Near infrared spectroscopy(NIRS)analysis technology has become an important process analysis tool in industrial and agricultural production,and has been widely used for qualitative and quantitative analysis in the fields of tobacco,agriculture,and pharmaceuticals.To address issues such as poor generalization ability and low prediction accuracy in NIRS modeling,a two-dimensional convolutional neural network(2DCNN)quantitative analysis model based on competitive adaptive reweighted sampling(CARS)and Gramian angular difference field(GADF)(CARS-GADF-2DCNN)was proposed.CARS-GADF-2DCNN used the CARS method to select an optimal wavelength set from the full spectrum,then employed GADF to encode the selection results into two-dimensional images,and finally used 2DCNN for prediction analysis.The 2DCNN model consisted of convolutional layers,parallel convolution modules,flattening layer,and fully connected layers.Simulation experiments were conducted on three public near-infrared(NIR)spectral datasets encompassing soil,tablet,and grain datasets to evaluate the CARS-GADF-2DCNN model.The results demonstrated that,compared to the one-dimensional convolutional neural network(1DCNN),the GADF-2DCNN model achieved 16.74％,23.40％,and 7.13％improvement in prediction accuracy for the soil,tablet,and grain datasets,respectively.Compared to GADF-2DCNN,VCPA-GADF-2DCNN,and IRIV-GADF-2DCNN models,the CARS-GADF-2DCNN model further improved prediction accuracy.For the soil dataset,prediction accuracy improved by 39.00％,30.78％and 4.13％;for the tablet dataset,the improvements were 9.52％,6.94％and 2.56％;for the grain dataset,the improvements were 20.57％,9.85％and 15.66％.In conclusion,CARS-GADF-2DCNN effectively selected the optimal wavelength subset from near infrared spectra,and revealed the latent features between different wavelengths.CARS-GADF-2DCNN addresses the issues of high complexity in prediction models and low prediction accuracy in near infrared spectral modeling,and could be effectively applied to near infrared spectral prediction analysis of different substances.

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 develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective:To explore the efficacy and safety of sivelestat sodium in patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU).Methods:Sixty patients with ARDS admitted to the ICU of the Fuyang Hospital Affiliated to Anhui Medical University from August 1, 2023 to November 1, 2024 were selected and divided into the control group (conventional treatment, 30 cases) and the sivelestat sodium group (treated with sivelestat sodium in addition to conventional treatment, 30 cases) by the random number table method. The clinical data such as inflammatory factors, blood gas analysis indicators, Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score and Sequential Organ Failure Assessment (SOFA) score of the two groups of patients before treatment and 3 days after treatment were compared. The prognostic indicators such as mechanical ventilation time, ICU stay time, total hospital stay time, 28-day mortality rate and clinical efficacy of the two groups of patients were compared.Results:Before treatment, there were no statistically significant differences in inflammatory factors, blood gas analysis indicators, APACHE Ⅱ score and SOFA score between the two groups of patients (all P>0.05). After 3 days of treatment, the improvement degrees of APACHE Ⅱ score, SOFA score, arterial partial pressure of oxygen (PaO 2), oxygenation index (PaO 2/FiO 2), procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) in the sivelestat sodium group were all greater than those in the control group. The differences were all statistically significant (all P<0.05); The mechanical ventilation time [(5.31±4.12) d vs (7.17±2.32)d] and ICU stay [(6.31±3.42)d vs (8.93±5.26)d] of patients in the sivelestat sodium group were significantly shorter than those in the control group, and the differences were statistically significant (all P<0.05). There was no statistically significant difference in the 28-day mortality rate between the sivelestat sodium group [20.00%(6/30)] and the control group [43.33%(13/30)] ( P>0.05). The total effective rate of treatment in the sivelestat sodium group was significantly higher than that in the control group [80.00%(24/30) vs 56.67%(17/30)], and the difference was statistically significant (χ 2=4.167, P=0.041). Conclusions:Sivelestat sodium is helpful in improving the physiological parameters of patients with ARDS, effectively reducing the levels of inflammatory factors in the body, shortening the duration of mechanical ventilation and ICU stay, but has no significant effect on the 28-day mortality rate.

