OBJECTIVE: To explore the clinical characteristics and prognostic factors of patients with extranasal NK/T-cell lymphoma (NKTCL). METHODS: The clinical data of 138 patients with NKTCL diagnosed in 10 medical centers of Huaihai Lymphoma Working Group from June 2015 to April 2021 were collected and analyzed retrospectively. The differences in clinicopathological characteristics of patients with different involvement and efficacy of pegaspargase regimen were compared, as well as perform survival analysis. RESULTS: A total of 138 extranasal NKTCL patients were included, with a median age of 46 years, and the ratio of males to females was approximately 2∶1. There were 39 patients with gastrointestinal involvement, 32 patients with oropharyngeal involvement, 17 patients with skin involvement, 11 patients with lymph node involvement, 11 patients with orbital involvement, and 28 patients with other parts involvement. Patients with skin involvement had a higher proportion of advanced disease and a lower proportion of CD56 positive rate compared to those with oropharyngeal involvement. Among the patients with gastrointestinal involvement, the survival rate of patients who received pegaspargase regimen was significantly higher than those who were treated without pegaspargase (P < 0.01). Multivariate analysis showed that serum creatinine was an independent prognostic factor for patients with skin involvement ( HR =1.027, 95%CI : 1.001-1.054, P =0.040), ECOG PS and EBV DNA were independent prognostic factors for patients with gastrointestinal involvement ( HR =2.635, 95%CI : 1.096-6.338, P =0.030; HR =4.772, 95% CI : 1.092-20.854, P =0.038), and ECOG PS and CA stage were independent prognostic factors for patients with oropharyngeal involvement ( HR =13.875, 95%CI : 2.517-76.496, P =0.002; HR =20.261, 95%CI : 2.466-166.470, P =0.005). CONCLUSION The clinicopathological characteristics of extranasal NKTCL patients with different sites of involvement are vary, and effective individualized treatment need to be further explored.
Adult ; Aged ; Female ; Humans ; Male ; Middle Aged ; Asparaginase/therapeutic use* ; Lymphoma, Extranodal NK-T-Cell/pathology* ; Prognosis ; Retrospective Studies ; Survival Rate ; Polyethylene Glycols

Liposomes serve as critical carriers for drugs and vaccines, with their biological effects influenced by their size. The microfluidic method, renowned for its precise control, reproducibility, and scalability, has been widely employed for liposome preparation. Although some studies have explored factors affecting liposomal size in microfluidic processes, most focus on small-sized liposomes, predominantly through experimental data analysis. However, the production of larger liposomes, which are equally significant, remains underexplored. In this work, we thoroughly investigate multiple variables influencing liposome size during microfluidic preparation and develop a machine learning (ML) model capable of accurately predicting liposomal size. Experimental validation was conducted using a staggered herringbone micromixer (SHM) chip. Our findings reveal that most investigated variables significantly influence liposomal size, often interrelating in complex ways. We evaluated the predictive performance of several widely-used ML algorithms, including ensemble methods, through cross-validation (CV) for both liposome size and polydispersity index (PDI). A standalone dataset was experimentally validated to assess the accuracy of the ML predictions, with results indicating that ensemble algorithms provided the most reliable predictions. Specifically, gradient boosting was selected for size prediction, while random forest was employed for PDI prediction. We successfully produced uniform large (600 nm) and small (100 nm) liposomes using the optimised experimental conditions derived from the ML models. In conclusion, this study presents a robust methodology that enables precise control over liposome size distribution, offering valuable insights for medicinal research applications.

