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 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 correlation between serum residual lipoprotein cholesterol(RLP-C)and triglyceride glucose(TyG)index in postmenopausal type 2 diabetes(T2DM)patients.Methods A total of 389 postmenopausal T2DM patients who attended Ruili People's Hospital from May 2019 to May 2023 were included.RLP-C and TyG index were calculated,and the study subjects were divided into the low TyG index group(n=194)and the high TyG index group(n=195)based on the median of TyG index(9.23).Differences in general clinical data and metabolism-related indexes between the two groups of patients were compared,and Spearman rank correlation was performed to explore their correlation.Single-factor and multivariate Logistic regressions were used to explore their influencing factors,and ROC curve was used to analyze their diagnostic value.Results Compared with the low TyG index group,the history of hypertension(79.49%vs 70.62%),hyperlipidemia(22.05%vs 13.40%),hemoglobin(Hb)(120.34±19.96g/L vs 114.97±21.32g/L),fasting blood glucose(FBG)[3.97(3.03,5.10)mmol/L vs 3.64(2.99,4.74)mmol/L],total cholesterol(TC)[5.00(4.40,5.95)mmol/L vs 4.36(3.78,5.30)mmol/L],triacylglycerol(TG)[2.11(1.60,3.00)mmol/L vs 1.20(0.91,1.54)mmol/L],low-density lipoprotein(LDL-C)[[2.99(2.43,3.93)mmol/L vs 2.71(2.13,3.38)mmol/L],non-high-density lipoprotein(nonHDL-C)[3.94(3.22,4.82)mmol/L vs 3.15(2.53,3.94)mmol/L]and RLP-C concentration[0.76(0.52,1.08)mmol/L vs 0.44(0.29,0.59)mmol/L]of the high TyG index group were higher,while high-density lipoprotein(HDL-C)concentration was lower,and the differences were statistically significant(x2=4.09,4.99;t=-2.56;Z=-2.34,-5.15,-12.08,-3.04,-6.23,-9.15,-3.99,all P<0.05).Spearman correlation analysis showed that TyG in all samples was positively correlated with TC,TG,LDL-C,RLP-C and nonHDL-C(r=0.304,0.769,0.179,0.386,0.571,all P<0.001),but was negatively correlated with HDL-C(r=-0.306,P<0.001).The correlations between TyG and TC,LDL-C in the high TyG index group were not significant(all P>0.05).Single-actor logistic regression analysis showed that history of hypertension,hyperlipidemia,HDL-C,LDL-C,and RLP-C were the factors influencing TyG levels[OR(95%CI)=1.61(1.01～2.57),1.83(1.07～3.12),0.28(0.14～0.54),1.21(1.02～1.43),17.58(8.11～38.11),all P<0.05],while the multivariate regression analysis showed that only RLP-C was an independent risk factor for elevated TyG[OR(95%CI)=13.17(5.71～30.37),P<0.001).ROC curves showed that the AUC(95%CI)for the diagnosis of TyG index elevation by RLP-C was 0.768(95%CI:0.721～0.816),with the cutoffvalue of 0.59 mmol/L.The sensitivity and specificity were 69.71%and 75.77%,respectively.Conclusion RLP-C is an independent risk factor for elevated TyG in postmenopausal T2DM patients,which has some clinical value in predicting insulin resistance.

Objective:To investigate the prevalence of Mycoplasma pneumoniae ( Mp) in children undergoing physical examination. Methods:This study randomly enrolled 1 303 children at the age of 6-12 years who underwent physical examination in 2023. Their oral and pharyngeal swabs as well as venous blood samples were collected. The prevalence of Mp in these subjects was detected using isolation and culturing, nucleic acid detection and serological test. Chi-square test was used for statistical analysis. Results:Among the 1 303 children, the detection rate of Mp was 4.1% (53/1 303) by culturing, 7.3% (95/1 303) by nucleic acid detection and 13.6% (177/1 303) by serological test. Statistical analysis showed that there were significant differences in the the detection rates of Mp among children undergoing physical examination between the three methods ( P<0.05). Conclusions:The detection rate of Mp in children undergoing physical examination in 2023 was about 4.1%. Isolation and culturing was more accurate than nucleic acid detection and serological test in the detection of Mp in healthy population as the latter two methods would overestimate the rate.

As a non-invasive pure blood pool imaging technology, contrast-enhanced ultrasound can be applied to the diagnosis of breast cancer, the judgment of lymph node status, the evaluation of neoadjuvant chemotherapy efficacy and prognosis, and provide more effective information for the diagnosis and treatment of breast cancer, which is conducive to the development of personalized treatment plan and avoid over treatment. This article reviews the clinical application of contrast-enhanced ultrasound in breast cancer in recent years.

Pulsed electric fields can induce various kinds of biological effects that are essentially different from the normal electric fields, especially the interactions of Nanosecond Pulsed electric field (nsPEF) with cells. The biological effects of different pulsed electric fields on cell membranes, cytoplasmic matrixes, cell growth are introduced in this paper. Based on these effects, some applications of pulsed electric fields in cancer therapy, gene therapy, and delivery of drugs are reviewed in details.
Cell Membrane ; metabolism ; radiation effects ; Cell Physiological Phenomena ; Electromagnetic Fields ; Electrophysiology ; Electroporation ; Genetic Therapy ; methods ; Neoplasms ; therapy