ObjectiveTo investigate the changes in coagulation system in acute decompensated cirrhosis （ADC） patients with or without sepsis and the association of these changes with short-term prognosis. MethodsA prospective study was conducted among 116 ADC patients who were hospitalized in Nanfang Hospital from January 2021 to July 2023， among whom there were 86 patients with sepsis and 30 patients without sepsis， and 54 patients with sepsis alone who had no chronic liver disease were enrolled as control group. Thromboelastography （TEG） and other conventional coagulation parameters were used to comprehensively evaluate the coagulation function of patients. The data including TEG results and short-term prognosis were collected， and a correlation analysis was performed. The independent-samples t test was used for comparison of normally distributed continuous data between two groups， and the Mann-Whitney U test was used for comparison of non-normally distributed continuous data between two groups； the chi-square test was used for comparison of categorical data between two groups. The Spearman correlation coefficient was calculated to investigate the correlation between different variables. The Logistic regression model was used to perform the univariate and multivariate analyses. ResultsFor the ADC patients with sepsis， the lungs and bloodstream were the main infection sites， and bacteria were the main pathogenic microorganism. TEG results showed that compared with the patients with sepsis alone， the patients with ADC and sepsis had a significant reduction in median maximum amplitude （MA）， a significant increase in coagulation time （K time）， and a significant reduction in α angle （all P<0.05）； the patients with ADC and sepsis had a significantly longer reaction time （R time） than those with ADC alone （P=0.02）， and the patients with sepsis alone had a significantly longer R time than those with ADC and sepsis （P=0.04）. There was no correlation between MA and platelet count in the patients with ADC and sepsis （r=-0.133， P=0.057）， while there was a significant correlation between MA and platelet count in the patients with sepsis alone （r=0.595， P=0.001）. SOFA score was negatively correlated with MA in sepsis patients with or without ADC （r=-0.503 and -0.561， both P<0.001）， and for the patients with ADC and sepsis， R time， K time， and α angle were weakly correlated with SOFA score and had a relatively strong correlation with APTT （all P<0.05）. The patients with ADC alone all survived within 90 days， and compared with the death group， the patients with sepsis alone who survived had significantly higher values of MA and α angle （all P<0.05）； there was a significant difference in α angle on day 90 between the survival group and the death group， no matter whether the patients were comorbid with ADC or not （both P<0.01）， while for the patients with ADC and sepsis， there was no significant difference in MA value on day 90 between the survival group and the death group （P>0.05）. ConclusionFor ADC patients comorbid with sepsis， coagulation function assessment and monitoring should be taken seriously in clinical practice， and TEG parameters and SOFA score should be monitored when necessary to develop individualized treatment regimens.

BACKGROUND: The incidence and mortality rates of lung cancer remain on the rise, creating an urgent need for screening among high-risk populations and early prevention. This study aims to explore the association and interaction between multidimensional lifestyle, socioeconomic status, and the incidence of lung cancer, and to provide scientific evidence for screening high-risk populations and preventing lung cancer. METHODS: Healthy lifestyle score was constructed using information on smoking, alcohol consumption, exercise, diet and sleep obtained through a questionnaire survey. Socioeconomic status was evaluated based on information on education, employment, and family income, and genetic testing data were used to assess the risk of genetic variation. A proportional hazards assumption test was conducted, and the Cox proportional hazards model was applied to analyze the associations between healthy lifestyle scores, socioeconomic status, and lung cancer, as well as the interactions among various factors, after adjusting for the risk of genetic variation, age, gender, diabetes, hypertension and the living environment score. RESULTS: A total of 245,538 samples that entered the cohort from March, 2006 to October, 2010 were included and followed up until December 31, 2022. The participants were divided into the case group (n=1472) and the control group (n=244,066). The analysis results showed that after adjusting for covariates, there was still an association between the healthy lifestyle score, socioeconomic status, and the incidence of lung cancer: compared with participants with a high healthy lifestyle score, the risk of lung cancer in participants with medium and low healthy lifestyle scores was significantly increased, with hazard ratios (HR) of 2.12 (95%CI: 1.86-2.41) and 3.36 (95%CI: 2.82-3.99) respectively; compared with participants with high socioeconomic status, the risk of lung cancer in participants with medium and low socioeconomic status was significantly increased, with HR of 1.29 (95%CI: 1.13-1.48) and 1.67 (95%CI: 1.46-1.90) respectively. Moreover, there were interactions between smoking status and socioeconomic status (Pfor interaction=0.05), as well as the other four lifestyle factors (Pfor interaction=0.02). CONCLUSIONS This study identified the association between multidimensional lifestyle factors and socioeconomic status with the incidence of lung cancer, as well as interactions between smoking and socioeconomic status and four other lifestyle factors, providing a scientific basis for screening and prevention in high-risk populations for lung cancer.
Humans ; Lung Neoplasms/epidemiology* ; Male ; Female ; Middle Aged ; Incidence ; Life Style ; Social Class ; Aged ; Adult ; Risk Factors

