BACKGROUND:In recent years,postmenopausal osteoporosis has been widely concerned by the society.With the gradual deepening of the research on animal models,animal models have become an important means of modern traditional Chinese medicine research as well as the experimental basis.Therefore,it is particularly important to understand the current status,hotspots,and development trend of the research on animal models.OBJECTIVE:To analyze the research status,hotspots,and development trend of animal models of postmenopausal osteoporosis at home and abroad,and to provide a certain theoretical foundation and reference basis for subsequent research.METHODS:Using"Subject=Postmenopausal osteoporosis AND Subject line=Mouse+Rabbit+Dog+Pig+Sheep+Monkey+Fish+Laboratory animals+Animal experimentation"in Chinese as the search formula,we searched for relevant literature published from January 1,1999 to October 1,2023 in CNKI and WanFang databases.Meanwhile,the articles published from January 1,1999 to October 1,2023 in the Web of Science Core Collection database were searched using"TS=(Postmenopausal osteoporosis)AND TS=(mouse OR mice OR rat OR rabbit OR dog OR swine OR pig OR sheep OR monkey OR fish OR flies OR"laboratory animal"OR"experiment animal"as the search formula.CiteSpace software was applied to visualize and analyze the authors,institutions,countries,keywords,and literature co-citations.RESULTS AND CONCLUSION:(1)After analyzing and screening,1 238 documents were included in the CNKI and WanFang databases,and 3 419 documents were included in the Web of Science Core Collection database.The overall number of articles issued since 1999 has been on an upward trend,with the highest research centrality in the United States,the highest number of articles issued in China.Moreover,the institution with the highest research centrality is the University of California.(2)After removing the keywords directly related to the article topics and synthesizing the co-occurrence frequency and centrality,"biomechanics,""Zuo Gui Wan,""alendronate,""breast cancer,"and"biochemical indicators"are in the core of the research field.(3)According to the analysis of co-citations,among the top 10 cited documents,5 of them are related to the treatment measures and clinical efficacy of postmenopausal osteoporosis.(4)Based on the analysis of keywords and co-cited literature,investigations on osteoblast formation and the mechanism of bone formation,the mechanism of metabolomics,the treatment of traditional Chinese medicine,signaling pathway,parathyroid hormone treatment,and sclerostin antibody treatment are the hot research topics now and the future research trends.

The aim of this research was to investigate the mechanism of chelidonine on H2 O2-in-duced inflammatory injury in porcine intestinal epithelial cells(IPEC-J2)through transcriptome sequencing.IPEC-J2 cells in the logarithmic growth phase were divided into the blank group(K group),H2O2 group(S group)and chelidonine group(L group),with three replicates in each group.Total RNA was isolated from each group for the purpose of constructing a sequencing li-brary.The assembled data underwent functional annotation,differential gene analysis,as well as GO and KEGG enrichment analyses.qPCR was used to confirm the expression of key differentially expressed genes(DEGs),and ELISA was utilized to assess the effect of chelidonine on the permea-bility of IPEC-J2 cells.The results indicated that the sequencing data met the necessary criteria and demonstrated a strong correlation between samples.The GO functional annotation results suggest that the intervention effects of chelidonine involve biological processes such as oxidative stress re-sponse and G2/M phase transition regulation of the mitotic cycle,and are closely associated with molecular functions,such as transmembrane transport activity.The KEGG enrichment analysis indicates that following H2 O2 treatment,DEGs in IPEC-J2 cells are predominantly enriched in the p53 signaling pathway,the coagulation cascade,the FoxO signaling pathway,and various other sig-naling pathways.Following pretreatment with chelidonine,the DEGs exhibit significant enrichment in several signaling pathways related to inflammation,including the TNF signaling pathway,syn-aptic vesicle cycle,and IL-17 signaling pathway.The results of qPCR were consistent with the se-quencing results.Chelidonine has also been found to effectively inhibit LDH release,elevate GLN content,and decrease DOA content.In conclusion,it can be seen that chelidonine can reduce cell permeability and alleviate H2 O2-induced inflammatory injury in IPEC-J2 cells by modulating in-flammation-related pathways such as the TNF signaling pathway.

