In a paper published in National Science Review, the cubic-phase α-MoC_1−x nanoparticles were incorporated into a carbon matrix and coupled with cobalt phthalocyanine molecules for the co-reduction of CO₂ and H₂O. During the reaction process, a dense hydrogen bond network was formed on the catalyst surface induced by rearranged water molecules, thereby enhancing water dissociation, accelerating proton transfer, and improving the overall performance of CO₂RR.

A research paper just published in Science China Life Sciences reports a gastric-adaptive hydrogen polysulfide microreactor (GAPSR), which can release a large amount of hydrogen polysulfide (H₂S_n, n≥2) and inactivate H. pylori glucose-6-phosphate dehydrogenase (G6PDH) by interfering with electron transfer from glucose-6-phosphate (G6P) to nicotinamide adenine dinucleotide phosphate (NADP⁺). This discovery provides a new scheme for the treatment of Helicobacter pylori infection.

This comprehensive review analyzes cutting-edge tools and technologies in modern pharmaceutical research, focusing on artificial intelligence, multi-omics technologies, and experimental methods. The study highlights how computational methods enhance drug discovery efficiency, while omics technologies provide systematic frameworks for investigating drug mechanisms. The integration of these advanced approaches has enabled more diverse and personalized treatment strategies, though challenges remain in drug development complexity, cost-effectiveness, and operational feasibility.

The findings reveal that, although the ensemble mean of the ECMWF model has limited forecasting ability for extreme cold events after two weeks, some ensemble members exhibit significantly high forecasting skill. The members with high forecasting skill can accurately predict the rapid change of surface air temperature and the intensity of the minimum temperature during an extreme cold event. This mainly depends on the accurate prediction of the atmospheric circulation situation in Eurasia (sea level pressure and 500-hPa geopotential height).

Behavioral scoring based on clinical observations remains the gold standard for screening, diagnosing,and evaluating infantile epileptic spasm syndrome (IESS). The accurate identification of seizures is crucial for clinical diagnosis and assessment. In this study, we propose an innovative seizure detection method based on video feature recognition of patient spasms. To capture the temporal characteristics of the spasm behavior presented in the videos effectively, we incorporate asymmetric convolution and convolution–batch normalization–ReLU (CBR) modules. Specifically within the 3D-ResNet residual blocks, we split the larger convolutional kernels into two asymmetric 3D convolutional kernels. These kernels are connected in series to enhance the ability of the convolutional layers to extract key local features, both horizontally and vertically. In addition, we introduce a 3D convolutional block attention module to enhance the spatial correlations between video frame channels efficiently. To improve the generalization ability, we design a composite loss function that combines cross-entropy loss with triplet loss to balance the classification and similarity requirements. We train and evaluate our method using the PLA IESS-VIDEO dataset, achieving an average seizure recognition accuracy of 90.59%, precision of 90.94%, and recall of 87.64%. To validate its generalization capability further, we conducted external validation using six different patient monitoring videos compared with assessments by six human experts from various medical centers. The final test results demonstrate that our method achieved a recall of 0.647 6, surpassing the average level achieved by human experts (0.559 5), while attaining a high F1-score of 0.721 9. These findings have substantial significance for the long-term assessment of patients with IESS.