Background: About 40,000 infertile couples visit Tu Du Hospital, Vietnam for consultation and treatment of infertility per year. Depression in infertile female patients not only influences mental wellbeing, but also affects the effectiveness of infertility treatment. The study aimed to determine the depression prevalence in infertile female patients and associated factors. Methods: A cross-sectional study was conducted during April–July 2016 with 401 infertile women visiting the Department of Infertility at Tu Du Hospital . The PHQ-9 scale was used to measure depressive symptoms. Face-to-face interviewing was conducted using a structured questionaire. Participants were also inquired about demographic characteristics, socio-economic status, infertility related characteristics and family and social relationships. Results: The depression prevalence was 12.2%, with a cut-off score ≥10 on PHQ-9 scale. Depression in infertile female patients was associated with infertility caused by the husband (AOR=3.09, 95% CI=1.44–6.63), infertility caused by both spouses (AOR=3.63, 95% CI=1.26–10.48), alcohol-addicted husband (AOR=4.83, 95% CI=1.32–17.58), and with wife’s previous antidepressant use (AOR=48.1, 95% CI=4.83–47.96) Conclusions: Assessment of depressive symptoms should be assessed at an early stage among infertile female patients for timely mental health support.

This study aims to determine the current status of thalassemia and find out some related factors to screening of thalassemia in pregnant women visiting the hospital. Materials and Methods: a cross-sectional study, including quantitative and qualitative research. A total of 550 women who visited the hospital from April 2020 to March 2021 were screened by full blood count, hemoglobin electrophoresis, and thalassemia mutation test. In-depth interviews with 17 pregnant women to find out their awareness about some related factors to screening of thalassemia. Results: The prevalence of pregnant women screened positive for thalassemia was 12.7%. The α-thalassemia accounted for the largest proportion of 67.1%, the β-thalassemia form accounted for 12.9%. Hemoglobin E and other combinations have also been detected. Risk factors affected screening for thalassemia include ethnicity, obstetric history, and anemia status. Besides, family and socio-cultural factors also affected the decision of pregnant women. Most pregnant women didn’t know about thalassemia, so they were very nervous about the health of their fetuses and future births. Conclusion: From the above findings, awareness among pregnant women about thalassemia is still low, they do not pay attention to early screening for thalassemia. Therefore, it is necessary to raise awareness for pregnant women and their families about thalassemia disease.

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The prevalence of thalassemia among the Vietnamese population was studied, and clinical decision support systems (CDSSs) for prenatal screening of thalassemia were created. A cross-sectional study was conducted on pregnant women and their husbands visiting from October 2020 to December 2021. A total of 10,112 medical records of first-time pregnant women and their husbands were collected. CDSS including two different types of systems for prenatal screening for thalassemia (expert system [ES] and four artificial intelligence [AI]-based CDSS) was built. 1,992 cases were used to train and test machine learning (ML) models while 1,555 cases were used for specialized ES evaluation. There were 10 key variables for AI-based CDSS for ML. The four most important features in thalassemia screening were identified. Accuracy of ES and AI-based CDSS was compared. The rate of patients with alpha thalassemia is 10.73% (1,085 patients), the rate of patients with beta-thalassemia is 2.24% (227 patients), and 0.29% (29 patients) of patients carry both alpha-thalassemia and beta-thalassemia gene mutations. ES showed an accuracy of 98.45%. Among AI-based CDSS developed, multilayer perceptron model was the most stable regardless of the training database (accuracy of 98.50% using all features and 97.00% using only the four most important features). AI-based CDSS showed satisfactory results. Further development of such systems is promising with a view to their introduction into clinical practice.

In this work, we study the generation of a negative refractive index based on electromagnetically induced transparency (EIT) in a Rb four-level N-type atomic gas medium. We derive analytic expressions for the relative permittivity and relative permeability of the medium according to the parameters of the probe, pump, and signal laser fields. We then investigate the variation of the real parts of the relative permittivity and relative permeability with respect to the intensity and frequency of the pump and signal laser fields. In the presence of the pump laser beam, the medium becomes transparent to the probe laser beam even in the resonant region. At the same time, the real parts of the relative permittivity and relative permeability are simultaneously negative (i.e., the medium exhibits a negative refractive index) in the EIT spectral domain. In the presence of the signal laser beam, the EIT effect occurs over two different frequency domains of the probe beam, so a negative refractive index is also generated in these two frequency domains. The investigation of the real parts of the relative permittivity and relative permeability with intensity and frequency of the pump and signal laser fields allowed us to find the laser parameters for the appearance of the negative refractive index, which can be useful for experimental observations.