The combination of Aidi Injection(ADI) and doxorubicin(DOX) is a common strategy in the treatment of cancer, which can achieve synergistic anti-tumor effects while attenuating the cardiotoxicity caused by DOX. This study aims to investigate the mechanism of ADI in attenuating DOX-induced cardiotoxicity by multi-omics. DOX was used to induce cardiotoxicity in mice, and the cardioprotective effects of ADI were evaluated based on biochemical indicators and pathological changes. Based on the results, transcriptomics, proteomics, and metabolomics were employed to analyze the changes of endogenous substances in different physiological states. Furthermore, data from multiple omics were integrated to screen key regulatory pathways by which ADI attenuated DOX-induced cardiotoxicity, and important target proteins were selected for measurement by ELISA kits and immunohistochemical analysis. The results showed that ADI significantly reduced the levels of cardiac troponin T(cTnT) and N-terminal pro-B-type natriuretic peptide(NT-proBNP) and effectively ameliorated myocardial fibrosis and intracellular vacuolization, indicating that ADI showed therapeutic effect on DOX-induced cardiotoxicity. The transcriptomics analysis screened out a total of 400 differentially expressed genes(DEGs), which were mainly enriched in inflammatory response, oxidative stress, and myocardial fibrosis. After proteomics analysis, 70 differentially expressed proteins were selected, which were mainly enriched in the inflammatory response, cardiac function, and energy metabolism. A total of 51 differentially expressed metabolites were screened by the metabolomics analysis, and they were mainly enriched in multiple signaling pathways, including the inflammatory response, lipid metabolism, and energy metabolism. The integrated data of multiple omics showed that linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism pathways played an important role in DOX-induced cardiotoxicity, and ADI may exert therapeutic effects by modulating these pathways. Target validation experiments suggested that ADI significantly regulated abnormal protein levels of cyclooxygenase-1(COX-1), cyclooxygenase-2(COX-2), prostaglandin H2(PGH2), and prostaglandin D2(PGD2) in the model group. In conclusion, ADI may attenuate DOX-induced cardiotoxicity by regulating linoleic acid metabolism, arachidonic acid metabolism, and glycerophosphate metabolism, thus alleviating inflammation of the body.
Doxorubicin/toxicity* ; Animals ; Mice ; Cardiotoxicity/genetics* ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Proteomics ; Metabolomics ; Injections ; Humans ; Multiomics

The chemical constituents from Cephalotaxus lanceolata were isolated and purified by using multiple chromatographic techniques, including octadecylsilane(ODS), silica gel, Sephadex LH-20 column chromatography, and semi-preparative high-performance liquid chromatography(HPLC). A total of 17 compounds obtained were identified by using spectroscopic methods such as nuclear magnetic resonance(NMR), mass spectrometry(MS), and ultraviolet(UV) combined with literature data. Compound 1 was a new alkaloid dimer, named cephalancetine E. The known compounds were determined as cephalancetine A(2), 11-hydroxycephalotaxine(3), 4-hydroxycephalotaxine(4), cephalotaxine(5), epicephalotaxine(6), cephalotaxine β-N-oxide(7), acetylcephalotaxine(8), cephalotine A(9), cephalotine B(10), 11-hydroxycephalotaxine hemiketal(11), 3-deoxy-3,11-epoxy-cephalotaxine(12), cephalotaxinone(13), isocephalotaxinone(14), 2,11-epoxy-1,2-dihydro-8-oxo-cephalotaxine(15), cephalotaxamide(16), and drupacine(17), respectively. Compounds 11, 12, and 15 were isolated from the Cephalotaxus genus for the first time. The biological activity was tested for compounds 1-17. The results reveal that compound 17 displays potent inhibitory activities against three human cancer cell lines(HepG-2, MCF-7, and SH-SY5Y).
Cephalotaxus/chemistry* ; Humans ; Cell Line, Tumor ; Drugs, Chinese Herbal/pharmacology* ; Harringtonines/pharmacology* ; Molecular Structure ; Dimerization ; Alkaloids/isolation & purification* ; Magnetic Resonance Spectroscopy

