With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Signal transducer and activator of transcription 3 (STAT3) is aberrantly expressed in a variety of cancer stem cells,and it strongly correlated with cellular carcinogenesis.Therefore,designing and in-vestigating new STAT3 inhibitors is an important and effective strategy to combat cancer.In this paper,we propose a novel STAT3 inhibitor design method based on the recurrent neural network (RNN) algo-rithm,and evaluate the effectiveness of this method through molecular simulation studies.We first used the RNN algorithm to construct a STAT3 inhibitor generation model,so that it can generate brand new in-hibitors.Then based on machine learning algorithms,we established a molecular classification prediction model for STAT3 inhibitors,conducted a hierarchical virtual screening based on molecular docking on molecules classified as STAT3 inhibitors,and selected three molecules with the highest scores in the extra precision (XP) screen,which as potential inhibitors for the next step of the study.The potential inhibi-tors were subjected to binding free energy computation,and tprediction of absorption,distribution,me-tabolism,excretion toxicity (ADMET) .In addition,an independent gradient model (IGM) analysis was used to further explore the pharmacogenetic properties.The experimental results in this paper show that novel potential STAT3 inhibitors with good drugability can be effectively generated by using the above methods.In sum,our work can provide a reference for subsequent STAT3 drug development.

Signal transducer and activator of transcription 3 (STAT3) is aberrantly expressed in a variety of cancer stem cells,and it strongly correlated with cellular carcinogenesis.Therefore,designing and in-vestigating new STAT3 inhibitors is an important and effective strategy to combat cancer.In this paper,we propose a novel STAT3 inhibitor design method based on the recurrent neural network (RNN) algo-rithm,and evaluate the effectiveness of this method through molecular simulation studies.We first used the RNN algorithm to construct a STAT3 inhibitor generation model,so that it can generate brand new in-hibitors.Then based on machine learning algorithms,we established a molecular classification prediction model for STAT3 inhibitors,conducted a hierarchical virtual screening based on molecular docking on molecules classified as STAT3 inhibitors,and selected three molecules with the highest scores in the extra precision (XP) screen,which as potential inhibitors for the next step of the study.The potential inhibi-tors were subjected to binding free energy computation,and tprediction of absorption,distribution,me-tabolism,excretion toxicity (ADMET) .In addition,an independent gradient model (IGM) analysis was used to further explore the pharmacogenetic properties.The experimental results in this paper show that novel potential STAT3 inhibitors with good drugability can be effectively generated by using the above methods.In sum,our work can provide a reference for subsequent STAT3 drug development.

OBJECTIVETo investigate the effect of atrial fibrillation on the accuracy of parameters monitored by transpulmonary thermodilution method.

METHODSTotally 12 patients from emergency intensive care unit with paroxysmal atrial fibrillation were enrolled. The hemodynamic parameters such as heart rate, mean arterial pressure, cardiac index, systemic vascular resistance index, intrathoracic blood volume index, and extravascular lung water index were monitored by transpulmonary thermodilution method before paroxysmal atrial fibrillation and during atrial fibrillation, the number of B-lines was detected by lung ultrasonography before and during paroxysmal atrial fibrillation. The changes of all the parameters were analyzed.

RESULTSWhen the paroxysmal atrial fibrillation happened, the heart rate increased significantly [(123.3±20.0) beat/min vs. (98.9±12.3) beat/min, P=0.006]; the mean arterial pressure [(86.9±10.2) mmHg vs. (93.0±12.5) mmHg, P=0.058], cardiac index [(2.82±0.62) L/(min·m(2)) vs. (3.31±1.02) L/(min·m(2)), P=0.058] and systemic vascular resistance index [(2254±947) dyn·s·cm(-5)·m(2) vs. (2302±828) dyn·s·cm(-5)·m(2), P=0.351] had no obvious change; however, the intrathoracic blood volume index significantly increased [(1333±90) ml/m(2) vs. (937±111) ml/m(2), P<0.001]; extravascular lung water index also increased significantly [(16.1±1.1) ml/kg vs. (6.5±1.9) ml/kg, P<0.001]. No significant difference was found in the number of B-lines detected by lung ultrasonography before and during atrial fibrillation (10.0±4.2 vs. 9.4±4.4, P=0.180).

CONCLUSIONBoth intrathoracic blood volume and extravascular lung water monitored by transpulmonary thermodilution method were overvalued during paroxysmal atrial fibrillation, which may mislead the clinical judgment and decision-making.

