Meta-Analysis-Based Comparison of Annual Fall Risk between Older Adults with Alzheimer's Disease and Mild Cognitive Impairment.

Background: Falls are a primary cause of injuries and hospitalization in older adults. It has been reported that cognitive impairments and dementia can increase fall risk in the older population; however, it remains unknown if fall risk differs among subgroups of dementia. This meta-analysis summarized previous studies reporting the annual fall risk of people with Alzheimer's disease (AD) or mild cognitive impairment (MCI) and compared the fall risk between these two groups of people with dementia. Methods: Thirty-five studies enrolling 7844 older adults with AD or MCI were included. The annual fall prevalence and average number of falls of the included studies were meta-analyzed and compared by random-effects models with inverse variance weights. Results: The annual fall prevalence in people with AD (43.55%) was significantly higher than MCI (35.26%, p < 0.001). A χ2 test indicated that the pooled fall prevalence is significantly higher in people with AD than MCI χ2 = 158.403, p < 0.001). Additionally, the yearly average number of falls in AD was higher than in MCI (1.30 vs 0.77 falls/person). Conclusions: The results showed that older people with AD experience a higher annual fall prevalence with a larger number of falls than older adults with MCI. The results suggested that the fall risk measurements should be reported separately between people with AD and MCI. The findings could provide preliminary guidance for the identification of individuals with dementia who experience a high fall risk.

Vitrification-enabled enhancement of proton conductivity in hydrogen-bonded organic frameworks.

Hydrogen-bonded organic frameworks (HOFs) are versatile materials with potential applications in proton conduction. Traditional approaches involve incorporating humidity control to address grain boundary challenges for proton conduction. This study finds vitrification as an alternative strategy to eliminate grain boundary effect in HOFs by rapidly melt quenching the kinetically stable HOF-SXU-8 to glassy state HOF-g. Notably, a remarkable enhancement in proton conductivity without humidity was achieved after vitrification, from 1.31 × 10-7 S cm-1 to 5.62× 10-2 S cm-1 at 100 °C. Long term stability test showed negligible performance degradation, and even at 30 °C, the proton conductivity remained at high level of 1.2 × 10-2 S cm-1. Molecule dynamics (MD) simulations and X-ray total scattering experiments reveal the HOF-g system is consisted of three kinds of clusters, i.e., 1,5-Naphthalenedisulfonic acid (1,5-NSA) anion clusters, N,N-dimethylformamide (DMF) molecule clusters, and H+-H2O clusters. In which, the H+ plays an important role to bridge these clusters and the high conductivity is mainly related to the H+ on H3O+. These findings provide valuable insights for optimizing HOFs, enabling efficient proton conduction, and advancing energy conversion and storage devices.

Shoe sole impedes leg muscle activation and impairs dynamic balance responding to a standing-slip.

The shoe sole is identified as a fall risk factor since it may impede the afferent information about the outside world collected by the plantar sensory units. However, no study has directly quantified how the shoe sole compromises body balance and increases fall risk. This study aimed to inspect how the sole affects human balance after an unexpected standing-slip. It was hypothesized that individuals wearing the sole, relative to their barefoot counterparts, would exhibit 1) more impaired stability and 2) disrupted lower limb muscle activation following a standing-slip. Twenty young adults were evenly randomized into two groups: soled and barefoot. The soled group wore a pair of customized 10-mm thick soles, while the other group was bare-footed. Full-body kinematics and leg muscle electromyography (EMG) were collected during a standardized and unexpected standing-slip. The EMG electrodes were placed on the tibialis anterior, gastrocnemius, rectus femoris, and biceps femoris bilaterally. Dynamic stability, spatiotemporal gait parameters, and the EMG latency of the leg muscles were compared between groups. The sole impeded the initiation of the recovery step possibly because it interfered with the accurate detection of the external perturbation and subsequently activated the leg muscles later in the soled group than in the barefoot group. As a result, individuals in the soled group experienced a longer slip distance and were more unstable than the barefoot group at the recovery foot liftoff. The findings of this study could augment our understanding of how the shoe sole impairs body balance and increases the fall risk.

