Alzheimer’s disease (AD) is a neurodegenerative disorder characterized by progressive cognitive decline, functional impairment, and neuropsychiatric symptoms. It represents the most prevalent form of dementia among the elderly population. Accumulating evidence indicates that oxidative stress plays a pivotal role in the pathogenesis of AD. Notably, elevated levels of oxidative stress have been observed in the brains of AD patients, where excessive reactive oxygen species (ROS) can cause extensive damage to lipids, proteins, and DNA, ultimately compromising neuronal structure and function. Amyloid β‑protein (Aβ) has been shown to induce mitochondrial dysfunction and calcium overload, thereby promoting the generation of ROS. This, in turn, exacerbates Aβ aggregation and enhances tau phosphorylation, leading to the formation of two pathological features of AD: extracellular Aβ plaque deposition and intracellular neurofibrillary tangles (NFTs). These events ultimately culminate in neuronal death, forming a vicious cycle. The interplay between oxidative stress and these pathological processes constitutes a core link in the pathogenesis of AD. The signaling pathways mediating oxidative stress in AD include Nrf2, RCAN1, PP2A, CREB, Notch1, NF‑κB, ApoE, and ferroptosis. Nrf2 signaling pathway serves as a key regulator of cellular redox homeostasis, exerts important antioxidant capacity and protective effects in AD. RCAN1 signaling pathway, as a calcineurin inhibitor, and modulates AD progression through multiple mechanisms. PP2A signaling pathway is involved in regulating tau phosphorylation and neuroinflammation processes. CREB signaling pathway contributes to neuroplasticity and memory formation; activation of CREB improves cognitive function and reduce oxidative stress. Notch1 signaling pathway regulates neuronal development and memory, participates in modulation of Aβ production, and interacts with Nrf2 toco-regulate antioxidant activity. NF‑κB signaling pathway governs immune and inflammatory responses; sustained activation of this pathway forms “inflammatory memory”, thereby exacerbating AD pathology. ApoE signaling pathway is associated with lipid metabolism; among its isoforms, ApoE-ε4 significantly increases the risk of AD, leading to elevated oxidative stress, abnormal lipid metabolism, and neuroinflammation. The ferroptosis signaling pathway is driven by iron-dependent lipid peroxidation, and the subsequent release of lipid peroxidation products and ROS exacerbate oxidative stress and neuronal damage. These interconnected pathways form a complex regulatory network that regulates the progression of AD through oxidative stress and related pathological cascades. In terms of therapeutic strategies targeting oxidative stress, among the drugs currently used in clinical practice for AD treatment, memantine and donepezil demonstrate significant therapeutic efficacy and can improve the level of oxidative stress in AD patients. Some compounds with antioxidant effects (such asα-lipoic acid and melatonin) have shown certain potential in AD treatment research and can be used as dietary supplements to ameliorate AD symptoms. In addition, non-drug interventions such as calorie restriction and exercise have been proven to exerted neuroprotective effects and have a positive effect on the treatment of AD. By comprehensively utilizing the therapeutic characteristics of different signaling pathways, it is expected that more comprehensive multi-target combination therapy regimens and combined nanomolecular delivery systems will be developed in the future to bypass the blood-brain barrier, providing more effective therapeutic strategies for AD.