To explore the feasibility of using UAV-LiDAR for measuring the leaf area index (LAI) of crop canopies, we employed UAV-LiDAR to scan sugarcane canopies during the tillering and elongation stages, acquiring canopy point cloud data. Subsequently, features such as average row height, projected row area, point cloud density at different canopy layers, and the ratios between these parameters were extracted. Three feature selection methods-partial least squares regression (PLSR), XGBoost feature importance (XGBoost-FI), and random forest-recursive feature elimination (RF-RFE)-were adopted to evaluate and identify the optimal input variables for modeling. With these selected variables, LAI inversion models were developed based on random forest (RF) and adaptive boosting (AdaBoost) algorithms, and their performance was assessed. Among the extracted features, the projected row area S_p and the total row point count C_total exhibited strong correlations with LAI, with correlation coefficients of 0.73 and 0.72, respectively. The AdaBoost-based LAI inversion model, using the projected row area S_p, average height H_avg, mid-layer point cloud density C_m, and total row point count C_total as input variables, achieved the best performance, with a coefficient of determination (R_v²) of 0.713 and a root mean square error (RMSE_v) of 0.25 on the validation set. This study provides an effective method for high-throughput acquisition of LAI in field crops, offering valuable scientific support for sugarcane field management and breeding efforts.
Plant Leaves/growth & development* ; Saccharum/growth & development* ; Algorithms ; Unmanned Aerial Devices ; Remote Sensing Technology/methods* ; Crops, Agricultural/growth & development*

Accurately obtaining the crop quantity and density is not only crucial for the demand-based input of water and fertilizer in the field but also vital for ensuring the yield and quality of crops. Aerial photography by unmanned aerial vehicles (UAVs) can quickly acquire the distribution image information of crops over a large area. However, the accurate recognition of a single type of dense targets is a huge challenge for most recognition algorithms. Taking banana seedlings as an example in this study, we captured the images of banana plantations by UAVs from high altitudes to explore an efficient recognition method for dense targets. We proposed a strategy of "cut-recognition-stitch" and constructed a counting method based on the improved Faster R-CNN algorithm. First, the images containing highly dense targets were cropped into a large number of image tiles according to different sizes (simulating different flight altitudes), and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm was adopted to improve the image quality. A banana seedling dataset containing 36 000 image tiles was constructed. Then, the Faster R-CNN network with optimized parameters was used to train the banana seedling recognition model. Finally, the recognition results were reversely stitched together, and a boundary deduplication algorithm was designed to correct the final counting results to reduce the repeated recognition caused by image cropping. The results show that the recognition accuracy of the Faster R-CNN with optimized parameters for banana image datasets of different sizes can reach up to 0.99 at most. The deduplication algorithm can reduce the average counting error for the original aerial images from 1.60% to 0.60%, and the average counting accuracy of banana seedlings reaches 99.4%. The proposed method effectively addresses the challenge of recognizing dense small objects in high-resolution aerial images, providing an efficient and reliable technical solution for intelligent crop density monitoring in precision agriculture.
Musa/growth & development* ; Crops, Agricultural/growth & development* ; Algorithms ; Neural Networks, Computer ; Unmanned Aerial Devices ; Seedlings/growth & development* ; Image Processing, Computer-Assisted/methods* ; Photography ; Agriculture/methods*

From January 23, 2020 to May 29, 2021, there were 17,550 clinical research registrations on the China Clinical Trial Registry Platform. The index of clinical research on COVID-19 showed 840 registrations, accounting for 4.79% (840/17 550) of the total registrations. According to the trend chart of epidemic development, 487 clinical research projects of COVID-19 registered from January 23, 2020 to March 18, 2020 were set as the early stage of epidemic discovery in the region, the following 15 months were divided into stable initial stage, stable later stage and the later stage of the first wave of domestic epidemic every five months. The proportion of COVID-19 research projects registered in each stage was 29.80%, 5.87%, 0.76% and 1.15% of all registered projects respectively. The registration ratios of COVID-19 in each clinical stage was 57.98%, 28.81%, 5.95% and 7.26% respectively. Intervention studies were 61.60% (300/487) in the early stage of the epidemic, and most of them were in biological agent and drug therapy, which were 40.49%, 50.00% and 73.77% respectively in the later stages. Stable initial stage biological agent projects decreased. Then the vaccine research program became a focused research program. Among the 58 registration projects involving vaccines, 25 were clinical trials of vaccines in different stages, with the major research units being biopharmaceutical companies. Of the 487 clinical studies registered in the early stage, 10.23%(50/487) were withdrawn, of which 38 were intervention treatment studies, most notably drug studies (17) and blood biological products (16), but they were still registered in the stable initial stage. It is necessary to reflect on the scientific and ethical nature of early COVID-19 clinical trials or research, including whether the three core values of equal respect, help to alleviate pain and fairness can be achieved. Whether the "ethics review meeting should pay special attention to the scientificity, safety, fairness, subject protection, informed consent documents and informed consent process, avoidance of conflicts of interest, etc. of clinical trials or research" has been implemented. Competent government departments at all levels and all clinical medical research institutions should organize evaluation, and then establish an effective evaluation mechanism when new clinical trials or research projects are registered in the future.

