OBJECTIVE: To investigate the clinical characteristics and prognosis of primary pulmonary lymphoma (PPL). METHODS: The clinical data of 17 patients with PPL admitted to Gansu Provincial Hospital from January 2013 to June 2023 were collected, and their clinical characteristics and prognosis were retrospectively analyzed and summarized. RESULTS: The median age of the 17 patients was 56 (29-73) years old. There were 8 males and 9 females. According to Ann Arbor staging system, there were 9 patients with stage I-II and 8 patients with stage III-IV. There were 14 patients with IPI score of 0-2 and 3 patients with IPI score of 3-4. All 17 patients had symptoms at the initial diagnosis, most of the first symptoms were cough, and 6 patients had B symptoms.Among the 17 patients, there were 8 cases of diffuse large B-cell lymphoma (DLBCL), 5 cases of mucosa-associated lymphoid tissue (MALT) lymphoma, 1 case of gray zone lymphoma (GZL), and 3 cases of Hodgkin's lymphoma (HL). 15 patients received chemotherapy, of which 3 cases received autologous hematopoietic stem cell transplantation(ASCT) and 3 cases received radiotherapy; 2 patients did not receive treatment. The median number of chemotherapy courses was 6(2-8). The short-term efficacy was evaluated, 12 patients achieved complete remission (CR) and 3 patients achieved partial remission (PR). The age, pathological subtype, sex, Ann Arbor stage, β₂-microglobulin(β₂-MG) level, lactate dehydrogenase(LDH) level were not correlated with CR rate (P >0.05), while IPI score was correlated with recent CR rate (P < 0.05 ). The median follow-up time was 31(2-102) months. One of the 12 CR patients died of COVID-19, and the rest survived. Among the 3 patients who did not reach CR, 1 died after disease progression, while the other 2 survived. One of the 2 untreated patients died one year after diagnosis. Both the median progression-free survival (PFS) time and overall survival (OS) time of the 17 patients were both 31 (2-102) months. CONCLUSION The incidence of PPL is low, and the disease has no specific clinical manifestations, which is easily missed and misdiagnosed. The pathological subtypes are mainly MALT lymphoma and DLBCL, and the treatment is mainly combined chemotherapy. The IPI score is related to the treatment efficacy.
Humans ; Middle Aged ; Male ; Female ; Adult ; Prognosis ; Aged ; Lung Neoplasms/therapy* ; Retrospective Studies ; Neoplasm Staging ; Lymphoma/therapy* ; Lymphoma, Large B-Cell, Diffuse

Objective To analyze the risk factors for meconium-stained amniotic fluid(MSAF)in preterm infants and the clinical outcome and prognosis of preterm infants.Methods Preterm infants with gestational age＜37 weeks delivered in Zhangjiajie People's Hospital from January 2022 to December 2023 were used as the study subjects,31 cases with MSAF were in MSAF group,and 31 cases of preterm infants hospitalized during the same period without MSAF were randomly paired in the ratio of 1∶1 to select with gestational age-body mass matching as non-MSAF group.Retrospective collection and analysis of pregnancy and perinatal conditions of mothers of preterm infants in two groups,comparing the differences of related factors between two groups of children;Logistic regression analysis of risk factors related to MSAF in preterm infants;comparing the complications and clinical outcomes of preterm infants in two groups.Results A total of 387 preterm infants with gestational age＜37 weeks were collected during the study period,including 31 preterm infants with comorbid MSAF,and the prevalence of MSAF in preterm infants was 8.0％.MSAF group had a higher incidence of advanced maternal age,premature rupture of membranes＞18 hours,antepartum fever,and cholestasis during pregnancy than non-MSAF group.Logistic regression analysis suggested that combined cholestasis during pregnancy and white blood cell count ≥ 30× 109/L within 6 hours after birth increased the incidence of MSAF in preterm infants.There was no statistically significant difference in the results of postnatal umbilical artery blood gas analysis between two groups of preterm infants.The proportion of leukocyte count ≥30×109/L,ultrasensitive C-reactive protein＞0.8 mg/L,and interleukin 6＞6 pg/L in MSAF group was higher than that of non-MSAF group in the 6 hours after birth.MSAF group had a higher incidence of intrauterine infectious pneumonia,feeding intolerance,and necrotizing small bowel colitis in neonates than non-MSAF group.Conclusion Advanced maternal age,intrauterine infections,and combined intrahepatic cholestasis during pregnancy may be the major risk factors for MSAF in preterm infants.MSAF preterm infants have a higher prevalence of intrauterine infectious pneumonitis,feeding intolerance,and necrotizing small bowel colitis in newborns,as well as longer hospital stays.

