Background Air pollution has widespread adverse health effects on human health, but research on its impact on reproductive health and related physiological mechanisms remains insufficient. Objective To investigate the associations between short-term exposure to particulate matter and nitrogen dioxide (NO₂) and serum levels of anti-Müllerian hormone (AMH) and testosterone in healthy adults. Methods A total of 152 healthy adults were recruited for a panel study, undergoing four follow-up visits from September 2019 to January 2020. Basic demographic information and serum samples were collected. Reproductive hormone levels were measured by enzyme-linked immunosorbent assay kits. Concurrently, environmental data in the study area were monitored, including fine particulate matter (PM_2.5), black carbon (BC), NO₂, particle number concentrations in the 5-560 nm size fraction (PNC_{5_560}), and meteorological variables. Linear mixed-effects models were constructed to estimate the effects of short-term exposure to these particulate matter on serum reproductive hormone levels. Results The participants had an average age of (23.9±2.4) years, with 102 women. For each interquartile range (IQR) increment in pollutant moving average concentrations, PM_2.5, PNC_{5_25}, PNC_{100_560}, BC, and NO₂ were associated with relative decreases in female serum AMH levels by 10.2%-16.9%, 46.2%-58.0%, 16.9%-21.2%, 14.8%-26.5%, and 11.3%-17.6%, respectively. These negative associations were stronger in females with waist-to-hip ratio > 0.76 and physical activity levels ≤ 1217 metabolic equivalents (MET) min per week. Similarly, in males, an IQR increase in the same pollutants was associated with relative decreases in serum free testosterone by 11.1%-17.2%, 35.2%, 10.5%-18.9%, 18.7%-24.2%, and 10.3%-26.0%, respectively. These effects were more pronounce in male with a waist-to-hip ratios > 0.81 and physical activity levels ≤ 1498 MET-min per week. Conclusion Short-term exposure to particulate matter and NO₂ may reduce serum levels of AMH and testosterone in healthy adults. Furthermore, these adverse effects appears to be stronger in adults with higher waist-to-hip ratios.

As a patented characteristic medicine of Yi ethnic minority， Pingxuan capsules have the effects of nourishing the liver and kidney， pacifying the liver， and subduing Yang. With the main indications of dizziness， headache， palpitations， tinnitus， insomnia， dreaminess， waist and knee soreness caused by liver-kidney deficiency and liver Yang upward disturbance， Pingxuan capsules are widely used in the treatment of posterior circulation ischemic vertigo， vestibular migraine， benign paroxysmal positional vertigo. However， the current knowledge is limited regarding the efficacy， syndrome differentiation， and safety of this medicine. On the basis of summarizing the experience of clinicians and the existing evidence， this study invites clinical experts of traditional Chinese and Western medicine， pharmaceutical experts， and methodological experts from relevant fields across China to conduct evidence-based evaluation of Pingxuan capsules. The evaluation follows the Specifications for the Development of Clinical Expert Consensus on Chinese Patent Medicines issued by the Standardization Office of the China Association of Chinese Medicine， and reaches 5 recommendations and 16 consensus suggestions. The consensus clarifies the clinical applications， efficacy， dose， course of treatment， combination of medicines， precautions， and contraindications of Pingxuan capsules in the treatment of vertigo and explains the safety of clinical application. This consensus is applicable to clinicians （traditional Chinese medicine， Western medicine， and integrated traditional Chinese and Western medicine） and pharmacists in tertiary hospitals， secondary hospitals， and community-level medical and health institutions across China， providing a reference for the rational use of Pingxuan capsules in the treatment of vertigo. It is hoped that the promotion of this consensus can facilitate the rational use of drugs in clinical practice， reduce the risk of drug use， and give full play to the advantages of Pingxuan capsules in the treatment of vertigo diseases. This consensus has been reviewed and published by the China Association of Chinese Medicine， with the number GS/CACM330-2023.