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

OBJECTIVES: To explore the mediating role of sleep duration in the relationship between depression symptoms and myopia among middle school students. METHODS: This study was a cross-sectional research conducted using a stratified cluster random sampling method. A total of 1 728 middle school students were selected from two junior high schools and two senior high schools in certain urban areas and farms of the Xinjiang Production and Construction Corps. Questionnaire surveys and vision tests were conducted among the students. Spearman analysis was used to analyze the correlation between depression symptoms, sleep duration, and myopia. The Bootstrap method was employed to investigate the mediating effect of sleep duration between depression symptoms and myopia. RESULTS: The prevalence of myopia in the overall population was 74.02% (1 279/1 728), with an average sleep duration of (7.6±1.0) hours. The rate of insufficient sleep was 83.62% (1 445/1 728), and the proportion of students exhibiting depression symptoms was 25.29% (437/1 728). Correlation analysis showed significant negative correlations between visual acuity in both eyes and sleep duration with depressive emotions as measured by the Center for Epidemiologic Studies Depression Scale (with correlation coefficients of -0.064, -0.084, and -0.199 respectively; P<0.01), as well as with somatic symptoms and activities (with correlation coefficients of -0.104, -0.124, and -0.233 respectively; P<0.01) and interpersonal relationships (with correlation coefficients of -0.052, -0.059, and -0.071 respectively; P<0.05). The correlation coefficients for left and right eye visual acuity and sleep duration were 0.206 and 0.211 respectively (P<0.001). Sleep duration exhibited a mediating effect between depression symptoms and myopia (indirect effect=0.056, 95%CI: 0.029-0.088), with the mediating effect value for females (indirect effect=0.066, 95%CI: 0.024-0.119) being higher than that for males (indirect effect=0.042, 95%CI: 0.011-0.081). CONCLUSIONS Sleep duration serves as a partial mediator between depression symptoms and myopia in middle school students.
Humans ; Myopia/etiology* ; Male ; Female ; Depression/physiopathology* ; Cross-Sectional Studies ; Sleep ; Adolescent ; Students ; Child ; Time Factors ; Sleep Duration

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:A retrospective analysis was conducted on the clinical characteristics and polysomnography (PSG) features of patients with obstructive sleep apnea (OSA) of different ages.Methods:From January 2015 to March 2024, the patients who underwent overnight PSG monitoring at the Sleep Respiratory Disease Diagnosis and Treatment Center, Department of Otolaryngology, Head and Neck Surgery, Sixth Medical Center of the PLA General Hospital were sequentially enrolled.A total of 4 396 patients[aged from 18 to 97(46.04±12.60)years] with OSA who met the criteria were finally enrolled and divided into the youth group (18-44 years old, n=2 099), middle-aged group (45-59 years old, n=1 641), and elderly group (≥60 years old, n=656).The differences in general condition, Epworth sleepiness Scale (ESS) score, rapid eye movement sleep (REM) sleep time in total sleep time, micro-awakening index, apnea hypopnea index (AHI), minimum oxygen saturation at night (LSpO 2), oxygen hypoxia index (ODI) and so on were compared.Multivariate Logistic regression was used to analyze the relationship between age stratification and different severity of OSA (mild 5≤AHI≤15, moderate 1530, Times per hour). Results:The neck circumference [(39.11±3.71) cm], body mass index [BMI, (26.63±3.70) kg/m2], total sleep time [(386.13±66.09) min], the percentage of deep sleep in total sleep time [(3.05±4.80)%], the percentage of REM sleep time in total sleep time [(16.73±6.83)%], apnea-hypopnea index [AHI, (31.17±19.20) events/h], oxygen desaturation index [ODI, (46.57±33.54) events/h], microarousal index [(14.82±12.27) events/h], and Epworth Sleepiness Scale (ESS) score (8.29±5.92) in the elderly group were all lower than those in the young group [neck circumference (41.36±3.37) cm, BMI (28.55±4.21) kg/m2, total sleep time [(424.67±62.21) min], the percentage of deep sleep in total sleep time (5.89±6.79)%, the percentage of REM sleep time in total sleep time (17.95±6.67)%, AHI (39.55±27.89) events/h, ODI (61.88±34.60) events/h, microarousal index (22.77±19.31) events/h, ESS score (9.63±5.82)] and the middle-aged group [neck circumference (40.75±3.59) cm, BMI (27.89±3.55)kg/m2, total sleep time [(410.98±63.02)min], the percentage of deep sleep in total sleep time (3.59±5.11)%, the percentage of REM sleep time in total sleep time (17.47±6.63)%, AHI (36.01±23.63) events/h, ODI (57.20±34.75) events/h, microarousal index (19.23±15.59) events/h, ESS score (9.98±6.11)], with F values of 102.62, 62.15, 95.87, 95.94, 8.71, 29.60, 49.72, 59.11, and 19.03, respectively, and all P values were <0.001. The proportion of females [29.57% (94/656)], the proportion of patients with hypertension [37.20% (244/656)], and the nocturnal LSpO 2 [(79.09±9.88)%] in the elderly group were all higher than those in the young group [proportion of females [6.48% (136/2 099)], proportion of patients with hypertension 18.10% (380/2 099), nocturnal LSpO 2(75.19±12.90)%] and the middle-aged group [proportion of females [15.84%(260/1 641)], proportion of patients with hypertension 33.09% (543/1 641), nocturnal LSpO 2(76.15±12.44)%], with F values of 242.62, 150.90, and 25.05, respectively, and all P values were <0.001.There were no significant differences in neck circumference, percentage of REM sleep time in total sleep time, AHI and LSpO 2 at night between young and middle-aged groups ( P>0.05). After adjusting for gender, neck circumference, BMI, and hypertension confounders, the risk of severe OSA in the older group was increased by 27.4% compared with the younger group ( OR=1.274, 95% CI=1.036-1.566, P<0.05). Further stratified analysis showed that neck circumference and BMI were independent factors for severe OSA in the young and middle-aged groups ( P<0.01), while, only neck circumference was independently associated with severe OSA in the elderly group (OR=1.119, 95% CI=1.039-1.207, P<0.01). Conclusion:The severity of OSA in young and middle-aged patients is more serious than that in elderly patients. Obesity is an important factor affecting the degree of OSA in young and middle-aged patients. After controlling for confounding factors such as obesity, the risk of severe OSA in elderly patients is higher than that in young patients, which is considered to be related to factors such as pharyngeal collapse and relaxation of pharyngeal muscles.