Cardiovascular disease (CVD), chronic kidney disease (CKD), and metabolic disorders are the 3 major chronic diseases threatening human health, which are closely related and often coexist, significantly increasing the difficulty of disease management. In response, the American Heart Association (AHA) proposed a novel disease concept of “cardiovascular-kidney-metabolic (CKM) syndrome” in October 2023, which has triggered widespread concern about the co-treatment of heart and kidney diseases and the prevention and treatment of metabolic disorders around the world. This review posits that effectively managing CKM syndrome requires a new and multidimensional paradigm for diagnosis and risk prediction that integrates biological insights, advanced technology and social determinants of health (SDoH). We argue that the core pathological driver is a “metabolic toxic environment”, fueled by adipose tissue dysfunction and characterized by a vicious cycle of systemic inflammation and oxidative stress, which forms a common pathway to multi-organ injury. The at-risk population is defined not only by biological characteristics but also significantly impacted by adverse SDoH, which can elevate the risk of advanced CKM by a factor of 1.18 to 3.50, underscoring the critical need for equity in screening and care strategies. This review systematically charts the progression of diagnostic technologies. In diagnostics, we highlight a crucial shift from single-marker assessments to comprehensive multi-marker panels. The synergistic application of traditional biomarkers like NT-proBNP (reflecting cardiac stress) and UACR (indicating kidney damage) with emerging indicators such as systemic immune-inflammation index (SII) and Klotho protein facilitates a holistic evaluation of multi-organ health. Furthermore, this paper explores the pivotal role of non-invasive monitoring technologies in detecting subclinical disease. Techniques like multi-wavelength photoplethysmography (PPG) and impedance cardiography (ICG) provide a real-time window into microcirculatory and hemodynamic status, enabling the identification of early, often asymptomatic, functional abnormalities that precede overt organ failure. In imaging, progress is marked by a move towards precise, quantitative evaluation, exemplified by artificial intelligence-powered quantitative computed tomography (AI-QCT). By integrating AI-QCT with clinical risk factors, the predictive accuracy for cardiovascular events within 6 months significantly improves, with the area under the curve (AUC) increasing from 0.637 to 0.688, demonstrating its potential for reclassifying risk in CKM stage 3. In the domain of risk prediction, we trace the evolution from traditional statistical tools to next-generation models. The new PREVENT equation represents a major advancement by incorporating key kidney function markers (eGFR, UACR), which can enhance the detection rate of CKD in primary care by 20%-30%. However, we contend that the future lies in dynamic, machine learning-based models. Algorithms such as XGBoost have achieved an AUC of 0.82 for predicting 365-day cardiovascular events, while deep learning models like KFDeep have demonstrated exceptional performance in predicting kidney failure risk with an AUC of 0.946. Unlike static calculators, these AI-driven tools can process complex, multimodal data and continuously update risk profiles, paving the way for truly personalized and proactive medicine. In conclusion, this review advocates for a paradigm shift toward a holistic and technologically advanced framework for CKM management. Future efforts must focus on the deep integration of multimodal data, the development of novel AI-driven biomarkers, the implementation of refined SDoH-informed interventions, and the promotion of interdisciplinary collaboration to construct an efficient, equitable, and effective system for CKM screening and intervention.

