Diagnosis of pregnancy disorder in the first-trimester patient plasma with Raman spectroscopy and protein analysis.

Gestational diabetes mellitus (GDM) is a pregnancy disorder associated with short- and long-term adverse outcomes in both mothers and infants. The current clinical test of blood glucose levels late in the second trimester is inadequate for early detection of GDM. Here we show the utility of Raman spectroscopy (RS) for rapid and highly sensitive maternal metabolome screening for GDM in the first trimester.

Experimental and Computational Studies of Steric Factors in the Isomerization of Internal Alkynes within the Coordination Environment of Half-Sandwich Metal Complexes.

The isomerization of internal alkynes ArC≡CAr within the coordination environment of low-valent half-sandwich [Ru(dppe)Cp] complexes via a 1,2-migration process affords vinylidene species [Ru{=C=C(Ar)Ar}(dppe)Cp]. The rearrangement reactions of symmetrically and asymmetrically substituted substrates featuring different electron-donating and -withdrawing groups and of varying steric bulk were modelled using density functional theory (DFT), and the conclusions supported by experimental observations. Examination of the reaction pathway and associated activation barriers reveal a high solvent dependency for the generation of the key intermediate species [Ru(dppe)Cp] from [RuCl(dppe)Cp] by halide dissociation in the presence of Na salts of weakly coordinating anions, with the lattice enthalpy of the NaCl by-product playing a critical role in the overall thermochemical balance of the reaction.

Particle formation in response to different protein formulations and containers: Insights from machine learning analysis of particle images.

Subvisible particle count is a biotherapeutics stability indicator widely used by pharmaceutical industries. A variety of stresses that biotherapeutics are exposed to during development can impact particle morphology. By classifying particle morphological differences, stresses that have been applied to monoclonal antibodies (mAbs) can be identified.

Predicting the onset of preeclampsia by longitudinal monitoring of metabolic changes throughout pregnancy with Raman spectroscopy.

Preeclampsia is a life-threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy.