Background Microparticles (MPs) are one of the main medium of inflammatory reaction with an important role in atherosclerotic progression. Studies on association of air pollution exposure and levels of peripheral blood MPs are limited among human. Objective To evaluate the effects of short-term exposure to air pollution on levels of peripheral blood MPs. Method A panel of 73 healthy adults was followed with 4 repeated follow-ups in Beijing, China, from November 2014 to January 2016. During each visit, we collected questionnaire information, fasting venous blood, urine, and exposures to fine particulate matter (PM_2.5), black carbon, nitric oxide, nitrogen dioxide, nitrogen oxide, sulfur dioxide, carbon monoxide, and ozone. We used linear mixed-effect models to analyze associations of air pollution exposure with levels of total MPs (TMPs) and MPs derived from various cells. Stratified analysis was conducted by levels of C-reactive protein (CRP) and malondialdehyde (MDA). Results The results showed significant associations between air pollution exposure and peripheral blood TMPs at 2 h-6 d prior to the follow-ups (P<0.05), while no statistical associations were found for MPs derived from different cell types. Significant increases in TMPs of 7.8% (95%CI: 0.7%, 15.3%) and 14.3% (95%CI: 2.8%, 27.2%) were observed with each interquartile range (IQR) increase in PM_2.5 (IQR=64.9 μg·m⁻³) at prior 18 h and NO (IQR=40.5 μg·m⁻³) at prior 48 h. Among participants with low levels of CRP and MDA, significantly positive associations were observed between air pollution exposure and levels of TMPs (P<0.05). Conclusion Short-term exposure to air pollution is significantly associated with increased levels of circulating MPs in healthy adults, and in people with lower systemic inflammation, peripheral blood MPs levels are more easily affected after exposure to air pollutants.

Background/Aims: Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy. Methods: We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues. Results: We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts. Conclusion Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.

OBJECTIVE: To explore the feasibility of incorporating simple bedside indicators into death predictive model for elderly critically ill patients based on interpretability machine learning algorithms, providing a new scheme for clinical disease assessment. METHODS: Elderly critically ill patients aged ≥ 65 years who were hospitalized in the intensive care unit (ICU) of Tacheng People's Hospital of Ili Kazak Autonomous Prefecture from June 2017 to May 2020 were retrospectively selected. Basic parameters including demographic characteristics, basic vital signs and fluid intake and output within 24 hours after admission, as well acute physiology and chronic health evaluation II (APACHE II), Glasgow coma score (GCS) and sequential organ failure assessment (SOFA) were also collected. According to outcomes in hospital, patients were divided into survival group and death group. Four datasets were constructed respectively, namely baseline dataset (B), including age, body temperature, heart rate, pulse oxygen saturation, respiratory rate, mean arterial pressure, urine output volume, infusion volume, and crystal solution volume; B+APACHE II dataset (BA), B+GCS dataset (BG), and B+SOFA dataset (BS). Then three machine learning algorithms, Logistic regression (LR), extreme gradient boosting (XGboost) and gradient boosting decision tree (GBDT) were used to develop the corresponding mortality predictive models within four datasets. The feature importance histogram of each prediction model was drawn by SHapley additive explanation (SHAP) method. The area under curve (AUC), accuracy and F1 score of each model were compared to determine the optimal prediction model and then illuminate the nomogram. RESULTS: A total of 392 patients were collected, including 341 in the survival group and 51 in the death group. There were statistically significant differences in heart rate, pulse oxygen saturation, mean arterial pressure, infusion volume, crystal solution volume, and etiological distribution between the two groups. The top three causes of death were shock, cerebral hemorrhage, and chronic obstructive pulmonary disease. Among the 12 prognostic models trained by three machine learning algorithms, overall performance of prognostic models based on B dataset was behind, whereas the LR model trained by BA dataset achieved the best performance than others with AUC of 0.767 [95% confidence interval (95%CI) was 0.692-0.836], accuracy of 0.875 (95%CI was 0.837-0.903) and F1 score of 0.190. The top 3 variables in this model were crystal solution volume with first 24 hours, heart rate and mean arterial pressure. The nomogram of the model showed that the total score between 150 and 230 were advisable. CONCLUSION The interpretable machine learning model including simple bedside parameters combined with APACHE II score could effectively identify the risk of death in elderly patients with critically illness.
Humans ; Critical Illness ; Machine Learning ; Aged ; Algorithms ; Intensive Care Units ; Retrospective Studies ; APACHE ; Prognosis ; Organ Dysfunction Scores ; Hospital Mortality ; Male ; Female