ObjectiveTransfer RNA-derived fragments (tRFs) are a recently characterized and rapidly expanding class of small non-coding RNAs, typically ranging from 13 to 50 nucleotides in length. They are derived from mature or precursor tRNA molecules through specific cleavage events and have been implicated in a wide range of cellular processes. Increasing evidence indicates that tRFs play important regulatory roles in gene expression, primarily by interacting with target messenger RNAs (mRNAs) to induce transcript degradation, in a manner partially analogous to microRNAs (miRNAs). However, despite their emerging biological relevance and potential roles in disease mechanisms, there remains a significant lack of computational tools capable of systematically predicting the interaction landscape between tRFs and their target mRNAs. Existing databases often rely on limited interaction features and lack the flexibility to accommodate novel or user-defined tRF sequences. The primary goal of this study was to develop a machine learning based prediction algorithm that enables high-throughput, accurate identification of tRF:mRNA binding events, thereby facilitating the functional analysis of tRF regulatory networks. MethodsWe began by assembling a manually curated dataset of 38 687 experimentally verified tRF:mRNA interaction pairs and extracting seven biologically informed features for each pair: (1) AU content of the binding site, (2) site pairing status, (3) binding region location, (4) number of binding sites per mRNA, (5) length of the longest consecutive complementary stretch, (6) total binding region length, and (7) seed sequence complementarity. Using this dataset and feature set, we trained 4 distinct machine learning classifiers—logistic regression, random forest, decision tree, and a multilayer perceptron (MLP)—to compare their ability to discriminate true interactions from non-interactions. Each model’s performance was evaluated using overall accuracy, receiver operating characteristic (ROC) curves, and the corresponding area under the ROC curve (AUC). The MLP consistently achieved the highest AUC among the four, and was therefore selected as the backbone of our prediction framework, which we named tRF Prospect. For biological validation, we retrieved 3 high-throughput RNA-seq datasets from the gene expression omnibus (GEO) in which individual tRFs were overexpressed: AS-tDR-007333 (GSE184690), tRF-3004b (GSE197091), and tRF-20-S998LO9D (GSE208381). Differential expression analysis of each dataset identified genes downregulated upon tRF overexpression, which we designated as putative targets. We then compared the predictions generated by tRF Prospect against those from three established tools—tRFTar, tRForest, and tRFTarget—by quantifying the number of predicted targets for each tRF and assessing concordance with the experimentally derived gene sets. ResultsThe proposed algorithm achieved high predictive accuracy, with an AUC of 0.934. Functional validation was conducted using transcriptome-wide RNA-seq datasets from cells overexpressing specific tRFs, confirming the model’s ability to accurately predict biologically relevant downregulation of mRNA targets. When benchmarked against established tools such as tRFTar, tRForest, and tRFTarget, tRF Prospect consistently demonstrated superior performance, both in terms of predictive precision and sensitivity, as well as in identifying a higher number of true-positive interactions. Moreover, unlike static databases that are limited to precomputed results, tRF Prospect supports real-time prediction for any user-defined tRF sequence, enhancing its applicability in exploratory and hypothesis-driven research. ConclusionThis study introduces tRF Prospect as a powerful and flexible computational tool for investigating tRF:mRNA interactions. By leveraging the predictive strength of deep learning and incorporating a broad spectrum of interaction-relevant features, it addresses key limitations of existing platforms. Specifically, tRF Prospect: (1) expands the range of detectable tRF and target types; (2) improves prediction accuracy through multilayer perceptron model; and (3) allows for dynamic, user-driven analysis beyond database constraints. Although the current version emphasizes miRNA-like repression mechanisms and faces challenges in accurately capturing 5'UTR-associated binding events, it nonetheless provides a critical foundation for future studies aiming to unravel the complex roles of tRFs in gene regulation, cellular function, and disease pathogenesis.

