BACKGROUND: This study aimed to investigate programmed death-ligand 1 tumor proportion score in predicting the safety and efficacy of PD-1/PD-L1 antibody-based therapy in treating patients with advanced non-small cell lung cancer (NSCLC) in a real-world setting. METHODS: This retrospective, multicenter, observational study enrolled adult patients who received PD-1/PD-L1 antibody-based therapy in China and met the following criteria: (1) had pathologically confirmed, unresectable stage III-IV NSCLC; (2) had a baseline PD-L1 tumor proportion score (TPS); and (3) had confirmed efficacy evaluation results after PD-1/PD-L1 treatment. Logistic regression, Kaplan-Meier analysis, and Cox regression were used to assess the progression-free survival (PFS), overall survival (OS), and immune-related adverse events (irAEs) as appropriate. RESULTS: A total of 409 patients, 65.0% ( n = 266) with a positive PD-L1 TPS (≥1%) and 32.8% ( n = 134) with PD-L1 TPS ≥50%, were included in this study. Cox regression confirmed that patients with a PD-L1 TPS ≥1% had significantly improved PFS (hazard ratio [HR] 0.747, 95% confidence interval [CI] 0.573-0.975, P = 0.032). A total of 160 (39.1%) patients experienced 206 irAEs, and 27 (6.6%) patients experienced 31 grade 3-5 irAEs. The organs most frequently associated with irAEs were the skin (52/409, 12.7%), thyroid (40/409, 9.8%), and lung (34/409, 8.3%). Multivariate logistic regression revealed that a PD-L1 TPS ≥1% (odds ratio [OR] 1.713, 95% CI 1.054-2.784, P = 0.030) was an independent risk factor for irAEs. Other risk factors for irAEs included pretreatment absolute lymphocyte count >2.5 × 10 9 /L (OR 3.772, 95% CI 1.377-10.329, P = 0.010) and pretreatment absolute eosinophil count >0.2 × 10 9 /L (OR 2.006, 95% CI 1.219-3.302, P = 0.006). Moreover, patients who developed irAEs demonstrated improved PFS (13.7 months vs. 8.4 months, P <0.001) and OS (28.0 months vs. 18.0 months, P = 0.007) compared with patients without irAEs. CONCLUSIONS A positive PD-L1 TPS (≥1%) was associated with improved PFS and an increased risk of irAEs in a real-world setting. The onset of irAEs was associated with improved PFS and OS in patients with advanced NSCLC receiving PD-1/PD-L1-based therapy.
Humans ; Carcinoma, Non-Small-Cell Lung/metabolism* ; Male ; Female ; Retrospective Studies ; Middle Aged ; Lung Neoplasms/metabolism* ; Aged ; B7-H1 Antigen/metabolism* ; Programmed Cell Death 1 Receptor/metabolism* ; Adult ; Aged, 80 and over ; Immune Checkpoint Inhibitors/therapeutic use*

Objective:To explore the predictive value of the difference in average CT attenuation values between renal pelvis urine and bladder urine on the risk of pyonephrosis, and to establish a nomogram model in combination with other parameters and to verify its predictive ability.Methods:The clinical data of patients with obstructive hydronephrosis who came to our hospital for emergency percutaneous nephrostomy (PCN) between January 2020 and December 2022 were retrospectively analyzed. There were 16 men and 33 women, with the age of (57.59, 14.67) years. Body mass index was (23.34, 3.11) kg/m 2. Urine nitrite was positive in 14 cases. Urine culture was positive in 21 cases. In the routine blood test, the median white blood cell count was 10.96 (7.21, 15.15) ×10 9/L, haemoglobin (115.08, 22.71) g/L and platelets (263.00, 97.20)×10 9/L. The difference in average CT attenuation values between renal pelvis urine and bladder urine (mean CT value of pyelonephritis-mean CT value of cysturia) was -2.19 (-7.04, 4.05) HU. Patients were divided into pyonephrosis group and hydronephrosis group according to whether the drainage fluid after PCN was pus. Single-factor and multi-factor logistic regression models were used to analyze the independent predictors of pyonephrosis and to construct nomograms. The discrimination of the model was assessed by the area under the curve (AUC) of the receiver operating characteristics (ROC), the accuracy by the calibration curve and the clinical efficacy by the decision curve analysis (DCA). Results:There were 49 cases in this study, 31 cases in the hydronephrosis group and 18 