ObjectiveTo clarify the associations between plasma fibrinogen （Fbg） and volumetric bone mineral density （vBMD） as well as osteoporosis measured by quantitative computed tomography （QCT）， and to explore the role of plasma Fbg in early screening and diagnosis of osteoporosis. MethodsPatients with hypertension who were hospitalized in the Department of Endocrinology of Sun Yat-sen Memorial Hospital of Sun Yat-sen University from January 2018 to June 2022 and underwent QCT examinations were included for cross-sectional analysis. The study analyzed the correlation between plasma Fbg and osteoporosis in patients. The diagnostic efficacy of plasma Fbg for osteoporosis was evaluated by the area under the receiver operating characteristic （ROC） curve （AUC）. ResultsTotally 441 subjects were included in the analysis， with an average age of 46.0±14.5 years and a prevalence of osteoporosis of 6.4% （28/441）. As the level of plasma fibrinogen increased， the incidence of osteoporosis significantly increased （P<0.000 1）while the average bone mineral density of L1 and L2 were significantly decreased （P<0.05）. Compared with the first quartile of plasma Fbg（1.99g/L -2.37g/L）， the risk of osteoporosis in the fourth quartile of plasma Fbg （3.67g/L-4.46g/L） increased by 8.85 times after adjusting for related confounding factors. ConclusionThis study found a negative correlation between plasma fibrinogen levels and bone density in patients with hypertension. Plasma fibrinogen levels may serve as a potential screening indicator for osteoporosis， aiding in early diagnosis and therapeutic monitoring. This discovery offers a new perspective for the study of bone metabolic diseases and warrants further investigation.

Objective To investigate the relationship between cyclin A2 (CCNA2) and the prognosis of colon cancer, and its possible mechanism from the perspective of immune infiltration. Methods We downloaded the transcriptome data of colon cancer patients from The Cancer Genome Atlas database. Clinicopathological feature analysis and survival analysis were performed based on the expression levels of CCNA2. A total of 75 specimens of colon cancer and normal tissues were collected, and the expression level of CCNA2 was analyzed using immunohistochemical methods. Multivariate analysis was conducted to explore its relationship with clinicopathological features. Gene Set Enrichment Analysis (GSEA) was used to assess the potential molecular functions of CCNA2 in colon cancer. CIBERSORT algorithm was applied to calculate the correlation between CCNA2 and immune-cell infiltration in colon cancer. Results Database and immunohistochemical analyses indicated that CCNA2 was expressed at a significantly higher level in colon cancer tissues than normal tissues (P<0.001). The overall survival, disease-specific survival, and progression-free interval were all longer in the group with high CCNA2 expression than the group with low expression (all P<0.05). In tumor tissues, the expression level of CCNA2 decreased with increased pathological and TNM stages (P<0.05). The expression level of CCNA2 in normal tissues was consistently lower than that in colon cancer tissues across all clinical stages (all P<0.001). GSEA suggested that Wnt/β-catenin, KRAS, and other signaling pathways were enriched when CCNA2 was lowly expressed. CIBERSORT analysis revealed an increase in the infiltration of immune cells such as regulatory T cells and macrophages M0 when CCNA2 expression was low. Conclusion CCNA2 is highly expressed in colon cancer and closely associated with grade of pathology and TNM stage. It may recruit regulatory T cells through the KRAS and Wnt/β-catenin pathways, thereby reducing immune-cell infiltration and promoting colon cancer progression, leading to poor prognosis.