ObjectiveTo establish a model that can quickly identify the aroma components in different parts of Nardostachys jatamansi， so as to provide a quality control basis for the market circulation and clinical use of N. jatamansi. MethodsHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） combined with Smart aroma database and National Institute of Standards and Technology（NIST） database were used to characterize the aroma components in different parts of N. jatamansi， and the aroma components were quantified according to relative response factor（RRF） and three internal standards， and the markers of aroma differences in different parts of N. jatamansi were identified by orthogonal partial least squares-discriminant analysis（OPLS-DA） and cluster thermal analysis based on variable importance in the projection（VIP） value >1 and P<0.01. The odor data of different parts of N. jatamansi were collected by Heracles Ⅱ Neo ultra-fast gas phase electronic nose， and the correlation between compound types of aroma components collected by the ultra-fast gas phase electronic nose and the detection results of HS-SPME-GC-MS was investigated by drawing odor fingerprints and odor response radargrams. Chromatographic peak information with distinguishing ability≥0.700 and peak area≥200 was selected as sensor data， and the rapid identification model of different parts of N. jatamansi was established by principal component analysis（PCA）， discriminant factor alysis（DFA）， soft independent modeling of class analogies（SIMCA） and statistical quality control analysis（SQCA）. ResultsThe HS-SPME-GC-MS results showed that there were 28 common components in the underground and aboveground parts of N. jatamansi， of which 22 could be quantified and 12 significantly different components were screened out. Among these 12 components， the contents of five components（ethyl isovalerate， 2-pentylfuran， benzyl alcohol， nonanal and glacial acetic acid，） in the aboveground part of N. jatamansi were significantly higher than those in the underground part（P<0.01）， the contents of β-ionone， patchouli alcohol， α-caryophyllene， linalyl butyrate， valencene， 1，8-cineole and p-cymene in the underground part of N. jatamansi were significantly higher than those in the aboveground part（P<0.01）. Heracles Ⅱ Neo electronic nose results showed that the PCA discrimination index of the underground and aboveground parts of N. jatamansi was 82， and the contribution rates of the principal component factors were 99.94% and 99.89% when 2 and 3 principal components were extracted， respectively. The contribution rate of the discriminant factor 1 of the DFA model constructed on the basis of PCA was 100%， the validation score of the SIMCA model for discrimination of the two parts was 99， and SQCA could clearly distinguish different parts of N. jatamansi. ConclusionHS-SPME-GC-MS can clarify the differential markers of underground and aboveground parts of N. jatamansi. The four analytical models provided by Heracles Ⅱ Neo electronic nose（PCA， DFA， SIMCA and SQCA） can realize the rapid identification of different parts of N. jatamansi. Combining the two results， it is speculated that terpenes and carboxylic acids may be the main factors contributing to the difference in aroma between the underground and aboveground parts of N. jatamansi.

Objective:To evaluate the efficacy of gadolinium ethoxybenzyl- diethy-lenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced MRI features in the preoperative prediction of tertiary lymphoid structures (TLS) within hepatocellular carcinoma (HCC) lesions.Methods:This retrospective cross-sectional study included clinical and pathological data from 297 HCC patients treated at the Southwest Hospital, Army Medical University between June 2021 and November 2022. Based on postoperative pathology, patients were categorized into TLS-negative ( n=93) and TLS-positive ( n=204) groups. MRI features of HCC lesions using Gd-EOB-DTPA enhancement and relevant clinical data were analyzed. Intergroup comparisons of imaging features and laboratory findings were performed using independent sample t-test, Mann-Whitney U test, χ2 test, or Fisher exact test, as appropriate. The logistic regression analysis was conducted to identify independent predictors of TLS positivity. A predictive model was constructed and visualized using a nomogram. The model′s predictive performance and clinical utility were assessed using the receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The area under the ROC curve (AUC) was compared using the DeLong test. Results:Significant differences were observed between the TLS-negative and TLS-positive groups in alpha-fetoprotein (AFP) levels, intratumoral hemorrhage, and peritumoral satellite nodules in the hepatobiliary phase ( P<0.05). Multivariate logistic regression identified intratumoral hemorrhage ( OR=0.123, 95% CI 0.070-0.216, P<0.001) and peritumoral satellite nodules in the hepatobiliary phase ( OR=0.236, 95% CI 0.093-0.596, P=0.002) as independent predictive factors for TLS-positivity. The imaging model based on these two features yielded an AUC of 0.764 (95% CI 0.709-0.809) for predicting TLS-positivity. When combined with AFP levels, the resulting clinical-imaging model achieved a superior AUC of 0.784 (95% CI 0.732-0.829), which was significantly higher than that of the imaging model alone ( Z=2.20, P=0.028). A nomogram was constructed based on the clinical-imaging model. The calibration curve demonstrated good predictive performance of the nomogram, and the DCA showed that the curve remained above the default line across a range of reasonable threshold probabilities, indicating that patients could derive clinical benefit. Conclusion:A nomogram model based on Gd-EOB-DTPA enhanced MRI features combined with AFP levels can effectively predict the presence of TLS in HCC.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