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:A retrospective analysis was conducted on the clinical characteristics and polysomnography (PSG) features of patients with obstructive sleep apnea (OSA) of different ages.Methods:From January 2015 to March 2024, the patients who underwent overnight PSG monitoring at the Sleep Respiratory Disease Diagnosis and Treatment Center, Department of Otolaryngology, Head and Neck Surgery, Sixth Medical Center of the PLA General Hospital were sequentially enrolled.A total of 4 396 patients[aged from 18 to 97(46.04±12.60)years] with OSA who met the criteria were finally enrolled and divided into the youth group (18-44 years old, n=2 099), middle-aged group (45-59 years old, n=1 641), and elderly group (≥60 years old, n=656).The differences in general condition, Epworth sleepiness Scale (ESS) score, rapid eye movement sleep (REM) sleep time in total sleep time, micro-awakening index, apnea hypopnea index (AHI), minimum oxygen saturation at night (LSpO 2), oxygen hypoxia index (ODI) and so on were compared.Multivariate Logistic regression was used to analyze the relationship between age stratification and different severity of OSA (mild 5≤AHI≤15, moderate 1530, Times per hour). Results:The neck circumference [(39.11±3.71) cm], body mass index [BMI, (26.63±3.70) kg/m2], total sleep time [(386.13±66.09) min], the percentage of deep sleep in total sleep time [(3.05±4.80)%], the percentage of REM sleep time in total sleep time [(16.73±6.83)%], apnea-hypopnea index [AHI, (31.17±19.20) events/h], oxygen desaturation index [ODI, (46.57±33.54) events/h], microarousal index [(14.82±12.27) events/h], and Epworth Sleepiness Scale (ESS) score (8.29±5.92) in the elderly group were all lower than those in the young group [neck circumference (41.36±3.37) cm, BMI (28.55±4.21) kg/m2, total sleep time [(424.67±62.21) min], the percentage of deep sleep in total sleep time (5.89±6.79)%, the percentage of REM sleep time in total sleep time (17.95±6.67)%, AHI (39.55±27.89) events/h, ODI (61.88±34.60) events/h, microarousal index (22.77±19.31) events/h, ESS score (9.63±5.82)] and the middle-aged group [neck circumference (40.75±3.59) cm, BMI (27.89±3.55)kg/m2, total sleep time [(410.98±63.02)min], the percentage of deep sleep in total sleep time (3.59±5.11)%, the percentage of REM sleep time in total sleep time (17.47±6.63)%, AHI (36.01±23.63) events/h, ODI (57.20±34.75) events/h, microarousal index (19.23±15.59) events/h, ESS score (9.98±6.11)], with F values of 102.62, 62.15, 95.87, 95.94, 8.71, 29.60, 49.72, 59.11, and 19.03, respectively, and all P values were <0.001. The proportion of females [29.57% (94/656)], the proportion of patients with hypertension [37.20% (244/656)], and the nocturnal LSpO 2 [(79.09±9.88)%] in the elderly group were all higher than those in the young group [proportion of females [6.48% (136/2 099)], proportion of patients with hypertension 18.10% (380/2 099), nocturnal LSpO 2(75.19±12.90)%] and the middle-aged group [proportion of females [15.84%(260/1 641)], proportion of patients with hypertension 33.09% (543/1 641), nocturnal LSpO 2(76.15±12.44)%], with F values of 242.62, 150.90, and 25.05, respectively, and all P values were <0.001.There were no significant differences in neck circumference, percentage of REM sleep time in total sleep time, AHI and LSpO 2 at night between young and middle-aged groups ( P>0.05). After adjusting for gender, neck circumference, BMI, and hypertension confounders, the risk of severe OSA in the older group was increased by 27.4% compared with the younger group ( OR=1.274, 95% CI=1.036-1.566, P<0.05). Further stratified analysis showed that neck circumference and BMI were independent factors for severe OSA in the young and middle-aged groups ( P<0.01), while, only neck circumference was independently associated with severe OSA in the elderly group (OR=1.119, 95% CI=1.039-1.207, P<0.01). Conclusion:The severity of OSA in young and middle-aged patients is more serious than that in elderly patients. Obesity is an important factor affecting the degree of OSA in young and middle-aged patients. After controlling for confounding factors such as obesity, the risk of severe OSA in elderly patients is higher than that in young patients, which is considered to be related to factors such as pharyngeal collapse and relaxation of pharyngeal muscles.