This study explores the mechanism of Guben Jiannao Liquid on Alzheimer's disease(AD) by regulating autophagy based on the liver kinase B1(LKB1)/adenosine monophosphate-activated protein kinase(AMPK)/mammalian target of rapamycin(mTOR) pathway. Male SD rats were randomly divided into the blank group, model group, low-dose and high-dose groups of Guben Jiannao Liquid, and rapamycin group, with 10 rats in each group. Except for the blank group, all other groups of rats were injected bilaterally in the hippocampus with β-amyloid(Aβ)_(1-42) to establish the AD model. The low-dose(6.21 g·kg~(-1)) and high-dose(12.42 g·kg~(-1)) groups of Guben Jiannao Liquid and rapamycin group(1 mg·kg~(-1)) were given the corresponding drugs by gavage, and the blank and model groups were given an equal volume of saline by gavage for four weeks. Morris water maze was used to test the learning and memory ability of rats in each group; hematoxylin-eosin(HE) and Nissl staining were used to observe the morphological and quantitative changes of neurons and Nissl bodies in the CA1 region of rat hippocampus; immunohistochemistry was utilized to detect Aβ-positive cell expression in the CA1 region of rat hippocampus; transmission electron microscopy was employed to observe ultrastructural changes in rat hippocampal tissue, and Western blot was used to examine the protein expression levels of LKB1, p-AMPK/AMPK, p-mTOR/mTOR, Beclin1, p62, and LC3-Ⅱ in the hippocampal tissue of the rats. The results showed that compared with those in the blank group, rats in the model group had elevated evasion latency and decreased number of platform transversal and residence time in the platform quadrant. The number of neurons in the hippocampal area was reduced, and the morphology was impaired. The average integral optical density value of Aβ-positive cells was elevated; the expression levels of LKB1, p-AMPK/AMPK, Beclin1, and LC3-Ⅱ were decreased, and the expression levels of p-mTOR/mTOR and p62 were increased. Compared with those in the model group, rats in the low-dose and high-dose groups of Guben Jiannao Liquid had shorter evasion latency, higher number of platform transversal, longer residence time in the platform quadrant, increased number of neurons, decreased expression of Aβ-positive cells and average integral optical density values, and increased number of autophagic lysosomes in hippocampal tissue. The expression levels of LKB1, Beclin1, and LC3-Ⅱ were elevated in the hippocampus of rats in the low-dose group of Guben Jiannao Liquid. The expression levels of LKB1, p-AMPK/AMPK, Beclin1, and LC3-Ⅱ were elevated in the hippocampal tissue of rats in the high-dose group of Guben Jiannao Liquid, and the expression levels of p-mTOR/mTOR and p62 were decreased. The findings suggest that Guben Jiannao Liquid can improve cognitive impairment in AD rats, and its mechanism of action may be related to the activation of the LKB1/AMPK/mTOR signaling pathway and the up-regulation of autophagy level.
Animals ; Alzheimer Disease/physiopathology* ; Male ; TOR Serine-Threonine Kinases/genetics* ; Autophagy/drug effects* ; Rats, Sprague-Dawley ; Protein Serine-Threonine Kinases/genetics* ; AMP-Activated Protein Kinases/genetics* ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Signal Transduction/drug effects* ; AMP-Activated Protein Kinase Kinases ; Humans ; Hippocampus/metabolism*

