Objective To investigate the epidemiological characteristics of cross-county imported dengue fever cases within Yunnan province in 2023, so as to provide insights into formulation of preventive and control measures for intra-provincial spread of dengue fever. Methods All data pertaining cross-county imported dengue fever cases within Yunnan Province in 2023 were collected, and the temporal, regional and population distributions of the cases were descriptively analyzed. Results A total of 1 664 intra-provincial cross-county imported dengue fever cases were reported in 95 counties (cities, districts) cross 16 profectures (cities) in Yunnan Province in 2023, accounting for 12.34% of total cases in the province. Cross-county imported dengue fever cases were predominantly reported during the period between August and October (1 516 cases, 91.11% of total cases), and peaked in September (659 cases), with a single-day peak on October 8 (36 cases). During the period from September 4 to 10, five counties (cities) with local dengue fever epidemics, including Jinghong City of Xishuangbanna Dai Autonomous Prefecture, Gengma Dai and Wa Autonomous County of Lincang City, Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, Mengla Coun ty of Xishuangbanna Dai Autonomous Prefecture, and Zhenkang County of Lincang City, exported 165 cross-county imported dengue fever cases to the rest of the province. Among the 1 644 intra-provincial cross-county imported dengue fever cases, the male to female ratio was 1.40∶1.00, and 1 329 cases were at ages of 15 to 55 years (79.87%), with farmers as the predominant occupation (886 cases, 53.25%). The top 5 counties (cities/districts) reporting the highest number of intra-provincial cross-county imported dengue fever cases included Simao District (266 cases) and Lancang Lahu Autonomous County (118 cases) of Pu’er City, Mengla County (91 cases) and Menghai County (91 cases) of Xishuangbanna Dai Autonomous Prefecture, and Mangshi City (73 cases) of Dehong Dai and Jingpo Autonomous Prefecture, which accounting for 38.40% of total imported cases. These intra-provincial cross-county imported dengue fever cases originated from 7 counties (cities/districts) in 4 prefectures (cities), including 1 261 cases (76.70%) from Jinghong City of Xishuangbanna Dai Autonomous Prefecture, 224 cases (13.63%) from Ruili City of Dehong Dai and Jingpo Autonomous Prefecture, 103 cases (6.27%) from Gengma Dai and Wa Autonomous County of Lincang City, 31 cases (1.89%) from Mengla County of Xishuangbanna Dai Autonomous Prefecture, 30 cases (1.82%) from Zhenkang County of Lincang City, 10 cases (0.61%) from Cangyuan Wa Autonomous County of Lincang City, and 5 cases (0.30%) from Mohan-Boten Economic Cooperation Zone of Kunming City. In addition, local dengue fever epidemics following intra-provincial cross-county importation of dengue fevers cases in Simao District, Jinggu Dai and Yi Autonomous County, Mangshi City, Longchuan County, and Cangyuan Wa Autonomous County. Conclusions Farmers and students are high-risk populations for intra-provincial cross-county imported dengue fever cases in Yunnan Province, and health education pertaining personal protection against dengue fever should be strengthened among these high-risk populations by governments at all levels. There is a high risk of local out-break of dengue fever following continuous introduction of intra-provincial cross-county imported cases. Standardized management of intra-provincial cross-county imported dengue fever cases should be reinforced to reduce the risk of local epidemics.

Objective:To explore the value of prenatal three-dimensional ultrasonography（3DUS）in displaying auricular morphotyping and dimensions for predicting congenital aural atresia（CAA）.Methods:A retrospective collection of 227 fetuses who underwent ultrasound scans and retained auricular 3DUS volumes from January 2018 to December 2023 at the First Affiliated Hospital of Sun Yat-sen University was conducted. Fetuses were divided into two groups：a CAA group（52 fetuses，62 auricles）and a non-CAA group（175 fetuses，202 auricles），based on the presence or absence of external auditory canal identified through postnatal examination. According to 3DUS auricular contour and presence or absence of the concha，the auricles were divided into 4 types：type Ⅰ，C-shaped auricle with a concha；type Ⅱ，Irregular auricle with a concha；type Ⅲ，C-shaped auricle without a concha；type Ⅳ，Irregular auricle without a concha. And auricular length（AL）and width（AW）were measured to calculate the product of the auricular length and width（ALW）. Normal reference ranges for ALW from the non-CAA group were developed. Differences of the auricular morphotyping and Z-score of ALW（ALWZ）were compared between the two groups. Receiver operating characteristic（ROC）curves were used to evaluate the diagnostic efficiency of auricular morphotyping，ALWZ and the regression model. A Logistic regression model for CAA based on auricular morphotyping and ALWZ were established.Results:The auricular morphotyping and ALWZ between the two groups were different statistically（both P<0.05）. The AUC of the auricular morphotyping and ALWZ predicting CAA were 0.960（95% CI = 0.923 - 0.997）and 0.975（95% CI = 0.959 - 0.991）individually. Formula for CAA prediction model combining the two indicators（5.379 × morphotyping - 2.386 × ALWZ - Conclusions:The auricular morphotyping and dimensions can effectively predict CAA.