Metabolic dysfunction-associated fatty liver disease (MAFLD) is an increasingly common liver disease worldwide. MAFLD is diagnosed based on the presence of steatosis on images, histological findings, or serum marker levels as well as the presence of at least one of the three metabolic features: overweight/obesity, type 2 diabetes mellitus, and metabolic risk factors. MAFLD is not only a liver disease but also a factor contributing to or related to cardiovascular diseases (CVD), which is the major etiology responsible for morbidity and mortality in patients with MAFLD. Hence, understanding the association between MAFLD and CVD, surveillance and risk stratification of MAFLD in patients with CVD, and assessment of the current status of MAFLD management are urgent requirements for both hepatologists and cardiologists. This Taiwan position statement reviews the literature and provides suggestions regarding the epidemiology, etiology, risk factors, risk stratification, nonpharmacological interventions, and potential drug treatments of MAFLD, focusing on its association with CVD.

Background/Aims: Despite the high efficacy of direct-acting antivirals (DAAs), approximately 1–3% of hepatitis C virus (HCV) patients fail to achieve a sustained virological response. We conducted a nationwide study to investigate risk factors associated with DAA treatment failure. Machine-learning algorithms have been applied to discriminate subjects who may fail to respond to DAA therapy. Methods: We analyzed the Taiwan HCV Registry Program database to explore predictors of DAA failure in HCV patients. Fifty-five host and virological features were assessed using multivariate logistic regression, decision tree, random forest, eXtreme Gradient Boosting (XGBoost), and artificial neural network. The primary outcome was undetectable HCV RNA at 12 weeks after the end of treatment. Results: The training (n=23,955) and validation (n=10,346) datasets had similar baseline demographics, with an overall DAA failure rate of 1.6% (n=538). Multivariate logistic regression analysis revealed that liver cirrhosis, hepatocellular carcinoma, poor DAA adherence, and higher hemoglobin A1c were significantly associated with virological failure. XGBoost outperformed the other algorithms and logistic regression models, with an area under the receiver operating characteristic curve of 1.000 in the training dataset and 0.803 in the validation dataset. The top five predictors of treatment failure were HCV RNA, body mass index, α-fetoprotein, platelets, and FIB-4 index. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of the XGBoost model (cutoff value=0.5) were 99.5%, 69.7%, 99.9%, 97.4%, and 99.5%, respectively, for the entire dataset. Conclusions Machine learning algorithms effectively provide risk stratification for DAA failure and additional information on the factors associated with DAA failure.