Fundamental Research最新文献

Peri-operative neurocognitive disorders (PNDs) include postoperative delirium (POD) and postoperative cognitive dysfunction (POCD). Children and the elderly are the two populations most vulnerable to the development of POD and POCD, which results in both high morbidity and mortality. There are many factors, including neuroinflammation and oxidative stress, that are associated with POD and POCD. General anesthesia is a major risk factor of PNDs. However, the molecular mechanisms of PNDs are poorly understood. Dexmedetomidine (DEX) is a useful sedative agent with analgesic properties, which significantly improves POCD in elderly patients. In this review, the current understanding of anesthesia in PNDs and the protective effects of DEX are summarized, and the underlying mechanisms are further discussed.

The activation of spinal astrocytes accounts for opioid-induced hyperalgesia (OIH), but the underlying mechanisms remain elusive. The presence of astrocyte-neuron lactate shuttle (ANLS) makes astrocytes necessary for some neural function and communication. The aim of this study was to explore the role of ANLS in the occurrence and maintenance of OIH. After 7 days consecutive morphine injection, a mice OIH model was established and astrocytic pyruvate dehydrogenase kinase 4 (PDK4), phosphorylated pyruvate dehydrogenase (p-PDH) and accumulation of L-lactate was elevated in the spinal dorsal horn. Intrathecally administration of inhibitors of PDK, lactate dehydrogenase 5 and monocarboxylate transporters to decrease the supply of L-lactate on neurons was observed to attenuate hypersensitivity behaviors induced by repeated morphine administration and downregulate the expression of markers of central sensitization in the spinal dorsal horns. The astrocyte line and the neuronal line were co-cultured to investigate the mechanisms in vitro. In this study, we demonstrated that morphine-induced hyperalgesia was sustained by lactate overload consequent upon aberrant function of spinal ANLS. In this process, PDK-p-PDH-lactate axis serves a pivotal role, which might therefore be a new target to improve long-term opioid treatment strategy in clinical practice.

DNA barcodes, short and unique DNA sequences, play a crucial role in sample identification when processing many samples simultaneously, which helps reduce experimental costs. Nevertheless, the low quality of long-read sequencing makes it difficult to identify barcodes accurately, which poses significant challenges for the design of barcodes for large numbers of samples in a single sequencing run. Here, we present a comprehensive study of the generation of barcodes and develop a tool, PRO, that can be used for selecting optimal barcode sets and demultiplexing. We formulate the barcode design problem as a combinatorial problem and prove that finding the optimal largest barcode set in a given DNA sequence space in which all sequences have the same length is theoretically NP-complete. For practical applications, we developed the novel method PRO by introducing the probability divergence between two DNA sequences to expand the capacity of barcode kits while ensuring demultiplexing accuracy. Specifically, the maximum size of the barcode kits designed by PRO is 2,292, which keeps the length of barcodes the same as that of the official ones used by Oxford Nanopore Technologies (ONT). We validated the performance of PRO on a simulated nanopore dataset with high error rates. The demultiplexing accuracy of PRO reached 98.29% for a barcode kit of size 2,922, 4.31% higher than that of Guppy, the official demultiplexing tool. When the size of the barcode kit generated by PRO is the same as the official size provided by ONT, both tools show superior and comparable demultiplexing accuracy.

Understanding thermal transport at the submicron scale is crucial for engineering applications, especially in the thermal management of electronics and tailoring the thermal conductivity of thermoelectric materials. At the submicron scale, the macroscopic heat diffusion equation is no longer valid and the phonon Boltzmann transport equation (BTE) becomes the governing equation for thermal transport. However, previous thermal simulations based on the phonon BTE have two main limitations: relying on empirical parameters and prohibitive computational costs. Therefore, the phonon BTE is commonly used for qualitatively studying the non-Fourier thermal transport phenomena of toy problems. In this work, we demonstrate an ultra-efficient and parameter-free computational method of the phonon BTE to achieve quantitatively accurate thermal simulation for realistic materials and devices. By properly integrating the phonon properties from first-principles calculations, our method does not rely on empirical material properties input. It can be generally applicable for different materials and the predicted results can match well with experimental results. Moreover, by developing a suitable ensemble of advanced numerical algorithms, our method exhibits superior numerical efficiency. The full-scale (from ballistic to diffusive) thermal simulation of a 3-dimensional fin field-effect transistor with 13 million degrees of freedom, which is prohibitive for existing phonon BTE solvers even on supercomputers, can now be completed within two hours on a single personal computer. Our method makes it possible to achieve the predictive design of realistic nanostructures for the desired thermal conductivity. It also enables accurately resolving the temperature profiles at the transistor level, which helps in better understanding the self-heating effect of electronics.

Combining photodynamic therapy (PDT) with chemodynamic therapy (CDT) has been proven to be a promising strategy to improve the treatment efficiency of cancer, because of the synergistic therapeutic effect arising between the two modalities. Herein, we report an inorganic nanoagent based on ternary NiCoTi-layered double hydroxide (NiCoTi-LDH) nanosheets to realize highly efficient photodynamic/chemodynamic synergistic therapy. The NiCoTi-LDH nanosheets exhibit oxygen vacancy-promoted electron-hole separation and photogenerated hole-induced O₂-independent reactive oxygen species (ROS) generation under acidic circumstances, realizing in situ pH-responsive PDT. Moreover, due to the effective conversion between Co³⁺ and Co²⁺ caused by photogenerated electrons, the NiCoTi-LDH nanosheets catalyze the release of hydroxyl radicals (·OH) from H₂O₂ through Fenton reactions, resulting in CDT. Laser irradiation enhances the catalyzed ability of the NiCoTi-LDH nanosheets to promote the ROS generation, resulting in a better performance than TiO₂ nanoparticles at pH 6.5. In vitro and in vivo experimental results show conclusively that NiCoTi-LDH nanosheets plus irradiation lead to efficient cell apoptosis and significant inhibition of tumor growth. This study reports a new pH-responsive inorganic nanoagent with oxygen vacancy-promoted photodynamic/chemodynamic synergistic performance, offering a potentially appealing clinical strategy for selective tumor elimination.

Nitrate products are widely used in manufacturing as crucial raw materials and fertilizers. The traditional nitrate synthesis process involves high energy consumption and emission, thereby opposing the goals of zero-carbon emission and green chemistry. Thus, a sustainable roadmap for nitrate synthesis that uses green energy input, clean N sources, and direct catalytic processes is urgently required (e.g., developing a novel photosynthesis system). Here, we synthesized TiO₂-supported atomically dispersed Cu species for N₂ photofixation to nitrate in a flow reactor. The optimized photocatalyst yielded a high nitrate photosynthesis rate of 0.93 μmol h⁻¹ and selectivity of ∼90%, which is superior to most of the values reported thus far. Further, experimental results and in-situ investigations revealed that the atomically dispersed Cu sites in the as-designed sample significantly enhanced the separation and transfer efficiency of photogenerated carriers, adsorption and activation of reactants, and the formation of chemisorbed NO_x intermediates, thereby realizing the excellent photofixation of N₂ to nitrate.

The COVID-19 pandemic has posed severe threats to global sustainable development. However, a comprehensive quantitative assessment of the impacts of COVID-19 on Sustainable Development Goals (SDGs) is still lacking. This research quantified the post-COVID-19 SDG progress from 2020 to 2024 using projected GDP growth and population and machine learning models including support vector machine, random forest, and extreme gradient boosting. The results show that the overall SDG performance declined by 7.7% in 2020 at the global scale, with 12 socioeconomic SDG performance decreasing by 3.0%–22.3% and 4 environmental SDG performance increasing by 1.6%–9.2%. By 2024, the progress of 12 SDGs will lag behind for one to eight years compared to their pre-COVID-19 trajectories, while extra time will be gained for 4 environment-related SDGs. Furthermore, the pandemic will cause more impacts on countries in emerging markets and developing economies than those on advanced economies, and the latter will recover more quickly to be closer to their pre-COVID-19 trajectories by 2024. Post-COVID-19 economic recovery should emphasize in areas that can help decouple economic growth from negative environmental impacts. The results can help government and non-state stakeholders identify critical areas for targeted policy to resume and speed up the progress to achieve SDGs by 2030.

With the soaring generation of hazardous waste (HW) during industrialization and urbanization, HW illegal dumping continues to be an intractable global issue. Particularly in developing regions with lax regulations, it has become a major source of soil and groundwater contamination. One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites, which makes HW illegal dumping difficult to be found, thereby causing a long-term adverse impact on the environment. How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed. In this study, a novel machine learning model based on the positive-unlabeled (PU) learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases. Validation of the random forest-based PU model showed that the predicted top 30% of high-risk areas could cover 68.1% of newly reported cases in the studied region, indicating the reliability of the model prediction. This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

To study the influence of an elbow inlet on the rotating stall characteristics of a waterjet propulsion pump (WJPP), a three-dimensional internal flow field in a WJPP under a straight-pipe inlet and elbow inlet is numerically simulated. By comparing the hydraulic performance of WJPP under the two inlet conditions, the internal relationship between the inlet mode and the flow pattern in the pump is clarified. Based on unsteady pressure fluctuation characteristics and wavelet analysis, the influence of the inlet mode on the rotating stall is revealed, and the stall transient propagation characteristics under critical stall conditions are analyzed. The disturbance effects of the inlet channel geometry disappear under low flow rate conditions, the main disturbance is induced by the high-speed countercurrent, and the flow pattern under the elbow inlet is better than that under the straight-pipe inlet. Under the straight-pipe inlet, the single-stall nucleus in the WJPP temporarily experiences a low-frequency and high-amplitude disturbance, which subsequently transforms into a mode of multi-stall nuclei with high-frequency circumferential disturbance. Under the elbow inlet, the rotating stall always maintains a mode of high-amplitude and low-frequency disturbance, which represents the transient characteristics of a single stall core propagating in the circumferential direction inside the channel. The results of this study have a reference value for structural design optimization in a WJPP.