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Preterm birth is a major public health problem, occurring in more than half a million births per year in the United States. A number of maternal conditions have been recognized as risk factors for preterm birth, but for the majority of cases, the etiology is not completely understood. Chlamydia trachomatis is one of the most prevalent sexually transmitted infections in the world. However, its role in adverse pregnancy outcome in women is still debated. In order to determine if genitourinary tract infection with C. trachomatis during pregnancy was associated with preterm birth, we conducted a case-control study on women who delivered at Boston Medical Center, an urban “safety-net” hospital that serves a socioeconomically disadvantaged and racially diverse population. Women with known risk factors for preterm birth or immune suppression were excluded. Variables collected on enrolled subjects included demographics; diagnosis of C. trachomatis during or prior to pregnancy; tobacco, alcohol, and illicit substance use; gestational age; and birthweight and gender of the newborn. We also collected urine for chlamydia testing at the time of delivery and placental biopsies for nucleic acid amplification and histological studies. A total of 305 subjects were enrolled: 100 who delivered preterm and 205 who delivered full term. Among those subjects, we identified 19 cases of pregnancy-associated C. trachomatis infection: 6/100 preterm and 13/205 full term, a difference which was not statistically significant. Only two cases of untreated chlamydia infection were identified postpartum, and both occurred in women who delivered at term. We conclude that genitourinary tract infection with C. trachomatis during pregnancy, when appropriately treated, is not associated with preterm birth.

Molybdenum phosphide (MoP) catalyzes the hydrogenation of CO, CO2, and their mixtures to methanol, and it is investigated as a high-activity catalyst that overcomes deactivation issues (e.g., formate poisoning) faced by conventional transition metal catalysts. MoP as a new catalyst for hydrogenating CO2 to methanol is particularly appealing for the use of CO2 as chemical feedstock. Herein, we use a colloidal synthesis technique that connects the presence of MoP to the formation of methanol from CO2, regardless of the support being used. By conducting a systematic support study, we see that zirconia (ZrO2) has the striking ability to shift the selectivity towards methanol by increasing the rate of methanol conversion by two orders of magnitude compared to other supports, at a CO2 conversion of 1.4% and methanol selectivity of 55.4%. In situ X-ray Absorption Spectroscopy (XAS) and in situ X-ray Diffraction (XRD) indicate that under reaction conditions the catalyst is pure MoP in a partially crystalline phase. Results from Diffuse Reflectance Infrared Fourier Transform Spectroscopy coupled with Temperature Programmed Surface Reaction (DRIFTS-TPSR) point towards a highly reactive monodentate formate intermediate stabilized by the strong interaction of MoP and ZrO2. This study definitively shows that the presence of a MoP phase leads to methanol formation from CO2, regardless of support and that the formate intermediate on MoP governs methanol formation rate.

Introduction Delivery Resources, Experiences, and Advocacy for Moms (DREAM) is an interprofessional service-learning program that empowers preclinical medical students by training them to provide labor support. Boston Medical Center is a safety-net hospital featuring an in-house doula service with limited coverage capacity. Consequently, many patients do not receive continuous labor support, although evidence shows that continuous labor support improves outcomes and may help reduce birth-outcome health disparities. We present a pragmatic approach to integrating preclinical students as labor-support staff and outline the methods and content of the training process as well as the evaluations used to assess program effectiveness. Methods Students were trained by doulas (Birth Sisters) and midwives to provide prenatal, labor, and postpartum support. Students completed an orientation and training workshop and then partnered with a Birth Sister for one prenatal visit, labor, and postpartum visit prior to working independently. Student leaders provided structure, mentoring, and support for preclinical students. Pre- and postsurveys assessed student confidence and obstetric knowledge acquisition. Budget, logistics, and program evaluation process are reviewed. Results Students demonstrated increased knowledge, as well as confidence in communication, advocacy, and support. Although balancing DREAM with academics was stressful, students continued to meet academic standards and felt their participation was gratifying and worthwhile. Student reflections and patient statements on their experience show the program was mutually beneficial. Discussion Preclinical students need gratifying clinical opportunities to develop confidence in communication and advocacy skills. Partnering them with underserved women to provide labor support is a pragmatic and clinically valuable intervention.

Diabetic Retinopathy (DR) is a complication of diabetes that can lead to blindness if not readily discovered. Automated screening algorithms have the potential to improve identification of patients who need further medical attention. However, the identification of lesions must be accurate to be useful for clinical application. The bag-of-visual-words (BoVW) algorithm employs a maximum-margin classifier in a flexible framework that is able to detect the most common DR-related lesions such as microaneurysms, cotton-wool spots and hard exudates. BoVW allows to bypass the need for pre- and post-processing of the retinographic images, as well as the need of specific ad hoc techniques for identification of each type of lesion. An extensive evaluation of the BoVW model, using three large retinograph datasets (DR1, DR2 and Messidor) with different resolution and collected by different healthcare personnel, was performed. The results demonstrate that the BoVW classification approach can identify different lesions within an image without having to utilize different algorithms for each lesion reducing processing time and providing a more flexible diagnostic system. Our BoVW scheme is based on sparse low-level feature detection with a Speeded-Up Robust Features (SURF) local descriptor, and mid-level features based on semi-soft coding with max pooling. The best BoVW representation for retinal image classification was an area under the receiver operating characteristic curve (AUC-ROC) of 97.8% (exudates) and 93.5% (red lesions), applying a cross-dataset validation protocol. To assess the accuracy for detecting cases that require referral within one year, the sparse extraction technique associated with semi-soft coding and max pooling obtained an AUC of 94.2 ± 2.0%, outperforming current methods. Those results indicate that, for retinal image classification tasks in clinical practice, BoVW is equal and, in some instances, surpasses results obtained using dense detection (widely believed to be the best choice in many vision problems) for the low-level descriptors.