MOF-derived multi-"hotspot" 3D Au/MOF-808 (Zr) nanostructures as SERS substrates for the ultrasensitive determination of thiram.

A convenient self-assembly method is proposed for synthesis of 3D Au/MOF-808 (Zr) composite nanostructures with a cerium metal-organic framework loaded with gold nanoparticles. We combine adsorption properties of MOF materials with surface plasmon resonance of noble metals to construct hotspot-dense 3D Au/MOF-808 (Zr) SERS substrates, by using a two-step method of solvothermal and reduction reactions. The results show that optimal SERS substrates are obtained from a volume ratio of gold nanoparticles to MOF-808 (Zr) solution of 4:1 and a self-assembly time of 2 h. Rhodamine 6G (R6G) is used as a molecular probe to characterize and analyze SERS properties of substrates of 3D Au/MOF-808 (Zr) prepared under the optimal process conditions, where the substrates are capable to detect R6G concentrations down to 10-10 M with a relative standard deviation of 8.81%. Finally, we applied the SERS substrates of 3D Au/MOF-808 (Zr) to the detection of pesticide thiram, and establish a quantitative determination method. 3D Au/MOF-808 (Zr) provides a sensitive detection of thiram in lake water by SERS with a detection limit of 1.49 × 10-9 M. Application tests show that a SERS enhancement factor of the MOF-based SERS substrates for the detection of thiram can be significantly increased to 5.91 × 105. Thus, the above results indicate that such substrate has high sensitivity, good adsorption, homogeneity, and reproducibility, which can be extended for sensitive detection of pesticide residues in food and environment.

Impact of standing perturbation intensities on fall and stability outcomes in healthy young adults.

Motorized treadmills have been extensively used in investigating reactive balance control and developing perturbation-based interventions for fall prevention. However, the relationship between perturbation intensity and its outcome has not been quantified. The primary purpose of this study was to quantitatively analyze how the treadmill belt's peak velocity affects the perturbation outcome and other metrics related to the reactive balance in young adults while the total belt displacement is controlled at 0.36 m. Thirty-one healthy young adults were randomly assigned into three groups with different peak belt speeds: low (0.9 m/s), medium (1.2 m/s), and high (1.8 m/s). Protected by a safety harness, participants were exposed to a forward support surface translation while standing at an unexpected timing on an ActiveStep treadmill. The primary (perturbation outcome: fall vs. recovery) and secondary (dynamic stability, hip descent, belt distance at liftoff, and recovery step latency) outcome measures were compared among groups. Results revealed that a higher perturbation intensity is correlated with a greater faller rate (p < 0.001). Compared to the low- and medium-intensity groups, the high-intensity group was less stable (p < 0.001) with a larger hip descent (p < 0.001) and a longer belt distance (p < 0.001) at the recovery step liftoff. The results suggest that the increased perturbation intensity raises the risk of falling with larger instability and poorer reactive performance after a support surface translation-induced perturbation in healthy young adults. The findings could furnish preliminary guidance for us to design and select the optimal perturbation intensity that can maximize the effects of perturbation-based training protocols.

EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction.

BACKGROUND: The seizure prediction algorithms have demonstrated their potential in mitigating epilepsy risks by detecting the pre-ictal state using ongoing electroencephalogram (EEG) signals. However, most of them require high-density EEG, which is burdensome to the patients for daily monitoring. Moreover, prevailing seizure models require extensive training with significant labeled data which is very time-consuming and demanding for the epileptologists. METHOD: To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction. RESULTS: Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time. CONCLUSIONS: Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients' quality of life.

Movement strategies during obstacle crossing in people with Parkinson disease: A systematic review with meta-analysis.

