Objective To analyze the influencing factors of malaria infection of labor service exported to overseas in Langfang City, in order to establish a visualization tool to assist clinicians in predicting the risk of malaria. Methods A total of 4 774 expatriate employees of the Nibei Pipeline Project of the Pipeline Bureau from October 2021 to August 2023 were taken as the subjects, and the gender, age, overseas residence area and Knowledge of malaria controlscores of the study subjects were investigated by questionnaire survey, and the possible risk factors of malaria were screened by logistic regression model. At the same time, the nomogram prediction model was established, and the subjects were divided into the training group and the validation group at a ratio of 2:1, and the area under the curve (ROC) and the decision curve were plotted to evaluate the prediction ability and practicability of the prediction model in this study. Results Among the 4 774 study subjects, 96 cases of malaria occurred, and the detection rate was 2.01%. Junior school （OR=1.723，95% CI:1.361-2.173）， and residence in rural areas（OR=2.091，95%CI:1.760 -3.100）were risk factors (OR>1), while protective measures（OR=0.826，95% CI : 0.781 - 0.901） and high malaria education scores （OR=0.872，95% CI : 0.621 - 0.899）were protective factors.The nomogram prediction model results showed that the area under the curve of the nomogram prediction model in the training group was 0.94 (95% CI : 0.85 - 1.00), while the validation group was 0.93 (95% CI : 0.80 - 1.00). The results of the decision curve showed that when the threshold probability of the population was 0-0.9, the nomogram model was used to predict the risk of malaria occurrence with the highest net income. Conclusion The nomogram prediction model (including gender, education, region, protection and malaria education score) established and validated in this study is of great value for clinicians to screen high-risk patients with malaria.

Tumor drug resistance is an important problem in the failure of chemotherapy and targeted drug therapy, which is a complex process involving chromatin remodeling. SWI/SNF is one of the most studied ATP-dependent chromatin remodeling complexes in tumorigenesis, which plays an important role in the coordination of chromatin structural stability, gene expression, and post-translation modification. However, its mechanism in tumor drug resistance has not been systematically combed. SWI/SNF can be divided into 3 types according to its subunit composition: BAF, PBAF, and ncBAF. These 3 subtypes all contain two mutually exclusive ATPase catalytic subunits (SMARCA2 or SMARCA4), core subunits (SMARCC1 and SMARCD1), and regulatory subunits (ARID1A, PBRM1, and ACTB, etc.), which can control gene expression by regulating chromatin structure. The change of SWI/SNF complex subunits is one of the important factors of tumor drug resistance and progress. SMARCA4 and ARID1A are the most widely studied subunits in tumor drug resistance. Low expression of SMARCA4 can lead to the deletion of the transcription inhibitor of the BCL2L1 gene in mantle cell lymphoma, which will result in transcription up-regulation and significant resistance to the combination therapy of ibrutinib and venetoclax. Low expression of SMARCA4 and high expression of SMARCA2 can activate the FGFR1-pERK1/2 signaling pathway in ovarian high-grade serous carcinoma cells, which induces the overexpression of anti-apoptosis gene BCL2 and results in carboplatin resistance. SMARCA4 deletion can up-regulate epithelial-mesenchymal transition (EMT) by activating YAP1 gene expression in triple-negative breast cancer. It can also reduce the expression of Ca²⁺ channel IP3R3 in ovarian and lung cancer, resulting in the transfer of Ca²⁺ needed to induce apoptosis from endoplasmic reticulum to mitochondria damage. Thus, these two tumors are resistant to cisplatin. It has been found that verteporfin can overcome the drug resistance induced by SMARCA4 deletion. However, this inhibitor has not been applied in clinical practice. Therefore, it is a promising research direction to develop SWI/SNF ATPase targeted drugs with high oral bioavailability to treat patients with tumor resistance induced by low expression or deletion of SMARCA4. ARID1A deletion can activate the expression of ANXA1 protein in HER2+ breast cancer cells or down-regulate the expression of progesterone receptor B protein in endometrial cancer cells. The drug resistance of these two tumor cells to trastuzumab or progesterone is induced by activating AKT pathway. ARID1A deletion in ovarian cancer can increase the expression of MRP2 protein and make it resistant to carboplatin and paclitaxel. ARID1A deletion also can up-regulate the phosphorylation levels of EGFR, ErbB2, and RAF1 oncogene proteins.The ErbB and VEGF pathway are activated and EMT is increased. As a result, lung adenocarcinoma is resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Although great progress has been made in the research on the mechanism of SWI/SNF complex inducing tumor drug resistance, most of the research is still at the protein level. It is necessary to comprehensively and deeply explore the detailed mechanism of drug resistance from gene, transcription, protein, and metabolite levels by using multi-omics techniques, which can provide sufficient theoretical basis for the diagnosis and treatment of poor tumor prognosis caused by mutation or abnormal expression of SWI/SNF subunits in clinical practice.