Objective:To develop a deep learning-based artificial intelligence system for assessing image quality in early pregnancy ultrasound，and to evaluate its performance in anatomical structure identification and quality control.Methods:A retrospective study was conducted by collecting 17 910 static ultrasound images of 8 quality-control planes from fetuses at 11 to 13 +6 weeks of gestation who underwent routine first-trimester ultrasound examinations at the First Affiliated Hospital of Sun Yat-sen University from June 2018 to June 2024. The dataset was divided into a training set（12 536 images），a test set（3 582 images），and a validation set（1 792 images）in a 7∶2∶1 ratio to develop a prenatal-screening artificial intelligence system（PSAIS）and to evaluate its performance in the automatic recognition and quality control of standard planes during early pregnancy. The average precision and mean average precision（mAP）were used to measure the model's ability to recognize the anatomical structures on each plane. Intraclass correlation coefficient（ICC）and Kappa statistics were used to assess the consistency between PSAIS and expert-level sonographers in both plane image quality assessment and standardization. The efficiency of PSAIS was also compared to manual quality control. Results:In the test set，the mAP values for recognizing the anatomical structures of the 8 quality-control planes all exceeded 0.800. In the validation set，PSAIS demonstrated moderate to good agreement with two experts in image quality evaluation：the ICC ranged from 0.713 to 0.843 for one expert and 0.678 to 0.788 for the other，while the Kappa values ranged from 0.590 to 0.768 and 0.530 to 0.702，respectively. In terms of plane standardization scoring，PSAIS showed particularly high agreement with expert ratings on the transventricular view（compliance rate 94.6%，Kappa=0.860）and the four-chamber cardiac view with blood flow（compliance rate 94.1%，Kappa=0.778），with agreement above 70% for the remaining planes. Compared with manual quality-control，PSAIS significantly increased processing speed：the total processing time was only 413 seconds，markedly less than the 77 008 seconds and 94 918 seconds required for manual QC（ P<0.001）. Conclusions:The PSAIS system performs well in recognizing and controlling the quality of standard ultrasound planes in early pregnancy，demonstrating high consistency with expert evaluations and significantly improved processing efficiency. It has potential application value in enhancing the quality and efficiency of early pregnancy screening.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Objective:To develop an artificial intelligence system using fully convolutional neural networks（FCN）to assist echocardiographers in the quantitative assessment of mitral regurgitation（MR）severity.Methods:From August 2021 to June 2024，echocardiographic images of 441 patients with MR were prospectively collected from Renmin Hospital of Wuhan University and the Central Hospital of Wuhan. After screening，a total of 269 patients（4 917 frames）were included in the study. Of these，3 644 frames（128 patients）of apical four-chamber color Doppler MR flow convergence images from Renmin Hospital of Wuhan University were selected as the training/validation set，while images from 121 patients（813 frames）were used as the internal test set. Additionally，images from 20 patients（460 frames）from the Central Hospital of Wuhan were selected as the external test set. The FCN algorithm was employed to capture features and segment the MR color region on the left atrial side，simultaneously outputting the regurgitant radius（r）for the calculation of the effective regurgitant orifice area and regurgitant volume. The severity of MR was then classified according to the 2017 guidelines of the American Society of Echocardiography. The segmentation and classification performance of the model was evaluated，and the measurement results of the AI system was compared with that of both senior and junior physicians.Results:In the internal test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.48，0.81，0.86，and 0.87，respectively. In the external test set，the accuracy of r identification for cases classified as Grade Ⅰ to Ⅳ was 0.60，0.77，0.64，and 0.77，respectively. The average accuracy of MR classification in the internal and external test sets was 0.91 and 0.88，respectively.Conclusions:The FCN model is capable of segmenting the left atrial side regurgitant areas in apical four-chamber heart color Doppler images，aiding physicians in obtaining quantitative assessment parameters for MR，and assisting junior physicians in accurately assessing the severity of MR.