cases in the pyonephrosis group. The average CT attenuation values of renal pelvis urine were 3.35(0.56, 8.96) HU and 7.78 (3.75, 18.38) HU, respectively, and the difference between the two groups was statistically significant ( P=0.023). The average CT attenuation values of bladder urine were (7.81±6.15)HU and (7.22±7.50)HU, respectively, and there was no significant difference between the two groups ( P=0.780). The difference in average CT attenuation values between renal pelvis urine and bladder urine were -3.98(-7.54, 0.60)HU and 2.13 (-5.15, 9.36)HU, respectively, and the difference between the two groups was not statistically significant ( P=0.059); the white blood cells were 9.82(7.04, 12.46) ×10 9/L and 13.99(9.75, 18.44) ×10 9/L, respectively, and the difference between the two groups was statistically significant ( P=0.048). Platelet counts were (248.06±87.87)×10 9/L and (288.72±109.29)×10 9/L, respectively, and there was no statistical difference between the two groups ( P=0.189). The proportion of urine protein positive was higher in the pyonephrosis group (17 vs. 20, P=0.048). Between the two groups, sex, age, body mass index, clinical symptoms (with or without low back pain), surgical history of upper urinary tract stones, underlying diseases (including hypertension, diabetes, coronary heart disease, etc.), location of hydronephrosis (left, right, and both sides), reasons of obstruction [upper urinary tract stones, other factors (such as tumor, ureteral stricture, etc.)], haemoglobin, were not statistical different. There were no significant difference in blood glucose, blood potassium, blood sodium, urine leukocytes, urine erythrocytes, urine nitrite and urine culture ( P>0.05). Multivariate logistic regression analysis showed that the difference in average CT attenuation values between renal pelvis urine and bladder urine ( OR=1.196, 95% CI 1.055-1.437, P=0.018), white blood cells ( OR=1.252, 95% CI 1.036-1.615, P=0.038), and platelets ( OR=1.014, 95% CI 1.003-1.030, P=0.025) were independent predictors of pyonephrosis. According to the above indicators, the nomogram model was established and the AUC value of the model was 0.767 (95% CI 0.616-0.918), the sensitivity was 0.611 and the specificity was 0.935. The calibration curve showed that there is a good fit between the observed value and the predicted value. The DCA analysis showed that the nomogram model has a net gain in a wide threshold range, demonstrating its predictive accuracy and clinical practicality in predicting the risk of pyonephrosis. When the cut-off value of the difference between the average CT values of pyelonephritis and cystourethrosis was 6.54 HU, the AUC value of the independent prediction of pyonephrosis was 0.690(95% CI 0.564-0.816), the sensitivity was 0.444 and the specificity was 0.935. Conclusions:The difference in average CT attenuation values between renal pelvis urine and bladder urine is an independent risk factor for predicting pyonephrosis, and the nomogram constructed by combining it with white blood cells and platelets has a good predictive effect for predicting the risk of pyonephrosis. If the difference in average CT attenuation values between renal pelvis urine and bladder urine is greater than 6.54HU, it should be alert to the occurrence of pyonephrosis.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective:To explore the predictive value of the difference in average CT attenuation values between renal pelvis urine and bladder urine on the risk of pyonephrosis, and to establish a nomogram model in combination with other parameters and to verify its predictive ability.Methods:The clinical data of patients with obstructive hydronephrosis who came to our hospital for emergency percutaneous nephrostomy (PCN) between January 2020 and December 2022 were retrospectively analyzed. There were 16 men and 33 women, with the age of (57.59, 14.67) years. Body mass index was (23.34, 3.11) kg/m 2. Urine nitrite was positive in 14 cases. Urine culture was positive in 21 cases. In the routine blood test, the median white blood cell count was 10.96 (7.21, 15.15) ×10 9/L, haemoglobin (115.08, 22.71) g/L and platelets (263.00, 97.20)×10 9/L. The difference in average CT attenuation values between renal pelvis urine and bladder urine (mean CT value of pyelonephritis-mean CT value of cysturia) was -2.19 (-7.04, 4.05) HU. Patients were divided into pyonephrosis group and hydronephrosis group according to whether the drainage fluid after PCN was pus. Single-factor and multi-factor logistic regression models were used to analyze the independent predictors of pyonephrosis and to construct nomograms. The discrimination of the model was assessed by the area under the curve (AUC) of the receiver operating characteristics (ROC), the accuracy by the calibration curve and the clinical efficacy by the decision curve analysis (DCA). Results:There were 49 cases in this study, 31 cases in the hydronephrosis group and 18 cases in the pyonephrosis group. The average CT attenuation values of renal pelvis urine were 3.35(0.56, 8.96) HU and 7.78 (3.75, 18.38) HU, respectively, and the difference between the two groups was statistically significant ( P=0.023). The average CT attenuation values of bladder urine were (7.81±6.15)HU and (7.22±7.50)HU, respectively, and there was no significant difference between the two groups ( P=0.780). The difference in average CT attenuation values between renal pelvis urine and bladder urine were -3.98(-7.54, 0.60)HU and 2.13 (-5.15, 9.36)HU, respectively, and the difference between the two groups was not statistically significant ( P=0.059); the white blood cells were 9.82(7.04, 12.46) ×10 9/L and 13.99(9.75, 18.44) ×10 9/L, respectively, and the difference between the two groups was statistically significant ( P=0.048). Platelet counts were (248.06±87.87)×10 9/L and (288.72±109.29)×10 9/L, respectively, and there was no statistical difference between the two groups ( P=0.189). The proportion of urine protein positive was higher in the pyonephrosis group (17 vs. 20, P=0.048). Between the two groups, sex, age, body mass index, clinical symptoms (with or without low back pain), surgical history of upper urinary tract stones, underlying diseases (including hypertension, diabetes, coronary heart disease, etc.), location of hydronephrosis (left, right, and both sides), reasons of obstruction [upper urinary tract stones, other factors (such as tumor, ureteral stricture, etc.)], haemoglobin, were not statistical different. There were no significant difference in blood glucose, blood potassium, blood sodium, urine leukocytes, urine erythrocytes, urine nitrite and urine culture ( P>0.05). Multivariate logistic regression analysis showed that the difference in average CT attenuation values between renal pelvis urine and bladder urine ( OR=1.196, 95% CI 1.055-1.437, P=0.018), white blood cells ( OR=1.252, 95% CI 1.036-1.615, P=0.038), and platelets ( OR=1.014, 95% CI 1.003-1.030, P=0.025) were independent predictors of pyonephrosis. According to the above indicators, the nomogram model was established and the AUC value of the model was 0.767 (95% CI 0.616-0.918), the sensitivity was 0.611 and the specificity was 0.935. The calibration curve showed that there is a good fit between the observed value and the predicted value. The DCA analysis showed that the nomogram model has a net gain in a wide threshold range, demonstrating its predictive accuracy and clinical practicality in predicting the risk of pyonephrosis. When the cut-off value of the difference between the average CT values of pyelonephritis and cystourethrosis was 6.54 HU, the AUC value of the independent prediction of pyonephrosis was 0.690(95% CI 0.564-0.816), the sensitivity was 0.444 and the specificity was 0.935. Conclusions:The difference in average CT attenuation values between renal pelvis urine and bladder urine is an independent risk factor for predicting pyonephrosis, and the nomogram constructed by combining it with white blood cells and platelets has a good predictive effect for predicting the risk of pyonephrosis. If the difference in average CT attenuation values between renal pelvis urine and bladder urine is greater than 6.54HU, it should be alert to the occurrence of pyonephrosis.

