Mitophagy activation by rapamycin enhances mitochondrial function and cognition in 5×FAD mice.

Alzheimer's disease (AD) is the most prevalent form of dementia, characterized by severe mitochondrial dysfunction, which is an intracellular process that is significantly compromised in the early stages of AD. Mitophagy, the selective removal of damaged mitochondria, is a potential therapeutic strategy for AD. Rapamycin, a mammalian target of rapamycin (mTOR) inhibitor, augmented autophagy and mitigated cognitive impairment. Our study revealed that rapamycin enhances cognitive function by activating mitophagy, alleviating neuronal loss, and improving mitochondrial dysfunction in 5 ×FAD mice. Interestingly, the neuroprotective effect of rapamycin in AD were negated by treatment with 3-MA, a mitophagy inhibitor. Overall, our findings suggest that rapamycin ameliorates cognitive impairment in 5 ×FAD mice via mitophagy activation and its downstream PINK1-Parkin pathway, which aids in the clearance of amyloid-ß (Aß) and damaged mitochondria. This study reveals a novel mechanism involving mitophagy regulation underlying the therapeutic effect of rapamycin in AD. This study provides new insights and therapeutic targets for rapamycin in the treatment of AD. However, there are still some shortcomings in this topic; if we can further knock out the PINK1/Parkin gene in animals or use siRNA technology, we can further confirm the experimental results.

Multifocal multiview imaging and data compression based on angular-focal-spatial representation.

Multifocal multiview (MFMV) is an emerging high-dimensional optical data that allows to record richer scene information but yields huge volumes of data. To unveil its imaging mechanism, we present an angular-focal-spatial representation model, which decomposes high-dimensional MFMV data into angular, spatial, and focal dimensions. To construct a comprehensive MFMV dataset, we leverage representative imaging prototypes, including digital camera imaging, emerging plenoptic refocusing, and synthesized Blender 3D creation. It is believed to be the first-of-its-kind MFMV dataset in multiple acquisition ways. To efficiently compress MFMV data, we propose the first, to our knowledge, MFMV data compression scheme based on angular-focal-spatial representation. It exploits inter-view, inter-stack, and intra-frame predictions to eliminate data redundancy in angular, focal, and spatial dimensions, respectively. Experiments demonstrate the proposed scheme outperforms the standard HEVC and MV-HEVC coding methods. As high as 3.693 dB PSNR gains and 64.22% bitrate savings can be achieved.

Potential risk factors for mild cognitive impairment among patients with type 2 diabetes experiencing hypoglycemia.

AIMS: This study examined the association between hypoglycemia and mild cognitive impairment (MCI) among patients with type 2 diabetes mellitus (T2DM) and identified risk factors for MCI in patients with hypoglycemia. METHODS: In this retrospective study, 328 patients with T2DM were screened in 2019 and followed up in 2022. Cognitive performance was assessed using the Montreal Cognitive Assessment (MoCA). The diagnosis of MCI was based on established criteria. Risk ratio (RR) with 95 % confidence intervals (CI) was calculated to estimate the risk of MCI. Univariate and multivariate logistic regression analyses were conducted to identify risk factors for MCI in those with hypoglycemia. RESULTS: Patients with hypoglycemia had lower cognitive performance 3 years later. The RR of MCI was 2.221 (95 % CI 1.269-3.885). Multivariate logistic analysis showed that low grip strength, existing diabetic retinopathy (DR), and multiple hypoglycemia episodes were associated with higher odds of MCI in patients with hypoglycemia (adjusted odds ratio [OR] 0.909 [95 % CI 0.859-0.963]), 3.078 [95 % CI 1.158-12.358], and 4.642 [95 % CI 1.284-16.776], respectively, all P < 0.05). CONCLUSIONS: Hypoglycemia increased MCI risk among patients with T2DM. Low grip strength, DR, and multiple hypoglycemia episodes may be potential risk factors for hypoglycemia-associated MCI.

Activation of mitophagy improves cognitive dysfunction in diabetic mice with recurrent non-severe hypoglycemia.