OBJECTIVE: The study aim was to investigate the effects of exposure to multiple environmental organic pollutants on cardiopulmonary health with a focus on the potential mediating role of oxidative stress. METHODS: A repeated-measures randomized crossover study involving healthy college students in Beijing was conducted. Biological samples, including morning urine and venous blood, were collected to measure concentrations of 29 typical organic pollutants, including hydroxy polycyclic aromatic hydrocarbons (OH-PAHs), bisphenol A and its substitutes, phthalates and their metabolites, parabens, and five biomarkers of oxidative stress. Health assessments included blood pressure measurements and lung function indicators. RESULTS: Urinary concentrations of 2-hydroxyphenanthrene (2-OH-PHE) ( β = 4.35% [95% confidence interval ( CI): 0.85%, 7.97%]), 3-hydroxyphenanthrene ( β = 3.44% [95% CI: 0.19%, 6.79%]), and 4-hydroxyphenanthrene (4-OH-PHE) ( β = 5.78% [95% CI: 1.27%, 10.5%]) were significantly and positively associated with systolic blood pressure. Exposures to 1-hydroxypyrene (1-OH-PYR) ( β = 3.05% [95% CI: -4.66%, -1.41%]), 2-OH-PHE ( β = 2.68% [95% CI: -4%, -1.34%]), and 4-OH-PHE ( β = 3% [95% CI: -4.68%, -1.29%]) were negatively associated with the ratio of forced expiratory volume in the first second to forced vital capacity. These findings highlight the adverse effects of exposure to multiple pollutants on cardiopulmonary health. Biomarkers of oxidative stress, including 8-hydroxy-2'-deoxyguanosine and extracellular superoxide dismutase, mediated the effects of multiple OH-PAHs on blood pressure and lung function. CONCLUSION Exposure to multiple organic pollutants can adversely affect cardiopulmonary health. Oxidative stress is a key mediator of the effects of OH-PAHs on blood pressure and lung function.
Humans ; Oxidative Stress/drug effects* ; Male ; Cross-Over Studies ; Female ; Young Adult ; Environmental Pollutants/toxicity* ; Environmental Exposure/adverse effects* ; Biomarkers/blood* ; Adult ; Blood Pressure/drug effects* ; Polycyclic Aromatic Hydrocarbons/urine* ; Beijing

Objective To study the medication law of Professor Zhang Yunling in the treatment of headache based on data mining technology;To provide ideas for the clinical treatment of headache.Methods Professor Zhang Yunling's outpatient TCM prescription data for the treatment of headache from Sep.2017 to Dec.2020 were collected,and the Ancient and Modern Medical Record Cloud Platform 2.3.5 was used to mine the selected TCM prescriptions for herbal medicine frequency statistics,property,taste and meridian tropism statistics,herbal medicine efficacy statistics,correlation analysis,clustering analysis,complex network analysis,etc.Results Through collection and screening,totally 332 prescriptions were included,involving 178 kinds of Chinese materia medica,with a total frequency of 5 380 times.The top 10 kinds of Chinese materia medica were Chuanxiaong Rhizoma,Paeoniae Radix alba,Atractyodis Macrocephalae Rhizoma,Testudinis Carapax et Plastrum,Bambusae Caulis in Taenia,Glycyrrhizae Radix et Rhizoma,Astragali Radix,Amomi Fructus Rotundus,Pinelliae Rhizoma Praeparatum,and Polygalae Radix.They were mainly warm,mild and slightly cold in properties,sweet,pungent and bitter in tastes,and liver,lung,spleen meridian in meridian tropism.In the statistics of herbal medicine efficacy,expelling wind and relieving pain,suppressing liver yang,promoting blood circulation and qi,clearing heart and relieving restlessness,clearing heat and detoxifying,softening liver and relieving pain were used more frequently.The combinations in herbal medicines association included"Atractyodis Macrocephalae Rhizoma-Chuanxiaong Rhizoma","Testudinis Carapax et Plastrum-Paeoniae Radix alba","Glycyrrhizae Radix et Rhizoma-Paeoniae Radix alba","Testudinis Carapax et Plastrum-Chuanxiaong Rhizoma","Bambusae Caulis in Taenia-Chuanxiaong Rhizoma".Herbal medicines clustering clustered 32 kinds of high-frequency herbal medicines used more than 60 times into 6 categories.Complex network analysis screened out the core formula for the treatment of headache:Chuanxiaong Rhizoma,Paeoniae Radix alba,Atractyodis Macrocephalae Rhizoma,Bambusae Caulis in Taenia,Pinelliae Rhizoma Praeparatum,Testudinis Carapax et Plastrum,Astragali Radix,Amomi Fructus Rotundus,and Glycyrrhizae Radix et Rhizoma.Conclusion In the treatment of headache,Professor Zhang Yunling holds that the pathogenesis of headache is deficiency in origin and excess in superficiality,deficiency of qi and blood,loss of nourishment of brain collaterals,stagnation of phlegm and dampness,disturbance of wind pathogen,obstruction of brain collaterals,and the location of the disease is related to the five viscera and involves the stomach.Focuses on tonifying deficiency and reducing excess,treats exterior and interior separately,and gives consideration to both the symptoms and the root causes,which often uses drugs to treat headache,such as dispelling wind and relieving pain,promoting blood circulation and relieving pain,relieving spasm and relieving pains,eliminating phlegm and dampness,invigorating qi and spleen,nourishing blood and yin,eliminating dampness and regulating stomach,relieving depression and restlessness,which can provide some reference for the clinical treatment of headache.