OBJECTIVE To assess the scientific rigor， clarity and feasibility of the recommendations of the Guidelines for Evidence-based Use of Biological Agents for the Clinical Treatment of Osteoporosis （hereinafter referred to as the Guideline） through external review， in order to further revise and improve the Guideline recommendations. METHODS This study employed a cross-sectional survey research design， a convenience sampling method was adopted to select frontline medical workers in the field of osteoporosis （including clinical doctors， clinical pharmacists， and nurses） as well as patients or their family members. External review was conducted through a combination of closed-ended and open-ended electronic questionnaires to get feedback from them on the appreciation，clarity and feasibility of the 32 preliminary recommendations in the Guideline. RESULTS A total of 90 external review subjects from 15 hospitals were collected， including 45 clinical doctors， 15 clinical pharmacists， 15 nurses and 15 patients or their family members. The overall appreciation degree of recommendations was 99.38%， the overall clarity degree of recommendations was 98.92%， and the overall feasibility degree of recommendations was 99.65%. At the same time， 111 subjective suggestions were collected， which provided an important reference for the further improvement of the Guideline recommendations. Based on the above feedback， the Guideline steering committee and core expert group revised the wording of 12 draft recommendations without deletion， and finally determined 32 recommendations. CONCLUSIONS The external review provides an important basis for the final formation of the Guideline， further improves the scientific rigor， clarity and feasibility of the recommendations， and ensures the standardization， practicality and implementability of the Guideline.

Objective: To compare and analyze the antibacterial effects of platelets against five common clinical pathogenic bacteria including MRSA, SE, SA, E. coli, and CRKP, and to preliminarily explore the role of DCD sensitivity in the observed variations of antibacterial effects. Methods: The same number of platelets were used to establish co-culture systems of platelets and platelet lysates with the five pathogenic bacteria. The antibacterial effects of platelets and platelet lysates on the five pathogenic bacteria were evaluated by observing the turbidity of the bacterial solution, measuring the OD value of the bacterial solution and counting the colonies. The supernatant protein of platelets co-cultured with MRSA was collected for quantitative proteomics analysis to explore the important antibacterial proteins of platelets. The content of DCD in the supernatant after co-culture of platelets and platelet lysates with the five pathogenic bacteria was detected by ELISA to preliminarily analyze the reasons for the different antibacterial effects of platelets on the five pathogenic bacteria. Results: Compared with the control group of MRSA, SA, and SE, the turbidity of the bacterial solution decreased after co-culture of platelets and platelet lysates with MRSA, SA, and SE for 12 h, and the OD value and colony count were significantly reduced (P<0.05). The turbidity of the bacterial solution did not change significantly after co-culture of platelets and platelet lysates with E. coli for 24 h, but the OD value decreased (P<0.05), and the colony count decreased to 10 CFU/mL but the difference was not statistically significant (P>0.05). Compared with the control group of CRKP, the turbidity, OD value, and colony count of the bacterial solution did not change significantly after co-culture of platelets and platelet lysates with CRKP (P>0.05). Proteomics results showed that after co-culture with MRSA, important proteins related to platelet activation, including collagen, fibrinogen, von Willebrand factor, integrin αIIbβ3, platelet glycoprotein V and IV were significantly up-regulated. ELISA results showed that after co-culture with the five pathogenic bacteria, platelets could secrete a large amount of DCD, with the content around 3 μg/mL. Conclusion: The antibacterial effect of platelets on Gram-positive bacteria MRSA, SA, and SE is better than that on Gram-negative bacteria E. coli and CRKP, and platelets have the best antibacterial effect on MRSA. The differences in antibacterial effects of platelets on the five pathogenic bacteria may be related to the sensitivity of DCD antibacterial peptides to the five pathogenic bacteria.

Cervical cancer is one of the most common gynecological malignant tumors in China. Given their lack of obviously early symptoms, more than half of patients with cervical cancer are diagnosed in the middle and late stages of this malignancy, resulting in poor prognosis. Finding new therapeutic targets is the current research direction. Metabolomics, as a new omics technology, is expected to provide new targets for tumor precision diagnosis and treatment through the analysis of the changes and potential mechanisms of metabolites in tumor occurrence and development by chromatography, mass spectrometry, and other technologies. Herein, we review the research methods of metabolomics; metabolic characteristics of cervical cancer; and progress of the research on metabolomics in cervical cancer diagnosis, curative effect prediction, and prognosis evaluation to provide new ideas for the precise diagnosis and treatment of cervical cancer.