Objective:To evaluate the role of the AMP-activated protein kinase (AMPK)-silent information regulator 1 (SIRT1)-nuclear factor-κB (NF-κB) signaling pathway in reduction of brain injury by panax notoginseng saponins (PNS) in mechanically ventilated rats.Methods:Seventy-two SPF-grade male Sprague-Dawley rats, aged 10 weeks, weighing 357-377 g, were divided into 6 groups ( n=12 each) by a random number table method: sham operation group, model group, PNS low dose group, PNS medium dose group, PNS high dose group, and PNS high dose+ compound C group. PNS 12.5, 25 and 50 mg/kg were intraperitoneally injected in PNS low dose group, PNS medium dose group and PNS high dose group, respectively. In PNS high dose+ compound C group, PNS 50 mg/kg was intraperitoneally injected, and 10 min later compound C 0.2 mg/kg was injected via the tail vein. Normal saline 10 ml/kg was intraperitoneally injected in sham operation group and model group. Drugs or normal saline was injected at 30 min before mechanical ventilation in each group. Mechanical ventilation model: The animals were mechanically ventilated for 6 h, with ventilation frequency 40 times/min, tidal volume 40 ml/kg in model group; The animals were mechanically ventilated for 6 h, with tidal volume 10 ml/kg in sham operation group. Morris water maze test was used to detect the learning and memory function of rats, the concentrations of serum interleukin-1beta (IL-1β), IL-6, tumor necrosis factor-alpha (TNF-α) and dopamine (DA) were detected by enzyme-linked immunosorbent assay, the neuronal counts in the hippocampal CA1 region were determined by Nissl staining, and the expression of P2Y1 purine receptor (P2Y1R), dysbindin-1 and AMPK in hippocampal CA1 region was detected by Western blot. The phosphorylated SIRT1 (p-SIRT1) to SIRT1 ratio and phosphorylated NF-κB p65 (p-NF-κB p65) to NF-κB ratio in hippocampal CA1 region was calculated. Results:Compared with sham operation group, the escape latency was significantly prolonged, the number of crossing the original platform quadrant was reduced, the time of staying at the target platform quadrant was shortened, the serum concentrations of IL-1β, IL-6 and TNF-α were increased, the serum DA concentration was decreased, the nerve count in hippocampal CA1 region was decreased, the expression of P2Y1R and dysbindin-1 was up-regulated, the expression of AMPK was down-regulated, the p-SIRT1/SIRT1 ratio was decreased, and the p-NF-κB p65/NF-κB p65 ratio was increased in model group ( P<0.05). Compared with model group, the escape latency was significantly shortened, the number of crossing the original platform quadrant was increased, the time of staying at the target platform quadrant was prolonged, the serum concentrations of IL-1β, IL-6 and TNF-α were decreased, the serum DA concentration was increased, the nerve count in hippocampal CA1 region was increased, the expression of P2Y1R and dysbindin-1 was down-regulated, the expression of AMPK was up-regulated, the p-SIRT1/SIRT1 ratio was increased, and the p-NF-κB p65/NF-κB p65 ratio was decreased in PNS low dose group, PNS medium dose group and PNS high dose group ( P<0.05). Compared with PNS high dose group, the escape latency was significantly prolonged, the number of crossing the original platform quadrant was reduced, the time of staying at the target platform quadrant was shortened, the serum concentrations of IL-1β, IL-6 and TNF-α were increased, the serum DA concentration was decreased, the nerve count in hippocampal CA1 region was decreased, the expression of P2Y1R and dysbindin-1 was up-regulated, the expression of AMPK was down-regulated, the p-SIRT1/SIRT1 ratio was decreased, and the p-NF-κB p65/NF-κB p65 ratio was increased in PNS high dose+ compound C group ( P<0.05). Conclusions:The mechanism by which PNS reduces brain injury may be related to activation of the AMPK-SIRT1-NF-κB signaling pathway in mechanically ventilated rats.