Objective:To establish a model based on the characteristics of breast cancer ultrasound images through deep learning methods to predict the risk of axillary lymph node metastasis（ALNM）in patients with breast cancer（maximum diameter ≤2.5 cm）before surgery.Methods:A total of 419 patients（3 433 breast tumor ultrasound images）with breast cancer（maximum diameter ≤2.5 cm）who underwent axillary lymph node dissection at Beijing Tiantan Hospital，Capital Medical University from January 2019 to December 2024 were retrospectively included. According to the pathological results of axillary lymph nodes，they were divided into 220 cases in the ALNM occurrence group（positive group）and 199 cases in the non-ALNM occurrence group（negative group）. The breast cancer ultrasound images of the two groups of cases were randomly classified into the training set（2 404 images），the validation set（687 images）and the test set（342 images）according to a ratio of 7∶2∶1. YOLOv8 was used as the basic model of You Only Look Once（YOLO）and optimized. The optimized model was applied to locate and capture the potential ultrasound features of breast cancer cases in the training set. A prediction model was constructed based on the captured ultrasound features. The model was adjusted and optimized through the validation set，and then matched with the case images in the test set. The confusion classification matrix graph and the curve graph for measuring the model performance were used to evaluate the model prediction performance and interpret the model，and the efficacy of this model in identifying breast cancer patients at risk of ALNM was analyzed.Results:There were statistically significant differences between the positive and negative groups in terms of the pathological maximum diameter of breast tumors，pathological T staging，the differentiation degree，the presence of distant metastasis，the maximum diameter measured by ultrasound，the quadrant of breast tumor occurrence，the Breast Imaging - Reporting and Data System（BI-RADS）classification of breast tumors，and the presence of abnormal ultrasound features of lymph node（all P<0.05）. The established deep learning model could automatically perform bounding box localization for the breast cancer of patients.The breast tumors in the positive group had potential ultrasound features that could be captured by the model compared with those in the negative group. The mean average precision（mAP）50 was 0.883，mAP 50-95 was 0.636，PR-AUC was 0.884 5，strict PR-AUC was 0.636 4，the sensitivity was 90.5%，and the specificity was 91.2%，and it had a good predictive efficacy. Conclusions:This prediction model based on the ultrasound characteristics of breast cancer through deep learning can effectively predict breast cancer（maximum diameter ≤ 2.5 cm）with the risk of ALNM，providing an effective basis for the clinical management of axillary lymph nodes in breast cancer patients.

Objective To evaluate the noise hazard level of a coal mining enterprise, and identify high-risk operation types and people, and to provide a basis for preventing and controlling the health damage caused by noise. Methods A large coal mining enterprise in Shaanxi Province was selected as the research object. The noise monitoring data of the coal mine over the years was used to calculate the noise exposure matrix of each post in the enterprise, and the classification of occupational hazards at workplaces (GBZ/T 229.4-2012) was used to assess the occupational health risk levels. Results Among the 22 noise-exposed positions in the enterprise, the 8-hour working day equivalent sound level in positions of shearer driver, horseshoe driver, crusher driver, shuttle driver, relaxation screen driver, and grading screen driver were all higher than the occupational exposure limit of noise. In 2021, the noise exposure levels of shearer drivers, crusher drivers, and coal-selecting workers were all higher than 90 dB (A), and the occupational hazard level was moderate hazard level. In addition, the noise exposure levels of most other jobs also exceeded the occupational exposure limit. Conclusion The noise hazards in the coal mine industry are mainly concentrated in the posts of the coal mining system, tunneling system, and screening workshop. Among them, the shearer driver, the crusher driver, and the coal preparation workers have higher noise exposure levels. It is recommended to take corresponding noise reduction measures and strengthen the protection level to reduce the noise exposure risk of workers.