OBJECTIVE: Navigating obstacles involves adjusting walking patterns, particularly when stepping over them. This task may be particularly challenging for people with Parkinson disease (PD) for several reasons. This review aims to compare the spatiotemporal gait parameters of people with and without PD while stepping over obstacles. LITERATURE SURVEY: A systematic literature search was conducted in six databases (PubMed, Scopus, Web of Science, EBSCO, Embase, and SciELO) from inception to September 2023. METHODOLOGY: Studies were selected that evaluated gait parameters of people with and without PD while walking over obstacles. Two independent researchers evaluated the eligibility and extracted gait parameters during obstacle crossing. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Heterogeneity was assessed using I2-tests. Random effects models were determined for effect sizes as standardized mean differences (SMD). SYNTHESIS: Twenty-five studies were included in the review and 17 in the meta-analysis. Most of the studies (58%) showed a low risk of bias. People with PD exhibit a shorter step when landing after crossing an obstacle (SMD = -0.50 [-0.69 to -0.31]). Compared to people without PD, people with PD also widen their support base (SMD = 0.27 [0.07-0.47]) and reduce gait velocity (SMD = -0.60 [-0.80 to -0.39]) when crossing the obstacle. CONCLUSIONS: People with PD adopt a more conservative motor behavior during obstacle crossing than those without PD, with a shorter step length when landing after crossing an obstacle, greater step width and lower crossing speed.

Synthesis, Characterization, and Properties of Heat-Resistant Energetic Materials Based on C-C Bridged Dinitropyrazole Energetic Materials.

Polycyclic energetic materials make up a distinctive class of conjugated structures that consist of two or more rings. In this work, 1,3-bis(3,5-dinitro-1H-pyrazol-4-yl)-4,6-dinitrobenzene (BDPD) was synthesized and investigated in detail as a polycyclic heat-resistant energetic molecule that can be deprotonated by bases to obtain its anionic (3-5) salts. All compounds were thoroughly characterized by 1H and 13C NMR, infrared spectroscopy, high-resolution mass spectrometry, and elemental analysis. The structural features of BDPD and its salts were investigated by single-crystal X-ray diffraction and analyzed by different kinds of computing software, like Multiwfn, Gaussian 09W, and so on. In addition, their thermal decomposition temperatures were evaluated by differential scanning calorimetry to be 319.8-329.0 °C, revealing that they possessed high thermal stabilities. The results of impact sensitivity and friction sensitivity analysis confirm that these energetic compounds were insensitive. The detonation properties of neutral compound BDPD and all its nonmetallic salts were calculated by the EXPLO5 v6.05.04 program. The results revealed that their detonation performances were higher than those of the widely used heat-resistant explosive 2,2',4,4',6,6'-hexanitrostilbene (HNS). Combining the above results, it is reasonable to suggest that these compounds have the potential to be heat-resistant energetic materials.

Lanosterol synthase deficiency promotes tumor progression by orchestrating PDL1-dependent tumor immunosuppressive microenvironment.

Lipid metabolic reprogramming is closely related to tumor progression with the mechanism not fully elucidated. Here, we report the immune-regulated role of lanosterol synthase (LSS), an essential enzyme in cholesterol synthesis. Database analysis and clinical sample experiments suggest that LSS was lowly expressed in colon and breast cancer tissues, which indicates poor prognosis. The biological activity of tumor cell lines and tumor progression in NOD scid gamma (NSG) mice were not affected after LSS knockdown, whereas LSS deficiency obviously aggravated tumor burden in fully immunized mice. Flow cytometry analysis showed that LSS knockdown significantly promoted the formation of tumor immunosuppressive microenvironment, characterized by the increase in M2 macrophages and polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs), as well as the decrease in anti-tumoral T lymphocytes. With the inhibition of myeloid infiltration or loss function of T lymphocytes, the propulsive effect of LSS knockdown on tumor progression disappeared. Mechanistically, LSS knockdown increased programmed death ligand 1 (PDL1) protein stability by 2,3-oxidosqualene (OS) binding to PDL1 protein. Anti-PDL1 therapy abolished LSS deficiency-induced immunosuppressive microenvironment and cancer progression. In conclusion, our results show that LSS deficiency promotes tumor progression by establishing an OS-PDL1 axis-dependent immunosuppressive microenvironment, indicative of LSS or OS as a potential hallmark of response to immune checkpoint blockade.