[Objective] To analyze the clinicopathological characteristics of treatment-naive patients with highly suspected prostate cancer (PCa) with prostate-specific antigen (PSA) level ≥20 ng/mL, to provide reference for promoting early screening of PCa. [Methods] A retrospective analysis was conducted on the clinical data of treatment-naive patients with PSA level ≥20 ng/mL, undergoing prostate biopsy for highly suspected PCa at the Department of Urology, Tianjin Medical University Second Hospital during Jan.2013 and Jun.2023. The correlation between patients' age, body mass index (BMI), PSA, prostate volume (PV), prostate cancer-specific antigen density (PSAD), prostate imaging reporting and data system (PI-RADS) score, and International Society of Urological Pathology (ISUP) grade with highly suspected PCa metastasis and PSA stratification were analyzed. [Results] A total of 1778 suspected patients were enrolled. Pathological findings confirmed PCa in 1465 cases (82.4%), with 487(33.2%) diagnosed as metastatic PCa. Over the past decade, the number of patients undergoing prostate biopsy for highly suspected PCa and being confirmed has been increasing annually, with the proportion of metastatic cases remaining at around 30%. Compared with those with PSA level being 20-50 ng/mL, patients with PSA level >50 ng/mL had older age, lower BMI, higher PSAD, higher PI-RADS, higher ISUP, more diverse pathological types, and a higher incidence of metastasis (P<0.05) with lower proportion of urban residents. Additionally, analysis of metastatic PCa cases showed that 46.8%(228/487) had oligometastasis (≤5 metastatic lesions), including 99.0% bone metastasis, 4.1% extraregional lymph node metastasis, and 4.3% other organ metastasis. [Conclusion] Over the past 10 years, there has been a continuous increase in the number of treatment-naive biopsied cases and newly diagnosed cases of highly suspicious PCa with PSA level ≥20 ng/mL, while the proportion of metastatic cases remains high. Therefore, proactive efforts should be made to promote early screening of high-risk suspected cases.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Abstract Modern western medicine typically focuses on treating specific symptoms or diseases, and traditional Chinese medicine (TCM) emphasizes the interconnections of the body’s various systems under external environment and takes a holistic approach to preventing and treating diseases. Phenomics was initially introduced to the field of TCM in 2008 as a new discipline that studies the laws of integrated and dynamic changes of human clinical phenomes under the scope of the theories and practices of TCM based on phenomics. While TCM Phenomics 1.0 has initially established a clinical phenomic system centered on Zhenghou (a TCM definition of clinical phenome), bottlenecks remain in data standardization, mechanistic interpretation, and precision intervention. Here, we systematically elaborates on the theoretical foundations, technical pathways, and future challenges of integrating digital medicine with TCM phenomics under the framework of “TCM phenomics 2.0”, which is supported by digital medicine technologies such as artificial intelligence, wearable devices, medical digital twins, and multi-omics integration. This framework aims to construct a closed-loop system of “Zhenghou–Phenome–Mechanism–Intervention” and to enable the digitization, standardization, and precision of disease diagnosis and treatment. The integration of digital medicine and TCM phenomics not only promotes the modernization and scientific transformation of TCM theory and practice but also offers new paradigms for precision medicine. In practice, digital tools facilitate multi-source clinical data acquisition and standardization, while AI and big data algorithms help reveal the correlations between clinical Zhenghou phenomes and molecular mechanisms, thereby improving scientific rigor in diagnosis, efficacy evaluation, and personalized intervention. Nevertheless, challenges persist, including data quality and standardization issues, shortage of interdisciplinary talents, and insufficiency of ethical and legal regulations. Future development requires establishing national data-sharing platforms, strengthening international collaboration, fostering interdisciplinary professionals, and improving ethical and legal frameworks. Ultimately, this approach seeks to build a new disease identification and classification system centered on phenomes and to achieve the inheritance, innovation, and modernization of TCM diagnostic and therapeutic patterns.