Objective:To explore the value of prenatal three-dimensional ultrasonography（3DUS）in displaying auricular morphotyping and dimensions for predicting congenital aural atresia（CAA）.Methods:A retrospective collection of 227 fetuses who underwent ultrasound scans and retained auricular 3DUS volumes from January 2018 to December 2023 at the First Affiliated Hospital of Sun Yat-sen University was conducted. Fetuses were divided into two groups：a CAA group（52 fetuses，62 auricles）and a non-CAA group（175 fetuses，202 auricles），based on the presence or absence of external auditory canal identified through postnatal examination. According to 3DUS auricular contour and presence or absence of the concha，the auricles were divided into 4 types：type Ⅰ，C-shaped auricle with a concha；type Ⅱ，Irregular auricle with a concha；type Ⅲ，C-shaped auricle without a concha；type Ⅳ，Irregular auricle without a concha. And auricular length（AL）and width（AW）were measured to calculate the product of the auricular length and width（ALW）. Normal reference ranges for ALW from the non-CAA group were developed. Differences of the auricular morphotyping and Z-score of ALW（ALWZ）were compared between the two groups. Receiver operating characteristic（ROC）curves were used to evaluate the diagnostic efficiency of auricular morphotyping，ALWZ and the regression model. A Logistic regression model for CAA based on auricular morphotyping and ALWZ were established.Results:The auricular morphotyping and ALWZ between the two groups were different statistically（both P<0.05）. The AUC of the auricular morphotyping and ALWZ predicting CAA were 0.960（95% CI = 0.923 - 0.997）and 0.975（95% CI = 0.959 - 0.991）individually. Formula for CAA prediction model combining the two indicators（5.379 × morphotyping - 2.386 × ALWZ - Conclusions:The auricular morphotyping and dimensions can effectively predict CAA.

Objective:To develop a deep learning-based artificial intelligence system for assessing image quality in early pregnancy ultrasound，and to evaluate its performance in anatomical structure identification and quality control.Methods:A retrospective study was conducted by collecting 17 910 static ultrasound images of 8 quality-control planes from fetuses at 11 to 13 +6 weeks of gestation who underwent routine first-trimester ultrasound examinations at the First Affiliated Hospital of Sun Yat-sen University from June 2018 to June 2024. The dataset was divided into a training set（12 536 images），a test set（3 582 images），and a validation set（1 792 images）in a 7∶2∶1 ratio to develop a prenatal-screening artificial intelligence system（PSAIS）and to evaluate its performance in the automatic recognition and quality control of standard planes during early pregnancy. The average precision and mean average precision（mAP）were used to measure the model's ability to recognize the anatomical structures on each plane. Intraclass correlation coefficient（ICC）and Kappa statistics were used to assess the consistency between PSAIS and expert-level sonographers in both plane image quality assessment and standardization. The efficiency of PSAIS was also compared to manual quality control. Results:In the test set，the mAP values for recognizing the anatomical structures of the 8 quality-control planes all exceeded 0.800. In the validation set，PSAIS demonstrated moderate to good agreement with two experts in image quality evaluation：the ICC ranged from 0.713 to 0.843 for one expert and 0.678 to 0.788 for the other，while the Kappa values ranged from 0.590 to 0.768 and 0.530 to 0.702，respectively. In terms of plane standardization scoring，PSAIS showed particularly high agreement with expert ratings on the transventricular view（compliance rate 94.6%，Kappa=0.860）and the four-chamber cardiac view with blood flow（compliance rate 94.1%，Kappa=0.778），with agreement above 70% for the remaining planes. Compared with manual quality-control，PSAIS significantly increased processing speed：the total processing time was only 413 seconds，markedly less than the 77 008 seconds and 94 918 seconds required for manual QC（ P<0.001）. Conclusions:The PSAIS system performs well in recognizing and controlling the quality of standard ultrasound planes in early pregnancy，demonstrating high consistency with expert evaluations and significantly improved processing efficiency. It has potential application value in enhancing the quality and efficiency of early pregnancy screening.