AIM: To quantitatively evaluate the clinical characteristics of haze after transepithelial photorefractive keratectomy(TPRK)for astigmatism using corneal densitometry.METHODS:In this retrospective clinical study, a total of 74 patients(106 eyes)with astigmatism ≥1.25 D who underwent TPRK in our hospital from October 2022 to December 2024 were continuously collected. All of the study subjects were divided into transparent group(65 eyes)and haze group(41 eyes)based on whether haze occurred after surgery. Pentacam examination was performed before and after surgery, and corneal densitometry was recorded at the time points of preoperation, 1 mo postoperation in the transparent group and the most severe haze degree in the haze group. The collected corneal densitometry included the average densitometry of the entire corneal layer in the central 2 mm, 2-6 mm, and 6-10 mm areas, as well as the average densitometry of the entire layer of the corneal section in the center 6 mm of the astigmatism axis(astigmatism expressed in negative cylindrical form)and orthogonal axis(the axis perpendicular to the astigmatism axis), and the average densitometry of the entire layer of the corneal section in the nasal and temporal 2-6 mm areas of the astigmatism axis in the haze group of patients with regular astigmatism. The change in corneal densitometry after surgery compared with that before surgery was calculated.RESULTS:There was no statistically significant difference in baseline data such as gender, age, and spherical equivalent between the transparent group and the haze group(all P>0.05). The change in corneal densitometry in the 2-6 mm area of the haze group was greater than that in the transparent group(Z=-2.226, P=0.026), while there was no significant difference in the change of corneal densitometry in the central 2 mm and 6-10 mm areas between the two groups(both P>0.05). There was no significant difference in the change of corneal densitometry between the transparent group and haze group along the orthogonal axis(all P>0.05), while the change of corneal densitometry in the haze group along the astigmatism axis was greater than that in the transparent group(Z=-2.371, P=0.018). The temporal corneal densitometry of patients with regular astigmatism in the haze group after surgery was higher than that of the nasal side, and the change in corneal densitometry was also greater than that of the nasal side(Z=-4.288, P<0.001; Z=-4.043, P<0.001).CONCLUSION:Unlike spherical correction for myopia and hyperopia, haze after TPRK for astigmatism was mainly manifested in the peripheral cutting area of the astigmatism axis, and patients with regular astigmatism had a higher probability or severity of haze on the temporal side of the astigmatism axis than on the nasal side.

OBJECTIVE To investigate the mechanism of catalpol affecting the differentiation of helper T cell 17 （Th17） by interfering the expressions of pyruvate kinase M2 （PKM2） and lactate dehydrogenase A （LDHA）. METHODS The naive CD4+ T cells were selected from the spleen of C57BL/6 mice， and were differentiated into Th17 cells by adding directional differentiation stimulants for 72 hours. At the same time， the cells were treated with 0 （directed control）， 20， 40 and 80 μg/mL catalpol. The flow cytometry was used to detect the proportion of Th17 cell differentiation in cells； the colorimetric method was adopted to detect the levels of pyruvate and lactate in cell culture supernatant； mRNA expressions of retinoid-related orphan nuclear receptor gamma t （RORγt）， PKM2 and LDHA were detected by qRT-PCR method； Western blot was used to detect the expression levels of PKM2， LDHA， signal transducer and activator of transcription 3 （STAT3）， and phosphorylated STAT3 （p-STAT3） proteins in cells. RESULTS Compared with the directed control group， after 72 hours of treatment with 20， 40， 80 μg/mL catalpol， the differentiation ratio of Th17 cells were decreased by 6.74%， 8.41%， 9.24%， and the levels of pyruvate and lactate in the cell culture supernatant， the mRNA expressions of PKM2， LDHA and RORγt as well as the protein expressions of PKM2 and LDHA and the phosphorylation of STAT3 were significantly reduced （P＜0.05）. CONCLUSIONS Catalpol can reduce the glycolysis level by down-regulating the expressions of PKM2 and LDHA， thereby inhibiting the differentiation of Th17 cells.

