Allogeneic hematopoietic stem cell transplantation (allo-HSCT) is a curative method for various hematological diseases. With the optimization of transplantation technology, the clinical application of allo-HSCT is more and more mature. Post-transplant liver injury is one of the common postoperative complications, which seriously affects the quality of life and long-term survival of patients. The causes of liver injury after allo-HSCT can be divided into non-infectious and infectious factors, which show similar clinical manifestations and different treatment principles. Timely diagnosis of post-transplant liver injury and the identification of the disease cause will be beneficial for early prevention or targeted treatment, thereby improving patients' prognosis. This review focuses on the etiology, clinical features, and treatment options of liver injury after allo-HSCT, aiming to deepen the understanding of hematologists on liver injury after allo-HSCT.

In recent years， data mining algorithms have been widely employed in scientific research within the field of traditional Chinese medicine （TCM）. The data mining algorithms are used to effectively handle and analyze the complex data in TCM formulas， providing a rational explanation for the mechanism of action. This method has proven particularly useful in uncovering patterns of compatibility and frequent combinations of herbs in TCM， thereby enhancing the reliability and accuracy of clinical diagnosis， target screening， and the study of new drugs. This paper reviews and analyzes 147 papers on TCM formula research that utilize data mining algorithms. The results indicate that data mining algorithms play a unique advantage in six sub- areas， including the study on the mechanism of action in TCM formula， the dose-efficacy of TCM formulas， the identification of core drugs pairs/groups， mining the relationships among “formulas-drug-symptom”， the discovery of new formulas， and mining the compatibility law. Notably， association rules and clustering algorithms are the most representative.

Flow cytometry (FCM) is a technique that can perform the multiparametric analysis of single cell. Recently, FCM has become an important tool to assist the management of chronic diseases. The clinical application of FCM has expanded from blood diseases such as leukemia and lymphoma to other chronic diseases such as solid tumors and chronic obstructive lung. Besides, FCM plays an important role in improving the efficacy of early diagnosis, monitoring on the immune status of patients, understanding of the disease progression, and assessment of prognosis of patients. Henceforth, more assistance could be provided by FCM to optimize the management of chronic diseases with the continuous innovation.

Flow cytometry (FCM) is an interdisciplinary cell analysis technology that integrates optics, fluid dynamics, electronics, and computer science. While FCM is widely utilized in diagnosing and monitoring hematologic malignancies, its application in nonhematopoietic neoplasms (NHN) remains in its nascent stages. However, recent advancements in science and technology have led to the emergence of innovative FCM technologies, such as mass spectrometry flow cytometry (CyTOF) and spectral flow cytometry (SFC), offering promising avenues for their clinical application aiming to assist the clinical diagnosis of NHN patients. This review summarizes the features of fundamentals of traditional FCM, CyTOF, and SFC technologies, along with their applications and future prospective in NHN diagnosis and treatment, aiming to offer updated insights for the continued expansion and utilization of FCM technology in clinical laboratory settings.

Lung cancer remains the leading cause of cancer deaths worldwide and is the most common cancer in males. Immune-checkpoint inhibitors (ICIs) that target programmed cell death protein-1 (PD-1) or programmed cell death-ligand 1 (PD-L1) have achieved impressive efficacy in the treatment of non-small-cell lung cancer (NSCLC) (Pardoll, 2012; Champiat et al., 2016; Gao et al., 2022). Although ICIs are usually well tolerated, they are often accompanied by immune-related adverse events (irAEs) (Doroshow et al., 2019). Non-specific activation of the immune system produces off-target immune and inflammatory responses that can affect virtually any organ or system (O'Kane et al., 2017; Puzanov et al., 2017). Compared with adverse events caused by chemotherapy, irAEs are often characterized by delayed onset and prolonged duration and can occur in any organ at any stage of treatment, including after cessation of treatment (Puzanov et al., 2017; von Itzstein et al., 2020). They range from rash, pneumonitis, hypothyroidism, enterocolitis, and autoimmune hepatitis to cardiovascular, hematological, renal, neurological, and ophthalmic irAEs (Nishino et al., 2016; Kumar et al., 2017; Song et al., 2020). Hence, we conducted a retrospective study to identify validated factors that could predict the magnitude of the risk of irAEs in patients receiving PD-1/PD-L1 inhibitors; our approach was to analyze the correlation between the clinical characteristics of patients at the start of treatment and relevant indicators such as hematological indices and the risk of developing irAEs. Then, we developed an economical, practical, rapid, and simple model to assess the risk of irAEs in patients receiving ICI treatment, as early as possible.
Male ; Humans ; Carcinoma, Non-Small-Cell Lung/drug therapy* ; Lung Neoplasms/drug therapy* ; Immune Checkpoint Inhibitors/adverse effects* ; Programmed Cell Death 1 Receptor ; Retrospective Studies ; Apoptosis