Objective:To evaluate the efficacy and safety of belimumab combined with standard regimen in the treatment of active lupus nephritis (LN).Methods:It was a single-center, pre - and post-control retrospective study. The Data of active LN patients treated with belimumab combined with standard regimen in the Department of Nephrology, the First Affiliated Hospital of Sun Yat-sen University from June 1, 2020 to June 30, 2022 were collected for analyzing the renal response rate and adverse reactions after belimumab treatment.Results:A total of 17 patients were included, including 14 females (82.35%). The age of the first medication was (26.06±2.64) years old, the median time of illness before the use of belimumab was 24.00 (8.50, 48.50) months, and the recurrence times before the use of belimumab was (1.24±1.03) times. All the 17 patients underwent renal biopsy. The main pathological types were type IV in 11 cases (11/17), type Ⅲ+V in 2 cases (2/17), type IV+V in 3 cases (3/17), and type V in 1 case (1/17). The dose of glucocorticoids was (22.95±8.30) mg/d in 1 year before belimumab administration. In 12 patients with LN who completed 24 weeks of belimumab treatment plan, the 24-hour urinary protein showed a downward trend, and there was a statistically significant difference compared with the baseline at 24 week [0.49 (0.15, 2.19) g vs. 2.83 (1.14, 4.11) g, Z=-2.100, P=0.036]. Compared with the baseline, serum albumin at 24 week increased by 29.36%, with statistically significant difference [(34.50±3.34) g/L vs. (26.67±5.75) g/L, t=-3.840, P=0.030]. The systemic lupus erythematosus disease activity index-2K score continued to decline, with statistically significant difference compared with baseline at 24 week (5.00±3.02 vs. 12.00±2.82, t=6.163, P<0.001). The lymphocyte count increased, and the difference was statistically significant compared with the baseline at 24 week [0.72(0.28, 2.39)×10 9/L vs. 0.30(0.19,0.34)×10 9/L, Z=-2.073, P=0.038]. There was a statistically significant difference between the glucocorticoids dosage at 24 week and the average glucocorticoids dosage 1 year before treatment [(11.25±6.35) mg/d vs. (22.60±9.75) mg/d, t=4.225, P=0.003]. After observation of belimumab for (38.13±22.93) weeks, patients had a complete response rate of 64.71% (11/17), a partial response rate of 17.65% (3/17), and an overall response rate of 82.35% (14/17). Relapse occurred in 1 case.No infusion-related reactions occurred in 17 patients. During the treatment, a total of 5 adverse events occurred, including 2 cases of pulmonary infection, 1 case each of sepsis, upper respiratory tract infection, and cytomegalovirus infection, which all improved after treatment and the subsequent treatment was not affected. Conclusion:Belimumab combined with standard regimen can improve the response rate of LN, reduce the recurrence rate, reduce the dosage of glucocorticoids, and control the overall adverse events with good prognosis.

Objective:To assess the clinical effectiveness and safety of Omalizumab for treating pediatric allergic asthma in real world in China.Methods:The clinical data of children aged 6 to 11 years with allergic asthma who received Omalizumab treatment in 17 hospitals in China between July 6, 2018 and September 30, 2020 were retrospectively analyzed.Such information as the demographic characteristics, allergic history, family history, total immunoglobulin E (IgE) levels, specific IgE levels, skin prick test, exhaled nitric oxide (FeNO) levels, eosinophil (EOS) counts, and comorbidities at baseline were collected.Descriptive analysis of the Omalizumab treatment mode was made, and the difference in the first dose, injection frequency and course of treatment between the Omalizumab treatment mode and the mode recommended in the instruction was investigated.Global Evaluation of Treatment Effectiveness (GETE) analysis was made after Omalizumab treatment.The moderate-to-severe asthma exacerbation rate, inhaled corticosteroid (ICS) dose, lung functions were compared before and after Omalizumab treatment.Changes in the Childhood Asthma Control Test (C-ACT) and Pediatric Asthma Quality of Life Questionnaire (PAQLQ) results from baseline to 4, 8, 12, 16, 24, and 52 weeks after Omalizumab treatment were studied.The commodity improvement was assessed.The adverse event (AE) and serious adverse event (SAE) were analyzed for the evaluation of Omalizumab treatment safety.The difference in the annual rate of moderate-to-severe asthma exacerbation and ICS reduction was investigated by using t test.The significance level was set to 0.05.Other parameters were all subject to descriptive analysis.A total of 200 allergic asthma patients were enrolled, including 75.5% ( n=151) males and 24.5% ( n=49) females.The patients aged (8.20±1.81) years. Results:The median total IgE level of the 200 patients was 513.5 (24.4-11 600.0) IU/mL.Their median treatment time with Omalizumab was 112 (1-666) days.Their