A novel predictive model for optimizing diabetes screening in older adults.

INTRODUCTION: The fasting blood glucose test is widely used for diabetes screening. However, it may fail to detect early-stage diabetes characterized by elevated postprandial glucose levels. Hence, we developed and internally validated a nomogram to predict the diabetes risk in older adults with normal fasting glucose levels. MATERIALS AND METHODS: This study enrolled 2,235 older adults, dividing them into a Training Set (n = 1,564) and a Validation Set (n = 671) based on a 7:3 ratio. We employed the least absolute shrinkage and selection operator regression to identify predictors for constructing the nomogram. Calibration and discrimination were employed to assess the nomogram's performance, while its clinical utility was evaluated through decision curve analysis. RESULTS: Nine key variables were identified as significant factors: age, gender, body mass index, fasting blood glucose, triglycerides, alanine aminotransferase, the ratio of alanine aminotransferase to aspartate aminotransferase, blood urea nitrogen, and hemoglobin. The nomogram demonstrated good discrimination, with an area under the receiver operating characteristic curve of 0.824 in the Training Set and 0.809 in the Validation Set. Calibration curves for both sets confirmed the model's accuracy in estimating the actual diabetes risk. Decision curve analysis highlighted the model's clinical utility. CONCLUSIONS: We provided a dynamic nomogram for identifying older adults at risk of diabetes, potentially enhancing the efficiency of diabetes screening in primary healthcare units.

Erratum: Author correction to 'Discovery and identification of EIF2AK2 as a direct key target of berberine for anti-inflammatory effects' [Acta Pharmaceutica Sinica B 13 (2023) 2138-2151].

[This corrects the article DOI: 10.1016/j.apsb.2022.12.009.].

Azobenzene-Based Linker Strategy for Selective Activation of Antibody-Drug Conjugates.

Existing antibody-drug conjugate (ADC) linkers, whether cleavable or non-cleavable, are designed to release highly toxic payloads or payload derivatives upon internalisation of the ADCs into cells. However, clinical studies have shown that only <1 % of the dosed ADCs accumulate in tumour cells. The remaining >99 % of ADCs are nonspecifically distributed in healthy tissue cells, thus inevitably leading to off-target toxicity. Herein, we describe an intelligent tumour-specific linker strategy to address these limitations. A tumour-specific linker is constructed by introducing a hypoxia-activated azobenzene group as a toxicity controller. We show that this azobenzene-based linker is non-cleavable in healthy tissues (O2 >10 %), and the corresponding payload derivative, cysteine-appended azobenzene-linker-monomethyl auristatin E (MMAE), can serve as a safe prodrug to mask the toxicity of MMAE (switched off). Upon exposure to the hypoxic tumour microenvironment (O2<1 %), this linker is cleaved to release MMAE and fully restores the high cytotoxicity of the ADC (switched on). Notably, the azobenzene linker-containing ADC exhibits satisfactory antitumour efficacy in vivo and a larger therapeutic window compared with ADCs containing traditional cleavable or non-cleavable linkers. Thus, our azobenzene-based linker sheds new light on the development of next-generation ADC linkers.