Objective To use feature selection and Likert grading method to quantify the data of lung cancer medical records,to construct a deep extreme learning machine model optimized by the sparrow search algorithm,to classify and predict the syndrome types of traditional Chinese medicine medical record data of lung cancer,and to provide scientific and effective research on syndrome type classification of traditional Chinese medicine.means.Methods The medical records of 497 cases diagnosed with lung cancer from January 2015 to December 2021 were collected from the Affiliated Hospital of Jiangxi University of Traditional Chinese Medicine,and 412 medical records were screened as the research objects.Syndromic factors of different syndromes were summarized by feature selection and feature importance ranking,and the syndrome factors were quantified by Likert grading method.Build a deep extreme learning machine optimized based on the sparrow search algorithm,and train and test the model.Finally,the model built in this paper is compared with other machine learning models according to three evaluation criteria.Results The average classification accuracy of the SSA-DELM model established in this paper was 88.44%,while the average accuracy of the support vector machine and Bayesian network was 83.39%and 84.53%,respectively.The recall rate and F1 value of the SSA-DELM model on the five syndrome types are mostly above 80%,which is also better than other traditional machine learning models.Conclusion The results of the study show that the use of feature selection combined with Likert grading method to quantify the lung cancer medical record data,compared with the 0-1 processing data,can show the characteristics of the data,improve the accuracy of the classification model,SSA-DELM new Compared with other traditional machine learning classification models,the model has better representation learning ability and learning speed.This model not only provides a scientific and technical means for the clinical treatment of lung cancer,but also provides a useful reference for the informatization and intelligent development of TCM syndrome differentiation and treatment.

Objective:To explore the influence of substance abuse history on anxiety and depression of male prisoners during their imprisonment, and its relationship with violent behavior.Methods:A questionnaire survey was conducted among 1 455 prisoners from October to November 2019.Self-administered personal substance abuse history questionnaires were used to collect the information on substance abuse (alcohol, tobacco, and drug use). The generalized anxiety scale (GAD-7) and patient health questionnaire (PHQ-9) were used to investigate anxiety and depression.All subjects were divided into substance abuse group ( n=871) and non substance abuse group ( n=584) according to whether they had a history of substance abuse or not.SPSS 21.0 software was used for statistical analysis.The statistical methods were t-test, chi square test and Logistic regression analysis. Results:(1)The scores of GAD-7 ((4.95±5.88) vs (3.35±5.33), t=-5.407, P<0.01) and PHQ-9 ((6.69±6.50) vs (4.48±5.73), t=-6.821, P<0.01) scales in the substance abuse group were higher than those in the no-substance abuse group.(2)Somatic disease( β=0.700, OR=2.014, 95% CI=1.599-2.538, P<0.05), history of alcohol abuse( β=0.434, OR=1.543, 95% CI=1.176-2.025, P<0.05), history of tobacco abuse( β=0.387, OR=1.473, 95% CI=1.154-1.880, P<0.05), age ≤ 45( β=0.372, OR=1.450, 95% CI=1.118-1.881, P<0.05) were the risk factors of anxiety among prisoners.Somatic disease( β=0.686, OR=1.986, 95% CI=1.581-2.496, P<0.05), history of tobacco abuse( β=0.488, OR=1.629, 95% CI=1.286-2.063, P<0.05), age ≤ 45( β=0.484, OR=1.622, 95% CI=1.260-2.089, P<0.05), history of alcohol abuse( β=0.344, OR=1.410, 95% CI=1.073-1.854, P<0.05) were the risk factors of depression among prisoners.(3) Years of education ≤ 9 years( β=0.900, OR=2.459, 95% CI=1.855-3.261, P<0.05), age ≤ 45( β=0.788, OR=2.199, 95% CI=1.690~2.860, P<0.05), unmarried( β=0.683, OR=1.980, 95% CI=1.421-2.759, P<0.05), history of alcohol abuse( β=0.308, OR=1.361, 95% CI=1.053-1.758, P<0.05), history of drug abuse( β=0.557, OR=1.745, 95% CI=1.055-2.885, P<0.05) were risk factors for violent behavior of prisoners. Conclusion:The history of substance abuse may be a risk factor for anxiety and depression of prisoners during their imprisonment.Alcohol and drug abuse are both factors influencing the occurrence of violent behavior.