Alzheimer's disease (AD), a prototypical neurodegenerative disorder, encompasses multifaceted pathological processes. As pivotal cellular structures within the central nervous system, ion channels play critical roles in regulating neuronal excitability, synaptic transmission, and neurotransmitter release. Extensive research has revealed significant alterations in the expression and function of ion channels in AD, implicating an important role of ion channels in the pathogenesis of abnormal Aβ deposition, neuroinflammation, oxidative stress, and disruptions in calcium homeostasis and neural network functionality. This review systematically summarizes the crucial roles and underlying mechanisms of ion channels in the onset and progression of AD, highlighting how these channel abnormalities contribute to AD pathophysiology. We also discuss the therapeutic potential of ion channel modulation in AD treatment, emphasizing the importance of addressing multifactorial nature and heterogeneity of AD. The development of multi-target drugs and precision therapies is proposed as a future direction of scientific research.
Alzheimer Disease/therapy* ; Humans ; Ion Channels/physiology* ; Oxidative Stress ; Animals ; Amyloid beta-Peptides/metabolism* ; Synaptic Transmission ; Calcium/metabolism*

Dopamine, as a catecholamine neurotransmitter widely distributed in the central nervous system, is involved in physiological functions such as motivation, arousal, reinforcement, and movement through various dopamine signaling pathways. The hippocampus receives dopaminergic neuron projections from regions such as the ventral tegmental area, locus coeruleus, and substantia nigra. Through D1-like and D2-like receptors, dopamine exerts significant regulatory effects such as spatial navigation, episodic memory, fear, anxiety, and reward. This review mainly summarizes the research progress on the functions of dopamine in the hippocampus from aspects including the sources of dopamine, receptor distribution and function, and the association of hippocampal dopamine system dysregulation with neurodegenerative diseases. The aim is to provide insights into the involvement of the dopamine system in hippocampal functions and the diagnosis and treatment of related diseases.
Hippocampus/physiology* ; Dopamine/physiology* ; Humans ; Animals ; Receptors, Dopamine D2/physiology* ; Memory/physiology* ; Signal Transduction/physiology* ; Neurodegenerative Diseases/physiopathology*

This review delves into the pivotal role of necrotic apoptosis in Alzheimer's disease(AD),with a focus on treatment strategies,drug development,prospects,and challenges,highlighting its significance in the progression of the disease.Firstly,necrotic apoptosis plays a crucial role in the pathogenesis of AD,particularly in association with the abnormal metabolism of β-amyloid(Aβ)and Tau proteins.The primary focus of drug design is to regulate the metabolism pathways of these two proteins to slow down or inhibit the progression of necrotic apoptosis.Secondly,the progress in drug development further emphasizes the importance of necrotic apoptosis in treating AD.Current research mainly focuses on drugs that affect the metabolism of Aβ and Tau proteins,such as lecanemab.Still,inconsistent result underscore the necessity for a more comprehensive understanding of the molecular mechanisms of necrotic apoptosis.Finally,the prospects and challenges of necrotic apoptosis research in AD are thoroughly discussed.A deeper understanding of necrotic apoptosis contributes to a better comprehension of the pathological mechanisms of AD but also may reveal new therapeutic targets.However,challenges such as multifactorial influences and the selection of treatment timing necessitate further in-depth research in the future.In conclusion,this review advocates for future research to deepen the understanding of the molecular mechanisms of necrotic apoptosis,enhance research on treatment strategies,gain a deeper understanding of its cross-regulation with other cell death pathways,and promote collaboration between basic research and clinical practice to advance the comprehensive understanding and treatment of Alzheimer's disease and necrotic apoptosis.

