Objective To describe the significance of the pretreatment inflammatory indicators in predicting the prognosis of patients with esophageal squamous cell carcinoma (ESCC) after undergoing radical radiotherapy. Methods The data of 246 ESCC patients who underwent radical radiotherapy were retrospectively collected. Receiver operating characteristic (ROC) curves were drawn to determine the optimal cutoff values for platelet-lymphocyte ratio (PLR), neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII). The Kaplan-Meier method was used for survival analysis. We conducted univariate and multivariate analyses by using the Cox proportional risk regression model. Software R (version 4.2.0) was used to create the nomogram of prognostic factors. Results The results of the ROC curve analysis showed that the optimal cutoff values of PLR, NLR, and SII were 146.06, 2.67, and 493.97, respectively. The overall response rates were 77.6% and 64.5% in the low and high NLR groups, respectively (P<0.05). The results of the Kaplan-Meier survival analysis revealed that the prognosis of patients in the low PLR, NLR, and SII group was better than that of patients in the high PLR, NLR, and SII group (all P<0.05). The results of the multivariate Cox regression analysis showed that gender, treatment modalities, T stage, and NLR were independent factors affecting the overall survival (OS). In addition, T stage and NLR were independent factors affecting the progression-free survival (PFS) (all P<0.05). The nomogram models of OS and PFS prediction were established based on multivariate analysis. The C-index values were 0.703 and 0.668. The calibration curves showed excellent consistency between the predicted and observed OS and PFS. Conclusion The pretreatment values of PLR, NLR, and SII are correlated with the prognosis of patients with ESCC who underwent radical radiotherapy. Moreover, NLR is an independent factor affecting the OS and PFS of ESCC patients. The NLR-based nomogram model has a good predictive ability.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective: Whether elevation in plasma levels of amyloid and tau protein biomarkers are better indicators of cognitive decline than higher baseline levels in patients with amnestic mild cognitive impairment (aMCI) and Alzheimer’s disease (AD) remains understudied. Methods: We included 67 participants with twice testing for AD-related plasma biomarkers via immunomagnetic reduction (IMR) assays (amyloid beta [Aβ]1-40, Aβ1-42, total tau [t-Tau], phosphorylated tau [p-Tau] 181, and alpha-synuclein [α-Syn]) and the Mini-Mental State Examination (MMSE) over a 1-year interval. We examined the correlation between biomarker levels (baseline vs. longitudinal change) and annual changes in the MMSE scores. Receiver operating characteristic curve analysis was conducted to compare the biomarkers. Results: After adjustment, faster cognitive decline was correlated with lower baseline levels of t-Tau (β=0.332, p=0.030) and p-Tau 181 (β=0.369, p=0.015) and rapid elevation of t-Tau (β=-0.330, p=0.030) and p-Tau 181 levels (β=-0.431, p=0.004). However, the levels (baseline and longitudinal changes) of Aβ1-40, Aβ1-42, and α-Syn were not correlated with cognitive decline. aMCI converters had lower baseline levels of p-Tau 181 (p=0.002) but larger annual changes (p=0.001) than aMCI non-converters. The change in p-Tau 181 levels showed better discriminatory capacity than the change in t-Tau levels in terms of identifying AD conversion in patients with aMCI, with an area under curve of 86.7% versus 72.2%. Conclusion We found changes in p-Tau 181 levels may be a suitable biomarker for identifying AD conversion.