Atrial Fibrillation ; physiopathology ; Blood Pressure ; Blood Volume ; Cardiac Output ; Extravascular Lung Water ; Heart Rate ; Hemodynamics ; Humans ; Intensive Care Units ; Thermodilution ; Vascular Resistance

OBJECTIVE: To investigate the genetic polymorphisms of 15 X-STR loci in Shandong Han population in order to establish the forensic application database. METHODS: The multi-PCR primers of these loci were designed by Primer Premier 5.0 software and labeled by 4 fluoresceins (FAM, VIC, NED and TET). The developed multi-PCR was used to investigate 15 X-STR loci (DXS10011, DXS101, GATA 165B12, DXS6795, DXS6800, DXS6801, DXS6803, DXS7132, DXS7133, DXS7423, DXS7424, DXS8377, DXS8378, DXS9898 and HPRTB) selected from the X chromosome of 481 unrelated individuals (295 females and 186 males) in Shandong Han population. RESULTS: Among the 15 X-STR loci, GATA 165B12, DXS6800, DXS6803, DXS7133 and DXS7423 showed moderate polymorphisms, while the rest 10 X-STR loci showed high polymorphisms (PIC > 0.5 and H > 0.5). No shared haplotype was detected among the males in Shandong Han population. CONCLUSION The developed multi-PCR system with fluorescence detection provides an effective way to establish X-STR loci database of population genetics in Shandong Han population and shows its forensic application.
Asian People/genetics* ; China ; Chromosomes, Human, X/genetics* ; DNA Fingerprinting ; DNA Primers ; Female ; Forensic Genetics ; Gene Frequency ; Genetic Linkage ; Genetics, Population ; Genotype ; Humans ; Male ; Microsatellite Repeats ; Multiplex Polymerase Chain Reaction ; Polymorphism, Genetic

Objective In this study,we investigated the relationship between oxidative stress,selenoproteins level and onset of Keshan disease (KD) through detecting the expression of 8-hydroxy-2-deoxy guanosine (8-OH-dG),thioredoxin reductase 1 (TrxR1) and glutathione peroxidase 1 (GPx1) in myocardial tissue.Methods Myocardium samples of autopsy patients including 8 cases of KD (KD group included 4 acute KD and 4 chronic KD) and 9 cases of non-KD (control group) were immunohistochemically stained for 8-OH-dG,TrxR1 and GPx1.The staining intensities subsequently quantified by using Olympus Image-Pro Plus 6.0 software.Results The positive rate of 8-OH-dG expression in myocardial nuclei was higher in the case group[(68.6 ± 20.4)％] than that of the control group[(2.4 ± 1.5)％,t =8.515,P ＜ 0.05].In addition,the positive rate of 8-OH-dG expression in acute KD[(91.7 ± 3.7)％] was significantly higher than that of chronic KD[(53.2 ± 7.9)％,t =6.409,P＜0.05].The distribution of TrxR1 and GPx1 was not associated with the distribution of myocardial damage.The expression of these two selenoproteins in KD group (401340 ± 59865,497590 ± 197082) were both lower than that of control group(2790300 ± 379298,1348400 ±615840; t =-28.493,-6.016,respectively,all P＜0.01).Conclusions Oxidative damage is detected in myocardium tissue of KD,and 8-OH-dG expression is associated with the degree of myocardial damage in KD.Selenoproteins,TrxR1 and GPx1,may be closely related to the pathogenesis of KD.

Induction of common knowledge or regularities from large-scale clinical data is a vital task for Chinese medicine (CM). In this paper, we propose a data mining method, called the Symptom-Herb-Diagnosis topic (SHDT) model, to automatically extract the common relationships among symptoms, herb combinations and diagnoses from large-scale CM clinical data. The SHDT model is one of the multi-relational extensions of the latent topic model, which can acquire topic structure from discrete corpora (such as document collection) by capturing the semantic relations among words. We applied the SHDT model to discover the common CM diagnosis and treatment knowledge for type 2 diabetes mellitus (T2DM) using 3 238 inpatient cases. We obtained meaningful diagnosis and treatment topics (clusters) from the data, which clinically indicated some important medical groups corresponding to comorbidity diseases (e.g., heart disease and diabetic kidney diseases in T2DM inpatients). The results show that manifestation sub-categories actually exist in T2DM patients that need specific, individualised CM therapies. Furthermore, the results demonstrate that this method is helpful for generating CM clinical guidelines for T2DM based on structured collected clinical data.
Diabetes Mellitus, Type 2 ; diagnosis ; therapy ; Diagnosis, Differential ; Humans ; Medicine, Chinese Traditional ; Models, Theoretical

OBJECTIVETo evaluate the potential role of hepatocyte growth factor (HGF) combined with bone marrow mesenchymal stem cells (BMSC) autograft for the treatment of silicosis.