Recurrent non-severe hypoglycemia (RH) in patients with diabetes might be associated with cognitive impairment. Previously, we found that mitochondrial dysfunction plays an important role in this pathological process; however, the mechanism remains unclear. The objective of this study was to determine the molecular mechanisms of mitochondrial damage associated with RH in diabetes mellitus (DM). We found that RH is associated with reduced hippocampal mitophagy in diabetic mice, mainly manifested by reduced autophagosome formation and impaired recognition of impaired mitochondria, mediated by the PINK1/Parkin pathway. The same impaired mitophagy initiation was observed in an in vitro high-glucose cultured astrocyte model with recurrent low-glucose interventions. Promoting autophagosome formation and activating PINK1/Parkin-mediated mitophagy protected mitochondrial function and cognitive function in mice. The results showed that impaired mitophagy is involved in the occurrence of mitochondrial dysfunction, mediating the neurological impairment associated with recurrent low glucose under high glucose conditions.

High dimensional optical data - varifocal multiview imaging, compression and evaluation.

Varifocal multiview (VFMV) is an emerging high-dimensional optical data in computational imaging and displays. It describes scenes in angular, spatial, and focal dimensions, whose complex imaging conditions involve dense viewpoints, high spatial resolutions, and variable focal planes, resulting in difficulties in data compression. In this paper, we propose an efficient VFMV compression scheme based on view mountain-shape rearrangement (VMSR) and all-directional prediction structure (ADPS). The VMSR rearranges the irregular VFMV to form a new regular VFMV with mountain-shape focusing distributions. This special rearrangement features prominently in enhancing inter-view correlations by smoothing focusing status changes and moderating view displacements. Then, the ADPS efficiently compresses the rearranged VFMV by exploiting the enhanced correlations. It conducts row-wise hierarchy divisions and creates prediction dependencies among views. The closest adjacent views from all directions serve as reference frames to improve the prediction efficiency. Extensive experiments demonstrate the proposed scheme outperforms comparison schemes by quantitative, qualitative, complexity, and forgery protection evaluations. As high as 3.17 dB gains of peak signal-to-noise ratio (PSNR) and 61.1% bitrate savings can be obtained, achieving the state-of-the-art compression performance. VFMV is also validated could serve as a novel secure imaging format protecting optical data against the forgery of large models.

Amine-modified polyionic liquid supports enhance the efficacy of PdNPs for the catalytic hydrogenation of CO2 to formate.

Palladium nanoparticles stabilised by aniline modified polymer immobilised ionic liquid is a remarkably active catalyst for the hydrogenation of CO2 to formate; the initial TOF of 500 h-1 is markedly higher than either unmodified catalyst or its benzylamine and N,N-dimethylaniline modified counterparts and is among the highest to be reported for a PdNP-based catalyst.

MOF-based heterogeneous catalysis in continuous flow via incorporation onto polymer-based spherical activated carbon supports.

We present an approach to harnessing the tuneable catalytic properties of complex nanomaterials for continuous flow heterogeneous catalysis by combining them with the scalable and industrially implementable properties of carbon pelleted supports. This approach, in turn, will enable these catalytic materials, which largely currently exist in forms unsuitable for this application (e.g. powders), to be fully integrated into large scale, chemical processes. A composite heterogeneous catalyst consisting of a metal-organic framework-based Lewis acid, MIL-100(Sc), immobilised onto polymer-based spherical activated carbon (PBSAC) support has been developed. The material was characterised by focused ion beam-scanning electron microscopy-energy dispersive X-ray analysis, powder X-ray diffraction, N2 adsorption, thermogravimetric analysis, atomic absorption spectroscopy, light scattering and crush testing with the catalytic activity studied in continuous flow. The mechanically robust spherical geometry makes the composite material ideal for application in packed-bed reactors. The catalyst was observed to operate without any loss in activity at steady state for 9 hours when utilised as a Lewis acid catalyst for the intramolecular cyclisation of (±)-citronellal as a model reaction. This work paves the way for further development into the exploitation of MOF-based continuous flow heterogeneous catalysis.

