Background Exposure to polycyclic aromatic hydrocarbons (PAHs) is associated with the development of metabolic syndrome (MS). However, the role of amino acids in PAH-induced MS remains unclear. Objective To explore the impact of PAHs exposure on the incidence of MS among coking workers, and to determine potential modifying effect of amino acid on this relationship. Methods Unmatched nested case-control design was adopted and the baseline surveys of coking workers were conducted in two plants in Taiyuan in 2017 and 2019, followed by a 4-year follow-up. The cohort comprised 667 coking workers. A total of 362 participants were included in the study, with 84 newly diagnosed cases of MS identified as the case group and 278 as the control group. Urinary levels of 11 PAH metabolites and plasma levels of 17 amino acids were measured by ultrasensitive performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS). Logistic regression was used to estimate the association between individual PAH metabolites and MS. Stratified by the median concentration of amino acids, Bayesian kernel machine regression (BKMR) model was employed to assess the mixed effects of PAHs on MS. Due to the skewed data distribution, all PAH metabolites and amino acids in the analysis were converted by natural logarithm ln (expressed as lnv). Results The median age of the 362 participants was 37 years, and 83.2% were male. Compared to the control group, the case group exhibited higher concentrations of urinary 2-hydroxyphenanthrene (2-OHPhe), 9-hydroxyphenanthrene (9-OHPhe), and hydroxyphenanthrene (OHPhe) (P=0.005, P=0.049, and P=0.004, respectively), as well as elevated levels of plasma branched chain amino acid (BCAA) and aromatic amino acid (AAA) (P<0.05). After being adjusted for confounding factors, for every unit increase in lnv_2-OHPhe in urine, the OR (95%CI) of MS was 1.57 (1.11, 2.26), and for every unit increase in lnv_OHPhe, the OR (95%CI) of MS was 1.82 (1.16, 2.90). Tyrosine, leucine, and AAA all presented a significant nonlinear correlation with MS. At low levels, tyrosine, leucine, and AAA did not significantly increase the risk of MS, but at high levels, they increased the risk of MS. In the low amino acid concentration group, as well as in the low BCAA and low AAA concentration groups, it was found that compared to the PAH metabolite levels at the 50th percentile (P₅₀), the log-odds of MS when the PAH metabolite levels was at the 75th percentile (P₇₅) were 0.158 (95%CI: 0.150, 0.166), 0.218 (95%CI: 0.209, 0.227), and 0.262 (95% CI: 0.241, 0.282), respectively, However, no correlation between PAHs and MS was found in the high amino acid concentration group. Conclusion Amino acids modify the effect of PAHs exposure on the incidence of MS. In individuals with low plasma amino acid levels, the risk of developing MS increases with higher concentrations of mixed PAH exposure. This effect is partly due to the low concentrations of BCAA and AAA.

ObjectiveTo explore the clinical safety of Feilike Mixture （FLK） in the real world. MethodsThe safety of all children who received FLK from 29 institutions in 12 provinces between January 21，2021 and December 25，2021 was evaluated through prospective centralized surveillance and a nested case control study. ResultsA total of 3 035 juveniles were included. There were 29 research centers involved，which are distributed across 12 provinces，including one traditional Chinese medicine （TCM） hospital and 28 general hospitals. The average age among the juveniles was （4.77±3.56） years old，and the average weight was （21.81±12.97） kg. Among them，119 cases （3.92%） of juveniles had a history of allergies. Acute bronchitis was the main diagnosis for juveniles，with 1 656 cases （54.46%）. FLK was first used in 2 016 cases （66.43%），and 142 juvenile patients had special dosages，accounting for 4.68%. Among them，92 adverse drug reactions （ADRs） occurred，including 73 cases of gastrointestinal system disorders，10 cases of metabolic and nutritional disorders，eight cases of skin and subcutaneous tissue diseases，two cases of vascular and lymphatic disorders，and one case of systemic diseases and various reactions at the administration site. The manifestations of ADRs were mainly diarrhea，stool discoloration，and vomiting，and no serious ADRs occurred. The results of multi-factor analysis indicated that special dosages （the use of FLK）［odds ratio （OR） of 2.642， 95% confidence interval （CI） of 1.105-6.323］，combined administration： spleen aminopeptide （OR of 4.978， 95%CI of 1.200-20.655），and reason for combined administration： anti-infection （OR of 1.814， 95%CI of 1.071-3.075） were the risk factors for ADRs caused by FLK. Conclusion92 ADRs occurred among 3 035 juveniles using FLK. The incidence of ADRs caused by FLK was 3.03%，and the severity was mainly mild or moderate. Generally，the prognosis was favorable after symptomatic treatment such as drug withdrawal or dosage reduction，suggesting that FLK has good clinical safety.