METHODSBone marrow (100 ml) was aspirated from a severe silicosis patient. BMSCs isolated, purified and cultured in vitro. When BMSC came to 70% confluence at passage 3, the culture medium was added liposomes (lipo2000) and plasmid-HGF (p-HGF) and cultured for 2 d. HGF-MSCSs (5 × 10(7) cells) were resuspended in 50 ml 0.9% sodium chloride (NS) and infused Intravenous drip at 3 consecutive times (once a week). Clinical follow-up were performed before and after treatment: (1) pulmonary high-kV X-ray, chest CT examination; (2) pulmonary function test; (3) determination of serum ceruloplasmin.

RESULTSThe symptoms such as coughing, chest tightness disappeared at 12 months after treatment. Pulmonary function tests showed significant changes after treatment: forced vital capacity (FVC) increased from 64.6% to 81.0%, forced expiratory volume in one second (FEV(1.0)) increased from 68.7% to 90.1%, 1 second rate (FEV(1.0)/FVC%) reduced from 111.6% to 107.1%, the maximum mid-expiratory flow (FEF(25%∼75%) decreased from 100.2% to 94.6%, forced expiratory vital capacity 75% of the moment bit of gas flow (MEF(75%)) increased from 99.2% to 113.5%, forced expiratory vital capacity 50% of the moment bit of gas flow (MEF(50%)) increased from 125.3% to 130.2%, forced expiratory vital capacity 25% of the moment bit of gas flow (MEF(25%)) reduced from 86.9% to 71.7%; serum ceruloplasmin levels decreased from 690 mg/L to 180.6 mg/L; lung high-kV X-ray at 1st review showed that diffuse lung nodules had been absorbed and getting smaller than before treatment; chest CT showed that the distribution and number of small nodules at double lung fields decreased than before treatment.

CONCLUSIONHGF combined with BMSC transplantation may have some potential role for the treatment of silicosis patients.

Adult ; Bone Marrow Transplantation ; Female ; Follow-Up Studies ; Hepatocyte Growth Factor ; therapeutic use ; Humans ; Mesenchymal Stem Cell Transplantation ; Silicosis ; therapy ; Treatment Outcome

BACKGROUNDPeroxisome proliferator-activated receptor (PPAR) alpha is one of the subtypes of PPARs. It regulates metabolism of lipid and lipoprotein, as well as glucose homeostasis. In addition, PPARalpha influences cellular proliferation, inflammation, differentiation and apoptosis, which plays a vital role in cardiovascular diseases. The purpose of this study was to investigate the role and mechanisms of PPARa activation in relation to acute myocardial damage induced by isoproterenol in rats.

METHODSThirty male Wister rats were randomly divided into control group, isoproterenol (Iso) injured group and fenofibrate (FF) treatment group. Acute myocardial damage caused by isoproterenol intraperitoneal injection induced ischemia was established. We determined the levels of creatine kinase (CK) and lactic dehydrogenase (LDH) in serum as well as the concentrations of free fatty acids (FFA) in serum and myocardium. The mRNA expressions of PPARa, muscular type carnitine palmitransferase (M-CPT-I) and medium chain lipid acetyl coenzyme A dehydrogenase (MCAD) were analyzed by reverse transcription-polymerase chain reaction (RT-PCR).

RESULTSCompared with the control group, the levels of serum CK and LDH were significantly increased after FF and Iso treatments. Moreover, the concentrations of FFA in both serum and myocardium were obviously increased in the Iso group and FF group, while the mRNA expressions of PPARalpha, M-CPT-I and MCAD declined, respectively (P < 0.01). When compared with the Iso group, significant decreases in serum CK and LDH were observed in the FF group. The concentrations of FFA both in serum and myocardial tissue were markedly decreased in the FF group, while the expressions of PPARalpha, M-CPT-I and MCAD mRNA were increased (vs. Iso, P < or = 0.01).

CONCLUSIONSThe utilization of FFA was reduced in isoproterenol induced acute myocardial damage. PPARalpha activation by its activator fenofibrate may play a key role in energy metabolism in acute myocardial damage induced by isoproterenol in rats.

Animals ; Creatine Kinase ; blood ; Energy Metabolism ; Fatty Acids, Nonesterified ; metabolism ; Fenofibrate ; pharmacology ; Heart ; drug effects ; Isoproterenol ; toxicity ; L-Lactate Dehydrogenase ; blood ; Male ; PPAR alpha ; physiology ; Rats ; Rats, Wistar