Diazo Transfer Reactions to 1,3-Dicarbonyl Compounds with Tosyl azide

International audienceA practical protocol for the large-scale preparation of 2-diazo-1,3-dicarbonyl compounds is described by diazo-transfer reactions with tosyl azide followed by efficient chromatographic purifications on silica gel and/or alumina

Preparation of mono-substituted malonic acid half oxyesters (SMAHOs).

The use of mono-substituted malonic acid half oxyesters (SMAHOs) has been hampered by the sporadic references describing their preparation. An evaluation of different approaches has been achieved, allowing to define the best strategies to introduce diversity on both the malonic position and the ester function. A classical alkylation step of a malonate by an alkyl halide followed by a monosaponification gave access to reagents bearing different substituents at the malonic position, including functionalized derivatives. On the other hand, the development of a monoesterification step of a substituted malonic acid derivative proved to be the best entry for diversity at the ester function, rather than the use of an intermediate Meldrum acid. Both these transformations are characterized by their simplicity and efficiency, allowing a straightforward access to SMAHOs from cheap starting materials

Accuracy of a smartphone pedometer application according to different speeds and mobile phone locations in a laboratory context

Background - The purpose of this study was to compare the accuracy of a smartphone application and a mechanical pedometer for step counting at different walking speeds and mobile phone locations in a laboratory context. Methods - Seventeen adults wore an iPphone6© with Runtastic Pedometer© application (RUN), at 3 different locations (belt, arm, jacket) and a pedometer (YAM) at the waist. They were asked to walk on an instrumented treadmill (reference) at various speeds (2, 4 and 6 km/h). Results - RUN was more accurate than YAM at 2 km/h (p < 0.05) and at 4 km/h (p = 0.03). At 6 km/h the two devices were equally accurate. The precision of YAM increased with speed (p < 0.05), while for RUN, the results were not significant but showed a trend (p = 0.051). Surprisingly, YAM underestimates the number of step by 60.5% at 2 km/h. The best accurate step counting (0.7% mean error) was observed when RUN is attached to the arm and at the highest speed. Conclusions - RUN pedometer application could be recommended mainly for walking sessions even for low walking speed. Moreover, our results confirm that the smartphone should be strapped close to the body to discriminate steps from noise by the accelerometers (particularly at low speed)

Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Study Protocol of the Ally Micro-Randomized Trial

Background: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user’s context from smartphone sensor data is a promising approach to further enhance tailoring. Objective: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants’ states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. Methods: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. Results: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191; 19.4%) and collection of sensor data (35/191; 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%. 247/274) participants

Investigating Intervention Components and Exploring States of Receptivity for a Smartphone App to Promote Physical Activity: Protocol of a Microrandomized Trial

BACKGROUND: Smartphones enable the implementation of just-in-time adaptive interventions (JITAIs) that tailor the delivery of health interventions over time to user- and time-varying context characteristics. Ideally, JITAIs include effective intervention components, and delivery tailoring is based on effective moderators of intervention effects. Using machine learning techniques to infer each user's context from smartphone sensor data is a promising approach to further enhance tailoring. OBJECTIVE: The primary objective of this study is to quantify main effects, interactions, and moderators of 3 intervention components of a smartphone-based intervention for physical activity. The secondary objective is the exploration of participants' states of receptivity, that is, situations in which participants are more likely to react to intervention notifications through collection of smartphone sensor data. METHODS: In 2017, we developed the Assistant to Lift your Level of activitY (Ally), a chatbot-based mobile health intervention for increasing physical activity that utilizes incentives, planning, and self-monitoring prompts to help participants meet personalized step goals. We used a microrandomized trial design to meet the study objectives. Insurees of a large Swiss insurance company were invited to use the Ally app over a 12-day baseline and a 6-week intervention period. Upon enrollment, participants were randomly allocated to either a financial incentive, a charity incentive, or a no incentive condition. Over the course of the intervention period, participants were repeatedly randomized on a daily basis to either receive prompts that support self-monitoring or not and on a weekly basis to receive 1 of 2 planning interventions or no planning. Participants completed a Web-based questionnaire at baseline and postintervention follow-up. RESULTS: Data collection was completed in January 2018. In total, 274 insurees (mean age 41.73 years; 57.7% [158/274] female) enrolled in the study and installed the Ally app on their smartphones. Main reasons for declining participation were having an incompatible smartphone (37/191, 19.4%) and collection of sensor data (35/191, 18.3%). Step data are available for 227 (82.8%, 227/274) participants, and smartphone sensor data are available for 247 (90.1%, 247/274) participants. CONCLUSIONS: This study describes the evidence-based development of a JITAI for increasing physical activity. If components prove to be efficacious, they will be included in a revised version of the app that offers scalable promotion of physical activity at low cost. TRIAL REGISTRATION: ClinicalTrials.gov NCT03384550; https://clinicaltrials.gov/ct2/show/NCT03384550 (Archived by WebCite at http://www.webcitation.org/74IgCiK3d)

Quinones as dienophiles in the Diels-Alder reaction: history and applications in total synthesis

In the canon of reactions available to the organic chemist engaged in total synthesis, the Diels–Alder reaction is among the most powerful and well understood. Its ability to rapidly generate molecular complexity through the simultaneous formation of two carboncarbon bonds is almost unrivalled, and this is reflected in the great number of reported applications of this reaction. Historically, the use of quinones as dienophiles is highly significant, being the very first example investigated by Diels and Alder. Herein, we review the application of the Diels–Alder reaction of quinones in the total synthesis of natural products. The highlighted examples span some 60 years from the landmark syntheses of morphine (1952) and reserpine (1956) by Gates and Woodward, respectively, through to the present day examples, such as the tetracyclines