BACKGROUND: There is a gap in understanding the effects of different acupoints and treatment methods (acupuncture and moxibustion) on microcirculatory changes in the lumbar region. OBJECTIVE: This study aimed to assess the thermal effects of acupuncture at Weizhong (BL40), with acupuncture at Chize (LU5) and moxibustion at both acupoints as control interventions. DESIGN, SETTING, PARTICIPANTS AND INTERVENTIONS: In this randomized controlled trial, 140 healthy participants were equally divided into four groups: acupuncture at BL40 (Acu-BL40), acupuncture at LU5 (Acu-LU5), moxibustion at BL40 (Mox-BL40) and moxibustion at LU5 (Mox-LU5). Participants underwent a 30-minute session of their assigned treatment. Infrared thermal imaging was used to collect temperature data on the areas of interest for analysis. MAIN OUTCOME MEASURES: The primary measure was the change in average temperature of the observed area after the intervention. The secondary measures included periodic temperature changes every 5 min and the temperature changes of the Governor Vessel and Bladder Meridian in the observed area after the intervention. RESULTS: Significant interactions were observed between treatments and acupoints affecting temperature (P < 0.001). The Acu-BL40 group showed a notably higher increase in mean temperature after 30 min compared to the Acu-LU5 and Mox-BL40 groups, with increases of 0.29 (95% confidence interval [CI] = 0.17 to 0.41) and 0.24 (95% CI = 0.08 to 0.41) °C, respectively. CONCLUSION: Acupuncture at BL40 acupoint can significantly increase the mean temperature in the observed area, highlighting the specific thermal effect of acupuncture compared to moxibustion in the lumbar area. This suggests a potential therapeutic benefit of acupuncture at BL40 for managing lumbar conditions. TRIAL REGISTRATION ClinicalTrials.gov (NCT05665426). Please cite this article as: Zheng SY, Wang XY, Lin LN, Liu S, Huang XX, Liu YY, Yu XS, Pan W, Fang JQ, Liang Y. Lumbar temperature change after acupuncture or moxibustion at Weizhong (BL40) or Chize (LU5) in healthy adults: A randomized controlled trial. J Integr Med. 2025; 23(2): 145-151.
Adult ; Female ; Humans ; Male ; Young Adult ; Acupuncture Points ; Acupuncture Therapy ; Body Temperature ; Healthy Volunteers ; Lumbosacral Region/physiology* ; Moxibustion ; Adolescent

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

This study aims to systematically review and evaluate the quality of clinical research on the treatment of adenomyosis(AM) with traditional Chinese medicine(TCM) in recent ten years, using evidence graphs. Computer searches were conducted on eight Chinese and English databases, commonly used guideline databases, and guideline-related websites, covering the period from January 1, 2014, to October 1, 2024. Two researchers independently screened, extracted information, and evaluated the quality of the evidence. The distribution and quality of the clinical research evidence were presented using both text and charts. A total of 565 articles were included in the study, comprising 523 intervention studies, 23 observational studies, 18 systematic reviews/Meta-analysis, and 1 guideline. The overall publication volume has shown a downward trend in past two years. The sample sizes of the intervention and observational studies primarily focused on 60 to 120 cases. The intervention schemes mainly involved multi-therapy combinations, including 33 classic prescriptions and 25 Chinese patent medicines. Among these, 48 studies related to 17 classic prescriptions and 45 studies related to 10 types of Chinese patent medicines involved TCM syndrome types. Randomized controlled trial(RCT) tended to focus on overall clinical efficacy and the degree of dysmenorrhea as key outcome measures. Methodological quality issues were found in 97 RCTs related to TCM decoctions and 131 RCTs related to Chinese patent medicines, primarily involving unclear explanations of some information. The AMSTAR scores for the 18 systematic reviews/Meta-analysis ranged from 1 to 8 points, with 16 studies suggesting "evidence of potential therapeutic efficacy". The recommended level for the one included guideline was B-level. TCM shows significant advantages in treating AM. Future clinical research should further standardize study designs, reference relevant reporting guidelines, improve the quality of clinical research, generate higher-level evidence-based results, and promote the high-quality development of clinical research on TCM for treating AM.
Humans ; Adenomyosis/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Female ; Medicine, Chinese Traditional ; Randomized Controlled Trials as Topic