Disturbed hippocampal histidine metabolism contributes to cognitive impairment induced by recurrent nonsevere hypoglycemia in diabetes.

Hypoglycemia is a common adverse reaction to glucose-lowering treatment. Diabetes mellitus (DM) combined with recurrent nonsevere hypoglycemia (RH) can accelerate cognitive decline. Currently, the metabolic pattern changes in cognition-related brain regions caused by this combined effect of DM and RH (DR) remain unclear. In this study, we first characterized the metabolic profiles of the hippocampus in mice exposed to DR using non-targeted metabolomic platforms. Our results showed that DR induced a unique metabolic pattern in the hippocampus, and several significant differences in metabolite levels belonging to the histidine metabolism pathway were discovered. Based on these findings, in the follow-up experiment, we found that histidine treatment could attenuate the cognitive impairment and rescue the neuronal and synaptic damage induced by DR in the hippocampus, which are closely related to ameliorated mitochondrial injury. These findings provide new insights into the metabolic mechanisms of the hippocampus in the progression of DR, and l-histidine supplementation may be a potential metabolic therapy in the future.

Force field-inspired transformer network assisted crystal density prediction for energetic materials.

Machine learning has great potential in predicting chemical information with greater precision than traditional methods. Graph neural networks (GNNs) have become increasingly popular in recent years, as they can automatically learn the features of the molecule from the graph, significantly reducing the time needed to find and build molecular descriptors. However, the application of machine learning to energetic materials property prediction is still in the initial stage due to insufficient data. In this work, we first curated a dataset of 12,072 compounds containing CHON elements, which are traditionally regarded as main composition elements of energetic materials, from the Cambridge Structural Database, then we implemented a refinement to our force field-inspired neural network (FFiNet), through the adoption of a Transformer encoder, resulting in force field-inspired Transformer network (FFiTrNet). After the improvement, our model outperforms other machine learning-based and GNNs-based models and shows its powerful predictive capabilities especially for high-density materials. Our model also shows its capability in predicting the crystal density of potential energetic materials dataset (i.e. Huang & Massa dataset), which will be helpful in practical high-throughput screening of energetic materials.

Gradient Boosted Machine Learning Model to Predict H2, CH4, and CO2 Uptake in Metal-Organic Frameworks Using Experimental Data.

Predictive screening of metal-organic framework (MOF) materials for their gas uptake properties has been previously limited by using data from a range of simulated sources, meaning the final predictions are dependent on the performance of these original models. In this work, experimental gas uptake data has been used to create a Gradient Boosted Tree model for the prediction of H2, CH4, and CO2 uptake over a range of temperatures and pressures in MOF materials. The descriptors used in this database were obtained from the literature, with no computational modeling needed. This model was repeated 10 times, showing an average R2 of 0.86 and a mean absolute error (MAE) of ±2.88 wt % across the runs. This model will provide gas uptake predictions for a range of gases, temperatures, and pressures as a one-stop solution, with the data provided being based on previous experimental observations in the literature, rather than simulations, which may differ from their real-world results. The objective of this work is to create a machine learning model for the inference of gas uptake in MOFs. The basis of model development is experimental as opposed to simulated data to realize its applications by practitioners. The real-world nature of this research materializes in a focus on the application of algorithms as opposed to the detailed assessment of the algorithms.

Mitophagy disorder mediates cardiac deterioration induced by severe hypoglycemia in diabetic mice.

Severe hypoglycemia is closely related to adverse cardiovascular outcomes in patients with diabetes; however, the specific mechanism remains unclear. We previously found that severe hypoglycemia aggravated myocardial injury and cardiac dysfunction in diabetic mice, and that the mechanism of damage was related to mitochondrial oxidative stress and dysfunction. Based on the key regulatory role of mitophagy in mitochondrial quality control, this study aimed to further explore whether the myocardial damage caused by severe hypoglycemia is related to insufficient mitophagy and to clarify their underlying regulatory relationship. After severe hypoglycemia, mitochondrial reactive oxygen species increased, mitochondrial membrane potential and ATP content decreased, and pathological mitochondrial damage was aggravated in the myocardium of diabetic mice. This was accompanied by decreased mitochondrial biosynthesis, increased fusion, and downregulated PTEN-induced kinase 1 (PINK1)/Parkin-dependent mitophagy. Treating diabetic mice with the mitophagy activator and polyphenol metabolite urolithin A activated PINK1/Parkin-dependent mitophagy, reduced myocardial oxidative stress and mitochondrial damage associated with severe hypoglycemia, improved mitochondrial function, alleviated myocardial damage, and ultimately improved cardiac function. Thus, we provide insight into the prevention and treatment of diabetic myocardial injury caused by hypoglycemia to reduce adverse cardiovascular outcomes in patients with diabetes.