Based on the viewpoint of "sweat pore-qi and liquid-kidney collaterals"， it is believed that children's nephrotic syndrome is caused by the core mechanism of sweat pore constraint and closure， qi and liquid imbalance， and kidney collaterals impairment， and it is proposed that the treatment principle is to nourish the sweat pore， regulate qi and fluid， and supplement the kidney and unblock the collaterals. In clinic， guided by sequential therapy and according to the different disease mechanism characteristics of the four stages， including early stage of the disease， hormone induction stage， hormone reduction stage， hormone maintenance stage， the staged dynamic identification and treatment was applied. For early stage of the disease with edema due to yang deficiency， modified Zhenwu Decoction （真武汤） was applied to warm yang and drain water； for hormone induction stage with yin deficiency resulting in effulgent fire， modified Zhibai Dihuang Pill （知柏地黄丸） plus Erzhi Pill （二至丸） was used to enrich yin and reduce fire； for hormone reduction stage with qi and yin deficiency， modified Shenqi Dihuang Decoction （参芪地黄汤） was used to boost qi and nourish yin； for hormone maintenance stage， modified Shenqi Pill （肾气丸） was used to supplement yin and yang. Meanwhile， the treatment also attaches importance to the combination of vine-based or worm medicinals to dredge collaterals， so as to providing ideas for clinical treatment.

The growth and development of children is an important stage for health, and its monitoringand intervention are related to the long-term development of individuals. The construction of a standardized and multi-dimensional database of pediatric growth and development-related diseases is an important basis for realizing precise diagnosis and treatment and health management. Based on the needs of clinical practice, this study proposes to establish a specialized database of pediatric growth and development-related diseases that integrates facial phenotypes and clinical diagnosis and treatment information. This study elaborates on the construction process, including data sources, data collection content, and the operation and management of the database; and proposes key points for quality control, including the establishment of quality control nodes, database construction standards, and a full-process quality control framework. The above ensure the integrity, logic and effectiveness of the data, so that the database can provide an objective basis for the screening and diagnosis of pediatric growth and development-related diseases. On the basis of scientific data management and strict quality control, the database will help reveal the patterns of children's growth and development, and promote the level of children's health management.

Objective To investigate the species of ticks in Suizhou City, Hubei Province, so as to provide insights into management of ticks and tick-borne diseases. Methods During the period between May 2023 and June 2024, livestock breeding farms and vegetation neighboring the place of residence of confirmed and suspected patients with tick-borne disease were selected as sampling points in rural areas from Yindian Township, Gaocheng Township, Wanhe Township, Wushan Township, Xiaolin Township, Xihe Township, Hedian Township and Beijiao Street in Suizhou City, Hubei Province, where confirmed and suspected cases with tick-borne diseases had been reported. The parasitic ticks on the body surface of free-range livestock were captured with tweezers in livestock breeding farms, and free ticks on the vegetation surface were captured with the flagging method. Morphological identification of tick samples was performed under a microscope, and the gender and developmental stage of ticks were determined. One engorged adult tick, 2 to 3 blood-feeding but non-engorged adult ticks, 10 to 15 unfed female ticks, 15 to 20 unfed male ticks, and 30 to 40 tick nymphs or larvae were assigned into a group, respectively. Genomic DNA was extracted from tick samples in each group, and mitochondrial 16S rRNA gene was amplified. Sequence analysis was performed with the DNASTAR software, and phylogenetic analysis was performed using the software MEGA 7.0. In addition, the phylogenetic tree was generated using the maximum likelihood method based on the Kimura 2 parameter model. Results A total of 2 438 ticks were captured from Suizhou City, Hubei Province during the period between May 2023 and June 2024, including 595 free ticks and 1 483 parasitic ticks. Three developmental stages of ticks were captured, including larvae, nymphs, and adults, and 75.18% (1 899/2 438) of captured ticks were adult, in which 79.04% (1 501/1 899) were female. Morphological and molecular biological assays identified one family, three genera and four species of captured ticks, including 2 425 Haemaphysalis longicornis ticks (99.47%) and one H. flava tick (0.04%) of the genus Haemaphysalis, 11 Rhipicephalus microplus ticks (0.45%) of the genus Rhipicephalus, and one Ixodes sinensis tick (0.04%) of the genus Ixodes in the family Ixodidae. Phylogenetic analysis revealed that the H. longicornis sequence (SZ49) in this study was clustered with sequences from Yunnan Province (GenBank accession number: MH024510.1), Hebei Province (GenBank accession number: MK450606.1) and Henan Province (GenBank accession number: MZ230645.1) into a clade, and the H. flava sequence (SZ19) in this study was clustered with sequences from Japan (GenBank accession number: MW064044.1), South Korea (GenBank accession number: ON629585.1), and Jiangsu Province (GenBank accession number: PP494741.1) and Hebei Province of China (GenBank accession number: MH520685.1) into a clade, while the R. microplus sequence (SZ8) in this study was clustered with the sequences from India (GenBank accession number: MK621328.1), and Henan Province (GenBank accession number: MT555307.1) and Guizhou Province of China (GenBank accession number: PP446801.1) into a clade. The sequence of I. sinensis (SZ23) in this study had 99.51% homology with that (GenBank accession number: OM368265.1) of ticks sampled from Wuhan City, Hubei Province. Conclusion There are four tick species of H. longicornis, H. flava, R. microplus and I. sinensis in Suizhou City, Hubei Province, and H. longicornis is the dominant species. H. flava is firstly recorded in Suizhou City.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Purpose: This Phase II trial was objected to evaluate the efficacy and safety of adding fulvestrant to neoadjuvant chemotherapy in patients with estrogen receptor (ER)+/human epidermal growth factor receptor 2 (HER2)– locally advanced breast cancer (LABC). Additionally, the study aimed to investigate the association of 16α-18F-fluoro-17β-fluoroestradiol (18F-FES) positron emission tomography (PET)–computed tomography (CT) and metabolites with efficacy. Materials and Methods: Fulvestrant and EC-T regimen were given to ER+/HER2– LABC patients before surgery. At baseline, patients received 18F-FES PET-CT scan, and plasma samples were taken for liquid chromatography–mass spectrometry analysis. The primary endpoint was objective response rate (ORR). Secondary endpoints included total pathologic complete response (tpCR) and safety. Results: Among the 36 patients enrolled, the ORR was 86.1%, the tpCR rate was 8.3%. The incidence of grade ≥ 3 treatment-emergent adverse events was 22%. The decrease in ER value in sensitive patients was larger than that in non-sensitive patients, as was Ki-67 (p < 0.05). The maximum standardized uptake value, mean standardized uptake values, total lesion ER expression of 18F-FES PET-CT in sensitive patients were significantly higher than those in non-sensitive patients (p < 0.05). Moreover, these parameters were significantly correlated with Miller and Payne grade and the change in ER expression before and after treatment (p < 0.05). Thirteen differential expressed metabolites were identified, which were markedly enriched in 19 metabolic pathways. Conclusion This regimen demonstrated acceptable toxicity and encouraging antitumor efficacy. 18F-FES PET-CT might serve as a tool to predict the effectiveness of this therapy. Altered metabolites or metabolic pathways might be associated with treatment response.