Perinatal depression is a psychological disorder that occurs during pregnancy and within one year of delivery, which can seriously affect the physical and mental health of pregnant and postpartum women, as well as the cognitive and behavioral abilities of offspring, with potential multigenerational effects. Therefore, it is important to identify its potential modifiable risk factors. Phthalic acid esters (PAEs), as common environmental endocrine disruptors, can affect maternal estrogen through multiple mechanisms and are important potential modifiable risk factors for developing maternal perinatal depression. At present, studies on the correlation between PAEs and perinatal depression are still very limited, and the mechanisms by which PAEs affect perinatal depression have not been clarified. Based on existing epidemiological and toxicological studies at home and abroad, the article briefly introduced the characteristics of multiple pathways, high doses, and long-term exposure to maternal PAEs, focused on reviewing the current status of epidemiological studies, pointed out the possible associations between some specific PAEs exposure and elevated risk of perinatal depression. It also summarized the potential roles of hormone-neurotransmitter pathway, inflammation mediation, gene regulation, and other possible mechanisms in the association between exposure to PAEs and perinatal depression. The article concluded with a look at how future research on the association between exposure to PAEs and perinatal depression can be scientifically validated, with a view to providing more high-quality evidence for the scientific prevention of the onset and progression of maternal depressive symptoms.

Background The threshold limit values (TLVs) established and regularly updated by the American Conference of Governmental Industrial Hygienists (ACGIH) are widely adopted and referenced globally, serving as a crucial reference for China's occupational exposure limits (OELs). It is necessary to track it regularly and compare it with China's OELs. Objective To compare the OELs stipulated in Occupational exposure limits for hazardous agents in the workplace—Part 1: Chemical hazardous agents (GBZ 2.1—2019) and the ACGIH TLVs (2024) and to provide references for subsequent formulation and revision of OELs in China. Methods The OELs specified in GBZ 2.1—2019 and the TLVs issued by ACGIH were used to establish a database using Microsoft Excel 2019 software. Cross verification was conducted through matching Chemical Abstracts Service Registry Numbers (CAS Rn) and both Chinese and English names to ensure accuracy. Then, comparisons and analyses were carried out based on the type of limit values, which were matched as follows: permissible concentration-time weighted average (PC-TWA) with threshold limit value-time weighted average (TLV-TWA), permissible concentration-short term exposure limit (PC-STEL) with threshold limit value-short term exposure limit (TLV-STEL), and maximum allowable concentration (MAC) with threshold limit value-ceiling (TLV-C). Comparisons included types, quantities, and sizes of limits. Results The GBZ 2.1—2019 OELs and the ACGIH TLVs (2024) were generally consistent in terms of types and definitions, but there were differences in the number and size of the limits. In terms of the number of limits, GBZ 2.1—2019 specified 365 OELs for 358 chemical hazardous agents, while ACGIH TLVs (2024) included 316 corresponding limits. Among these, 148 (46.9%) limits were consistent, 38 (12.0%) were basically consistent, and 130 (41.1%) were inconsistent. In terms of the size of the limits, out of the 130 inconsistent limits, 51 OELs were lower than the corresponding TLVs, 67 OELs were higher than the corresponding TLVs, and 12 were under different limit types. For some chemical hazardous agents, their OELs were significantly lower or higher than their TLVs. Conclusion Some of the OELs for chemical hazardous agents specified in GBZ 2.1—2019 are significantly lower or higher than the TLVs. For these chemical hazardous factors, it is recommended to prioritize their inclusion in research projects and to complete the revisions as soon as possible based on the latest scientific evidence.

Objective To construct a risk prediction model for liver cancer in patients with chronic hepatitis C based on seven different machine learning algorithms and select the optimal model. Methods A total of 236 patients with chronic hepatitis C were selected as the research subjects. Patients were divided into a case group and a control group according to whether liver cancer occurs. Prediction models were constructed based on seven machine learning algorithms including classification and regression tree, random forest, gradient boosting decision tree, extreme gradient boosting (XGBoost), logistic regression, K-near neighbor, and support vector machine. The Shapley additive explanations (SHAP) algorithm was used to interpret the best prediction model. Results Among the seven models, the XGBoost model had the best comprehensive prediction performance (accuracy of 0.933, sensitivity of 0.775, specificity of 0.960, area under the ROC curve of 0.956, F1 score of 0.764). The SHAP algorithm suggested that AFP, age, AST, diabetes, BMI, PLT, ALT, liver cysts, FIB-4, and gender contributed to the model decision and are the risk factors for liver cancer in patients with chronic hepatitis C. Conclusion This study develops an interpretable machine learning model based on the XGBoost algorithm, which has a good reference value for individualized monitoring of liver cancer in patients with chronic hepatitis C.