Aim To explore the effect and mechanism of the artesunate(ART)on hepatocellular carcinoma(HCC).Methods The cell lines MHCC-97H and HCC-LM3 were used to be detected.MTT and clone formation were used to determine the cell proliferation;Wound healing was used to detect the cell migration;Transwell was used to test the cell invasion.Flow-cy-tometry was used to detect cell apoptosis and cell cy-cle.RNA-seq and qRT-PCR was used to detect the genes expression.Results The proliferation,migra-tion and invasion of treated cells were obviously inhibi-ted(P＜0.01).Moreover,the apoptosis rate in-creased significantly,so did the proportion of G2/M cells.Transcriptomic analysis identified GADD45A as a potential target of ART through RNA-sequencing da-ta,and suggested that ART might induce apoptosis and cell cycle arrest through regulating the expression of GADD45A.In addition,the results of mechanism studies and signaling analysis suggested that GADD45A had interaction with its upstream gene NACC1(nucle-us accumbens associated 1).Moreover,after ART treatment,the expressions of GADD45A and NACC1 were changed significantly.Conclusion ART may be a potential drug to resist HCC by affecting the expres-sion of GADD45A and its upstream gene NACC1,which provides a new drug,a new direction and a new method for the clinical treatment of HCC.

Objective:To investigate the predictive value of clinical factor model, deep learning model based on baseline plain CT images, and combination of both for predicting hematoma expansion in cerebral hemorrhage.Methods:The study was cross-sectional. Totally 471 cerebral hemorrhage patients who were firstly diagnosed in the Second Affiliated Hospital of Soochow University from January 2017 to December 2021 were collected retrospectively. These patients were randomly divided into a training dataset ( n=330) and a validation dataset ( n=141) at a ratio of 7∶3 by using the random function. All patients underwent two noncontrast CT examinations within 24 h and an increase in hematoma volume of >33% or an absolute increase in hematoma volume of >6 ml was considered hematoma enlargement. According to the presence or absence of hematoma enlargement, all patients were divided into hematoma enlargement group and hematoma non-enlargement group.Two-sample t test, Mann-Whitney U test or χ2 test were used for univariate analysis. The factors with statistically significant differences were included in multivariate logistic regression analysis, and independent influences related to hematoma enlargement were screened out to establish a clinical factor model. ITK-SNAP software was applied to manually label and segment the cerebral hemorrhage lesions on plain CT images to train and build a deep learning model based on ResNet50 architecture. A combination model for predicting hematoma expansion in cerebral hemorrhage was established by combining independent clinical influences with deep learning scores. The value of the clinical factor model, the deep learning model, and the combination model for predicting hematoma expansion in cerebral hemorrhage was evaluated using receiver operating characteristic (ROC) curves and decision curves in the training and validation datasets. Results:Among 471 cerebral hemorrhage patients, 136 cases were in the hematoma enlargement group and 335 cases were in the hematoma non-enlargement group. Regression analyses showed that male ( OR=1.790, 95% CI 1.136-2.819, P=0.012), time of occurrence ( OR=0.812, 95% CI 0.702-0.939, P=0.005), history of oral anticoagulants ( OR=2.157, 95% CI 1.100-4.229, P=0.025), admission Glasgow Coma Scale score ( OR=0.866, 95% CI 0.807-0.929, P<0.001) and red blood cell distribution width ( OR=1.045, 95% CI 1.010-1.081, P=0.011) were the independent factors for predicting hematoma