Sepsis-acquired weakness (SAW) is a common complication in critically ill patients, yet significant gaps remain in both mechanistic understanding and therapeutic interventions for this condition. SAW not only prolongs the duration of mechanical ventilation and hospitalization but is also closely associated with increased mortality. Even if these SAW patients survive, they often experience long-term physical dysfunction after hospital discharge, leading to diminished quality of life. Emerging evidence suggests that sustained mitochondrial dysfunction may constitute a pivotal pathophysiological basis for the development and progression of SAW, primarily encompassing five key aspects: dysregulated mitochondrial quality control (MtQC), impaired oxidative phosphorylation (OXPHOS), exacerbated oxidative stress, disrupted Ca²⁺; homeostasis, and their mediation of diverse myofiber injuries. This article systematically elucidates the central role of mitochondrial dysfunction in the pathogenesis of SAW. Furthermore, we explore potential therapeutic strategies targeting mitochondrial function, including mitigating mitochondrial oxidative stress, optimizing nutritional support, and supplementing with muscle-derived mesenchymal stem cells. These insights provide a critical theoretical framework for understanding SAW mechanisms and developing clinical interventions, with particular emphasis on the translational value of mitochondrial-targeted therapies in improving outcomes for septic patients.
Humans ; Sepsis/metabolism* ; Mitochondria/metabolism* ; Muscle Weakness/etiology* ; Oxidative Stress ; Oxidative Phosphorylation

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

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.

italic>Polygonum capitatum is a characteristic Miao medicine in Guizhou, commonly used in clinical practice to treat gastrointestinal and urinary tract infections. Research has found that it has good antibacterial and anti-inflammatory effects, and its main active ingredient is flavonoids. Lavonoid O-methyltransferase (FOMT) is a key enzyme for oxymethylation modification of flavonoid compounds. In order to understand the function and properties of FOMT protein in Polygonum capitatum, the transcriptome was sequenced by Illumina HiSeq 4000 high throughput sequencing technology. Then, the obtained transcripts were annotated and analyzed, and the whole genome of the FOMT gene family in Polygon capitalis was mined and identified. A total of 99 298 Unigenes were obtained, of which 71 514 were successfully annotated by the public database. In the genome of Polygonum capitatum, a total of 50 FOMT genes were identified. The phylogenetic tree showed that FOMT genes were divided into two subfamilies: caffeoyl CoA O-methyltransferase (CCoAOMT) and caffeic acid O-methyltransferase (COMT). Gene sequence analysis showed that the number of FOMT encoded amino acids ranged from 99 to 1 053 aa, the molecular weight ranged from 11 224.91 to 86 687.42 Da, and the isoelectric point ranged from 4.79 to 9.45. The 50 FOMT family members were hydrophobic proteins. Subcellular localization results showed that 54% of FOMT subfamily CCoAOMT and COMT members were located in cytoplasm and 28% were located in chloroplasts. The FOMT gene was tissue specific and highly expressed in the flowers of Polygonum capitatum, followed by the stems, and the least expressed in the roots. In this study, the FOMT gene family of Polygonum capitatum was identified and analyzed to provide theoretical basis for further study of FOMT function and biosynthesis of methylated flavonoids.

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.

Objective: To describe the distribution characteristics of hypertension among adult twins in the Chinese National Twin Registry (CNTR) and to provide clues for exploring the role of genetic and environmental factors on hypertension. Methods: A total of 69 220 (34 610 pairs) of twins aged 18 and above with hypertension information were selected from CNTR registered from 2010 to 2018. Random effect models were used to describe the population and regional distribution of hypertension in twins. To estimate the heritability, the concordance rates of hypertension were calculated and compared between monozygotic twins (MZ) and dizygotic twins (DZ). Results: The age of all participants was (34.1±12.4) years. The overall self-reported prevalence of hypertension was 3.8%(2 610/69 220). Twin pairs who were older, living in urban areas, married, overweight or obese, current smokers or ex-smokers, and current drinkers or abstainers had a higher self-reported prevalence of hypertension (P<0.05). Analysis within the same-sex twin pairs found that the concordance rate of hypertension was 43.2% in MZ and 27.0% in DZ, and the difference was statistically significant (P<0.001). The heritability of hypertension was 22.1% (95%CI: 16.3%- 28.0%). Stratified by gender, age, and region, the concordance rate of hypertension in MZ was still higher than that in DZ. The heritability of hypertension was higher in female participants. Conclusions: There were differences in the distribution of hypertension among twins with different demographic and regional characteristics. It is indicated that genetic factors play a crucial role in hypertension in different genders, ages, and regions, while the magnitude of genetic effects may vary.
Adult ; Female ; Humans ; Male ; Alcohol Drinking ; Diseases in Twins/genetics* ; Hypertension/genetics* ; Twins, Dizygotic/genetics* ; Twins, Monozygotic/genetics*