Objective To analyze the research status and hotspots in the field of astragalus polysaccharides;To provide references for further research.Methods Research literature about astragalus polysaccharides was retrieved from CNKI,Wanfang Data,VIP,PubMed,and Web of Science databases from 1st,Jan.2013 to 1st,July 2023.NoteExpress 3.7 software was used to manage the literature and ultimately establish a database.Excel 2019,CiteSpace 6.2.2R and VOSviewer 1.6.18 were used to visually analyze the publication volume,authors,institutions,and keywords of the included literature.Results A total of 2 462 articles were included,with 1 284 Chinese articles and 1 178 English articles.The main research institutions were Gansu University of Chinese Medicine,Shandong University of Traditional Chinese Medicine,and Beijing University of Chinese Medicine.The core authors of Chinese literature were Liu Yongqi,Wang Hongxin,Lu Meili,etc.The core authors of English literature included Zhang Wei,Li Ke,Yang Xiaojun,etc.High-frequency keywords of Chinese literature included Astragali Radix,rats,polysaccharides,cell apoptosis,and oxidative stress,etc.High frequency keywords in English literature included expression,in vitro,oxidative stress,apoptosis,etc.Conclusion The research on astragalus polysaccharides focuses on their pharmacological effects and mechanisms.Intestinal flora,immune regulation,autophagy and apoptosis are the hot action mechanisms in this field.The focus of disease research involves tumor and diabetes,and antiviral,anti infection and other pharmacological effects are the research trend.

Objective To investigate the efficacy of an interpretable integrated learning model combining clinical indicators,CT image features and radiomics features for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma,so as to provide references for clincal treatment decisions.Methods A retrospective analysis was conducted on clinical and imaging data from 220 patients(231 lesions)with primary non-small cell lung cancer at Jiangjin Central Hospital of Chongqing(Center 1)and 83 patients(84 lesions)at Chongqing General Hospital(Center 2).In Center 1,the squamous cell carcinoma group consisted of 60 patients(60 lesions),while the adenocarcinoma group included 160 patients(171 lesions).In Center 2,the squamous cell carcinoma group comprised 18 patients(18 lesions),and the adenocarcinoma group involved 65 patients(66 lesions).The patients were categorized into squamous cell carcinoma and adenocarcinoma groups based on pathological findings.Center 1 was randomly partitioned into a training set and a validation set at a 7∶3 ratio,while Center 2 served as the independent test set.Firstly,a deep learning model,VB-Net,was used to automatically segment the tumor region on the lung window image;secondly,the SMOTE(synthetic minority oversampling technique)method was used to balance the categories in the training set and standardize the extracted features with Z-scores;thirdly,the least absolute shrinkage and selection operator(LASSO)were used to select the optimal radiomics features and calculate the radiomics score(Radscore),and univariate and multivariate logistic regression was used to screen clinical indicators and independent clinical factors for differentiating lung squamous cell carcinoma and adenocarcinoma in CT image features;finally,three ensemble learning algorithms(AdaBoost,Bagging decision tree and XGBoost)were used to combine independent clinical factors and Radscore to construct the model.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of the models.SHAP technique was used to analyze the feature contribution and model decision-making process.Results Among the evaluated ensemble models,AdaBoost and Bagging decision trees demonstrated overfitting tendencies.In contrast,the XGBoost model showed the best performance,achieving AUC values of 0.939,0.887 and 0.853 in the training,validation and independent test sets,respectively.SHAP indicated that Radscore was the most important feature affecting the performance of the model.The decision diagram enabled the visualization of the diagnostic process of the model.Conclusion The interpretable integrated learning model based on clinical indicators,CT image and radiomics features is expected to non-invasively diagnose lung squamous cell carcinoma and adenocarcinoma before treatment and assist clinicians make treatment decisions as early as possible.[Chinese Medical Equipment Journal,2025,46(7):12-20]