Anthoceros angustus Steph. is rich in phenolic acids such as rosmarinic acid (RA). Phenylalanine ammonia-lyase (PAL) is an entry enzyme in the phenylpropanoid pathway of plants, playing an important role in the biosynthesis of RA. To investigate the important role of PAL in rosmarinic acid synthesis, two PAL genes (designated as AanPAL1 and AanPAL2) were cloned from A. angustus, encoding 755 and 753 amino acid residues, respectively. The AanPAL deduced amino acid sequences contain the conserved domains of PAL and the core active amino acid residues Ala-Ser-Gly. The phylogenetic analysis indicated that AanPAL1 and AanPAL2 were clustered with PALs from bryophytes and ferns and had the shortest evolutionary distance with the PALs from Physcomitrella patens. Quantitative real-time PCR results showed that the expression of AanPAL1 and AanPAL2 was induced by exogenous methyl jasmonate (MeJA). HPLC results showed that the MeJA treatment significantly increased the accumulation of RA. AanPAL1 and AanPAL2 were expressed in Escherichia coli and purified by histidine-tag affinity chromatography. The recombinant proteins catalyzed the conversion of L-phenylalanine to generate trans-cinnamic acid with high efficiency, with the best performance at 50 ℃ and pH 8.0. The K_m and k_cat of AanPAL1 were 0.062 mmol/L and 4.35 s^-1, and those of AanPAL2 were 0.198 mmol/L and 14.48 s^-1, respectively. The specific activities of AanPAL1 and AanPAL2 were 2.61 U/mg and 8.76 U/mg, respectively. The two enzymes had relatively poor thermostability but good pH stability. The high activity of AanPAL2 was further confirmed via whole-cell catalysis with recombinant E. coli, which could convert 1 g/L L-phenylalanine into trans-cinnamic acid with a yield of 100% within 10 h. These results give insights into the regulatory role of AanPAL in the biosynthesis of RA in A. angustus and provide candidate enzymes for the biosynthesis of cinnamic acid.
Phenylalanine Ammonia-Lyase/metabolism* ; Cloning, Molecular ; Cinnamates/metabolism* ; Recombinant Proteins/metabolism* ; Rosmarinic Acid ; Depsides/metabolism* ; Escherichia coli/metabolism* ; Amino Acid Sequence ; Plant Proteins/metabolism* ; Phylogeny ; Acetates/pharmacology* ; Cyclopentanes ; Oxylipins

Objectives:The aim is to analyze the cost structure and coefficient of variation for hospitalized patients with malnutrition based on Diagnosis-Related Groups(DRG),providing a reference for the further application and promotion of DRG.Method:Data were collected from patients admitted to Ningxia Hui Autonomous Region People's Hospital between March 2023 and August 2023.A diagnostic system based on artificial intelligence was used to identify malnourished patients.The composition of hospitalization costs for these individuals was described and analyzed,as was the coefficient of variation for various costs within DRG groupings.A multivariate regression analysis was conducted to identify the factors that influence patient hospitalization costs.Results:The average age of hospitalized patients with malnutrition was(68.12±16.43)years,with an average length of stay of(14.55±8.47)days,with an average hospitalization cost of(32 128.89±35 345.61)yuan.Among patients within the same DRG group,the coefficient of variation for various costs was found to be lower in the malnutrition group than in the normal group.This suggests that when assessed individually,the malnutrition group exhibited a higher degree of homogeneity in their cost structures.The factors influencing total hospitalization costs were found to be:length of hospital stay(P=0.001),nutritional monitoring fees(P=0.020),number of chronic diseases(P=0.003),and Karnofsky Performance Status(KPS)score(P=0.038).Hospitalization costs were positively correlated with both length of stay and nutritional assessment fees,but negatively correlated with the number of chronic diseases and KPS scores.Conclusions:Malnutrition has a profound impact on health outcomes,medical expenses,length of hospital stay,and disease severity.The implementation of the DRG system aims to standardize and improve the nutritional diagnosis and treatment process by categorizing different stages of malnutrition.This approach can minimize variations within DRG groups,making it easier to allocate medical resources more precisely and efficiently.Furthermore,it is a valuable reference tool for promoting DRG payment reform in different regions.