Objective:This study aimed to investigate the predictive value of N-terminal pro-B-type natriuretic peptide (NT-proBNP) trajectory on future major adverse cardiovascular events (MACE) in patients with stable coronary artery disease (SCAD) after percutaneous coronary intervention (PCI).Methods:A retrospective cohort study was conducted on SCAD patients admitted to the Department of Cardiology at Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, from January 2013 to December 2021. A total of 828 subjects were enrolled, comprising 592 males and 236 females, with an average age of (66.44±11.71) years. SCAD patients post-percutaneous coronary intervention (PCI) were stratified into three NT-proBNP trajectory groups: T1 Low-Low (219 cases), T2 Medium-Low (363 cases), and T3 High-High (246 cases). The median follow-up time was 2.1 years, and the maximum follow-up time was 9 years. The primary clinical endpoint event was MACE. The NT-proBNP concentration in patients′ serum was measured using enzyme-linked fluorescent assay, and different trajectory groups were determined using latent class trajectory modeling. The association between NT-proBNP trajectory and occurrence of MACE in SCAD patients was evaluated using Kaplan-Meier survival curves and multivariable Cox proportional hazards regression models.Results:A total of 67 (8.1%) major adverse cardiovascular events occurred, including 43 cases (5.2%) of all-cause mortality, 13 cases (1.6%) of heart failure death, 9 cases (1.1%) of non-fatal myocardial infarction, and 15 cases (1.8%) of non-fatal stroke. Kaplan-Meier survival curve analysis showed significant differences in survival rates among T1, T2, and T3 groups of SCAD patients for MACE, all-cause mortality, and heart failure death (all P<0.001). In the multivariable Cox regression analysis, the risk of MACE occurrence for patients in the T2 group and T3 group was 1.708 times (95% CI 0.72-4.05) and 3.842 times (95% CI 1.625-9.081) compared to the T1 group, respectively. Moreover, a statistically significant linear trend was observed for the risk of MACE occurrence across trajectory groups ( P<0.001). Conclusions:NT-proBNP trajectory groups after PCI in SCAD patients are strongly associated with the risk of MACE occurrence and can serve as an independent predictor for MACE. Dynamic monitoring of NT-proBNP during follow-up to obtain longitudinal trajectories helps identify high-risk SCAD patients and implement timely effective intervention measures.