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

Background: and Purpose Symptomatic intracranial hemorrhage (sICH) following endovascular thrombectomy (EVT) is a severe complication associated with adverse functional outcomes and increased mortality rates. Currently, a reliable predictive model for sICH risk after EVT is lacking. Methods: This study used data from patients aged ≥20 years who underwent EVT for anterior circulation stroke from the nationwide Taiwan Registry of Endovascular Thrombectomy for Acute Ischemic Stroke (TREAT-AIS). A predictive model including factors associated with an increased risk of sICH after EVT was developed to differentiate between patients with and without sICH. This model was compared existing predictive models using nationwide registry data to evaluate its relative performance. Results: Of the 2,507 identified patients, 158 developed sICH after EVT. Factors such as diastolic blood pressure, Alberta Stroke Program Early CT Score, platelet count, glucose level, collateral score, and successful reperfusion were associated with the risk of sICH after EVT. The TREAT-AIS score demonstrated acceptable predictive accuracy (area under the curve [AUC]=0.694), with higher scores being associated with an increased risk of sICH (odds ratio=2.01 per score increase, 95% confidence interval=1.64–2.45, P<0.001). The discriminatory capacity of the score was similar in patients with symptom onset beyond 6 hours (AUC=0.705). Compared to existing models, the TREAT-AIS score consistently exhibited superior predictive accuracy, although this difference was marginal. Conclusions The TREAT-AIS score outperformed existing models, and demonstrated an acceptable discriminatory capacity for distinguishing patients according to sICH risk levels. However, the differences between models were only marginal. Further research incorporating periprocedural and postprocedural factors is required to improve the predictive accuracy.

OBJECTIVE To investigate the relationship between 23-site chip genetic screening failures and the results of newborns hearing screening,and to provide clinical reference for the diagnosis and treatment of genetic screening failures.METHODS There were 1 916 newborns born in the Beijing area from November 2022 to May 2024,who did not pass the 23-site chip genetic screening tests and underwent newborn hearing screening with definite initial screening results.Chi-square test was used to analyze the relationship between different mutation types and genotypes and the initial hearing screening results.RESULTS The overall neonatal hearing screening failure rate was 5.27%(101/1 916),with a higher failure rate of 61.54%(56/91)for homozygous and compound heterozygous mutations than the failure rate of 2.54%(45/1 772)for heterozygous mutations,0%(0/34)for digenic gene heterozygous mutations,and 0(0/19)for mtDNA 12S rRNA mutations,with a statistically significant difference(P<0.001).Among the homozygous and compound heterozygous mutations,the failure rates of homozygous and compound heterozygous for GJB2 gene and SLC26A4 gene were 59.76%(49/82)and 77.78%(7/9),respectively,with no statistically significant difference between the two groups(P=0.488).The homozygous and compound heterozygous for GJB2 gene were divided into three groups based on genotype:c.109G>A homozygous mutations,c.109G>A compound heterozygous mutations,and other homozygous and compound heterozygous mutations.The hearing screening failure rates of the three groups,from highest to lowest,were as follow:other homozygous and compound heterozygous mutations(88.89%,8/9),c.109G>A homozygous mutations(65.12%,28/43),and c.109G>A compound heterozygous mutations(43.33%,13/30),with a statistically significant difference(P=0.029).The failure rates of heterozygous for GJB2 gene,SLC26A4 gene and GJB3 gene were 2.86%(40/1 398),1.25%(4/321)and 1.89%(1/53),respectively,with no statistically significant difference among the three groups(P=0.241).The failure rate of hearing screening for individuals with GJB2 heterozygotes of different genotypes and individuals with SLC26A4 heterozygotes of different genotypes did not show statistically significant differences.CONCLUSION The failure rate of newborn hearing screening for homozygous and compound heterozygous mutation of 23-site chip genetic screening is higher than that of other mutation types,verifying the effectiveness of the newborn hearing screening program.Some newborns of homozygous and compound heterozygous mutation can pass the hearing screening,especially those with the c.109G>A homozygous and compound heterozygous mutation,who need clinical follow-up.