volunteers.The corresponding clinical data including medical history,laboratory results and image data were collected and retrospectively analyzed.Results Significant differences were seen in the contents and expression profiles of serum energy metabolism-related products between the patients and the healthy volunteers.The patients had significantly reduced serum contents of cyclic AMP,succinate,citrate and cis-aconitate(P＜0.05),and elevated D-glucose 6-phosphate content(P＜0.05).The serum contents of citrate and succinate were negatively correlated with the risk stratification(low-,moderate-and high-risk)and clinical stage of the disease(P＜0.05).Meanwhile,there was a negative correlation between the contents of L-malic acid and citrate and the mid-term efficacy evaluation results,such as complete/partial response(CR/PR)or stable disease(SD)(P＜0.05).For patients with extranodal NK/T cell lymphoma(n=10),there were also significant reductions in the contents of cyclic AMP,succinate,citrate,isocitrate and cis-aconitate in the sera of patients compared with healthy volunteers(P＜0.05),and the contents of citrate and succinate were negatively correlated with the clinical stage(P＜0.05)and were rather correlated with mid-term efficacy evaluation results(CR/PR or SD)(P＜0.05).For patients with angioimmunoblastic T-cell lymphoma(n=6),the serum contents of cyclic AMP,citrate and succinate were significantly lower,while the content of D-glucose 6-phosphate was higher when compared with the healthy volunteers(P＜0.05),and the content of succinate was negatively correlated with both clinical stage and risk grade of the patients(P＜0.05).Conclusion There are 5 serum differential metabolites identified between patients with peripheral T-cell lymphoma and healthy controls,and succinate and citrate are expected to be serum biomarkers of peripheral T-cell lymphoma.

Objective:To explore the factors that promote and hinder exercise adherence in long dialysis duration hemodialysis patients, and to provide a reference for improving their exercise levels.Methods:From March to May 2023, a qualitative research method using phenomenon approach was conducted and 15 patients with peritoneal dialysis for at least 10 years at the People′s Liberation Army Central Command Headquarters Hospital (Hankou Hospital) were selected for in-depth interviews using purposive sampling method. The data were analyzed using Colaizzi 7-step method and the main themes were extracted.Results:Among the 15 interviewers, there were 5 males and 10 females, aged 39-76 years old.Conclusions:The exercise level of long dialysis duration hemodialysis patients is influenced by multiple factors. Medical staff should correct their cognitive biases and change their behavioral attitudes, strengthen external supportive environments and reduce subjective normative pressures, gradually provide more objective support, thereby promoting the exercise training of long dialysis duration hemodialysis patients.