Abstract:《2021 年阿尔茨海默病事实和数据》（Alzheimer’s Association） 强调了阿尔茨海默病（Alzheimer’s disease，AD） 是最常见的痴呆症. 伴随人类老龄化的日益严重，AD已被列为美国的第六大死因，在我国已成为第五大死因，且死亡率呈逐年上升趋势. 《2019 年世界阿尔茨海默病报告：对痴呆症的态度》估计全球有超过5 000 万人患有痴呆症，预计到2050 年，这个数字可能增加到1.52 亿［1］ . 面对AD对老年人健康日益加重的危害，各国投入了大量的人力、财力、物力以推动不同学科，从不同角度开展对AD的基础和临床研究. 其研究热点出现了一系列的变化趋势，如重视突触可塑性与认知能力、脑的能量代谢与线粒体的结构、溶酶体与脑内蛋白质稳态等研究，包括神经元损伤与修复、自噬与神经元变性死亡、神经元与神经胶质细胞的协调、外泌体的结构与功能、慢性炎症等. 另一方面，大数据与影像、临床、组学等多模数据结合与分析，在AD的病理、诊断、干预和治疗方面的应用，促进了我们对AD的认识和理解.
本期《生物化学与生物物理进展》刊出了4 篇论文，从不同的角度探讨和研究了有关AD的发病机制和潜在的治疗方法. 过氧化物酶体增殖物激活受体（peroxisome proliferators-activated receptor，PPAR） 属于核受体，在中枢神经系统中表达，具有调节能量代谢、神经传递、氧化还原稳态、线粒体等功能. 吕明媞等［2］综述了过氧化物酶体增殖物激活受体的一个亚型，PPARα，在控制突触可塑性和调节认知功能中的重要作用及其与AD的关系.高君妍等［3］介绍了诱导神经再生的方法及其在治疗AD的作用机制，展望了通过诱导神经再生，从而有效地改善AD的症状的前景. 金宇等［4］探讨了基于遗传数据的机器学习在AD研究中的应用，其主要内容包括：遗传数据与影像、临床、组学等多模数据结合的AD诊断和预后等. 通过AD患者数据库、脑片观察、小鼠模型的分析，路亚岚等［5］发现细胞凋亡抑制家族蛋白Survivin，可能通过NF-κB-Survivin 轴抑制细胞的凋亡，从而有望在延缓AD病理进程中发挥一定的作用. 上述4 篇论文选题和内容属于当前AD研究的前沿，基于作者的专业视野或研究结果为相关领域的研究提供了新的思路和线索. 在此集成《阿尔茨海默病研究专题》，以飨读者.

Abstract:Alzheimer’s disease (AD) is a neurodegenerative disease with progressive and persistent cognition and memory destruction. Its main pathological features are β-amyloid (Aβ) deposition and neurofibril tangles formed by hyperphosphorylated Tau protein, which is becoming a serious global health problem. Peroxisome proliferators-activated receptors (PPARs) is a nuclear receptor that expresses in the central nervous system and regulates energy metabolism, neurotransmission, redox homeostasis, mitochondrial function and other physiological processes. PPARα, as one of the subtypes, plays an important role in the control of synaptic plasticity and neuronal function. In this review, we discussed the possibility of PPARα as a therapeutic target for AD treatment. PPARα can reduce the production of soluble amyloid precursor protein (sAPP) and Aβ by regulating β secretase-1 (BACE-1) and reduce the accumulation of reactive oxygen species (ROS) by regulating the function of mitochondria, thereby decreasing oxidative stress damage. PPARα can down-regulate inflammatory factors and lessen neuroinflammation. It can also decrease blood lipids, alleviate insulin resistance, and regulate lipids metabolism. PPARα, as a promising target for the treatment of AD, is of great significance to new treatment strategies for AD.