Objective:To establish the prediction model for postoperative delirium in elderly patients with mild stroke undergoing non-cardiac and non-intracranial surgery.Methods:This was a nested case-control study. Seven hundred and fifty elderly patients of either sex with mild stroke, aged ≥65 yr, undergoing elective surgical procedures under general anesthesia in the Department of Gastrointestinal Surgery, Orthopedics and Urology at the Tangshan Workers Hospital from May to December 2023, were selected. The perioperative clinical data were collected. The incidence of postoperative delirium was assessed using the Confusion Assessment Scale 1-7 days after surgery or 1 day before discharge. The patients were assigned to the training set and the validation set in a ratio of 7∶3 using a simple random sampling method. Multivariate logistic regression was used to identify the risk factors for postoperative delirium, a postoperative delirium risk prediction model was established based on the risk factors, the nomogram was developed, and the receiver operating characteristic (ROC) curve, calibration curve and decision curve were plotted to assess the accuracy of the prediction model. The prediction model was verified using the validation set, and the calibration curve and ROC curve were plotted to assess the predictive performance of the model.Results:A total of 721 patients were finally included, and 108 patients developed postoperative delirium. Older age, high American Society of Anesthesiologists Physical Status classification, history of preoperative hypertension, short years of education, high preoperative Pittsburgh sleep quality index score, high preoperative National Institutes of Health Stroke Scale score, high intraoperative hypothermia, intraoperative hypotension and high postoperative numerical rating scale score were independent risk factors for postoperative delirium ( P<0.05). The area under the ROC curve of the training set prediction model was 0.996, with a sensitivity of 1.000, and specificity of 0.945. The slope of the calibration curve was close to 1, and the predicted risk of postoperative delirium was in good agreement with the actual risk. When the threshold probability of the decision curve was 0-0.9, the net return rate was higher than the null line. Validation set: In the calibration curve of the prediction model, the cohort and calibration curves were close to the ideal line, with an area under the ROC curve of 0.997, sensitivity of 1.000, and specificity of 0.962. Conclusions:Based on age, American Society of Anesthesiologists Physical Status classification, history of preoperative hypertension, years of education, preoperative Pittsburgh sleep quality index score, National Institutes of Health Stroke Scale score, intraoperative hypothermia and hypotension and postoperative numerical rating scale score, the prediction model for postoperative delirium is developed and has a good predictive performance in elderly patients with mild stroke undergoing non-cardiac and non-intracranial surgery.

Objective:To construct a risk prediction model for urinary retention in patients undergoing radical cervical cancer surgery based on machine learning, and the prediction effect of the model was internally verified and evaluated, in order to provide reference for the early prevention and treatment of urinary retention in patients undergoing radical cervical cancer surgery.Methods:A total of 981 patients who underwent radical cervical cancer surgery in the First Affiliated Hospital of Anhui Medical University from June 2017 to February 2022 were selected and divided into the training set (687 cases) and the test set (294 cases) according to a ratio of 7∶3. Through literature review and risk factor analysis, the influencing factors of urinary retention after radical treatment of cervical cancer were explored, and the risk prediction model of urinary retention was constructed by using XGBoost, random forest, support vector machine and decision tree in machine learning. The accuracy rate, recall rate, F1 value and AUC of four machine learning algorithms were calculated by using the method of 10-fold cross-validation, and the model with the highest predictive efficiency was selected.Results:Among the 981 patients included, the incidence of urinary retention after radical cervical cancer surgery was 18.86% (185/981). The median age of urinary retention group was 51 years old, and that of non urinary retention group was 50 years old. Statistically significant variables in the univariate analysis and influencing factors summarized by literature review were featured, including patient age, intraoperative blood loss, body mass index (BMI), cancer stage, surgical method, surgical resection scope, whether pelvic lymph node dissection was performed, comorbidities and residual urine. Among the four model building methods of machine learning, the random forest model has the best effect, its training set F1 value was 0.94, the test set F1 value was 0.77, the ROC was plotted and the AUC was calculated to be 0.73. Age, intraoperative blood loss, BMI, cancer stage and surgical method contributed significantly to the classification of random forest model.Conclusions:The prediction model of urinary retention risk after radical cervical cancer surgery based on random forest method has the best efficacy. It is useful to help nursing personnel evaluate the risk of the uroschesis for a patient and then take targeted nursing interventions to actively prevent postoperative urinary retention.