Isolated Random Forest Assisted Spatio-Temporal Ant Colony Evolutionary Algorithm for Cell Tracking in Time-Lapse Sequences.

Multi-Object tracking in real world environments is a tough problem, especially for cell morphogenesis with division. Most cell tracking methods are hard to achieve reliable mitosis detection, efficient inter-frame matching, and accurate state estimation simultaneously within a unified tracking framework. In this paper, we propose a novel unified framework that leverages a spatio-temporal ant colony evolutionary algorithm to track cells amidst mitosis under measurement uncertainty. Each Bernoulli ant colony representing a migrating cell is able to capture the occurrence of mitosis through the proposed Isolation Random Forest (IRF)-assisted temporal mitosis detection algorithm with the assumption that mitotic cells exhibit unique spatio-temporal features different from non-mitotic ones. Guided by prediction of a division event, multiple ant colonies evolve between consecutive frames according to an augmented assignment matrix solved by the extended Hungarian method. To handle dense cell populations, an efficient group partition between cells and measurements is exploited, which enables multiple assignment tasks to be executed in parallel with a reduction in matrix dimension. After inter-frame traversing, the ant colony transitions to a foraging stage in which it begins approximating the Bernoulli parameter to estimate cell state by iteratively updating its pheromone field. Experiments on multi-cell tracking in the presence of cell mitosis and morphological changes are conducted, and the results demonstrate that the proposed method outperforms state-of-the-art approaches, striking a balance between accuracy and computational efficiency.

All-aqueous droplets-templated tailorable core-shell alginate microspheres for constructing vascularized intestinal mucosain vitromodels.

Recently,in vitromodels of intestinal mucosa have become important tools for drug screening and studying the physiology and pathology of the intestine. These models enable the examination of cellular behavior in diseased states or in reaction to alterations in the microenvironment, potentially serving as alternatives to animal models. One of the major challenges in constructing physiologically relevantin vitromodels of intestinal mucosa is the creation of three-dimensional microstructures that accurately mimic the integration of intestinal epithelium and vascularized stroma. Here, core-shell alginate (Alg) microspheres were generated to create the compartmentalized extracellular matrix microenvironment needed to simulate the epithelial and vascularized stromal compartments of the intestinal mucosa. We demonstrated that NIH-3T3 and human umbilical vein endothelial cells embedded in the core of the microspheres can proliferate and develop a vascular network, while human colorectal adenocarcinoma cells (Caco-2) can form an epithelial monolayer in the shell. Compared to Caco-2 monolayer encapsulated within the shell, the presence of the vascularized stroma enhances their proliferation and functionality. As such, our core-shell Alg microspheres provide a valuable method for generatingin vitromodels of vascularized intestinal mucosa with epithelial and vascularized stroma arranged in a spatially relevant manner and demonstrating near-physiological functionality.

Semantically redundant training data removal and deep model classification performance: A study with chest X-rays.

Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest. Also, the common use of augmentation methods to generate variety in DL training could limit performance when indiscriminately applied to such data. We hypothesize that semantic redundancy would therefore tend to lower performance and limit generalizability to unseen data and question its impact on classifier performance even with large data. We propose an entropy-based sample scoring approach to identify and remove semantically redundant training data and demonstrate using the publicly available NIH chest X-ray dataset that the model trained on the resulting informative subset of training data significantly outperforms the model trained on the full training set, during both internal (recall: 0.7164 vs 0.6597, p<0.05) and external testing (recall: 0.3185 vs 0.2589, p<0.05). Our findings emphasize the importance of information-oriented training sample selection as opposed to the conventional practice of using all available training data.

[Summary and evaluation of randomized controlled trial on Chinese patent medicine in treatment of diabetic foot ulcer].