End-to-end varifocal multiview images coding framework from data acquisition end to vision application end.

The emerging data, varifocal multiview (VFMV) has an exciting prospect in immersive multimedia. However, the distinctive data redundancy of VFMV derived from dense arrangements and blurriness differences among views causes difficulty in data compression. In this paper, we propose an end-to-end coding scheme for VFMV images, which provides a new paradigm for VFMV compression from data acquisition (source) end to vision application end. VFMV acquisition is first conducted in three ways at the source end, including conventional imaging, plenoptic refocusing, and 3D creation. The acquired VFMV has irregular focusing distributions due to varying focal planes, which decreases the similarity among adjacent views. To improve the similarity and the consequent coding efficiency, we rearrange the irregular focusing distributions in descending order and accordingly reorder the horizontal views. Then, the reordered VFMV images are scanned and concatenated as video sequences. We propose 4-directional prediction (4DP) to compress the reordered VFMV video sequences. Four most similar adjacent views from the left, upper left, upper and upper right directions serve as reference frames to improve the prediction efficiency. Finally, the compressed VFMV is transmitted and decoded at the application end, benefiting potential vision applications. Extensive experiments demonstrate that the proposed coding scheme is superior to the comparison scheme in objective quality, subjective quality and computational complexity. Experiments on new view synthesis show that VFMV can achieve extended depth of field than conventional multiview at the application end. Validation experiments show the effectiveness of view reordering, the advantage over typical MV-HEVC, and the flexibility on other data types, respectively.

Enhancing Molecular Representations Via Graph Transformation Layers.

Molecular representation learning is an essential component of many molecule-oriented tasks, such as molecular property prediction and molecule generation. In recent years, graph neural networks (GNNs) have shown great promise in this area, representing a molecule as a graph composed of nodes and edges. There are increasing studies showing that coarse-grained or multiview molecular graphs are important for molecular representation learning. Most of their models, however, are too complex and lack flexibility in learning different granular information for different tasks. Here, we proposed a flexible and simple graph transformation layer (i.e., LineEvo), a plug-and-use module for GNNs, which enables molecular representation learning from multiple perspectives. The LineEvo layer transforms fine-grained molecular graphs into coarse-grained ones based on the line graph transformation strategy. Especially, it treats the edges as nodes and generates the new connected edges, atom features, and atom positions. By stacking LineEvo layers, GNNs can learn multilevel information, from atom-level to triple-atoms level and coarser level. Experimental results show that the LineEvo layers can improve the performance of traditional GNNs on molecular property prediction benchmarks on average by 7%. Additionally, we show that the LineEvo layers can help GNNs have more expressive power than the Weisfeiler-Lehman graph isomorphism test.

Force field-inspired molecular representation learning for property prediction.

Molecular representation learning is a crucial task to accelerate drug discovery and materials design. Graph neural networks (GNNs) have emerged as a promising approach to tackle this task. However, most of them do not fully consider the intramolecular interactions, i.e. bond stretching, angle bending, torsion, and nonbonded interactions, which are critical for determining molecular property. Recently, a growing number of 3D-aware GNNs have been proposed to cope with the issue, while these models usually need large datasets and accurate spatial information. In this work, we aim to design a GNN which is less dependent on the quantity and quality of datasets. To this end, we propose a force field-inspired neural network (FFiNet), which can include all the interactions by incorporating the functional form of the potential energy of molecules. Experiments show that FFiNet achieves state-of-the-art performance on various molecular property datasets including both small molecules and large protein-ligand complexes, even on those datasets which are relatively small and without accurate spatial information. Moreover, the visualization for FFiNet indicates that it automatically learns the relationship between property and structure, which can promote an in-depth understanding of molecular structure.