Objective:To explore the monitoring value of left ventricular functional parameters obtained by bedside ultrasound combined with clinically relevant indicators in patients with veno-arterial extracorporeal membrane oxygenation (VA-ECMO).Methods:A retrospective study was conducted. A total of 24 patients receiving VA-ECMO adjuvant support in Renmin Hospital of Wuhan University from June 2018 to January 2020 were selected. The bedside ultrasound was performed on the first day of ECMO support, the day before weaning, the clinical indicators before weaning were obtained. The differences in clinical indicators and the left ventricular functional parameters between the two groups of whether weaning successfully were compared; univariate Logistic regression analysis was used to screen out the related factors affecting weaning.Results:Sixteen patients were successful weaned and 8 patients failed. Compared with the weaning failure group, patients in the weaning success group required less continuous renal replacement therapy (CRRT, cases: 4 vs. 6, P < 0.05), mean arterial pressure (MAP) before weaning was higher [mmHg (1 mmHg = 0.133 kPa): 84.64±9.55 vs. 62.30±8.79, P < 0.05], and the pulse oxygen saturation (SpO 2) was also higher (0.966±0.670 vs. 0.866±0.061, P < 0.05), while vasoactive-inotropic score (VIS), serum creatinine (SCr) and serum lactic acid (Lac) were lower [VIS score: 7.27±1.42 vs. 16.93±8.52, SCr (μmol/L): 123.60±83.64 vs. 213.10±117.39, Lac (mmol/L): 1.94±0.91 vs. 5.62±5.48, all P < 0.05]. Univariate Logistic regression analysis showed that the MAP, VIS, SCr, Lac, SpO 2 before weaning were the related factors affecting weaning [odds ratio ( OR) were 0.306, -0.740, -0.011, -0.632, -4.069; 95% confidence interval (95% CI) were 1.065-1.732, 0.235-0.899, 0.979-0.999, 0.285-0.992 and 0.001-0.208; P values were 0.014, 0.022, 0.038, 0.047, 0.002]. In the weaning success group, left ventricular ejection fraction (LVEF), velocity of mitralannulus in systolic (LatSa), maximum flow velocity of aortic valve (AV-Vmax), velocity-time integral (VTI), left ventricular global longitudinal strain (LVGLS), left ventricular global longitudinal strain rate (LVGLSr) were all increased on the day before ECMO weaning compared with the first day of ECMO support [LVEF: 0.40±0.05 vs. 0.28±0.07, LatSa (cm/s): 6.81±0.91 vs. 4.62±1.02, AV-Vmax (cm/s): 104.81±33.98 vs. 64.44±16.85, VTI (cm): 14.56±3.11 vs. 7.96±1.98, LVGLS: (-8.95±2.59)% vs. (-5.26±1.28)%, LVGLSr (1/s): -0.48±0.11 vs. -0.29±0.09], whereas the ECMO flow was significantly reduced (L/min: 1.46±0.47 vs. 2.64±0.31), the differences were statistically significant (all P < 0.05). There was no significant difference in left ventricular functional parameters between the first day of ECMO support and the day before ECMO weaning in the weaning failure group. Compared with the weaning failure group, the weaning success group had higher LVEF, LatSa, AV-Vmax, VTI, LVGLS, LVGLSr on the day before ECMO weaning [LVEF: 0.40±0.05 vs. 0.26±0.07, LatSa (cm/s): 6.81±0.91 vs. 4.31±1.03, AV-Vmax (cm/s): 104.81±33.98 vs. 67.67±18.46, VTI (cm): 14.56±3.11 vs. 7.75±2.77, LVGLS: (-8.95±2.59)% vs. (-4.81±1.81)%, LVGLSr (1/s): -0.48±0.11 vs. -0.30±0.10, all P < 0.05] and lower ECMO flow (L/min: 1.46±0.47 vs. 2.20±0.62, P < 0.05). Conclusion:Bedside echocardiographic left ventricular function parameters (LVEF, LatSa, AV-Vmax, VTI, LVGLS, LVGLSr) combined with clinical indicators (MAP, VIS, SCr, Lac, SpO 2) were helpful to evaluate the therapeutic effect of patients receiving VA-ECMO support and can provide important guiding value in the selection of VA-ECMO weaning timing and the judgment of prognosis.