OBJECTIVE: To investigate whether electroacupuncture (EA) at sensitized acupoints could reduce sympathetic-sensory coupling (SSC) and neurogenic inflammatory response by interfering with 5-hydroxytryptamine (5-HT)ergic neural pathways to relieve colitis and somatic referred pain, and explore the underlying mechanisms. METHODS: Rats were treated with 5% dextran sodium sulfate (DSS) solution for 7 days to establish a colitis model. Twelve rats were randomly divided into the control and model groups according to a random number table (n=6). According to the "Research on Rat Acupoint Atlas", sensitized acupoints and non-sensitized acupoints were determined. Rats were randomly divided into the control, model, Zusanli-EA (ST 36), Dachangshu-EA (BL 25), and Xinshu (BL 15) groups (n=6), as well as the control, model, EA, and EA + GR113808 (a 5-HT inhibitor) groups (n=6). The rats in the control group received no treatment. Acupuncture was administered on 2 days after modeling using the stimulation pavameters: 1 mA, 2 Hz, for 30 min, with sparse and dense waves, for 14 consecutive days. GR113808 was injected into the tail vein at 5 mg/kg before EA for 10 min for 7 consecutive days. Mechanical sensitivity was assessed with von Frey filaments. Body weight and disease activity index (DAI) scores of rats were determined. Hematoxylin and eosin staining was performed to observe colon histopathology. SSC was analyzed by immunofluorescence staining. Immunohistochemical staining was performed to detect 5-HT and substance P (SP) expressions. The calcitonin gene-related peptide (CGRP) in skin tissue and tyrosine hydroxylase (TH) protein levels in DRG were detected by Western blot. The levels of hyaluronic acid (HA), bradykinin (BK), prostaglandin I2 (PGI2) in skin tissue, 5-HT, tryptophan hydroxylase 1 (TPH1), serotonin transporters (SERT), 5-HT 3 receptor (5-HT3R), and 5-HT 4 receptor (5-HT4R) in colon tissue were measured by enzyme-linked immunosorbent assay (ELISA). RESULTS: BL 25 and ST 36 acupoints were determined as sensitized acupoints, and BL 15 acupoint was used as a non-sensitized acupoint. EA at sensitized acupoints improved the DAI score, increased mechanical withdrawal thresholds, and alleviated colonic pathological damage of rats. EA at sensitized acupoints reduced SSC structures and decreased TH and CGRP expression levels (P<0.05). Furthermore, EA at sensitized acupoints reduced BK, PGI2, 5-HT, 5-HT3R and TPH1 levels, and increased HA, 5-HT4R and SERT levels in colitis rats (P<0.05). GR113808 treatment diminished the protective effect of EA at sensitized acupoints in colitis rats (P<0.05). CONCLUSION EA at sensitized acupoints alleviated DSS-induced somatic referred pain in colitis rats by interfering with 5-HTergic neural pathway, and reducing SSC inflammatory response.