Abstract:Alzheimer?s disease (AD) is a neurodegenerative disease characterized by progressive cognitive impairment and memory loss in the central nervous system, which has become one of the most difficult problems and urgent to solve in geriatrics. However, the pathological mechanism of AD is still unclear, and there is no specific medicine for AD. Currently, the exploration of nerve regeneration in AD has gradually attracted increasing attention. Increasing BDNF or NGF expression by neurotrophic solution or adeno-associated virus can regulate the survival of nerve cells and the plasticity of synapses. AChEI drugs can inhibit the decomposition of ACh and activate the nAChRs receptor, which enhance growth, communication and survival of neurons. Brain stimulation techniques such as repetitive transcranial magnetic stimulation (rTMS) and transcranial direct current stimulation (tDCS) can activate synaptic activity in neuronal circuits. Exogenous NSCs transplantation or BDNF combined with NSCs transplantation can not only directly increase the number of neurons, but also indirectly improve pathology surroundings by stimulating the secretion of neurotrophic factors and exosomes. Studies have demonstrated that the treatment of neurotrophic solution, physical stimulation or stem cell transplantation can enhance adult neurogenesis in the brain, which is considered to be effective strategy to alleviate pathological symptoms as well as cognitive impairment of AD. However, the optimal intervention strategy and the quality of treatment need to be further evaluated. Our paper reviews the methods of inducing nerve regeneration and elucidates its therapeutic mechanism of AD, which may provide a theoretical basis for the implementation of nerve regeneration therapy.

Abstract:Alzheimer’s disease (AD), a neurodegenerative disease, is closely related to the genetic and environmental factors, about 70% of which are caused by the genetic factors, but its pathogenesis is still unclear. Along with the advent of high-throughput gene sequencing, the processing of genetic data using machine learning (ML) has become a hot spot. In this paper, the applications of ML in AD are mainly reviewed, including the diagnosis and prognosis of AD based on genetic data, the analysis of genetic variation of AD, the analysis of gene expression profile of AD, and the further development of ML for AD. Firstly, during the diagnosis and prognosis of AD, the genetic data combining with other modalities, such as imaging data, clinical data and histological data, would be greatly improved the accuracy of ML methods. It is valuable for the early diagnosis of AD, and effectively delays the progression of AD. Secondly, the application of ML in the analysis of genetic variation of AD, single nucleotide polymorphisms (SNPs) of new genes were dug out, and the pathogenic mechanism of AD was further explored. Thirdly, the analysis of gene expression profile of AD mainly focuses on the discovery of the pathways of genes which could provide the possibility of gene targets for AD therapy. In the future, the multi-level model of ML might be developed for high-quality, diverse and large data, and provide scientific strategies for exploring the pathogenesis of AD.

Abstract:As the increasing aging population globally, Alzheimer’s disease (AD) has been the most common type of neurodegenerative disease, characterized with progressive cognitive impairment. The main pathological features were senile plaques, neurofibrillary tangles and neurons and synapse loss. Inhibitor of apoptosis family proteins (IAPs) are a class of endogenous apoptosis inhibitors, and their function in the pathological process of AD has not been clear. In this study, IAPs protein expression were analyzed from AD patient databases, AD animal models and brain slice models. NFκB signaling pathway was detected by EMSA and immunoblotting. The results show that Survivin acts as a co-upregulated gene in all models including AD patients, AD mouse models and Aβ, okadaic acid and LPS induced brain slices. NFκB signaling pathway was significantly activated, and they exhibited the similar expression profile. Therefore, the cell apoptosis in AD progression may be inhibited by NFκB/IAPs axis. Survivin may be an important target for AD prevention and treatment.

Abstract:The synergy of hippocampus (HPC) and prefrontal cortex (PFC) plays key roles in memory processing. A large amount of evidence shows that PFC and HPC support the formation, consolidation and retrieval of episodic memory through the synchronous action of characteristic neural rhythms (theta, gamma, sharp wave ripples(SWRs) rhythms). Based on the study of the neural rhythms in the HPC-PFC network, this review summarizes the role of synchronous interaction of theta, gamma and SWRs rhythms between two brain regions in episodic memory. With the development of animal learning and memory, the HPC-PFC coherence of theta rhythm, as well as gamma rhythms, were significantly increased. These data indicated that HPC and PFC communicate closely through oscillatory information flow in this network when animals have strong memory capabilities. HPC SWRs during either sleep or awake rest were coordinated with cortical SWRs and some other low-frequency rhythms to promote memory consolidation, while awake SWRs can further promote memory retrieval. On the other hand, mental illness is often accompanied by learning and memory dysfunction. EEG studies based on humans and animals have found that the coupling of neural rhythms between HPC and PFC was disordered, which could be treated as an important indicator of pathological cognitive impairment. This review also summarizes the abnormal performance of neural rhythms in HPC-PFC network in schizophrenia and depression and their underlying mechanisms, providing objective evidence for the rapid diagnosis of psychiatric diseases in the future.