Objective:To analyze the epidemiological and clinical characteristics of respiratory pathogens in China.Methods:This study was a cross-sectional study, which encompassed 19 core units of the clinical pathogen network and established a three-tiered clinical pathogen surveillance system. Thirty respiratory samples were collected every two weeks from various units from January to December 2024, and the clinical and pathogen diagnostic information were gathered. A total of 11 864 samples were tested using this system. The tier-1 clinical pathogen surveillance system covered influenza A virus (Flu-A), influenza B virus (Flu-B), respiratory syncytial virus (RSV), and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The tier-2 clinical pathogen surveillance system focused on 18 key respiratory pathogens. The tier-3 clinical pathogen surveillance system further clarified whether any emerging infectious diseases had occurred.Results:The tier-1 clinical pathogen surveillance system showed Flu-A predominated in December, Flu-B predominated in January, SARS-CoV-2 peaked in March and August, whereas RSV circulated sporadically throughout the year. Geographic trends were broadly consistent across the seven major regions, although Flu-A detection in December was notably higher in Northeast China (48.1%(111/231)) and East China (36.2%(148/409)), and RSV detection was concentrated in the Northwest and South China from January to March. Data from the tier-2 clinical pathogen surveillance system indicated that Streptococcus pneumoniae, Mycoplasma pneumoniae, rhinovirus, and adenovirus were detected year-round, of these, Streptococcus pneumoniae and rhinovirus showed elevated positive detection rates from August to September, while adenovirus peaked in January. Legionella pneumophila was not detected throughout the year, and other pathogens fluctuated throughout the year without a consistent pattern. The predominant etiologic agents of pediatric pneumonia were Mycoplasma pneumoniae (35.0%(105/300)), rhinovirus (25.7%(77/300)), and adenovirus (17.3%(52/300)), whereas adult pneumonia was mainly caused by Streptococcus pneumoniae (10.5%(29/277)), Staphylococcus aureus (6.9%(19/277)), Mycoplasma pneumoniae (6.9%(19/277)), and Flu-A (6.1%(17/277)). The tier-3 clinical pathogen surveillance system did not identify any emerging respiratory pathogens. Conclusion:Respiratory pathogens in China in 2024 exhibit distinct temporal and spatial distribution patterns and vary among different populations.

OBJECTIVES: To investigate the effect of in vitro cultured calculus bovis (ICCB) on cerebral ischemia/reperfusion injury (CIRI) and its mechanism. METHODS: A CIRI rat model and a cell model were induced by middle cerebral artery occlusion (MCAO) in Sprague Dawley rats and oxygen glucose deprivation/reperfusion (OGD/R) in BV2 cells, respectively. The CIRI rat model was evaluated using the modified neurological severity score (mNSS), brain water content, and cerebral infarction volume after 1.5 h of ischemia followed by 72 h of reperfusion. Histopathological changes in the cortex and hippocampal CA1 region were observed with hematoxylin and eosin staining. Microglial polarization and NOD-like receptor thermal protein domain associated protein (NLRP) 3 inflammasome expression in the cortex were examined by immunofluorescence. BV2 cell viability was measured via MTT assay after treatment with ICCB and Nigericin. The expressions of NLRP3, ASC, caspase-1 proteins and inflammatory cytokines were detected with Western blotting in OGD/R treated BV2 cells (0.5 h OGD+24 h reperfusion) and in cells pretreated with Nigericin for 24 h. RESULTS: ICCB treatment significantly improved neurological function, reduced cerebral infarct volume and brain water content, and mitigated pathological damage in the cortical and hippocampal CA1 regions of rats subjected to CIRI (all P<0.05). ICCB promoted the transition of cortical microglia from M1 to M2 phenotypes and suppressed NLRP3 activation in microglial cells (all P<0.01). ICCB significantly down-regulated the expression of NLRP3, ASC, and caspase-1 proteins, and reduced the secretion of IL-18 and IL-1β in BV2 cells of OGD/R model (all P<0.01). In addition, Nigericin significantly reversed the salvage effect of ICCB on model cells (both P<0.01) and the modulation of inflammatory cytokines (P<0.05). CONCLUSIONS ICCB exerts a protective effect against CIRI by mitigating neuroinflammation, through the reduction of M1 microglial polarization, promotion of M2 conversion, and suppression of the NLRP3/ASC/caspase-1 signaling pathway.
Animals ; Rats, Sprague-Dawley ; Reperfusion Injury/prevention & control* ; Microglia/metabolism* ; Rats ; NLR Family, Pyrin Domain-Containing 3 Protein ; Brain Ischemia/metabolism* ; Male