expansion in cerebral hemorrhage. ROC curve analysis showed that in the training dataset, the area under the curve (AUC) of clinical factor model, deep learning model and combination model were 0.688 (95% CI 0.635-0.738), 0.695 (95% CI 0.642-0.744) and 0.747 (95% CI 0.697-0.793) respectively. The AUC of the combination model was better than that of the clinical model ( Z=0.54, P=0.011) and the deep learning model ( Z=2.44, P=0.015). In the validation dataset, the AUC of clinical factor model, deep learning model and combination model were 0.687 (95% CI 0.604-0.763), 0.683 (95% CI 0.599-0.759) and 0.736 (95% CI 0.655-0.806) respectively, with no statistical significance. Decision curves showed that the combination model had the highest net benefit rate and strong clinical practicability. Conclusions:Both the deep learning model and the clinical factor model established in this study have some predictive value for hematoma expansion in cerebral hemorrhage; the combination model established by the two together has the highest predictive value and can be applied to predict hematoma expansion.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

Objective To explore the correlation between serum thymosin α1 level and left ventricular ejection fraction(LVEF)in patients with acute anterior wall ST-segment elevation myocardial infarction(STEMI)after receiving percutaneous coronary intervention(PCI).Methods A total of 74 patients with acute anterior wall STEMI(acute anterior wall STEMI group)and 38 patients having no myocardial infarction(control group),who were admitted to the Shanghai Sixth People's Hospital of China from December 2019 to February 2022,were enrolled in this study.According to the LVEF value after the recanalization of anterior descending coronary artery with PCI during hospitalization period,the patients of acute anterior wall STEMI group were divided into LVEF＜50％subgroup(n=33)and LVEF≥50％subgroup(n=41).Serum thymosin α1 level was determined by enzyme linked immunosorbent assay(ELISA),the results were compared between the groups.Logistic regression analysis was used to analyze the correlation between thymosin α1 level and LVEF.The receiver operating characteristic(ROC)curve of serum thymosin α1 level for predicting cardiac function in patients with acute anterior wall STEMI after receiving PCI was drawn.Results The serum thymosin α1 level in LVEF≥50％subgroup was significantly higher than that in the LVEF＜50％subgroup(P=0.032).During the post-PCI hospitalization period,the serum thymosin α1 level was positively correlated with LVEF.Logistic regression analysis revealed that serum thymosin α1 level was an independent predictor for LVEF＜50％in patients with acute anterior wall STEMI after receiving PCI.The area under ROC of serum thymosin α1 level for predicting LVEF≥50％in patients with acute anterior wall STEMI during hospitalization was 0.644(P=0.034).The area under ROC of serum thymosin α1 level combined with peak troponin I level and with peak NT-proBNP level for predicting LVEF＜50％in patients with acute anterior wall STEMI during hospitalization was 0.780(P＜0.01)and 0.702(P=0.003)respectively.When taking the median serum thymosin α1 level as the cut-off value,the proportion of LVEF≥50％patients was higher among the patients having the post-PCI serum thymosin α1 level＞2,890 ng/L.Conclusion In patients with acute anterior wall STEMI,the serum thymosin α1 level is closely related to the LVEF value during the post-PCI hospitalization period,it is an independent predictor for cardiac function improvement after PCI.It is expected that the serum thymosin α1 level may become a new indicator for predicting the improvement of cardiac function in patients with STEMI after recanalization of anterior descending coronary artery with PCI.