Caenorhabditis elegans,as an emerging model organism,has been widely used in multiple disciplines such as medicine,life science,and environmental science in recent years,due to its charac-teristics of short life cycle,clear genetic background,highly conserved evolution,complete genome analysis and excellent fitting between experimental data and human results.It also shows unique advan-tages in the field of toxicology.This paper summarizes its advantages in toxicological research starting from the biological characteristics of C.elegans,introduces the toxicological research methods and progress based on the C.elegans model,focuses on demonstrating its applications in environmental fo-rensic medicine and forensic toxicology,and looks forward to the application of the relevant results in the field of forensic science.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To develop a deep learning model based on fundus retinal images to improve the detection rate of mild cognitive impairment(MCI)in patients with coronary heart disease,achieve early intervention and improve prognosis.Methods The study was a single-center cross-sectional study that retrospectively included patients diagnosed with coronary heart disease(CHD)by coronary angiography(≥50％ stenosis of at least one coronary vessel)from Beijing Anzhen Hospital between November 2021 and December 2022.The whole data set was randomly divided into the training set and the testing set according to the ratio of 8∶2 for model development.After that,the patient data of the same center from January 2023 to April 2023 were included in the time verification method to verify the model.The diagnostic criteria for MCI were MMSE＜27 or MoCA＜26.Four kinds of convolutional neural network(CNN)architectures were used to train fundus images,and a comprehensive vision model of MCI detection was established through model integration.The area under the curve(AUC),sensitivity and specificity of the receiver operating curve(ROC)were used to evaluate the performance of the AI model.Results We collected 5 880 eligible fundus images from 3 368 CHD patients.Based on the results of the MMSE scale,the algorithm was labeled,including 2 898 males and 527 MCI patients.The AUC of the deep learning model in the test group is 0.733(95％CI 0.688-0.778),and the sensitivity of the algorithm in the test group is 0.577(95％CI 0.528-0.625)by using the operating point with the maximum sum of sensitivity and specificity.With a specificity of 0.758(95％CI 0.714-0.802),corresponding to a validated AUC of 0.710(95％CI 0.601-0.818).Based on the results of the MoCA scale,the algorithm labels 2 437 males and 1 626 MCI patients.The AUC of the deep learning model in the test group was 0.702(95％CI 0.671-0.733).The operating point with the maximum sum of sensitivity and specificity was selected,and the sensitivity of the algorithm was 0.749(95％CI 0.719-0.778)and the specificity was 0.561(95％CI 0.527-0.595),corresponding to the AUC value of the verification group was 0.674(95％CI 0.622-0.726).Conclusions The deep learning algorithm model based on fundus images has good diagnostic performance,and may be used as a new non-invasive,convenient and rapid screening method for MCI in CHD population.

Objective To explore the clinical characteristics and treatment scheme of periprosthetic joint infection(PJI)caused by Cutibacterium avidum(C.avidum).Methods The diagnosis and treatment process of a patient with PJI caused by C.avidum was summarized,and relevant literatures in the database were retrieved for review.Results A 65-year-old female patient with body mass index(BMI)of 31.1 kg/m2 underwent left humeral head prosthesis replacement surgery following a left proximal humerus fracture.Ten months after the surgery,the pa-tient exhibited poor wound healing and oozing,along with limited movement of the left shoulder joint,and was diag-nosed infection following shoulder arthroplasty.Patient underwent debridement of the infected lesion and removal of the prosthesis.The tissue,bone cement and prosthesis were cultured for C.avidum.Four literatures were re-trieved and screened,a total of 30 patients with PJI(28 cases hip joint infection and 2 cases shoulder joint infection)caused by C.avidum were reported through literature retrieval,and 78.6％(n=22)total hip arthroplasty(THA)surgeries were performed using direct anterior approach(DAA).The positive rate of preoperative joint fluid culture was 71.4％,29 cases underwent surgical combined with sensitive antimicrobials treatment.Except for one patient who had repeated infection and underwent three surgeries,other patients had a good prognosis.Conclusion PJI caused by C.avidum is mostly seen in THA patients who are obese and undergo DAA,with a few cases reported after shoulder arthroplasty.The high sensitivity of preoperative joint fluid culture provides an important basis for the development of surgical strategies and anti-infection protocols.

Objective To retrospectively analyze the research on the integrated medical and elderly care in China in re-cent ten years,and explore the research hotspots and future research trend of the integrated medical and elderly care.Methods Using the method of literature research,the journal articles with the theme of combination of medical care and nursing,integration of medical care and nursing or medical care and nursing service from 2013 to 2023 were searched in CNKI.The sources were lim-ited to Peking University Core,CSSCI,CSCD and AMI,and the selected articles were analyzed by CiteSpace.Results This ar-ticle takes 896 literatures as research objects,we found that the number of articles reached a peak in 2018,the higher number of articles issued by the institutions are medicine-related institutions.The high-frequency keywords are the integrated medical and elderly care,elderly services,senior citizens,elderly care institutions and elderly care model.The strongest emergent word de-tected is health care integration.Conclusion In the past ten years,domestic research on the integration of medical care and eld-erly care has shown diversification and innovation.Policy tools,pension insurance,community and rural pension,quality evalua-tion index system,influencing factors were the research focus of scholars.Future research needs to focus on the application of quantitative methods,empirical and practical analysis.