Rats ; Animals ; Electroacupuncture ; Rats, Sprague-Dawley ; Serotonin ; Acupuncture Points ; Pain, Referred ; Calcitonin Gene-Related Peptide ; Signal Transduction ; Colitis/therapy* ; Indoles ; Sulfonamides

Objective To investigate the microbiota composition and diversity between autogenous and anautogenous Culex pipiens pallens, so as to provide insights into unraveling the pathogenesis of autogeny in Cx. pipiens pallens. Methods Autogenous and anautogenous adult Cx. pipiens pallens samples were collected at 25 ℃, and the hypervariable regions of the microbial 16S ribosomal RNA (16S rRNA) gene was sequenced on the Illumina NovaSeq 6000 sequencing platform. The microbiota abundance and diversity were evaluated using the alpha diversity index, and the difference in the microbiota structure was examined using the beta diversity index. The microbiota with significant differences in the abundance between autogenous and anautogenous adult Cx. pipiens pallens samples was identified using the linear discriminant analysis effect size (LEfSe). Results The microbiota in autogenous and anautogenous Cx. pipiens pallens samples belonged to 18 phyla, 28 classes, 70 orders, 113 families, and 170 genera, and the dominant phyla included Proteobacteria, Bacteroidetes, and so on. At the genus level, Wolbachia was a common dominant genus, and the relative abundance was (77.6 ± 11.3)% in autogenous Cx. pipiens pallens samples and (47.5 ± 8.5)% in anautogenous mosquito samples, while Faecalibaculum (0.4% ± 0.1%), Dubosiella (0.5% ± 0.0%) and Massilia (0.5% ± 0.1%) were specific species in autogenous Cx. pipiens pallens samples. Alpha diversity analysis showed that higher Chao1 index and ACE index in autogenous Cx. pipiens pallens samples than in anautogenous samples (both P values > 0.05), and lower Shannon index (P > 0.05) and Simpson index (P < 0.05) in autogenous Cx. pipiens pallens samples than in anautogenous samples. LEfSe analysis showed a total of 48 significantly different taxa between autogenous and anautogenous Cx. pipiens pallens samples (all P values < 0.05). Conclusion There is a significant difference in the microbiota diversity between autogenous and anautogenous Cx. pipiens pallens.

Two methods including gas chromatography tandem mass spectrometry (GC-MS/MS) and high-performance liquid chromatography tandem mass spectrometry (LC-MS/MS) were established to detect common alkyl sulfonates and aryl sulfonates genotoxic impurities. Four alkyl sulfonates and methyl benzenesulfonate were determined by GC-MS/MS using butyl methanesulfonate as the internal standard, the chromatographic column was HP-5MS UI (30 mm × 0.25 mm, 0.25 µm), the carrier gas was helium, the flow rate was 1.0 mL·min^-1 in a constant flow mode, the sample inlet temperature was set to 250 ℃, the split ratio was 10∶1, and the initial temperature of the heating program was 80 ℃, maintained for 1 minute, and then increased to 240 ℃ at a heating rate of 30 ℃·min^-1 for 2 minutes. The mass spectrometry detector was an electron bombardment ion source (EI source), the data collection condition was multi reaction monitoring mode (MRM), and method validation using the raw material of clinical drug citalopram hydrobromide as a sample. The results showed that the linear range of four alkyl sulfonates and methyl benzenesulfonate were good at 3-50 ng·mL^-1 and 9-150 ng·mL^-1, with a correlation coefficient of r > 0.999, The spiked recovery was 80%-120%. The detection limits were 1 and 3 ng·mL^-1; Ten aryl sulfonates determined by LC-MS/MS, the chromatographic column was CSH Fluoro phenyl (100 mm × 2.1 mm, 1.7 µm), the mobile phase was methanol (B)-5 mmol·L^-1 ammonium formate (D), with a flow rate of 0.2 mL·min^-1, and gradient elution was performed. The gradient program (T/% B) was set as 0/20, 25/90, 35/90, 42/20. The mass spectrometer detector was electro spray ionization with positive ionization mode (ESI⁺), the data collection was in dynamic multi reaction monitoring mode (dMRM), and the method was validated using the raw material of the clinical drug citalopram hydrobromide as a sample. The results showed that the linear range of aryl sulfonates were good at 9-2 000 ng·mL^-1, 3-100 ng·mL^-1 and 0.9-30 ng·mL^-1, respectively. The correlation coefficient r > 0.999, the spiked recovery was 80%-120%. The detection limits were 30, 1 and 0.3 ng·mL^-1. Two detection methods did not detect potential sulfonate genotoxicity impurities in the above APIs. The established analytical methods are reliable and effective, which can provide reference for drug quality control and detection.