Low FT3/FT4 Ratio Is Linked to Poor Prognosis of Acute Myocardial Infarction in Euthyroid Patients with Type 2 Diabetes Mellitus.

This is an observational, retrospective, single-center study aimed to determine whether the free triiodothyronine (FT3) to free thyroxine (FT4) ratio was related to acute myocardial infarction (AMI) prognosis in individuals with type 2 diabetes mellitus (T2DM). A total of 294 euthyroid T2DM patients with new-onset AMI were enrolled. FT3/FT4 ratio tertiles were used to categorize patients into Group 1 (FT3/FT4 ≥ 4.3), Group 2 (3.5 ≤ FT3/FT4 < 4.3), and Group 3 (FT3/FT4 < 3.5). Major adverse cardiac events (MACE), including nonfatal myocardial infarction, target vessel revascularization (TVR), and cardiac mortality, served as the primary endpoint. Group 3 demonstrated a considerably higher incidence of MACE than the other two groups over the average follow-up duration of 21 ± 6.5 months (all p < 0.001). Multivariable Cox regression analysis showed that a low FT3/FT4 ratio was an independent risk factor for MACE after AMI (Group 1 as a reference; Group 2: hazard ratio [HR] 1.275, 95% confidence interval [CI]: 0.563−2.889, p = 0.561; Group 3: HR 2.456, 95% CI: 1.105−5.459, p = 0.027). Moreover, the area under the receiver-operating characteristic curve (AUC) indicates a good predictive value of FT3/FT4 ratio for MACE (AUC = 0.70). Therefore, in T2DM patients with AMI, a low FT3/FT4 ratio was strongly linked to poor prognosis.

Transition State Theory-Inspired Neural Network for Estimating the Viscosity of Deep Eutectic Solvents.

The lack of accurate methods for predicting the viscosity of solvent materials, especially those with complex interactions, remains unresolved. Deep eutectic solvents (DESs), an emerging class of green solvents, have a severe lack of viscosity data, resulting in their application still staying at the stage of random trial and error, and it is difficult for them to be implemented on an industrial scale. In this work, we demonstrate the successful prediction of the viscosity of DESs based on the transition state theory-inspired neural network (TSTiNet). The TSTiNet adopts multilayer perceptron (MLP) for the transition state theory-inspired equation (TSTiEq) parameters calculation and verification using the most comprehensive DESs viscosity data set to date. For the energy parameters of the TSTiEq, the constant assumption and the fast iteration with the help of MLP can allow TSTiNet to achieve the best performance (the average absolute relative deviation on the test set of 6.84% and R 2 of 0.9805). Compared with the traditional machine learning methods, the TSTiNet has better generalization ability and dramatically reduces the maximum relative deviation of prediction under the constraints of the thermodynamic formulation. It requires only the structural information on DESs and is the most accurate and reliable model available for DESs viscosity prediction.

Continuous Synthesis of Uniformly Dispersed Mesoporous SBA-15 Supported Silver Nanoparticles in a Coiled Flow Inverter Reactor.

Mesoporous silica supported nanocatalysts have shown great potential in industrial processes due to their unique properties, such as high surface area, large pore volume, good chemomechanical stability and so on. Controllable and tunable synthesis of supported nanocatalysts is a crucial problem. Continuous synthesis of supported nanoparticles has been reported to get uniformly dispersed nanomaterials. Here, a method for continuous synthesis of uniformly dispersed mesoporous SBA-15 supported silver nanoparticles in a coiled flow inverter (CFI) microreactor is described. Compared to Ag/SBA-15 synthesized in the conventional batch reactor and Ag synthesized in continuous flow, mesoporous silica nanocatalysts synthesized in continuous flow are found to have smaller average size (7-11 nm) and narrower size distribution. The addition of capping agents can effectively change the characteristic of catalysts. Moreover, two kinds of support with different surface area and pore size have been added into the continuous synthesis. This method can provide further understandings for the synthesis of uniformly dispersed supported nanocatalysts in continuous flow, especially for mesoporous nanomaterials, which provides the possibilities of large-scale yield process of supported nanocatalysts in industry.