Objective To explore the effects of chronic stress and stress cessation on hypothalamic appetite regulators in mice,and to explore the stress-dependent mechanism of appetite change.Methods A total of 32 male C57BL/6 mice were randomly divided into control(Ctrl)group(n=16)and chronic unpredictable mild stress(CUMS)group(n=16).The mice in the CUMS group were given CUMS to establish the stress model,and those in the Ctrl group were fed normally.The food intake and weight of mice were recorded.The CUMS model was verified through tail suspension experiments and forced swimming experiments.Eight mice in the Ctrl group and 8 mice in the CUMS group were randomly sacrificed at the 12th week.The Ctrl group was re-grouped into the cessation-control(C-Ctrl)group(n=8),the CUMS group was re-grouped into the cessation-stress(C-CUMS)group(n=8),and the mice were sacrificed at the 15th week.The mRNA and protein levels of appetite-regulating factors,including orexin 1 receptor(OX1R),leptin receptor(LEPR)and agouti-related protein(AgRP)in the hypothalamus,were detected by quantitative polymerase chain reaction and immunochemistry.Results From week 2 to week 11 of stress,the food intake of the mice in the CUMS group was significantly higher than that in the Ctrl group(all P＜0.05),while there was no significant difference in body weight between the 2 groups within 11 weeks(all P＞0.05).Compared with the Ctrl group,the immobility durations of forced swimming and tail suspension of the CUMS group were markedly longer after 11 weeks(both P＜0.01),indicating successful modeling.AgRP and OX1R mRNA expression in the hypothalamus of the CUMS group was significantly increased(both P＜0.01),while LEPR mRNA expression was significantly decreased(P＜0.01);AgRP protein in the hypothalamic arcuate nucleus of the CUMS group was significantly higher than that of the Ctrl group(P＜0.05),and LEPR protein was markedly lower than that of the Ctrl group(P＜0.01).However,after 3 weeks of stress cessation,the C-CUMS group had less food intake and lower body weight than the C-Ctrl group(both P＜0.05).The LEPR mRNA of the C-CUMS group was significantly increased(P＜0.01),while AgRP and OX1R mRNA were not significantly different(both P＞0.05).There was no significant difference in AgRP protein levels between the C-CUMS group and the C-Ctrl group(P＞0.05),while LEPR protein level of the C-CUMS group was significantly higher than that of the C-Ctrl group(P＜0.01).Conclusion CUMS can lead to increased appetite in mice,which may involve the functional regulation of LEPR and AgRP.After the stress cessation,the appetite decreases,which may involve the functional regulation of LEPR.

Objective To analyze the epidemic status of breast cancer globally and in China based on gender(female),age,and human development index(HDI)according to the Global cancer statistics 2022 published by the International Agency for Research on Cancer.Methods The epidemiological data of breast cancer from 185 countries and territories were sourced from the GLOBOCAN 2022 database.The HDI data were compiled based on the Human development report 2022 published by the United Nations Development Programme.The epidemiological data were stratified by age,gender,and HDI to describe the prevalence of breast cancer globally and in China.Pearson correlation analysis was used to evaluate the correlation of standardized incidence rate(SIR),standardized mortality rate(SMR)and mortality-to-incidence ratio(M/I)with HDI.Results The data of 175 countries and territories were included.The number of breast cancer incident cases worldwide was 2 297 000 in 2022,ranking the 2nd only to lung cancer,with an SIR of 46.8 per 100 000(ranking the 1st in the world).The number of breast cancer deaths globally was 666 000,ranking the 4th among all cancer deaths,with an SMR of 12.7 per 100 000(ranking the 2nd worldwide).In China,the number of breast cancer incident cases was 357 000,ranking the 6th among all cancers,accounting for 15.5％of the global breast cancers,with an SIR of 33.0 per 100 000(ranking the 2nd among all cancers in China).The number of breast cancer deaths in China was 75 000,ranking the 7th among all cancer deaths,accounting for 11.3％of the global breast cancer deaths,with an SMR of 6.1 per 100 000(ranking the 6th among all cancer deaths in China).For females around the world,the number of breast cancer incidents and deaths,SIR,and SMR ranked the 1st among all cancers.For Chinese females,both the incident number of breast cancer and SIR ranked the 2nd,while the deaths and SMR ranked the 5th and the 4th,respectively.SIR was positively correlated with HDI(r=0.76,P＜0.01),M/I was negatively correlated with HDI(r=-0.89,P＜0.01),while SMR was not correlated with HDI(r=-0.09,P=0.23).The incidence of breast cancer worldwide and in China increased with age from 25 to 29 years old.The number of breast cancer deaths in the world increased with age from 30 years old,peaked at 50-59 years old,and declined after 70 years old.The breast cancer deaths in China showed 2 small peaks at the age of 50-54 and 65-69 years old,and gradually decreased after 70 years old.The age-dependent trend of SIR varied in different countries and territories with different HDI levels,as well as globally and in China,whereas SMR continued to increase with age.Conclusion The disease burden of breast cancer is serious globally and in China.Especially in females,both SIR and SMR rank the 1st in the world;although they rank the 2nd and the 4th,respectively,in China,the number of incidents and deaths cannot be ignored.HDI is positively correlated with SIR and negatively correlated with M/I,however,with no correlation with SMR.The SIR in countries and territories with different HDI levels changes differently with age,while SMR increases with age in all.