OBJECTIVE: To investigate the effect of intraoperative blood salvage autotransfusion on local recurrence and long-term metastasis of patients after carotid body tumor resection. METHODS: We retrospectively reviewed a consecutive series of 61 patients undergoing elective carotid body tumor resection from August 2009 to December 2020. Among them, 14 received intraoperative blood salvage autotransfusion (autotrasfusion group) and 47 did not (non-autotransfusion). Data of general information, surgical status and postoperative follow-up results were collected. RESULTS: The proportion of Shamblin Ⅲ in the autotransfusion group was 85.7%, which was significantly higher than 31.9% in the non-autotransfusion group (P=0.003). The average operation time of the 14 patients in the autotransfusion group was (264±84) min, intraoperative blood loss was 1 200 (700, 2 700) mL, and autologous blood transfusion was 500 (250, 700) mL. Of these, 8 patients (57%) required concomitant allogeneic blood with 400 (260, 400) mL of allogeneic blood. The average operation time of the 47 patients in the non-autotransfusion group was (153±75) min, and the intraoperative blood loss was 300 (100, 400) mL. Of these, 6 (13%) required allogeneic blood transfusion, and 520 (400, 520) mL of allogeneic blood was used. Compared with the non-autotransfusion group, the average operation time in the autologous blood transfusion group was significantly longer (P < 0.001), and the intraoperative blood transfusion volume was larger (P=0.007). Of the 14 patients undergoing autotransfusion, 8 (57%) needed allogeneic blood at the same time; while in the 47 non-autologous transfusion patients, 6 (13%) needed allogeneic blood transfusion. The proportion of autotransfusion group using allogeneic blood at the same time was even higher (P=0.002). The incidence of nerve injury within 30 days after surgery was 29.5%, and there was no significant difference between the two groups. No early deaths occurred. The average follow-up was (76±37) months. One case of local recurrence occurred in the non-autotransfusion group. There was no distant metastasis. There were no tumor-related deaths. The estimated 5-year and 10-year overall survival rates were 96.4% and 83.8%, respectively. There was no significant difference in overall survival between the two groups (P=0.506). CONCLUSION The use of intraoperative blood salvage autotransfusion increased no risk of local recurrence and distant metastasis in patients with carotid body tumor, which is safe and effective in carotid body tumor resection.
Humans ; Blood Transfusion, Autologous/methods* ; Operative Blood Salvage/methods* ; Retrospective Studies ; Male ; Female ; Carotid Body Tumor/pathology* ; Middle Aged ; Prognosis ; Neoplasm Recurrence, Local ; Blood Loss, Surgical ; Aged ; Adult ; Operative Time

Poly(ADP-ribosyl)ation (PARylation) is a specific form of post-translational modification (PTM) predominantly triggered by the activation of poly-ADP-ribose polymerase 1 (PARP1). However, the role and mechanism of PARylation in the advancement of acute kidney injury (AKI) remain undetermined. Here, we demonstrated the significant upregulation of PARP1 and its associated PARylation in murine models of AKI, consistent with renal biopsy findings in patients with AKI. This elevation in PARP1 expression might be attributed to trimethylation of histone H3 lysine 4 (H3K4me3). Furthermore, a reduction in PARylation levels mitigated renal dysfunction in the AKI mouse models. Mechanistically, liquid chromatography-mass spectrometry indicated that PARylation mainly occurred in receptor for activated C kinase 1 (RACK1), thereby facilitating its subsequent phosphorylation. Moreover, the phosphorylation of RACK1 enhanced its dimerization and accelerated the ubiquitination-mediated hypoxia inducible factor-1α (HIF-1α) degradation, thereby exacerbating kidney injury. Additionally, we identified a PARP1 proteolysis-targeting chimera (PROTAC), A19, as a PARP1 degrader that demonstrated superior protective effects against renal injury compared with PJ34, a previously identified PARP1 inhibitor. Collectively, both genetic and drug-based inhibition of PARylation mitigated kidney injury, indicating that the PARylated RACK1/HIF-1α axis could be a promising therapeutic target for AKI treatment.

Tracheal intubation is the most commonly used way to establish artificial airway in intensive care unit patients, and it is the premise of respiratory support and treatment, the success of extubation is an important basis to measure the prognosis of patients. Influenced by many factors, extubation of patients may fail, resulting in prolonged hospitalization, increased medical expenses and increased incidence of complications. This article reviews the literature at home and abroad, and summarizes the main mechanism, risk factors and prevention strategies of extubation failure in intensive care unit patients, aiming at improving the recognition ability of clinical medical staff for extubation failure and providing reference for clinical management and follow-up research.