Cephalopelvic disproportion (CPD)-related obstructed labor is accountable for 3–8% of the maternal deaths worldwide. The consequence of CPD-related obstructive labor in the absence of a Caesarian section (C/S) is often maternal or perinatal mortality or morbidity to the mother and/or the infant. Accurate and timely referral of at-risk mothers to health facilities where C/S is a delivery option could reduce maternal mortality in the developing world. The goal of this work was to develop and test the feasibility of a safe, low-cost, easy-to-use, portable tool, using a Microsoft Kinect 3D camera, to identify women at risk for obstructed labor due to CPD. Magnetic resonance imaging (MRI) scans, 3D camera imaging, anthropometry and clinical pelvimetry were collected and analyzed from women 18–40 years of age, at gestational age ≥36+0 weeks with previous C/S due to CPD (n = 43), previous uncomplicated vaginal deliveries (n = 96), and no previous obstetric history (n = 148) from Addis Ababa, Ethiopia. Novel and published CPD risk scores based on anthropometry, clinical pelvimetry, MRI, and Kinect measurements were compared. Significant differences were observed in most anthropometry, clinical pelvimetry, MRI and Kinect measurements between women delivering via CPD-related C/S versus those delivering vaginally. The area under the receiver-operator curve from novel CPD risk scores base on MRI-, Kinect-, and anthropometric-features outperformed novel CPD risk scores based on clinical pelvimetry and previously published indices for CPD risk calculated from these data; e.g., pelvic inlet area, height, and fetal-pelvic index. This work demonstrates the feasibility of a 3D camera-based platform for assessing CPD risk as a novel, safe, scalable approach to better predict risk of CPD in Ethiopia and warrants the need for further blinded, prospective studies to refine and validate the proposed CPD risk scores, which are required before this method can be applied clinically.

Mucin-domain glycoproteins are densely O-glycosylated and play critical roles in a host of biological functions. In particular, the T cell immunoglobulin and mucin-domain containing family of proteins (TIM-1, -3, -4) decorate immune cells and act as key checkpoint inhibitors in cancer. However, their dense O-glycosylation remains enigmatic both in terms of glycoproteomic landscape and structural dynamics, primarily due to the challenges associated with studying mucin domains. Here, we present a mucinase (SmE) and demonstrate its ability to selectively cleave along the mucin glycoprotein backbone, similar to others of its kind. Unlike other mucinases, though, SmE harbors the unique ability to cleave at residues bearing extremely complex glycans which enabled improved mass spectrometric analysis of several mucins, including the entire TIM family. With this information in-hand, we performed molecular dynamics (MD) simulations of TIM-3 and -4 to demonstrate how glycosylation affects structural features of these proteins. Overall, we present a powerful workflow to better understand the detailed molecular structures of the mucinome.

High-field asymmetric waveform ion mobility spectrometry (FAIMS) separates glycopeptides in the gas phase prior to mass spectrometry (MS) analysis, thus offering the potential to analyze glycopeptides without prior enrichment. Several studies have demonstrated the ability of FAIMS to enhance glycopeptide detection but have primarily focused on N-glycosylation. Here, we evaluated FAIMS for O-glycoprotein and mucin-domain glycoprotein analysis using samples of varying complexity. We demonstrated that FAIMS was useful in increasingly complex samples, as it allowed for the identification of more glycosylated species. However, during our analyses, we observed a phenomenon called in FAIMS fragmentation (IFF) akin to in source fragmentation but occurring during FAIMS separation. FAIMS experiments showed a 2-5-fold increase in spectral matches from IFF compared to control experiments. These results were also replicated in previously published data, indicating that this is likely a systemic occurrence when using FAIMS. Our study highlights that although there are potential benefits to using FAIMS separation, caution must be exercised in data analysis because of prevalent IFF, which may limit its applicability in the broader field of O-glycoproteomics.

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