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 explore the efficacy and safety of sivelestat sodium in patients with acute respiratory distress syndrome (ARDS) in the intensive care unit (ICU).Methods:Sixty patients with ARDS admitted to the ICU of the Fuyang Hospital Affiliated to Anhui Medical University from August 1, 2023 to November 1, 2024 were selected and divided into the control group (conventional treatment, 30 cases) and the sivelestat sodium group (treated with sivelestat sodium in addition to conventional treatment, 30 cases) by the random number table method. The clinical data such as inflammatory factors, blood gas analysis indicators, Acute Physiology and Chronic Health Evaluation (APACHE) Ⅱ score and Sequential Organ Failure Assessment (SOFA) score of the two groups of patients before treatment and 3 days after treatment were compared. The prognostic indicators such as mechanical ventilation time, ICU stay time, total hospital stay time, 28-day mortality rate and clinical efficacy of the two groups of patients were compared.Results:Before treatment, there were no statistically significant differences in inflammatory factors, blood gas analysis indicators, APACHE Ⅱ score and SOFA score between the two groups of patients (all P>0.05). After 3 days of treatment, the improvement degrees of APACHE Ⅱ score, SOFA score, arterial partial pressure of oxygen (PaO 2), oxygenation index (PaO 2/FiO 2), procalcitonin (PCT), interleukin-6 (IL-6), and C-reactive protein (CRP) in the sivelestat sodium group were all greater than those in the control group. The differences were all statistically significant (all P<0.05); The mechanical ventilation time [(5.31±4.12) d vs (7.17±2.32)d] and ICU stay [(6.31±3.42)d vs (8.93±5.26)d] of patients in the sivelestat sodium group were significantly shorter than those in the control group, and the differences were statistically significant (all P<0.05). There was no statistically significant difference in the 28-day mortality rate between the sivelestat sodium group [20.00%(6/30)] and the control group [43.33%(13/30)] ( P>0.05). The total effective rate of treatment in the sivelestat sodium group was significantly higher than that in the control group [80.00%(24/30) vs 56.67%(17/30)], and the difference was statistically significant (χ 2=4.167, P=0.041). Conclusions:Sivelestat sodium is helpful in improving the physiological parameters of patients with ARDS, effectively reducing the levels of inflammatory factors in the body, shortening the duration of mechanical ventilation and ICU stay, but has no significant effect on the 28-day mortality rate.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To observe clinical efficacy and safety of less invasive surfactant administration(LISA)in treatment of respiratory distress syndrome(RDS)in extremely premature infants.Methods A total of 65 cases premature infants diagnosed with RDS admitted to neonatal intensive care unit of Xiaogan Central Hospital from January 2021 to December 2023,with gestational age of 28+0～31+6 weeks.They were divided into LISA group(n=33)and intubation administration of surfactant extubation(InSurE)group(n=32)using a random number table method.The incidence of adverse events,blood gas analysis before and after administration,pulse oxygen saturation(SpO2),changes in blood pressure,clinical efficacy,complications,and outcomes were compared between two groups.Results There was no statistically significant difference in the incidence of drug reflux,bradycardia,apnea,or SpO2＜80％between two groups of operations(P＞0.05).During the operation,SpO2 of LISA group was lower than that of InSurE group,and blood pressure monitoring at the 2nd and 4th minutes were lower than those of InSurE group at the corresponding time points,with statistically significant differences(P＜0.05).After 1 hour of treatment,arterial partial pressure of oxygen in LISA group was higher than that in InSurE group,and arterial partial pressure of carbon dioxide was lower than that in InSurE group,with statistically significant differences(P＜0.05).The mechanical ventilation ratio and oxygen therapy time within 72 hours in LISA group were lower than those in InSurE group,and the differences were statistically significant(P＜0.05).There was no statistically significant difference in reuse rate of pulmonary surfactant(PS),and hospitalization time between two groups(P＞0.05).The incidence of grade 3-4 periventricular intraventricular hemorrhage,P-IVH in LISA group was lower than that in InSurE group,and the difference was statistically significant(P＜0.05).There was no statistically significant difference in the incidence of other complications between two groups(P＞0.05).Conclusion Less invasive PS treatment for extremely premature infants with RDS can reduce the incidence of mechanical ventilation,shorten oxygen therapy time,and reduce the occurrence of severe P-IVH.

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Objective To investigate the clinical efficacy and safety of single-port thoracoscopic surgery for elderly patients with non-small cell lung cancer(NSCLC).Methods The clinical data of 93 patients with NSCLC who underwent thoracoscopic lobectomy or segmentectomy was collected,the patients were divided into uniportal operation group(40 cases,received single-port thoracoscopic surgery)and single-operation port operation group(53 cases,received single-operation port thoracoscopic surgery)according to the operation methods.The operation time,the amount of blood loss,the number of lymph node dissection,chest drainage volume 3 days after surgery,duration of indwelling drainage tube,postoperative hospital stay,visual analogue scale(VAS)score of postoperative pain,and incidence of postoperative complications of patients between the two groups were compared.The cumulative survival rate between the two groups was compared.Results The operation were successfully completed in both groups.There was no statistically significant difference in terms of operation time,the amount of blood loss,the number of lymph node dissection,chest drainage volume 3 days after surgery,duration of indwelling drainage tube,or postoperative hospital stay of patients between the two groups(P>0.05).There was significant difference in VAS score of postoperative pain of patients between the two groups(P<0.05).There was no early death within 1 months after surgery in both groups.There was no significant difference in the incidence of complications between the two groups(P>0.05).After 4 to 30 months of follow-up,there was no significant difference in the cumulative survival rate between the two groups(P>0.05).Conclusion Single-port thoracoscopic lobectomy or segmentectomy for elderly patients with NSCLC has high safety and feasibility,with less trauma,faster recovery and less postoperative pain.