This study systematically collected, analyzed, and evaluated randomized controlled trial(RCT) in the treatment of diabetic foot ulcer(DFU). The aim as provide references for future studies and to enhance the application of clinical evidence. The RCT of DFU treated with Chinese Patent Medicine was obtained and analyzed using the AI-Clinical Evidence Database of Chinese Patent Medicine(AICED-CPM). The analysis was supplemented with data from CNKI, Wanfang, VIP, SinoMed, PubMed, EMbase, Cochrane Library, and Web of Science. A total of 275 RCTs meeting the requirements were retrieved, with only 7 of them having a sample size of 200 or more. These trials involved 66 different Chinese patent medicine including 25 oral medications, 24 Chinese herbal injections, and 17 external drugs. Among the 33 different intervention/control designs identified, the most common design was Chinese patent medicine + conventional treatment vs conventional treatment(86 cases, 31.27%). Out of the 275 articles included in the literature, 50 did not provide information on the specific course of treatment(18.18%). A total of 10 counting indicators(with a frequency of 426) and 36 measuring indicators(with a frequency of 962) were utilized. The methodological quality of the RCT for the treatment of DFU with Chinese patent medicine was found to be low, with deficiencies in blind methods, other bias factors, study registration, and sample size estimation. There were noticeable shortcomings in the reporting of allocation hiding and implementation bias(blind method application). More studies should prioritize trial registration, program design, and strict quality control during implementation to provide valuable data for clinical practice and serve as a reference for future investigations.

Unraveling the enigma: A comprehensive review of solid pseudopapillary tumor of the pancreas.

Solid pseudopapillary tumor of the pancreas (SPTP) is a rare neoplasm predominantly observed in young females. Pathologically, CTNNB1 mutations, ß-catenin nuclear accumulation, and subsequent Wnt-signaling pathway activation are the leading molecular features. Accurate preoperative diagnosis often relies on imaging techniques and endoscopic biopsies. Surgical resection remains the mainstay treatment. Risk models, such as the Fudan Prognostic Index, show promise as predictive tools for assessing the prognosis of SPTP. Establishing three types of metachronous liver metastasis can be beneficial in tailoring individualized treatment and follow-up strategies. Despite advancements, challenges persist in understanding its etiology, establishing standardized treatments for unresectable or metastatic diseases, and developing a widely recognized grading system. This comprehensive review aims to elucidate the enigma by consolidating current knowledge on the epidemiology, clinical presentation, pathology, molecular characteristics, diagnostic methods, treatment options, and prognostic factors.

Sagittal balance parameters measurement on cervical spine MR images based on superpixel segmentation.

Introduction: Magnetic Resonance Imaging (MRI) is essential in diagnosing cervical spondylosis, providing detailed visualization of osseous and soft tissue structures in the cervical spine. However, manual measurements hinder the assessment of cervical spine sagittal balance, leading to time-consuming and error-prone processes. This study presents the Pyramid DBSCAN Simple Linear Iterative Cluster (PDB-SLIC), an automated segmentation algorithm for vertebral bodies in T2-weighted MR images, aiming to streamline sagittal balance assessment for spinal surgeons. Method: PDB-SLIC combines the SLIC superpixel segmentation algorithm with DBSCAN clustering and underwent rigorous testing using an extensive dataset of T2-weighted mid-sagittal MR images from 4,258 patients across ten hospitals in China. The efficacy of PDB-SLIC was compared against other algorithms and networks in terms of superpixel segmentation quality and vertebral body segmentation accuracy. Validation included a comparative analysis of manual and automated measurements of cervical sagittal parameters and scrutiny of PDB-SLIC's measurement stability across diverse hospital settings and MR scanning machines. Result: PDB-SLIC outperforms other algorithms in vertebral body segmentation quality, with high accuracy, recall, and Jaccard index. Minimal error deviation was observed compared to manual measurements, with correlation coefficients exceeding 95%. PDB-SLIC demonstrated commendable performance in processing cervical spine T2-weighted MR images from various hospital settings, MRI machines, and patient demographics. Discussion: The PDB-SLIC algorithm emerges as an accurate, objective, and efficient tool for evaluating cervical spine sagittal balance, providing valuable assistance to spinal surgeons in preoperative assessment, surgical strategy formulation, and prognostic inference. Additionally, it facilitates comprehensive measurement of sagittal balance parameters across diverse patient cohorts, contributing to the establishment of normative standards for cervical spine MR imaging.

