Single step syntheses of (1 S)-aryltetrahydroisoquinolines by norcoclaurine synthases

The 1-aryl-tetrahydroisoquinoline (1-aryl-THIQ) moiety is found in many biologically active molecules. Single enantiomer chemical syntheses are challenging and although some biocatalytic routes have been reported, the substrate scope is limited to certain structural motifs. The enzyme norcoclaurine synthase (NCS), involved in plant alkaloid biosynthesis, has been shown to perform stereoselective Pictet–Spengler reactions between dopamine and several carbonyl substrates. Here, benzaldehydes are explored as substrates and found to be accepted by both wild-type and mutant constructs of NCS. In particular, the variant M97V gives a range of (1 S)-aryl-THIQs in high yields (48–99%) and e.e.s (79–95%). A cocrystallised structure of the M97V variant with an active site reaction intermediate analogue is also obtained with the ligand in a pre-cyclisation conformation, consistent with (1 S)-THIQs formation. Selected THIQs are then used with catechol O-methyltransferases with exceptional regioselectivity. This work demonstrates valuable biocatalytic approaches to a range of (1 S)-THIQ

Sleep effort and its measurement: A scoping review

Insomnia disorder is characterized by disruption in sleep continuity and an overall dissatisfaction with sleep. A relevant feature of insomnia is sleep effort, which refers to both cognitive and behavioural conscious attempts to initiate sleep. The Glasgow Sleep Effort Scale is a self-report tool developed to assess this construct. The objective of the current scoping review was to map how sleep effort has been discussed in the literature and operationalized through its respective measure. Medline/PubMed, Scopus, Web of Science and PsycInfo databases were used to search for potential studies. The search query used in databases was the specific name of the self-reported tool itself (Glasgow Sleep Effort Scale) and “sleep effort” term. This scoping review followed JBI guidelines. To be included, records pertaining to any type of study that mentioned the Glasgow Sleep Effort Scale were considered. No language constraint was used. At the end, 166 initial records were retrieved. From those, 46 records met eligibility criteria and were analysed. Among the main findings, it was observed that the Glasgow Sleep Effort Scale has been increasingly used in recent years, with a notable observed upward trend, especially in the last 2 years. In addition to the original measure, only three published adapted versions of the instrument were identified. This suggests that there is limited research on adapting the scale for different populations or contexts. Sleep effort has been increasingly studied in the last few years. Nonetheless, more research on the Glasgow Sleep Effort Scale tool is recommended, including cross-cultural adaptations

Machine diagnosis of chronic obstructive pulmonary disease using a novel fast-response capnometer

Abstract Background Although currently most widely used in mechanical ventilation and cardiopulmonary resuscitation, features of the carbon dioxide (CO2) waveform produced through capnometry have been shown to correlate with V/Q mismatch, dead space volume, type of breathing pattern, and small airway obstruction. This study applied feature engineering and machine learning techniques to capnography data collected by the N-Tidal™ device across four clinical studies to build a classifier that could distinguish CO2 recordings (capnograms) of patients with COPD from those without COPD. Methods Capnography data from four longitudinal observational studies (CBRS, GBRS, CBRS2 and ABRS) was analysed from 295 patients, generating a total of 88,186 capnograms. CO2 sensor data was processed using TidalSense’s regulated cloud platform, performing real-time geometric analysis on CO2 waveforms to generate 82 physiologic features per capnogram. These features were used to train machine learning classifiers to discriminate COPD from ‘non-COPD’ (a group that included healthy participants and those with other cardiorespiratory conditions); model performance was validated on independent test sets. Results The best machine learning model (XGBoost) performance provided a class-balanced AUROC of 0.985 ± 0.013, positive predictive value (PPV) of 0.914 ± 0.039 and sensitivity of 0.915 ± 0.066 for a diagnosis of COPD. The waveform features that are most important for driving classification are related to the alpha angle and expiratory plateau regions. These features correlated with spirometry readings, supporting their proposed properties as markers of COPD. Conclusion The N-Tidal™ device can be used to accurately diagnose COPD in near-real-time, lending support to future use in a clinical setting. Trial registration: Please see NCT03615365, NCT02814253, NCT04504838 and NCT03356288