Perinatal Management and Long-Term Follow-up of a Primipara With Severe Pulmonary Arterial Hypertension Associated With Systemic Lupus Erythematosus.

BACKGROUND: Systemic lupus erythematosus (SLE) accounts for the largest portion of connective tissue disease-associated pulmonary arterial hypertension (PAH) in Asian countries, especially in China, and SLE-PAH poses multiple challenges during pregnancy and delivery. Patients with SLE-PAH tend to have lower survival rates and worse quality of life than other subgroups of PAH. CASE PRESENTATION: Presented in this report is a 28-year-old primipara who suffered from SLE for 13 years and SLE-PAH for nine years. She had cardiac care throughout these years. She was admitted at 26 weeks of gestation for progressive dyspnea on exertion and her condition improved after a three-week PAH-targeted therapy consisting of prostacyclin and PDE-5 inhibitor. At 29 weeks of gestation, she was infected with influenza H1N1 and her clinical status deteriorated with increased dyspnea. After two weeks of influenza therapy and maximization of PAH therapy, a cesarean delivery was performed under epidural anesthesia at 31 weeks of gestation. She was discharged ten days after delivery. Although the targeted therapy for both PAH and SLE was readjusted after delivery and regular follow-up showed a gradual recovery and a stable condition, she still died suddenly at home 12 months after delivery. The child is healthy. CONCLUSIONS: Sequential combination therapy of PAH and SLE and the structured perinatal management might lead to optimal short-term outcomes in the mother and fetus. Long-term outcomes in women with PAH who become pregnant are poor, with high rates of morbidity and mortality. Delivery strategies remain an important challenge for modern Pregnancy Heart Teams.

How to achieve auto-identification in Raman analysis by spectral feature extraction & Adaptive Hypergraph.

With the miniaturization of Raman spectrometers, Raman spectroscopy (including Surface-enhanced Raman spectroscopy) has been widely applied to various fields, especially towards rapid detection applications. In order to deal with the accompanied massive databases, large numbers of Raman spectra require to be handled and identified in an effective and automatic manner. This paper proposes an algorithm of material auto-identification, which makes use of machine learning methods to analyze Raman spectra. Firstly, a universal method of spectral feature extraction is designed to automatically process Raman spectra after the background subtraction. Secondly, the extracted feature vectors are used to classify and identify target materials by Adaptive Hypergraph (AH), an efficient classifier in the field of machine learning, in a manner of automation with an accuracy rate of ~99%. Compared with Support Vector Machine (SVM) and Random Forest (RF), two typical methods of classification, the AH classifier provides better performance free of tuning any parameter facing different targets. Thirdly, Cubic Spline Interpolation is introduced to enhance the universal of the proposed algorithm between different databases from different Raman spectrometers with variant vendors. The identification accuracy rate is up to 98% using the high frequency sampling spectra as the learning and the low frequency sampling ones as the testing, respectively.

Enhanced Stim1 expression is associated with acquired chemo-resistance of cisplatin in osteosarcoma cells.

Osteosarcoma is the most common primary malignant bone tumor. Although cisplatin is the primary chemotherapy used in osteosarcoma treatment, the cisplatin resistance remains a big challenge for improving overall survival. The store-operated calcium (Ca2+) entry (SOCE) and its major mediator Stim1 have been shown to be implicated in a number of pathological processes typical for cancer. In this study, we showed that Stim1 expression was significantly increased in chemo-resistant osteosarcoma tissues compared with chemo-sensitivity tissues. Patients with Sitm1 expression exhibited poorer overall survival than Stim1-negative patients. Moreover, un-regulation of Stim1 expression and SOCE were also observed in cisplatin-resistant MG63/CDDP cells compared with their parental cells. Cisplatin treatment obviously reduced Stim1 expression and SOCE in cisplatin-sensitivity MG63 cells, but had no effects on MG63/CDDP cells. In addition, cisplatin resulted in a more pronounced increase of endoplasmic reticulum (ER) stress in MG63 cells than in their resistant variants, which was evidenced by the activation of molecular markers of ER stress, GRP78, CHOP and ATF4. Knockdown of Stim1 using siRNA remarkably enhanced cisplatin-induced apoptosis and ER stress in MG63/CDDP cells, thereby sensitizing cancer cells to cisplatin. On the other hand, overexpression of Stim1 markedly reversed apoptosis and ER stress following cisplatin treatment. Taken together, these results demonstrate that Stim1 as well as Ca2+ entry contributes cisplatin resistance via inhibition of ER stress-mediated apoptosis, and provide important clues to the mechanisms involved in cisplatin resistance for osteosarcoma treatment. Stim1 represents as a target of cisplatin and blockade of Stim1-mediated Ca2+ entry may be a useful strategy to improve the efficacy of cisplatin to treat osteosarcoma.

An auto-adaptive background subtraction method for Raman spectra.

Background subtraction is a crucial step in the preprocessing of Raman spectrum. Usually, parameter manipulating of the background subtraction method is necessary for the efficient removal of the background, which makes the quality of the spectrum empirically dependent. In order to avoid artificial bias, we proposed an auto-adaptive background subtraction method without parameter adjustment. The main procedure is: (1) select the local minima of spectrum while preserving major peaks, (2) apply an interpolation scheme to estimate background, (3) and design an iteration scheme to improve the adaptability of background subtraction. Both simulated data and Raman spectra have been used to evaluate the proposed method. By comparing the backgrounds obtained from three widely applied methods: the polynomial, the Baek's and the airPLS, the auto-adaptive method meets the demand of practical applications in terms of efficiency and accuracy.