Abstract:Laser speckle contrast imaging (LSCI) is a powerful and simple non-scanning real-time hemodynamic imaging method, with the advantages of high spatial and temporal resolution, wide imaging field, high-speed imaging, low damage, relatively simple instrument structure. After decades of development, it already has had the ability to quantify flow changes with higher resolution. Although LSCI is limited to superficial tissue imaging due to the limitation of depth resolution, it has been playing an important role in the studies and clinical applications of biomedical fields such as dermatology and neurological disease research. This paper briefly introduces the basic principle, typical device and technical progress of LSCI, and reviews the recent progress in brain diseases such as stroke, drug addiction, Alzheimer's disease and other applications of brain science. Finally, we discuss the prospects for development of LSCI in the study of brain science.

Abstract:Biological samples play essential roles in biomedical studies, especially in “-omics” analyses. Recently, researchers have found that preanalytical effects and storage duration have direct effects on molecular biology-based procedures. The lack of guidelines for the collection, transport, and storage of samples could lead to the degradation of target molecules such as DNA, RNA, and proteins. These may also lead to inaccurate results in research laboratories. Therefore, sample quality should be assessed at the nucleic acid, metabolite, and protein levels depending on sample components, research objectives, and detection technologies. This review describes the specific biomarkers and proper tools for monitoring the quality of human blood in clinical laboratories. Genomic DNA (gDNA), messenger RNA (mRNA), cell-free DNA (cfDNA), and small noncoding RNA (miRNA) are the main nucleic-acid components of blood samples used in research. This review summarizes the techniques used for the purification, yield analysis, and integrity analysis of gDNA and total RNA, including UV spectrophotometric analysis, qubit fluorometric quantification, and agarose gel electrophoresis. The internal reference genes used in cfDNA and miRNA quality control are also listed. Quantitative real-time polymerase chain reaction analysis of reference genes is commonly conducted, and the results reflect the quantity and fragment integrity of cfDNA and miRNA. However, no effective markers have been used for mRNA quality control. Therefore, the development of effective biomarkers is required for quantifying mRNA and small RNA molecules. Moreover, effective markers can be used for detecting changes in mRNA expression levels in vitro. Regarding metabolism, nuclear magnetic resonance (NMR) spectroscopy is the most common analytical method for the study of small metabolic molecules in blood. Matching between the available NMR data and reference databases can be applied to assess significant changes in metabolites and to evaluate blood sample quality. In addition, both metabolites and proteins can be quantified through mass spectrometry (MS). Typically, they are first enriched through affinity interactions (e.g., chromatography, immunoaffinity, and magnetic approaches) and then quantified through MS. This review summarizes the metabolites, proteins, and peptides identified by MS as biomarkers for blood quality control. However, their accuracy and effectiveness have not been widely recognized. Because various techniques have been used and a series of markers have been identified, the stability and accuracy of these molecules still need to be verified for quality control. Moreover, due to the high cost of MS, these techniques are not widely used in laboratories. Therefore, in the future, new quality control parameters should be identified for blood sample assessment.

Abstract:In this paper, stable epileptiform discharges were induced on rat hippocampal slices by using the electrode array detection technique. To analyze and study the parameters of the hippocampal slices, the starting site of epileptiform discharges, the propagation direction and transmission rate, and the power spectral density of each frequency band during the inter-ictal discharges (IID) and ictal discharges (ID) when 130 Hz high-frequency stimulation (HFS) was applied to CA3 region. The results showed that HFS could effectively reduce the amplitude and duration of seizures, increase latency time, and inhibit the transition of epileptiform discharges from IID to ID. In conclusion, HFS resists epilepsy through promoting the inhibitory transmission system and resisting the excitatory connections between hippocampal neurons.