Continuous microfluidic synthesis of zirconium-based UiO-67 using a coiled flow invertor reactor.

Metal-organic frameworks (MOFs), particularly Zirconium based, have a wide variety of potential applications, such as catalysis and separation. However, these are held back by traditionally only being synthesised in long batch reactions, which causes the process to be expensive and limit the amount of reaction control available, leading to potential batch to batch variation in the products, such as particle size distributions. Microfluidics allows for batch reactions to be performed with enhanced mass/heat transfer, with the coiled flow inverter reactor (CFIR) setup narrowing the residence time distribution, which is key in controlling the particle size and crystallinity. In this work, a Zirconium based MOF, UiO-67, has been synthesised continuously using a microfluidic CFIR, which has allowed for the product to be formed in 30 min, a fraction of the traditional batch heating time of 24 h. The microfluidicially synthesised UiO-67 is also smaller product with a narrower particle size distribution (≈200 nm to ≈400 nm) than its batch counterpart (~500 nm to over 3 µm).

Dynamic Modification of Palladium Catalysts with Chain Alkylamines for the Selective Hydrogenation of Alkynes.

Selective hydrogenation of alkynes plays a pivotal role in the field of chemical production but still suffers from restrained catalytic activity and low alkene selectivity. Herein, a dynamic modification strategy was utilized by preferentially attaching diethylenetriamine (DETA) to the surface of the support to modify the Pd catalyst. The DETA-modified Pd catalyst demonstrates unprecedented reactivity (14,412 h-1) and selectivity as high as 94% for the semihydrogenation of 2-methyl-3-butyn-2-ol at 35 °C, presenting a 36-fold higher reactivity than the Lindlar catalyst. Moreover, the yield exceeds 98.2% at full conversion under no solvent and organic adsorbate conditions, indicating the potential applications for industrial production. Systematic studies reveal that flexible DETA serves in a reversible "breathing pattern" for the molecular discrimination by constructing dynamic metal-support interaction (DMSI), enabling selective exclusion of alkenes from the Pd surface. DETA is competent to dynamically adjust the adsorption behaviors of reactants and effectively boost the intrinsic activity of the modified catalyst. Impressively, the DETA-modified Pd catalyst exhibits exceptional stability even after being recycled 20 times. This work sheds light on a novel and applicable method for the rational design of heterogeneous catalysts via DMSI.

Correlation between Thyroid Homeostasis and Obesity in Subclinical Hypothyroidism: Community-Based Cross-Sectional Research.

OBJECTIVE: It remains unknown whether obesity has an effect on the pituitary-thyroid feedback control axis in subclinical hypothyroidism (SCH). We aimed to investigate the association of thyroid homeostasis with obesity in a SCH population. METHODS: Our study consisted of a community-based and cross-sectional study from the Epidemiological Survey of Thyroid Diseases in Fujian Province, China. A total of 193 subjects with SCH (90 males and 103 females) without a history of treatment of thyroid disease, such as surgery, radiation, and thyroid hormone or antithyroid medication, were included in the present study. Indices of obesity, including body mass index (BMI), waist circumference (WC), and waist-height ratio (WHtR) were measured. RESULTS: Our results showed that the secretory capacity of the thyroid gland (SPINA-GT) and Jostel's thyrotropin index (TSHI) were negatively correlated with BMI, WC, and WHtR, whereas the reciprocal of the thyrotroph thyroid hormone resistance index (TTSI-1) was positively correlated with BMI (all p < 0.05). After adjustment for age, sex, smoking, iodine status, and glucolipid metabolism, the associations between TSHI, TTSI (reciprocal transformation), and BMI still persisted (all p < 0.05). CONCLUSIONS: These results suggest that low levels of thyroid homeostasis indexes may be associated with overall obesity in SCH, rather than central adiposity.