The extracellular surface of the GLP-1 receptor is a molecular trigger for biased agonism

Ligand-directed signal bias offers opportunities for sculpting molecular events, with the promise of better, safer therapeutics. Critical to the exploitation of signal bias is an understanding of the molecular events coupling ligand binding to intracellular signaling. Activation of class B G protein-coupled receptors is driven by interaction of the peptide N terminus with the receptor core. To understand how this drives signaling, we have used advanced analytical methods that enable separation of effects on pathway-specific signaling from those that modify agonist affinity and mapped the functional consequence of receptor modification onto three-dimensional models of a receptor-ligand complex. This yields molecular insights into the initiation of receptor activation and the mechanistic basis for biased agonism. Our data reveal that peptide agonists can engage different elements of the receptor extracellular face to achieve effector coupling and biased signaling providing a foundation for rational design of biased agonists

Copper-Promoted Regioselective Intermolecular Diamination of Ynamides: Synthesis of Imidazo[1,2‑<i>a</i>]pyridines

A facile access to 3-heterosubstituted (3-oxazolidinonyl/indolyl/phenoxy) imidazo­[1,2-<i>a</i>]­pyridines from readily available 2-aminopyridines and electron-rich (internally activated) alkynes like ynamides/ynamines/ynol ethers is achieved via Cu­(OTf)₂-mediated intermolecular diamination under aerobic conditions. The reaction is highly regioselective, owing to internal electron bias, and thus led to a single regioisomer with heterosubstitution at C3

The study area.

A key obstacle in the fight against malaria is afebrile malaria. It remains undiagnosed and, therefore, is invisible to the health system. Apart from being a serious illness, it contributes to increased transmission. Existing studies in India have not adequately reported afebrile malaria and its determinants, including the use of long-lasting insecticide-treated nets (LLINs). This study used six waves of mass screening, which were conducted by the state government in the high-malaria-burden region of Chhattisgarh, a state in India, in 2020, 2021, and 2022. Each round of data collection included more than 15000 individuals. Descriptive statistics were used to analyse key indicators of malaria prevalence and LLIN use. Multivariate analyses were performed to identify the determinants of afebrile malaria and LLIN use. Malaria prevalence in the afebrile population varied from 0.6% to 1.4% across the different waves of mass screening. In comparison, malaria positivity among febrile individuals was greater than 33% in each wave. Afebrile malaria contributed to 19.6% to 47.2% of the overall malaria burden in the region. Indigenous communities (scheduled tribes) were more susceptible to malaria, including afebrile malaria. Individuals using LLINs were less likely to be affected by afebrile malaria. Overall, 77% of the individuals used LLINs in early monsoon season, and in winter the rate was lower at 55%. LLIN use was significantly associated with the number of LLINs the households received from the government. Although fever continues to be a primary symptom of malaria, afebrile malaria remains a significant contributor to the malaria burden in the region. The free distribution of LLINs should be expanded to include high-burden populations. Global policies must include strategies for surveillance and control of afebrile malaria in high-burden areas.</div

Socio-demographic profile of sample in each round.

Socio-demographic profile of sample in each round.</p

Prevalence of malaria by individual characteristics.

Prevalence of malaria by individual characteristics.</p

Results of logistic regression for determinants of LLIN-use by individuals (N = 90926).

Results of logistic regression for determinants of LLIN-use by individuals (N = 90926).</p

Malaria prevalence and share of afebrile malaria in six waves of mass screening in Chhattisgarh (2020 to 2022).

Malaria prevalence and share of afebrile malaria in six waves of mass screening in Chhattisgarh (2020 to 2022).</p

Minimal dataset of the study.

A key obstacle in the fight against malaria is afebrile malaria. It remains undiagnosed and, therefore, is invisible to the health system. Apart from being a serious illness, it contributes to increased transmission. Existing studies in India have not adequately reported afebrile malaria and its determinants, including the use of long-lasting insecticide-treated nets (LLINs). This study used six waves of mass screening, which were conducted by the state government in the high-malaria-burden region of Chhattisgarh, a state in India, in 2020, 2021, and 2022. Each round of data collection included more than 15000 individuals. Descriptive statistics were used to analyse key indicators of malaria prevalence and LLIN use. Multivariate analyses were performed to identify the determinants of afebrile malaria and LLIN use. Malaria prevalence in the afebrile population varied from 0.6% to 1.4% across the different waves of mass screening. In comparison, malaria positivity among febrile individuals was greater than 33% in each wave. Afebrile malaria contributed to 19.6% to 47.2% of the overall malaria burden in the region. Indigenous communities (scheduled tribes) were more susceptible to malaria, including afebrile malaria. Individuals using LLINs were less likely to be affected by afebrile malaria. Overall, 77% of the individuals used LLINs in early monsoon season, and in winter the rate was lower at 55%. LLIN use was significantly associated with the number of LLINs the households received from the government. Although fever continues to be a primary symptom of malaria, afebrile malaria remains a significant contributor to the malaria burden in the region. The free distribution of LLINs should be expanded to include high-burden populations. Global policies must include strategies for surveillance and control of afebrile malaria in high-burden areas.</div

Results of logistic regression models for determinants of malaria and afebrile malaria.

Results of logistic regression models for determinants of malaria and afebrile malaria.</p