first dose of Omalizumab was 300 (150-600) mg.Of the 200 cases, 114 cases (57.0%) followed the first Omalizumab dosage recommended in the instruction.After 4-6 months of Omalizumab treatment, 88.5% of the patients enrolled ( n=117) responded to Omalizumab.After 4 weeks of treatment with Omalizumab, asthma was well-controlled, with an increased C-ACT score [from (22.70±3.70) points to (18.90±3.74) points at baseline]. Four-six months after Omalizumab administration, the annual rate of moderate-to-severe asthma exacerbation had a reduction of (2.00±5.68) per patient year( t=4.702 5, P<0.001), the median ICS daily dose was lowered [0 (0-240) μg vs. 160 (50-4 000) μg at baseline] ( P<0.001), the PAQLQ score was improved [(154.90±8.57) points vs. (122.80±27.15) points at baseline], and the forced expiratory volume in one second % predicted (FEV 1%pred) was increased [(92.80±10.50)% vs. (89.70±18.17)% at baseline]. In patients with available evaluations for comorbidities, including allergic rhinitis, atopic dermatitis or eczema, urticaria, allergic conjunctivitis and sinusitis, 92.8%-100.0% showed improved symptoms.A total of 124 AE were reported in 58 (29.0%) of the 200 patients, and the annual incidence was 0(0-15.1) per patient year.In 53 patients who suffered AE, 44 patients (83.0%) and 9 patients (17.0%) reported mild and moderate AE, respectively.No severe AE were observed in patients.The annual incidence of SAE was 0(0-1.9) per patient year.Most common drug-related AE were abdominal pain (2 patients, 1.0%) and fever (2 patients, 1.0%). No patient withdrew Omalizumab due to AE. Conclusions:Omalizumab shows good effectiveness and safety for the treatment of asthma in children.It can reduce the moderate-to-severe asthma exacerbation rate, reduce the ICS dose, improve asthma control levels, and improve lung functions and quality of life of patients.

The fall armyworm (FAW), Spodoptera frugiperda, is a destructive pest native to America and has recently become an invasive insect pest in China. Because of its rapid spread and great risks in China, understanding of FAW genetic background and pesticide resistance is urgent and essential to develop effective management strategies. Here, we assembled a chromosome-level genome of a male FAW (SFynMstLFR) and compared re-sequencing results of the populations from America, Africa, and China. Strain identification of 163 individuals collected from America, Africa and China showed that both C and R strains were found in the American populations, while only C strain was found in the Chinese and African populations. Moreover, population genomics analysis showed that populations from Africa and China have close relationship with significantly genetic differentiation from American populations. Taken together, FAWs invaded into China were most likely originated from Africa. Comparative genomics analysis displayed that the cytochrome p450 gene family is extremely expanded to 425 members in FAW, of which 283 genes are specific to FAW. Treatments of Chinese populations with twenty-three pesticides showed the variant patterns of transcriptome profiles, and several detoxification genes such as AOX, UGT and GST specially responded to the pesticides. These findings will be useful in developing effective strategies for management of FAW in China and other invaded areas.
Animals ; China ; Genomics ; Humans ; Male ; Pesticides ; Spodoptera/genetics* ; Transcriptome

Objective    To study the diagnosis and treatment of aortopulmonary window (APW) associated with severe pulmonary hypertension. Methods    The clinical data of 23 patients with APW undergoing surgical treatment in The First Affiliated Hospital of Air Force Medical University from 2010 to 2018 were retrospectively reviewed. There were 9 male and 14 female patients. The age was 3-132 (4.63±2.14) months. The weight was 3.3-35.0 (17.3±3.6) kg. Results    Windows were situated in the proximal of semilunar valve (type Ⅰ) in 8 patients, and distal of the aorta (type Ⅱ) in 14 patients, from proximal to distal (type Ⅲ) in only 1 patient. Eleven patients were isolated APW, the others were combined with cardiac defects. The mean pulmonary artery pressure was 68.4±7.5 mm Hg. All patients underwent surgical correction under general anesthesia and hypothermia cardiopulmonary bypass. All patients were discharged uneventfully, with an average follow-up time of 4 years. The patients showed good outcomes and no residual shunt after surgery, and the pulmonary artery pressure decreased to normal. Conclusion    APW is an uncommon congenital cardiac anomaly. The clinical presentation is an excessive left-to-right shunt, and most patients present early in life. Development of pulmonary hypertension and pulmonary vascular resistance is usually rapid. Operative treatment is indicated as soon as the diagnosis is established, regardless of the patient’s age, and most patients after surgery have a good long-term outcome.