Loss of AMPK activity induces organelle dysfunction and oxidative stress during oocyte aging.

BACKGROUND: Oocyte quality is critical for the mammalian reproduction due to its necessity on fertilization and early development. During aging, the declined oocytes showing with organelle dysfunction and oxidative stress lead to infertility. AMP-activated protein kinase (AMPK) is a serine/threonine protein kinase which is important for energy homeostasis for metabolism. Little is known about the potential relationship between AMPK with oocyte aging. RESULTS: In present study we reported that AMPK was related with low quality of oocytes under post ovulatory aging and the potential mechanism. We showed the altered AMPK level during aging and inhibition of AMPK activity induced mouse oocyte maturation defect. Further analysis indicated that similar with its upstream regulator PKD1, AMPK could reduce ROS level to avoid oxidative stress in oocytes, and this might be due to its regulation on mitochondria function, since loss of AMPK activity induced abnormal distribution, reduced ATP production and mtDNA copy number of mitochondria. Besides, we also found that the ER and Golgi apparatus distribution was aberrant after AMPK inhibition, and enhanced lysosome function was also observed. CONCLUSIONS: Taken together, these data indicated that AMPK is important for the organelle function to reduce oxidative stress during oocyte meiotic maturation.

Optimizing prediction models for pancreatic fistula after pancreatectomy: Current status and future perspectives.

Postoperative pancreatic fistula (POPF) is a frequent complication after pancreatectomy, leading to increased morbidity and mortality. Optimizing prediction models for POPF has emerged as a critical focus in surgical research. Although over sixty models following pancreaticoduodenectomy, predominantly reliant on a variety of clinical, surgical, and radiological parameters, have been documented, their predictive accuracy remains suboptimal in external validation and across diverse populations. As models after distal pancreatectomy continue to be progressively reported, their external validation is eagerly anticipated. Conversely, POPF prediction after central pancreatectomy is in its nascent stage, warranting urgent need for further development and validation. The potential of machine learning and big data analytics offers promising prospects for enhancing the accuracy of prediction models by incorporating an extensive array of variables and optimizing algorithm performance. Moreover, there is potential for the development of personalized prediction models based on patient- or pancreas-specific factors and postoperative serum or drain fluid biomarkers to improve accuracy in identifying individuals at risk of POPF. In the future, prospective multicenter studies and the integration of novel imaging technologies, such as artificial intelligence-based radiomics, may further refine predictive models. Addressing these issues is anticipated to revolutionize risk stratification, clinical decision-making, and postoperative management in patients undergoing pancreatectomy.

Advancing Drug-Target Interaction prediction with BERT and subsequence embedding.

Exploring the relationship between proteins and drugs plays a significant role in discovering new synthetic drugs. The Drug-Target Interaction (DTI) prediction is a fundamental task in the relationship between proteins and drugs. Unlike encoding proteins by amino acids, we use amino acid subsequence to encode proteins, which simulates the biological process of DTI better. For this research purpose, we proposed a novel deep learning framework based on Bidirectional Encoder Representation from Transformers (BERT), which integrates high-frequency subsequence embedding and transfer learning methods to complete the DTI prediction task. As the first key module, subsequence embedding allows to explore the functional interaction units from drug and protein sequences and then contribute to finding DTI modules. As the second key module, transfer learning promotes the model learn the common DTI features from protein and drug sequences in a large dataset. Overall, the BERT-based model can learn two kinds features through the multi-head self-attention mechanism: internal features of sequence and interaction features of both proteins and drugs, respectively. Compared with other methods, BERT-based methods enable more DTI-related features to be discovered by means of attention scores which associated with tokenized protein/drug subsequences. We conducted extensive experiments for the DTI prediction task on three different benchmark datasets. The experimental results show that the model achieves an average prediction metrics higher than most baseline methods. In order to verify the importance of transfer learning, we conducted an ablation study on datasets, and the results show the superiority of transfer learning. In addition, we test the scalability of the model on the dataset in unseen drugs and proteins, and the results of the experiments show that it is acceptable in scalability.