OBJECTIVE：To study the ef fects of Gegen qinlian decoction （GGQLD）on blood lipid and blood glucose of hyperlipidemia（HLP）model rats ，and to explore its mechanism from the perspective of intestinal flora. METHODS ：Totally 48 rats were randomly divided into blank control group （n＝8）and modeling group （n＝40）. For consecutive 5 weeks，model group was given high-lipid diet to induce HLP model ；blank control group was given routine diet. After modeling ，30 modeling rats were randomly divided into model group ，simvastatin group （positive control ，10 mg/kg），GGQLD high-dose ，medium-dose and low-dose groups （14.85，4.95，1.65 g/kg，by crude drug ），with 6 rats in each group. Blank control group and model group were given constant volume of normal saline intragastrically ；administration groups were given relevant medicine intragastrically ，once a day，for consecutive 11 weeks. At the same time ，each group was continuously given corresponding diet. After the last medication ， body mass and body length of rats were determined ，and Lee ’s index was calculated. Serum levels of TG ，TC，HDL-C，LDL-C and fasting blood glucose （FBG）were determined in rats. DNA of rat caecum content was extracted for 16S rRNA V 3-V4 region sequencing. The Two-part model was used to analyze the correlation between intestinal flora with lipids and blood glucose. RESULTS：After 11 weeks of administration ，compared with blank control group ，the body mass ，body length ，Lee’s index ， serum levels of TC ，TG，HDL-C and FBG of model group were increased significantly （P＜0.05 or P＜0.01），while the level of HDL-C was decreased significantly （P＜0.05）. Compared with model group ，body mass and Lee ’s index and serum levels of TG ， FBG of rats in GGQLD high-dose group ，and serum levels of TC ，TG in GGQLD medium-dose group ，as well as serum level of TG of rats in GGQLD low-dose group was decreased significantly （P＜0.05 or P＜0.01）. Correlation analysis with intestinal flora showed that TC and TG shared 3 operational taxonomic units （OTU），including OTU 559，OTU701 and OTU 135（OTU135 was also shared with FBG ），which were all positively correlated with the level of TC ，TG and FBG （P＜0.01）. The three OTU were annotated as Tyzzerella of Spirillaceae ，Anaerotruncus of Verrucaceae and Peptoclostridium of Streptococcidae ，respectively. High-dose and low-dose GGQLD had a down-regulating effect on Tyzzerella and Anaerotruncus（P＜0.05 or P＜0.01），while had up-regulating effect on Peptoclostridium（P＜0.01）. CONCLUSIONS ：High-dose GGQLD （14.85 g/kg）can effectively reduce the body mass and blood lipid of HLP model rats ，and can prevent the abnormal increase of blood glucose of model rats. The mechanism may be associated with that the reduction of intestinal flora （Tyzzerella，Anaerotruncus）content.

Objective: To study the clinical features of dengue cases infected with hepatotropic virus and Mycobacterium tuberculosis in Xishuangbanna, and to provide evidences to set up effective treatment programs for the dengue patients infected with the other diseases for hospitals. Methods: The clinical characteristics of dengue cases infected hepatotropic virus and Mycobacterium tuberculosis were analyzed retrospectively on their symptoms and biochemical parameters from the People′s Hospital and the Infectious Disease Hospital of Xishuangbanna Prefecture in 2013 and 2015. Results: The clinical characteristics of dengue cases infected with hepatotropic virus were typical, and inclued low incidence of urinary abnormalities, coagulation disorders and high-lactate dehydrogenase. Dengue cases infected with Mycobacterium tuberculosis had high incidence of shock, high-hematocrit, renal function and coagulation abnormalities, which suggested a trend of more serious illness than other groups obviously. Conclusions The rate of severe disease was higher in dengue cases infected with Mycobacterium tuberculosis than those infected with hepatotropic virus, which suggests that the dengue cases infected with Mycobacterium tuberculosis should be treated timely to reduce the severity of the diseases in the hospital.