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.

Objective To compare regional pulmonary ventilation and perfusion and ventilation/perfusion(V/Q)matching in different body positions using electrical impedance tomography(EIT).Methods Ten healthy experimental pigs were selected to collect their EIT lung ventilation and perfusion data in supine,prone,left lateral and right lateral positions.The EIT data underwent analysis and image reconstruction using MATLAB R2022b and EIDORS v3.9.The effective regions with ventilation and perfusion were determined and the V/Q matching regions were computed with the maximum pixel value 20％as the threshold.Comparisons were carried out over the V/Q matching indexes including V/Q match％,dead space％and shunt％and ventilation and perfusion distribution in regions of interest(ROIs)including ROI1,ROI2,ROI3 and ROI4 in different body positions.Results The differences in V/Q match％,dead space％and shunt％of the experimental animals in varied positions were not statistically significant(P＞0.05).The regional distribution of pulmonary ventilation and perfusion changed in different positions,and the regional distributions differed in ROIl,ROI2 and ROI3 for ventilation(P＜0.05)and in ROI1 and ROI2 for perfusion(P＜0.05).The ventilation and perfusion regions were distributed consistently with the gravity-dependent areas in supine and prone positions whereas conversely in the right and left lateral positions.Conclusion The V/Q matching indexes of one subject have high test consistency in different body positions;gravity-dependent areas varied with the changes of the body positions,which affected the distribution of pulmonary ventilation and perfusion regions;EIT can be used for measuring the changed pulmonary ventilation and perfusion due to different positions and determining the influences of position changes on pulmonary ventilation and perfusion and V/Q matching.[Chinese Medical Equipment Journal,2024,45(5):1-7]

Lipopolysaccharides (LPS) are major outer membrane components of Gram-negative bacteria. Unlike most Gram-negative bacteria, Acinetobacter baumannii can still survive and acquire polymyxin resistance after complete loss of LPS. Previous studies of LPS-deficient Acinetobacter baumannii mostly focused on LPS-deficient strains induced by polymyxin, whose background was complex and unstable. To investigate LPS loss-mediated polymyxin resistant-Acinetobacter baumannii, this study constructed a stable and clear background LPS-deficient strain by knocking out lpxC gene in Acinetobacter baumannii ATCC 19606 with CIRSPR/Cas9, and then studied the phenotypic changes of the lpxC-deficient strain including morphology, growth rate, antibiotic susceptibility, virulence, membrane permeability, and membrane potential. Animal experiments were approved by the Animal Care and Welfare Committee Institute of Medicinal Biotechnology, CAMS and PUMC (approval number: IMB-20240119D₉). The results indicated that the lpxC gene of Acinetobacter baumannii ATCC 19606 was successfully knocked out. After losing the lpxC, the strain underwent the morphological change from rod-shaped to spherical. Furthermore, it leads to reduced growth rate, enhanced membrane permeability, decreased membrane potential, lower virulence, and increased antibiotic susceptibility to β-lactams, quinolones, aminoglycosides, macrolides, glycopeptides. The lpxC deletion results in significant changes in membrane homeostasis and adaptability of Acinetobacter baumannii. Understanding the phenotypic changes of colistin-resistant Acinetobacter baumannii mediated by LPS loss is useful for exploring the resistance mechanism of Acinetobacter baumannii and developing new therapeutic strategies.

Objective To propose a method for predicting weaning outcomes based on machine learning and electrical impedance tomography(EIT).Methods Firstly,EIT image features were extracted from a total of 84 samples from 30 patients,and the important features screened with the extreme gradient boosting(XGBoost)algorithm were used as inputs to the model.Secondly,the prediction model was built with six machine learning methods,namely random forest(RF),support vector machines(SVM),XGBoost,gradient boosting decision tree(GBDT),logistic regression(LR)and decision tree(tree).Then the prediction model had its prediction performance evaluated by AUC,accuracy,sensitivity and specificity under imbalanced dataset,over-sampling balanced dataset and random under-sampling balanced dataset.Results In terms of AUC,accuracy and specificity,the model under the over-sampling balanced dataset and the random under-sampling balanced dataset behaved better than that under the imbalanced dataset(P＜0.05);in terms of sensitivity,the difference in model performance between the over-sampling balanced dataset and the imbalanced dataset was not statistically significant(P＞0.05),and the model performance under the random under-sampling balanced dataset decreased when compared with that under the imbalanced dataset(P＜0.05).There were no significant differences between the model performance under the over-sampling balanced dataset and that under the random under-sampling balanced dataset(P＞0.05).The model based on XGBoost behaved the best under the over-sampling balanced dataset,with an AUC of 0.769,an accuracy of 0.808,a sensitivity of 0.938 and a specificity of 0.600.Conclusion The method based on machine learning and EIT predicts weaning outcomes of patients with prolonged mechanical ventilation,and thus can be used for auxiliary decision support for clinicians to determine the appropriate timing of weaning.[Chinese Medical Equipment Journal,2023,44(10):1-6]