Objective To conduct an epidemiological survey of common congenital heart disease(CHD)among children in ethnic minority areas in southeastern Guizhou and to explore the influencing factors of delayed medical treatment.Methods From January 2019 to July 2022,18 850 children aged 3 months to 14 years in Qiandongnan Miao and Dong Autonomous Prefecture were selected;105 children with CHD were included in the training set,and they were divided into delayed group(80 cases)and non-delayed group(25 cases)according to whether or not to delay medical treatment.In addition,children with CHD(35 cases)from July 2022 to December 2022 were included in the validation set.The general data of the subjects in the two groups were compared and ana-lyzed.Multivariate logistic regression was performed and risk scoring model was constructed.Results The preva-lence of CHD in 18 850 children was 5.57‰(105/18 850),with the highest prevalence in Liping County,and the lowest in Huangping County.The proportion of children with secondary atrial septal defect was the highest,and that of the aortic valve malformation was the lowest.Among the complex cases of CHD,the proportion of children with single type was the highest,and that of children with three or more types were the lowest.Among children with CHD,the rate of delayed medical treatment was 76.19% (80/105).The median delay in medical treatment was 12 months,with an average of(18.78±4.77)months.Multifactor logistic regression analysis showed that heart murmur(level 2～3),less-educated(primary and secondary school)guardian,family per capita income<2 000 yuan,and frequent drinking of the guardian were independent risk factors for delayed medical treatment(P<0.05),and commercial settlement of medical expenses was independent protective factor(P<0.05).Risk scoring model divided the children into three groups:low risk(≤80 points),medium risk(>80 points and≤134 points)and high(>134 points)risk group.The evaluation of the model show that it was accurate,effective,safe,and reliable.Conclusion The highest prevalence is observed in Liping County.The proportion of children with secondary atrial septal defect and the proportion of children with single type are the highest.Delayed medical treat-ment is found in most of the children with CHD.Cardiac murmur,education background of the guardian,per capita family income,guardian alcohol consumption,and medical expense settlement method are all independent influencing factors for delayed medical treatment.

Objective:To explore the correlation between high cholinergic pathway signaling and cognitive function in patients with Parkinson disease(PD) accompanied with sleep disorder.Methods:PD patients admitted from 2017 to 2022 were divided into PD with sleep disorder group (PD-SD group) ( n=56) and PD without sleep disorder group (PD-NSD group) ( n=41) according to the Parkinson's disease sleep scale (PDSS) score. All participants underwent magnetic resonance imaging examination.All patients were evaluated by the PDSS, Hoehn-Yahr (H-Y), Montreal cognitive assessment scale (MoCA), and cholinergic pathways hyper intensities scale (CHIPS). The difference of cognitive function between the two groups and the correlation between CHIPS and cognitive function were analyzed.Independent sample t-test, Spearman correlation analysis, and binary Logistic regression analysis were performed on the data by SPSS 26.0 statistical software. Results:(1)The MoCA score of the PD-SD group (22.00 (5.00)) was lower than that of the PD-NSD group (26.00 (5.00)) ( Z=-3.830, P<0.05). The total and all aspects scores of CHIPS in PD-SD group were higher than those in PD-NSD group(the total score of the low external capsule: 12.00(8.00), 0(8.00), the total score of the high external capsule: 12.00(2.00), 6.00(9.00), the total score of the radial crown: 8.00(0), 4.00(4.00), the total score of the centrum semiovale: 3.00(4.00), 0(2.00), the total score of the right side: 16.00(9.00), 5.00(10.00), the total score of the left side: 17.00(6.00), 7.00(9.00), the total score of CHIPS: 32.00(14.00), 14.00(20.00))( Z=-5.081, -5.873, -4.933, -3.211, -5.562, -6.232, -5.995, all P<0.05). (2)The correlation analysis between the score of CHIPS and cognitive function in the PD-SD group showed that, the total score of the low external capsule ( r=-0.286), the total score of the centrum semiovale ( r=-0.307), the total score of the right side ( r=-0.376), the total score of the left side ( r=-0.284) and the total score of CHIPS ( r=-0.349) were negatively correlated with MoCA(all P<0.05). (3)Binary Logistic regression analysis showed that white matter lesions in centrum semiovale, low inner capsule, right and left leukodystrophy were not influence factors for cognitive impairment (all P>0.05). Conclusion:PD patients with sleep disorders have lower cognitive function scores, higher CHIPS scores, and significant changes in white matter lesions compared to those without sleep disorders. In PD patients with sleep disorders, the higher the CHIPS score, the lower the cognitive function score, and the more significant the rate of cognitive impairment occurrence and development.