Objective To analyze the risk factors for meconium-stained amniotic fluid(MSAF)in preterm infants and the clinical outcome and prognosis of preterm infants.Methods Preterm infants with gestational age＜37 weeks delivered in Zhangjiajie People's Hospital from January 2022 to December 2023 were used as the study subjects,31 cases with MSAF were in MSAF group,and 31 cases of preterm infants hospitalized during the same period without MSAF were randomly paired in the ratio of 1∶1 to select with gestational age-body mass matching as non-MSAF group.Retrospective collection and analysis of pregnancy and perinatal conditions of mothers of preterm infants in two groups,comparing the differences of related factors between two groups of children;Logistic regression analysis of risk factors related to MSAF in preterm infants;comparing the complications and clinical outcomes of preterm infants in two groups.Results A total of 387 preterm infants with gestational age＜37 weeks were collected during the study period,including 31 preterm infants with comorbid MSAF,and the prevalence of MSAF in preterm infants was 8.0％.MSAF group had a higher incidence of advanced maternal age,premature rupture of membranes＞18 hours,antepartum fever,and cholestasis during pregnancy than non-MSAF group.Logistic regression analysis suggested that combined cholestasis during pregnancy and white blood cell count ≥ 30× 109/L within 6 hours after birth increased the incidence of MSAF in preterm infants.There was no statistically significant difference in the results of postnatal umbilical artery blood gas analysis between two groups of preterm infants.The proportion of leukocyte count ≥30×109/L,ultrasensitive C-reactive protein＞0.8 mg/L,and interleukin 6＞6 pg/L in MSAF group was higher than that of non-MSAF group in the 6 hours after birth.MSAF group had a higher incidence of intrauterine infectious pneumonia,feeding intolerance,and necrotizing small bowel colitis in neonates than non-MSAF group.Conclusion Advanced maternal age,intrauterine infections,and combined intrahepatic cholestasis during pregnancy may be the major risk factors for MSAF in preterm infants.MSAF preterm infants have a higher prevalence of intrauterine infectious pneumonitis,feeding intolerance,and necrotizing small bowel colitis in newborns,as well as longer hospital stays.

Objective To investigate the efficacy of an interpretable integrated learning model combining clinical indicators,CT image features and radiomics features for the differential diagnosis of lung squamous cell carcinoma and adenocarcinoma,so as to provide references for clincal treatment decisions.Methods A retrospective analysis was conducted on clinical and imaging data from 220 patients(231 lesions)with primary non-small cell lung cancer at Jiangjin Central Hospital of Chongqing(Center 1)and 83 patients(84 lesions)at Chongqing General Hospital(Center 2).In Center 1,the squamous cell carcinoma group consisted of 60 patients(60 lesions),while the adenocarcinoma group included 160 patients(171 lesions).In Center 2,the squamous cell carcinoma group comprised 18 patients(18 lesions),and the adenocarcinoma group involved 65 patients(66 lesions).The patients were categorized into squamous cell carcinoma and adenocarcinoma groups based on pathological findings.Center 1 was randomly partitioned into a training set and a validation set at a 7∶3 ratio,while Center 2 served as the independent test set.Firstly,a deep learning model,VB-Net,was used to automatically segment the tumor region on the lung window image;secondly,the SMOTE(synthetic minority oversampling technique)method was used to balance the categories in the training set and standardize the extracted features with Z-scores;thirdly,the least absolute shrinkage and selection operator(LASSO)were used to select the optimal radiomics features and calculate the radiomics score(Radscore),and univariate and multivariate logistic regression was used to screen clinical indicators and independent clinical factors for differentiating lung squamous cell carcinoma and adenocarcinoma in CT image features;finally,three ensemble learning algorithms(AdaBoost,Bagging decision tree and XGBoost)were used to combine independent clinical factors and Radscore to construct the model.The receiver operating characteristic(ROC)curve was used to evaluate the diagnostic performance of the models.SHAP technique was used to analyze the feature contribution and model decision-making process.Results Among the evaluated ensemble models,AdaBoost and Bagging decision trees demonstrated overfitting tendencies.In contrast,the XGBoost model showed the best performance,achieving AUC values of 0.939,0.887 and 0.853 in the training,validation and independent test sets,respectively.SHAP indicated that Radscore was the most important feature affecting the performance of the model.The decision diagram enabled the visualization of the diagnostic process of the model.Conclusion The interpretable integrated learning model based on clinical indicators,CT image and radiomics features is expected to non-invasively diagnose lung squamous cell carcinoma and adenocarcinoma before treatment and assist clinicians make treatment decisions as early as possible.[Chinese Medical Equipment Journal,2025,46(7):12-20]