Objective:To investigate the value of dual-detector spectral CTA in distinguishing infarct core from penumbra in patients with acute ischemic stroke(AIS), and to further explore the risk factors associated with infarct core and their predictive value.Methods:The imaging and clinical data of 163 patients with AIS who met the inclusion criteria admitted to the Second Affiliated Hospital of Soochow University from March 2022 to May 2023 were retrospectively analyzed. Patients from March 2022 to December 2022 were used as the training group, and patients from January 2023 to May 2023 were used as the validation group for internal validation. The head and neck spectral CTA and brain CT perfusion imaging with dual-layer detector spectral CT were all carried out on all patients. Using CTP as reference, the patients were divided into infarct core group and non-infarct core group according to whether an infarct core occurred in the hypoperfusion regions of brain tissue. Multivariate logistic regression analysis was used to screen predictors related to the infarct core. The receiver operating characteristic (ROC) curve was used to evaluate the predictive efficacy.Results:A total of 163 patients were included in the study, including 112 in the training group and 51 in the validation group. There were significant differences in iodine density, effective atomic number, hypertension, triglyceride and neutrophils between the two groups ( P< 0.05). The cutoff values for iodine density values and effective atomic number values were 0.215 mg/mL and 7.405, respectively. Multivariate logistic regression analysis showed that iodine density and hypertension were independent risk factors for infarct core in AIS, and triglyceride was an independent protective factor. The area under the ROC curve (AUC) of iodine density value was the largest (0.859), with a sensitivity of 70.27%, and a specificity of 90.67%, which had a good predictive value. The ROC curve analysis results for the validation group were consistent with the training group. Conclusions:Spectral CT parameters iodine density values and effective atomic number values have the potential to distinguish the infarct core area from the penumbra area in patients with AIS. Iodine density and hypertension were independent risk factors of infarct core in AIS, triglyceride was an independent protective factor, and iodine density values obtained by dual-layer spectral detector CT had a high predictive value.

ObjectiveTo discuss the thought of treatment of orifices in the Chinese herbal classics in the past dynasties based on the correspondence between drugs and symptoms to guide the clinical treatment based on syndrome differentiation. MethodAll the literature data of Chinese herbal classics were retrieved from the database of the Chinese Medical Dictionary， involving 76 works of Chinese herbal classics and covering representative works from the Han dynasty to the Ming and Qing dynasties. The information on Chines herbal drugs for the treatment of orifices was collected and sorted out. According to Chinese Materia Medica （11^th Edition） and Pharmacopoeia of the People's Republic of China （2020 Edition）， the nature， flavor， and meridian tropism of the selected Chinese herbal drugs were statistically analyzed. The pathogenesis elements in the treatment of orifices were classified and counted， and the contents of syndrome differentiation and treatment in various Chinese herbal classics were extracted. ResultIn 76 Chinese herbal classics in the past dynasties， 93 Chinese herbal drugs for the treatment of orifices were selected. The nature of drugs was mainly warm， followed by cold and mild. The flavor was mainly pungent， followed by bitter and sweet. In terms of meridian tropism， drugs mainly acted on the lung meridian， followed by stomach， heart， liver， spleen， and kidney meridians. The pathogenesis elements of orifices could be divided into six categories， i.e.， wind invasion， turbid obstruction and Qi stagnation， water and dampness stagnation， blood stasis and collaterals blockage， heat and toxin damage， deficiency of vital Qi and cold coagulation. ConclusionOrifices are mainly treated with drugs effective in dispelling wind and pathogenic factors， resolving turbidity and removing stagnation， inducing diuresis and eliminating dampness， promoting blood circulation and dredging collaterals， clearing heat and purging fire， tonifying deficiency and dispelling cold， which are used in combination. Eliminating pathogenic factors and dredging， tonifying deficiency and purging excess are the main characteristics of treatment of orifices based on syndrome differentiation， which is in line with the physiological dysfunction state of orifices in losing the function， evil Qi blockage and healthy Qi deficiency.