As artificial intelligence technology rapidly advances, its deployment within the medical sector presents substantial ethical challenges. Consequently, it becomes crucial to create a standardized, transparent, and secure framework for processing medical data. This includes setting the ethical boundaries for medical artificial intelligence and safeguarding both patient rights and data integrity. This consensus governs every facet of medical data handling through artificial intelligence, encompassing data gathering, processing, storage, transmission, utilization, and sharing. Its purpose is to ensure the management of medical data adheres to ethical standards and legal requirements, while safeguarding patient privacy and data security. Concurrently, the principles of compliance with the law, patient privacy respect, patient interest protection, and safety and reliability are underscored. Key issues such as informed consent, data usage, intellectual property protection, conflict of interest, and benefit sharing are examined in depth. The enactment of this expert consensus is intended to foster the profound integration and sustainable advancement of artificial intelligence within the medical domain, while simultaneously ensuring that artificial intelligence adheres strictly to the relevant ethical norms and legal frameworks during the processing of medical data.
Artificial Intelligence/legislation & jurisprudence* ; Humans ; Consensus ; Computer Security/standards* ; Confidentiality/ethics* ; Informed Consent/ethics*

Objective To investigate the risk factors for bronchopulmonary dysplasia(BPD)in twin preterm infants with a gestational age of<34 weeks,and to provide a basis for early identification of BPD in twin preterm infants in clinical practice.Methods A retrospective analysis was performed for the twin preterm infants with a gestational age of<34 weeks who were admitted to 22 hospitals nationwide from January 2018 to December 2020.According to their conditions,they were divided into group A(both twins had BPD),group B(only one twin had BPD),and group C(neither twin had BPD).The risk factors for BPD in twin preterm infants were analyzed.Further analysis was conducted on group B to investigate the postnatal risk factors for BPD within twins.Results A total of 904 pairs of twins with a gestational age of<34 weeks were included in this study.The multivariate logistic regression analysis showed that compared with group C,birth weight discordance of>25%between the twins was an independent risk factor for BPD in one of the twins(OR=3.370,95%CI:1.500-7.568,P<0.05),and high gestational age at birth was a protective factor against BPD(P<0.05).The conditional logistic regression analysis of group B showed that small-for-gestational-age(SGA)birth was an independent risk factor for BPD in individual twins(OR=5.017,95%CI:1.040-24.190,P<0.05).Conclusions The development of BPD in twin preterm infants is associated with gestational age,birth weight discordance between the twins,and SGA birth.

Methamphetamine (METH) abuse is associated with significant neurotoxicity, high addiction potential, and behavioral abnormalities. Recent studies have identified a connection between the gut microbiota and METH-induced neurotoxicity and behavioral disorders. However, the underlying causal mechanisms linking the gut microbiota to METH pathophysiology remain largely unexplored. In this study, we employed fecal microbiota transplantation (FMT) and antibiotic (Abx) intervention to manipulate the gut microbiota in mice administered METH. Furthermore, we supplemented METH-treated mice with short-chain fatty acids (SCFAs) and pioglitazone (Pio) to determine the protective effects on gut microbiota metabolism. Finally, we assessed the underlying mechanisms of the gut-brain neural circuit in vagotomized mice. Our data provide compelling evidence that modulation of the gut microbiome through FMT or microbiome knockdown by Abx plays a crucial role in METH-induced neurotoxicity, behavioral disorders, gut microbiota disturbances, and intestinal barrier impairment. Furthermore, our findings highlight a novel prevention strategy for mitigating the risks to both the nervous and intestinal systems caused by METH, which involves supplementation with SCFAs or Pio.