Objective: To describe the distribution characteristics of hyperlipidemia in adult twins in the Chinese National Twin Registry (CNTR) and explore the effect of genetic and environmental factors on hyperlipidemia. Methods: Twins recruited from the CNTR in 11 project areas across China were included in the study. A total of 69 130 (34 565 pairs) of adult twins with complete information on hyperlipidemia were selected for analysis. The random effect model was used to characterize the population and regional distribution of hyperlipidemia among twins. The concordance rates of hyperlipidemia were calculated in monozygotic twins (MZ) and dizygotic twins (DZ), respectively, to estimate the heritability. Results: The age of all participants was (34.2±12.4) years. This study's prevalence of hyperlipidemia was 1.3% (895/69 130). Twin pairs who were men, older, living in urban areas, married,had junior college degree or above, overweight, obese, insufficient physical activity, current smokers, ex-smokers, current drinkers, and ex-drinkers had a higher prevalence of hyperlipidemia (P<0.05). In within-pair analysis, the concordance rate of hyperlipidemia was 29.1% (118/405) in MZ and 18.1% (57/315) in DZ, and the difference was statistically significant (P<0.05). Stratified by gender, age, and region, the concordance rate of hyperlipidemia in MZ was still higher than that in DZ. Further, in within-same-sex twin pair analyses, the heritability of hyperlipidemia was 13.04% (95%CI: 2.61%-23.47%) in the northern group and 18.59% (95%CI: 4.43%-32.74%) in the female group, respectively. Conclusions: Adult twins were included in this study and were found to have a lower prevalence of hyperlipidemia than in the general population study, with population and regional differences. Genetic factors influence hyperlipidemia, but the genetic effect may vary with gender and area.
Adult ; Female ; Humans ; Male ; Middle Aged ; Young Adult ; China/epidemiology* ; Diseases in Twins/genetics* ; Hyperlipidemias/genetics* ; Metabolic Diseases ; Twins, Dizygotic ; Twins, Monozygotic/genetics*

italic>Ardisia crispa (Thunb.) A. DC. is a traditional Miao medicinal herb with significant therapeutic effects in the treatment of sore throat, tonsillitis, edema of nephritis and bruising and rheumatism, etc. Ardisia crispa var. amplifolia and Ardisia crispa var. dielsii are varieties of A. crispa. A. crispa var. amplifolia and A. crispa var. dielsii are controversial in terms of species evolutionary relationships and taxonomic identification. In this study, we sequenced the whole genome sequences of A. crispa var. amplifolia and A. crispa var. dielsii chloroplasts using Illumina platform, assembled, annotated and characterized them, compared the structural features and degree of variation among chloroplast genomes using bioinformatics methods, and also downloaded constructing phylogenetic trees to analyze the phylogenetic relationships of chloroplasts in Primulaceae and Myrsinaceae using whole genome sequence information. The results showed that the complete chloroplast genome sequences of A. crispa var. amplifolia and A. crispa var. dielsii were 156 749 bp and 156 748 bp in length, with 132 genes annotated, including 87 protein-coding genes; the codon preference of A/U was greater than that of G/C; The differences in the coding regions of rps15 and rpoB genes in the comparative genome analysis can be used as loci for molecular identification of the two species; the differences in the coding regions of ycf1, ycf2, rpoC1, ycf3, petD and rpl16 genes in the chloroplast genome compared with those of the same genus can be used as loci for identification of the genus. In the phylogenetic results, A. crispa var. amplifolia and A. crispa var. dielsii were clustered together with 100% support, indicating that they are closely related. In this research, we analyzed the chloroplast genome structure and phylogenetic relationships of A. crispa var. amplifolia and A. crispa var. dielsii, providing an important theoretical basis for their molecular identification, genetic variation, breeding and phylogenetic analysis.