Abstract:Temperature is an important parameter in organisms. Accurate measurement of cellular temperature fluctuations in the metabolic process can provide valuable information for more in-depth exploration of the energy production and diffusion process of cells, thereby promoting the research of cancer and other diseases. In this study, integrated microchips were fabricated in batch based on Micro-Electro-Mechanical System and microfluidic technology, which can monitor temperature fluctuations in a microenvironment during the process of cell metabolism. The microchip is composed of a C-shaped “micro-dam” structure, a “micro-slit” for liquid flow, and an electrode structure, which can complete cell culture and temperature monitoring on a microchip. The microchip with adherent cells was placed in a constant temperature environment of 37°C, and the constant current method was used to continuously real-time monitor the temperature changes of the cells in the metabolic process. The chip has a total of 9 detection units, each of which was completely independent and applied for detecting multiple cells’ temperature fluctuation parallelly. The accuracy and precision of the microchip were respectively better than 0.013°C and ±0.014°C with 0.1 s response speed. The linear fitting parameter R² between temperature and resistance of Ti/Pt temperature sensors of different thicknesses was greater than 0.999. Different cells trapped by this microchip were cultured on chip and monitored under a constant temperature environment of (37±0.015)℃. The temperature fluctuation range of human lung adenocarcinoma cell (H1975) (0.173℃) during metabolism was greater than that of hepatic stellate cell (HSC) (0.127℃). The average temperature of cancer cells H1975 (37.001℃) is higher than that of normal HSCs (36.989℃). In conclusion, this integrated microchip provides a tool of real-time monitoring cell temperature variation for the study of cell physiology and pathology.

Abstract:We demonstrate the feasibility of a four-focus switch Fourier-domain optical coherence tomography system for whole eye segment imaging, and in vivo visual axis parameter (VAP) measurement. In this study, we use two rotating disks to switch the focus position synchronously, to expand the detection depth range. Three parallel glass plates of different thicknesses are placed on a rotating disk along the path of the detection beam, so as to switch the focus position of the probing beam from the cornea, the anterior and posterior of the lens to the retina. On the other synchronously rotating disk, the light path of the reference arm can simultaneously extend the detecting optical path to match the probing beam. This multistage focusing method can make the probing beam completely focused on each segment of the human eye during the course of signal collection. Subsequently, we measured VAP several times, and the results show that our four-focus switch FD-OCT system exhibits good repeatability. In conclusion, we prove theoretically and experimentally that the proposed four-focus switched FD-OCT system can realize whole eye segment imaging and VAP measurement, including central corneal thickness, anterior chamber depth, lens thickness, and axial length. Our method will be beneficial for measurements in ophthalmology.

Abstract:Single nucleotide polymorphism (SNP) profiling is a commonly used genetic tool for individual identification and ancestry inference in forensic genetics. This study collected ancestry informative SNPs (AISNPs) from literature and public libraries, and applied softmax regression, support vector machine and random forest, which were used to infer ancestry origins of Northern Han, Japanese and Korean, the three major populations in the North of East Asia. We analyzed 428 AISNPs in 103 northern Han samples and 104 Japanese samples from the 1 000 Genomes Project and 100 Korean samples from the Asian Diversity Project, using multiple linear regression collinearity diagnostics and random forest mean decrease accuracy to screen and optimize high-information AISNPs combinations which were used for ancestry inference linear and nonlinear prediction models, respectively. We constructed two discriminant models of softmax regression and support vector machine with 67-plex AISNPs and a random forest discriminant model with 42-plex AISNPs, achieving high-precision division of Northern Han, Japanese and Korean. The accuracy rates of the 5 times 10-fold cross-validation test of the softmax regression model, support vector machine model and random forest model were 95.19%, 95.77%, and 94.53%, respectively. The 67-plex and 42-plex AISNP prediction models established in this study can be used for genetic inference of the three major populations in the North of East Asia with high practical application value.