OBJECTIVES: We aimed to quantify the temporal associations between cervical cancer incidence and cervical cancer-related factors and to predict the number of new cervical cancer cases averted under counterfactual scenarios compared to the status quo scenario. METHODS: We described temporal trends in cervical cancer and associated factors globally from 1990 to 2019. We then used generalized linear mixed models to explore the impact of tobacco use, sexually transmitted infections (STIs), human papillomavirus (HPV) vaccination, and cervical screening on cervical cancer incidence. A counterfactual analysis was performed to simulate the most effective scenario for reducing cervical cancer incidence. RESULTS: The worldwide incidence of cervical cancer showed a downward trend over the past 3 decades (estimated annual percentage change, -0.72%), although the incidence remained high (>30 cases per 100,000 persons) in sub-Saharan Africa, Latin America, and the Caribbean. Higher smoking and STI prevalence showed significant direct associations with the incidence of cervical cancer, whereas HPV vaccination and screening coverage showed significant inverse associations. If the strategic goals for accelerating the elimination of cervical cancer and tobacco control programs had been achieved in 2019, the largest decrease in the number of new cervical cancer cases would have been observed, with 54,169 fewer new cases of cervical cancer in 2019. CONCLUSIONS Our counterfactual analysis found that a comprehensive intervention program emphasizing scaled-up cervical screening coverage (70%), HPV vaccination coverage (90%), and tobacco control (30% relative reduction) would be the most effective program for reducing cervical cancer incidence.

Objective:To explore the feasibility of deep learning technology for renal artery recognition in retroperitoneal laparoscopic renal surgery videos.Methods:From January 2020 to July 2021, the video data of 87 cases of laparoscopic retroperitoneal nephrectomy, including radical nephrectomy, partial nephrectomy, and hemiurorectomy, were retrospectively analyzed. Two urological surgeons screened video clips containing renal arteries. After frame extraction, annotation, review, and proofreading, the labeled targets were divided into training set and test set by the random number table in a ratio of 4∶1. The training set was used to train the neural network model. The test set was used to test the ability of the neural network to identify the renal artery in scenes with different difficulties, which was uniformly transmitted to the YOLOv3 convolutional neural network model for training. According to the opinion of two senior doctors, the test set was divided into high, medium, and low discrimination of renal artery and surrounding tissue. High identification means a clean renal artery and a large exposed area. For middle recognition degree, the renal artery had a certain degree of blood immersion, and the exposed area was medium. Low identification means that the exposed area of the renal artery was small, often located at the edge of the lens, and the blood immersion was severe, which may lead to lens blurring. In the surgical video, the annotator annotated the renal artery truth box frame by frame. After normalization and preprocessing, all images were input into the neural network model for training. The neural network output the renal artery prediction box, and if the overlap ratio (IOU) with the true value box was higher than the set threshold, it was judged that the prediction was correct. The neural network test results of the test set were recorded, and the sensitivity and accuracy were calculated according to IOU.Results:In the training set, 1 149 targets of 13 videos had high recognition degree, 1 891 targets of 17 videos had medium recognition degree, and 349 targets of 18 videos had low recognition degree. In the test set, 267 targets in 9 videos had high recognition degree, 519 targets in 11 videos had medium recognition degree, and 349 targets in 18 videos had low recognition degree. When the IOU threshold was 0.1, the sensitivity and accuracy were 52.78% and 82.50%, respectively. When the IOU threshold was 0.5, the sensitivity and accuracy were 37.80% and 59.10%, respectively. When the IOU threshold was 0.1, the sensitivity and accuracy of high, medium and low recognition groups were 89.14% and 87.82%, 45.86% and 78.03%, 32.95%, and 76.67%, respectively. The frame rate of the YOLOv3 algorithm in real-time surgery video was ≥15 frames/second. The false detection rate and missed detection rate of neural network for renal artery identification in laparoscopic renal surgery video were 47.22% and 17.49%, respectively (IOU=0.1). The leading causes of false detection were similar tissue and reflective light. The main reasons for missed detection were image blurring, blood dipping, dark light, fascia interference, or instrument occlusion, etc.Conclusions:Deep learning-based renal artery recognition technology is feasible. It may assist the surgeon in quickly identifying and protecting the renal artery during the operation and improving the safety of surgery.

OBJECTIVE： To screen the best proportion of Astragalus membranaceus injection combined with Erigeron breviscapus injection against cerebral ischemia-reperfusion injury in rats. METHODS： Male SD rats were randomly divided into sham operation group， model group and administration group [different A. membranaceus injection-E. breviscapus injection proportion groups， being A（0 ∶ 10）， B（2 ∶ 8）， C（4 ∶ 6）， D（6 ∶ 4）， E（8 ∶ 2）， F（10 ∶ 0）groups， set by baseline geometric proportion increasing and decreasing design]， with 8 rats in each group. Except for sham operation group， reperfusion injury model of middle cerebral artery occlusion were induced by modified suture method in rats. The each administration group was given relevant medicine intraperitoneally once immediately after inducing model， and then given again after 24 hours （medication interval between the two injections of 30 min）. Constant volume of normal saline was given to rats in sham operation group and model group. Forty-eight hours after reperfusion， Longa scoring method was used to evaluate neurological impairment of rats， and neurological impairment score was recorded. Serum content of MDA and activity of SOD were measured by colorimetry assay. TTC assay was used to detect cerebral infraction， and cerebral infarction rate was calculated. Kim’s formula was used to calculate the synergistic index （q） of rats in administration groups. RESULTS： Compared with sham operation group， neurological impairment score and serum content of MDA were increased significantly in model group， while activity of SOD was decreased significantly （P＜0.01）. The area of cerebral infarction increased significantly， and the rate of cerebral infarction increased significantly （P＜0.01）. Compared with model group， neurological impairment scores and serum contents of MDA were decreased significantly in group A， B， C， D and E； neurological impairment score of group C was significantly lower than those of group A and F； serum contents of MDA in group B， C， D and E were significantly lower than that of group F （P＜0.05 or P＜0.01）. Activities of SOD in group A， B， C， D and E were increased significantly， and group C was significantly higher than group F （P＜0.05 or P＜0.01）. The cerebral infarction area of rats in each administration group was reduced to varying degrees. The cerebral infarction rates of rats in group B， C， D and E were significantly reduced， and group C and D were significantly lower than group F， while group C was significantly lower than group A （P＜0.05 or P＜0.01）. The q values of group B， C， D and E were 0.90， 1.30， 1.00， 0.70 （neurological impairment score） and 0.79， 1.27， 0.98， 0.82 （cerebral infarction rate）. CONCLUSIONS： Different ratios of A. membranaceus injection and E. breviscapus injection have certain protective effects on cerebral ischemia-reperfusion injury model rats， can relieve their neurological deficits， alleviate their oxidative stress and reduce their cerebral infarction areas. The effect of the combination of the two drugs is better than that of single use， and the optimum ratio is 4 ∶ 6.