OBJECTIVETo construct a forecasting model of influenza-like illness in Zhejiang Province.

METHODSThe number of influenza-like cases and related pathogens among outpatients and emergency patients were obtained from 11 sentinel hospitals in Zhejiang Province during 2012 to 2013 (total 104 weeks), and corresponding meteorological factors were also collected. The epidemiological characteristics of influenza during the period were then analyzed. Linear correlation and rank correlation analyses were conducted to explore the association between influenza-like illness and related factors. Optimal parameters were selected by cross validation. Support vector machine was used to construct the forecasting model of influenza-like illness in Zhejiang Province and verified by the historical data.

RESULTSCorrelation analysis indicated that 8 factors were associated with influenza-like illness occurred in one week. The results of cross validation showed that the optimal parameters were C=3, ε=0.009 and γ=0.4. The results of influenza-like illness forecasting model after verification revealed that support vector machine had the accuracy of 50.0% for prediction with the same level, while it reached 96.7% for prediction within the range of one level higher or lower.

CONCLUSIONSupport vector machine is suitable for early warning of influenza-like illness.

China ; epidemiology ; Forecasting ; Humans ; Influenza, Human ; epidemiology ; Sentinel Surveillance ; Support Vector Machine

H5 subtype avian influenza (AIV-H5) is a major causative agent of animalloimia a rapid and sensitive molecular biological diagnosis is crucial to the control program of AIV-H5. AIV-H5 real-time fluorescent reverse transcription loop-mediated isothermal amplification (qRT-LAMP) was established by means of heat treatment of the samples. The sensitivity, specificity and repeatability of this method were assessed and the performance of Calcein,SYBR Green I,HNB,SYTO 81 in colorimetric detection was comparatively analyzed to screen the optimum dye. The results showed the sensitivity of this method was 100 times higher than that of standard real-time fluorescent RT-PCR, and the detection limit was one copy of the gene per reaction. This method had no cross-reactivity with other common avian respiratory tract infectious disease-related pathogens such as IBV and NDV. The present study suggested Calcein was the optimum dye. Small-scale tests suggested this method was reliable for survey monitoring of AIV-H5 on the spot, indicating its potential applications in field investigation.
Animals ; Chickens ; Hemagglutinin Glycoproteins, Influenza Virus ; genetics ; Influenza A Virus, H5N1 Subtype ; genetics ; isolation & purification ; Influenza in Birds ; diagnosis ; virology ; Poultry Diseases ; diagnosis ; virology ; Reverse Transcriptase Polymerase Chain Reaction ; instrumentation ; methods ; Sensitivity and Specificity

OBJECTIVETo evaluate the efficacy and toxicity of combination therapy with surgery and recombinant adenovirus-p53 injection of recurrent malignant gliomas.

METHODS38 patients with recurrent malignant gliomas were included in this study. Among them, 18 patients of combined treatment group had Ommaya reservoirs placed into the tumor cavities after the resection of the tumors and received regular recombinant adenovirus-p53 injections after the operation. The other 20 patients received surgery alone.

RESULTSThe 6-month and 1-year survival rates after the combination therapy were 66.7% (14/18) and 44.4% (8/18), respectively. The median survival time was 9.7 months. Compared with the surgery-alone group, the combined treatment group achieved significant improvement (P < 0.05). The Karnofsky score was significantly improved at 6 months after the combination therapy compared with that before the treatment (P < 0.05).

CONCLUSIONThe recombinant adenovirus-p53 injection is safe and effective in treatment of recurrent malignant gliomas. The combination therapy of surgery and recombinant adenovirus-p53 injection may improve the life quality and the prognosis in patients with recurrent malignant gliomas.

