Low DHEAS: A Sensitive and Specific Test for the Detection of Subclinical Hypercortisolism in Adrenal Incidentalomas.

CONTEXT: Subclinical hypercortisolism (SH) occurs in 5% to 30% of adrenal incidentalomas (AIs). Common screening tests for adrenocorticotropin-independent hypercortisolism have substantial false-positive rates, mandating further time and resource-intensive investigations. OBJECTIVE: To determine whether low basal dehydroepiandrosterone sulfate (DHEAS) is a sensitive and specific screening test for SH in AI. SETTING AND PATIENTS: In total, 185 patients with AI were screened for adrenal medullary (plasma metanephrines) and cortical [1 mg overnight dexamethasone suppression test (ONDST), 24-hour urinary free cortisol (UFC), serum DHEAS, plasma renin, and aldosterone] hyperfunction. Positive ONDST [≥1.8 mcg/dL (≥50 nmol/L)] and/or UFC (more than the upper limit of reference range) results were further investigated. We diagnosed SH when at least 2 of the following were met: raised UFC, raised midnight serum cortisol, 48-hour dexamethasone suppression test (DST) cortisol ≥1.8 mcg/dL (≥50 nmol/L). RESULTS: 29 patients (16%) were diagnosed with SH. Adrenocorticotropin was 99%) and specific (91.9%) for the diagnosis of SH. Cortisol following 1 mg ONDST of 1.9 mcg/dL (53 nmol/L) was a sensitive (>99%) screening test for SH but had lower specificity (82.9%). The 24-hour UFC lacked sensitivity (69%) and specificity (72%). CONCLUSION: A single basal measurement of DHEAS offers comparable sensitivity and greater specificity to the existing gold-standard 1 mg DST for the detection of SH in patients with AIs

A transcriptomic axis predicts state modulation of cortical interneurons

Transcriptomics has revealed that cortical inhibitory neurons exhibit a great diversity of fine molecular subtypes1-6, but it is not known whether these subtypes have correspondingly diverse patterns of activity in the living brain. Here we show that inhibitory subtypes in primary visual cortex (V1) have diverse correlates with brain state, which are organized by a single factor: position along the main axis of transcriptomic variation. We combined in vivo two-photon calcium imaging of mouse V1 with a transcriptomic method to identify mRNA for 72 selected genes in ex vivo slices. We classified inhibitory neurons imaged in layers 1-3 into a three-level hierarchy of 5 subclasses, 11 types and 35 subtypes using previously defined transcriptomic clusters3. Responses to visual stimuli differed significantly only between subclasses, with cells in the Sncg subclass uniformly suppressed, and cells in the other subclasses predominantly excited. Modulation by brain state differed at all hierarchical levels but could be largely predicted from the first transcriptomic principal component, which also predicted correlations with simultaneously recorded cells. Inhibitory subtypes that fired more in resting, oscillatory brain states had a smaller fraction of their axonal projections in layer 1, narrower spikes, lower input resistance and weaker adaptation as determined in vitro7, and expressed more inhibitory cholinergic receptors. Subtypes that fired more during arousal had the opposite properties. Thus, a simple principle may largely explain how diverse inhibitory V1 subtypes shape state-dependent cortical processing

In situ conjugation of dithiophenol maleimide polymers and oxytocin for stable and reversible polymer–peptide conjugates

The in situ one-pot synthesis of peptide–polymer bioconjugates is reported. Conjugation occurs efficiently without the need for purification of dithiophenol maleimide functionalized polymer as a disulfide bridging agent for the therapeutic oxytocin. Conjugation of polymers was demonstrated to be reversible and to significantly improve the solution stability of oxytocin

The influence of solid state information and descriptor selection on statistical models of temperature dependent aqueous solubility.

Predicting the equilibrium solubility of organic, crystalline materials at all relevant temperatures is crucial to the digital design of manufacturing unit operations in the chemical industries. The work reported in our current publication builds upon the limited number of recently published quantitative structure-property relationship studies which modelled the temperature dependence of aqueous solubility. One set of models was built to directly predict temperature dependent solubility, including for materials with no solubility data at any temperature. We propose that a modified cross-validation protocol is required to evaluate these models. Another set of models was built to predict the related enthalpy of solution term, which can be used to estimate solubility at one temperature based upon solubility data for the same material at another temperature. We investigated whether various kinds of solid state descriptors improved the models obtained with a variety of molecular descriptor combinations: lattice energies or 3D descriptors calculated from crystal structures or melting point data. We found that none of these greatly improved the best direct predictions of temperature dependent solubility or the related enthalpy of solution endpoint. This finding is surprising because the importance of the solid state contribution to both endpoints is clear. We suggest our findings may, in part, reflect limitations in the descriptors calculated from crystal structures and, more generally, the limited availability of polymorph specific data. We present curated temperature dependent solubility and enthalpy of solution datasets, integrated with molecular and crystal structures, for future investigations