Anionic Coordination Control in Building Cu-Based Electrocatalytic Materials for CO2 Reduction Reaction.

Renewable energy-driven conversion of CO2 to value-added fuels and chemicals via electrochemical CO2 reduction reaction (CO2RR) technology is regarded as a promising strategy with substantial environmental and economic benefits to achieve carbon neutrality. Because of its sluggish kinetics and complex reaction paths, developing robust catalytic materials with exceptional selectivity to the targeted products is one of the core issues, especially for extensively concerned Cu-based materials. Manipulating Cu species by anionic coordination is identified as an effective way to improve electrocatalytic performance, in terms of modulating active sites and regulating structural reconstruction. This review elaborates on recent discoveries and progress of Cu-based CO2RR catalytic materials enhanced by anionic coordination control, regarding reaction paths, functional mechanisms, and roles of different non-metallic anions in catalysis. Finally, the review concludes with some personal insights and provides challenges and perspectives on the utilization of this strategy to build desirable electrocatalysts.

Indole alkaloids isolated from the Nicotiana tabacum-derived Aspergillus fumigatus 0338 as potential inhibitors for tobacco powdery mildew and their mode of actions.

To explore active natural products against tobacco powdery mildew caused by Golovinomyces cichoracearum, an extract from the fermentation of endophytic Aspergillus fumigatus 0338 was investigated. The mechanisms of action for active compounds were also studied in detail. As a result, 14 indole alkaloid derivatives were isolated, with seven being newly discovered (1-7) and the remaining seven previously described (8-14). Notably, compounds 1-3 are rare linearly fused 6/6/5 tricyclic prenylated indole alkaloids, with asperversiamide J being the only known natural product of this kind. The isopentenyl substitutions at the 5-position in compounds 4 and 5 are also rare, with only compounds 1-(5-prenyl-1H-indol-3-yl)-propan-2-one (8) and 1-(6-methoxy-5-prenyl-1H-indol3-yl)-propan-2-one currently available. In addition, compounds 6 and 7 are new framework indole alkaloid derivatives bearing a 6-methyl-1,7-dihydro-2H-azepin-2-one ring. The purified compounds were evaluated for their activity against G. cichoracearum, and the results revealed that compounds 7 and 9 demonstrated obvious anti-G. cichoracearum activities with an inhibition rate of 82.6% and 85.2%, respectively, at a concentration of 250 µg/mL, these rates were better than that of the positive control agent, carbendazim (78.6%). The protective and curative effects of compounds 7 and 9 were also better than that of positive control, at the same concentration. Moreover, the mechanistic study showed that treatment with compound 9 significantly increased the structural tightness of tobacco leaves and directly affect the conidiospores of G. cichoracearum, thereby enhancing resistance. Compounds 7 and 9 could also induce systemic acquired resistance (SAR), directly regulating the expression of defense enzymes, defense genes, and plant semaphorins, which may further contribute to increased plant resistance. Based on the activity experiments and molecular dockings, the indole core structure may be the foundation of these compounds' anti-G. cichoracearum activity. Among them, the indole derivative parent structures of compounds 6, 7, and 9 exhibit strong effects. Moreover, the methoxy substitution in compound 7 can enhance their activity. By isolating and structurally identifying the above indole alkaloids, new candidates for anti-powdery mildew chemical screening were discovered, which could enhance the utilization of N. tabacum-derived fungi in pesticide development.