Objective To analyze the research status and hotspots in the field of astragalus polysaccharides;To provide references for further research.Methods Research literature about astragalus polysaccharides was retrieved from CNKI,Wanfang Data,VIP,PubMed,and Web of Science databases from 1st,Jan.2013 to 1st,July 2023.NoteExpress 3.7 software was used to manage the literature and ultimately establish a database.Excel 2019,CiteSpace 6.2.2R and VOSviewer 1.6.18 were used to visually analyze the publication volume,authors,institutions,and keywords of the included literature.Results A total of 2 462 articles were included,with 1 284 Chinese articles and 1 178 English articles.The main research institutions were Gansu University of Chinese Medicine,Shandong University of Traditional Chinese Medicine,and Beijing University of Chinese Medicine.The core authors of Chinese literature were Liu Yongqi,Wang Hongxin,Lu Meili,etc.The core authors of English literature included Zhang Wei,Li Ke,Yang Xiaojun,etc.High-frequency keywords of Chinese literature included Astragali Radix,rats,polysaccharides,cell apoptosis,and oxidative stress,etc.High frequency keywords in English literature included expression,in vitro,oxidative stress,apoptosis,etc.Conclusion The research on astragalus polysaccharides focuses on their pharmacological effects and mechanisms.Intestinal flora,immune regulation,autophagy and apoptosis are the hot action mechanisms in this field.The focus of disease research involves tumor and diabetes,and antiviral,anti infection and other pharmacological effects are the research trend.

Objective To observe the efficacy and safety of Morinda officinalis oligosaccharides in the continuation treatment of mild and moderate depression.Methods An open,single-arm,multi-center design was adopted in our study.Adult patients with mild and moderate depression who had received acute treatment of Morinda officinalis oligosaccharides were enrolled and continue to receive Morinda officinalis oligosaccharides capsules for 24 weeks,the dose remained unchanged during continuation treatment.The remission rate,recurrence rate,recurrence time,and the change from baseline to endpoint of Hamilton Depression Scale(HAMD),Hamilton Anxiety Scale(HAMA),Clinical Global Impression-Severity(CGI-S)and Arizona Sexual Experience Scale(ASEX)were evaluated.The incidence of treatment-related adverse events was reported.Results The scores of HAMD-17 at baseline and after treatment were 6.60±1.87 and 5.85±4.18,scores of HAMA were 6.36±3.02 and 4.93±3.09,scores of CGI-S were 1.49±0.56 and 1.29±0.81,scores of ASEX were 15.92±4.72 and 15.57±5.26,with significant difference(P＜0.05).After continuation treatment,the remission rate was 54.59％(202 cases/370 cases),and the recurrence rate was 6.49％(24 cases/370 cases),the recurrence time was(64.67±42.47)days.The incidence of treatment-related adverse events was 15.35％(64 cases/417 cases).Conclusion Morinda officinalis oligosaccharides capsules can be effectively used for the continuation treatment of mild and moderate depression,and are well tolerated and safe.

Objective This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength. Methods We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.Results In the multimetal linear regression, Cu (β=-2.119), As (β=-1.318), Sr (β=-2.480), Ba (β=0.781), Fe (β= 1.130) and Mn (β=-0.404) were significantly correlated with grip strength (P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95％ confidence interval:-1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn (Pinteractions of 0.003 and 0.018, respectively).Conclusion In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.