【Objective】 To investigate the effect or endoscopic discectomy via interlaminar approach in the treatment of lumbar disc herniation under local or general anesthesia. 【Methods】 We selected 78 patients with lumbar disc herniation （L5-S1） who underwent endoscopic discectomy via interlaminar approach from January 2018 to February 2020. According to the anesthesia method， they were divided into local anesthesia group （40 patients） and general anesthesia group （38 patients）. Baseline data， complications， operation time， length of hospital stay， hospitalization expenses， preoperative and postoperative VAS scores were compared between the two groups to evaluate the difference in clinical outcomes. 【Results】 There was no significant difference between the two groups in gender （23/17 vs. 20/18）， age （38.45±13.59 vs 37.39±13.35） years， preoperative VAS score （7.05±1.32 vs. 6.95±1.47）， or other baseline data （P>0.05）. The operation was completed successfully in both groups， and the operation time （83.57±13.24 vs. 86.28±15.43） minutes did not statistically differ （P>0.05）. No complications such as cerebrospinal fluid leakage， nerve injury， or wound infection occurred. In the local anesthesia group， the VAS pain score at 1d， 1 month and 6 months after surgery was 3.05±1.34， 1.90±0.98 and 1.80±1.09， respectively， which were significantly lower than those before surgery （P<0.001）. The VAS pain scores of the general anesthesia group at the above three time points were 3.24±2.01， 2.03±1.20 and 1.59±1.31， respectively， which were also significantly lower than those before surgery （P<0.001）， but the difference was not statistically significant compared with that of the local anesthesia group at the same time point （P>0.05）. However， compared with the general anesthesia group， the length of hospital stay （2.93±0.92） d and the cost of hospitalization （29 397.97±1 398.09） yuan in the local anesthesia group were decreased by 25.8% and 11.7%， respectively， with statistical significance （P<0.05）. 【Conclusion】 Endoscopic discectomy through interlaminar approach under local or general anesthesia can achieve good clinical outcomes in the treatment of lumbar disc herniation （L5-S1）. Compared with general anesthesia， local anesthesia brings a lower overall cost and a shorter hospital stay.

Objective:To investigate the risk factors of refracture of the injured vertebrae after percutaneous vertebral augmentation for acute symptomatic thoracolumbar osteoporotic compression fractures (ASTOCFs).Methods:A case-control study was conducted to analyze the clinical data of 2 237 ASTOCFs patients admitted to three hospitals from January 2010 to January 2019. There were 569 males and 1 668 females, with age range of 50-85 years [(66.7±4.8)years]. The patients underwent percutaneous vertebroplasty (PVP) or percutaneous kyphoplasty (PKP). According to the radiographic outcomes, the patients were divided into refracture group ( n=315) and non-refracture group ( n=1 922). Data were recorded for the two groups, including basic demographics (gender, age, height and weight), personal habits (smoking and alcohol consumption), basic diseases (diabetes, hypertension, coronary heart disease and chronic obstructive pulmonary disease), preoperative bone mineral density, fracture segment, number of injured vertebrae, surgical method (PVP or PKP), surgical approach, bone cement viscosity, distance from cement to the upper and lower endplate, cement volume in injured vertebrae, cement leakage, postoperative exercise, and postoperative anti-osteoporosis treatment. The above data were analyzed to identify their correlation with postoperative refracture of the injured vertebrae by univariate analysis. The independent risk factors for postoperative refracture of the injured vertebrae were determined by multivariate Logistic regression analysis. Results:Univariate analysis showed that refracture of injured vertebrae was correlated with gender, age, diabetes, fracture segment, surgical method, distance from cement to the upper and lower endplate, postoperative exercise, and postoperative anti-osteoporosis treatment ( P<0.05 or 0.01), but there was no correlation with height, weight, smoking, alcohol consumption, hypertension, coronary heart disease, chronic obstructive pulmonary disease, preoperative bone mineral density, number of fractured vertebrae, surgical approach, bone cement viscosity, cement volume in injured vertebrae or cement leakage (all P>0.05). Multivariate Logistic regression analysis showed that female ( OR=1.92, 95% CI 1.34-2.64, P<0.01), age ≥80 years ( OR=1.21, 95%CI 1.17-1.25, P<0.01), diabetes ( OR=1.92, 95% CI 0.44-2.55, P<0.01), thoracolumbar fracture ( OR=1.46, 95% CI 1.82-7.51, P<0.05), PKP ( OR=4.56, 95% CI 0.86-1.44, P<0.05), no postoperative exercise ( OR=2.14,95% CI 0.27-0.38, P<0.01), and no postoperative anti-osteoporosis treatment ( OR=2.36,95% CI 0.13-0.47, P<0.05) were positively correlated with refracture of injured vertebrae. Conclusion:Female, age ≥80 years, diabetes, thoracolumbar fracture, PKP, no postoperative exercise, and no postoperative anti-osteoporosis treatment are independent risk factors for refracture of injured vertebrae after percutaneous vertebral augmentation for ASTOCFs.