Adenoviridae ; genetics ; Adult ; Brain Neoplasms ; pathology ; surgery ; therapy ; Combined Modality Therapy ; Female ; Follow-Up Studies ; Genes, p53 ; Genetic Therapy ; Glioma ; pathology ; surgery ; therapy ; Humans ; Male ; Middle Aged ; Neoplasm Recurrence, Local ; Recombinant Proteins ; therapeutic use ; Survival Rate ; Tumor Suppressor Protein p53 ; genetics ; therapeutic use

OBJECTIVETo explore the possible effects of 50 Hz magnetic fields (MF) exposure on HCG and progesterone secretion of human villous trophoblasts in vitro.

METHODSThe trophoblasts were isolated from human villus by trypsin digestion and incubated in DMEM medium. Then the trophoblasts were exposed to 0.2 mT, 0.4 mT 50 Hz MF for 6 h, 12 h, 24 h, 48 h and 72 h, respectively. Each exposure group was matched to one control group which was from the same villus and cultured with the same condition except the 50 Hz MF exposure. The concentration of human chorionic gonadotropin (HCG) and progesterone in the culture medium was detected by electrochemiluminescence immunoassay. Statistical significance of differences between means was determined by one way-ANOVA with P < 0.05 considered significant.

RESULTSExposure of trophoblasts to 50 Hz MF at 0.2 mT intensity within 72 h did not affect the secretion level of HCG and progesterone (compared with blank control, P > 0.05). There was also no significant change of the secretion level of HCG and progesterone when trophoblasts were exposed to 0.4 mT 50 Hz MF within 48 h (compared with blank control, P > 0.05). However, 50 Hz MF inhibited the HCG and progesterone secretion significantly with exposure for 72 h (compared with blank control, P < 0.05).

CONCLUSIONThe exposure to 50 Hz MF for long period could inhibit trophoblasts excreting the HCG and progesterone, and the threshold intensity may be between 0.2 mT and 0.4 mT.

Cells, Cultured ; Chorionic Gonadotropin ; metabolism ; Female ; Humans ; Magnetic Fields ; adverse effects ; Pregnancy ; Progesterone ; metabolism ; Trophoblasts ; metabolism ; secretion

OBJECTIVETo investigate the possible effect of exposure to GSM 1,800 MHz radiofrequency electromagnetic fields (RF EMF) on epidermal growth factor (EGF) receptor and its possible interference by noise magnetic fields (MF).

METHODSChinese hamster lung fibroblasts (CHL) were exposed to 1,800 MHz RF EMF (modulated by 217 Hz or 50 Hz, or unmodulated), 2 microT noise MF, and RF EMF combined with 2 microT noise MF for 15 min, respectively. The specific absorption rates (SARs) of RF EMF were 0.1, 0.5, 1.0, 2.0 and 4.0 W/kg. Commercial EGF (1 ng/ml) treatment was used as positive control. EGF receptors on the cell membrane were observed under a laser scanning confocal microscope after indirect immunofluorescence staining.

RESULTSEGF receptor clustering was induced after exposure to GSM 1,800 MHz RF EMF modulated by 217 Hz or 50 Hz MF at SARs of 0.5, 1.0, 2.0, 4.0 W/kg for 15 min as induced by 1 ng/ml EGF, but not at SAR of 0.1 W/kg. And no EGF receptor clustering was found in cells after exposure to unmodulated RF EMF or 2 microT noise MF. In addition, superposition of 2 microT noise MF could inhibit the EGF receptor clustering induced by GSM 1,800 MHz RF EMF.

CONCLUSIONEGF receptor clustering in CHL cells can be induced by GSM 1,800 MHz RF EMF at the lowest SAR of 0.5 W/kg and inhibited by noise MF. The modulation of wave may play an important role in the inducement of receptor clustering after RF exposure.

Animals ; Cell Line ; Cell Membrane ; metabolism ; radiation effects ; Cricetinae ; Cricetulus ; Dose-Response Relationship, Radiation ; Electromagnetic Fields ; Fibroblasts ; metabolism ; radiation effects ; Lung ; cytology ; Radio Waves ; Receptor, Epidermal Growth Factor ; metabolism