Purpose: This Phase II trial was objected to evaluate the efficacy and safety of adding fulvestrant to neoadjuvant chemotherapy in patients with estrogen receptor (ER)+/human epidermal growth factor receptor 2 (HER2)– locally advanced breast cancer (LABC). Additionally, the study aimed to investigate the association of 16α-18F-fluoro-17β-fluoroestradiol (18F-FES) positron emission tomography (PET)–computed tomography (CT) and metabolites with efficacy. Materials and Methods: Fulvestrant and EC-T regimen were given to ER+/HER2– LABC patients before surgery. At baseline, patients received 18F-FES PET-CT scan, and plasma samples were taken for liquid chromatography–mass spectrometry analysis. The primary endpoint was objective response rate (ORR). Secondary endpoints included total pathologic complete response (tpCR) and safety. Results: Among the 36 patients enrolled, the ORR was 86.1%, the tpCR rate was 8.3%. The incidence of grade ≥ 3 treatment-emergent adverse events was 22%. The decrease in ER value in sensitive patients was larger than that in non-sensitive patients, as was Ki-67 (p < 0.05). The maximum standardized uptake value, mean standardized uptake values, total lesion ER expression of 18F-FES PET-CT in sensitive patients were significantly higher than those in non-sensitive patients (p < 0.05). Moreover, these parameters were significantly correlated with Miller and Payne grade and the change in ER expression before and after treatment (p < 0.05). Thirteen differential expressed metabolites were identified, which were markedly enriched in 19 metabolic pathways. Conclusion This regimen demonstrated acceptable toxicity and encouraging antitumor efficacy. 18F-FES PET-CT might serve as a tool to predict the effectiveness of this therapy. Altered metabolites or metabolic pathways might be associated with treatment response.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.

Objective: Machine learning (ML) has been reported to have better predictive capability than traditional statistical techniques. The aim of this study was to assess the efficacy of ML algorithms and logistic regression (LR) for predicting depressive symptoms during the COVID-19 pandemic. Methods: Analyses were carried out in a national cross-sectional study involving 21,916 participants. The ML algorithms in this study included random forest (RF), support vector machine (SVM), neural network (NN), and gradient boosting machine (GBM) methods. The performance indices were sensitivity, specificity, accuracy, precision, F1-score, and area under the receiver operating characteristic curve (AUC). Results: LR and NN had the best performance in terms of AUCs. The risk of overfitting was found to be negligible for most ML models except for RF, and GBM obtained the highest sensitivity, specificity, accuracy, precision, and F1-score. Therefore, LR, NN, and GBM models ranked among the best models. Conclusion Compared with ML models, LR model performed comparably to ML models in predicting depressive symptoms and identifying potential risk factors while also exhibiting a lower risk of overfitting.