Objective To investigate the effect and mechanism of naringin on acute lung injury(ALI)in septic mice.Methods The acute lung injury mouse model of sepsis was established by intraperitoneal injection of 10 mg·kg-1 lipopolysaccharide.The mice were randomly divided into control group(injected with equal amounts of saline and phosphate buffer),model group(mouse model of sepsis acute lung injury),naringin group(50 mg·kg-1 naringin injected intraperitoneally 1 hour prior to lipopolysaccharide modeling)and BzATP group(50 mg·kg-1 naringin+5 mg·kg-1 BzATP injected intraperitoneally 1 hour prior to lipopolysaccharide modeling).After modeling,lung tissues were taken 24 h later,lung coefficients were calculated;lung tissue interleukin(-ILβ-1 β),interleukin-10(IL-10)and tumor necrosis factor-α(TNF-α)levels were detected by enzyme-linked immunosorbent assay;the expression of purinergic 2X7 receptor(P2X7R),nucleotide-binding oligomerization structural domain-like receptor protein 3(NLRP3)and nuclear factor-κB(NF-κB)proteins were detected by Western blotting.Results The lung coefficients in the control,model,naringin and BzATP groups were(6.26±0.31),(9.09±1.02),(7.02±0.45)and(8.79±0.55)mg·g-1;the contents of TNF-α were(56.41±0.35),(174.68±1.58),(85.23±1.68)and(162.97±3.42)pg·mL-1;the contents of IL-1β were(44.18±7.37),(119.91±17.16),(85.41±2.14)and(104.57±3.39)pg·mL-1;the contents of IL-10 were(50.82±2.89),(28.31±1.86),(42.82±1.98)and(25.19±1.69)pg·mL-1;P2X7 protein expression levels were 0.45±0.16,1.33±0.10,0.64±0.09 and 1.05±0.18;NF-κB protein expression levels were 0.38±0.19,1.29±0.09,0.57±0.11 and 0.92±0.07;NLRP3 protein expression levels were 0.72±0.14,1.28±0.23,0.75±0.09 and 1.27±0.23.Compared with the model group in the control group and naringin group,compared with the naringin group in the BzATP group,the differences of the above indexes were statistically significant(P＜0.01,P＜0.05).Conclusion Naringin attenuates acute lung injury in septic mice by inhibiting the P2X7 receptor-mediated NF-κB/NLRP3 signaling pathway.

Micro vesicles(MVs)are the membrane structures produced by the direct outgrowth of the cell membrane,which can be involved in the pathogenesis of many diseases;MVs can also be used in the diagnosis of diseases and the prediction of prognosis.In recent years,there are more and more researches related to MVs in the field of sepsis,and the mechanism of MVs in sepsis is now reviewed.

Tumors continue to be a major challenge in human survival that we have yet to overcome. Despite the variety of treatment options available, we have not yet found an effective method. As more and more research is conducted, attention has been turned to a new field for tumor treatment—the tumor microenvironment (TME). This is a dynamic and complex environment consisting of various matrix cells surrounding cancer cells, including surrounding immune cells, blood vessels, extracellular matrix, fibroblasts, bone marrow-derived inflammatory cells, signaling molecules, and some specific cell types. Firstly, endothelial cells play a key role in tumor development and the immune system’s protection of tumor cells. Secondly, immune cells, such as macrophages, Treg cells, Th17 cells, are widely involved in various immune responses and activities in the human body, such as inflammation responses promoting survival carefully orchestrated by the tumor. Even though many studies have extensively researched the TME and found many research schemes, so far, no key effective method has been found to treat tumors by affecting the TME. The TME is a key interaction area between the host immune system and the tumor. Cells within the TME influence each other and interact with cancer cells to affect cancer cell invasion, tumor growth, and metastasis. This is a new direction for cancer treatment. In the complex environment of the TME, post-translational modifications (PTMs) of proteins have been proven to play an important role in the TME. PTMs are dynamic, strictly regulated changes to proteins that control their function by regulating their structure, spatial location, and interaction. Among PTMs, a reversible post-translational modification called SUMOylation is a common regulatory mechanism in cellular processes. It is a post-translational modification that targets lysine residues with a small ubiquitin-like modifier (SUMO) in a reversible post-translational modification manner. SUMOylation is widely involved in carcinogenesis, DNA damage response, cancer cell proliferation, metastasis, and apoptosis, playing a pivotal role in the TME, such as DNA damage repair, tumor metastasis, and also participates in immune cell differentiation, activation, and inhibition of immune cells. On the other hand, SUMO or sentrin-specific protease (SENP) inhibitors can interfere with the SUMOylation process, thereby affecting many biological processes, including immune response, carcinogenesis, cell cycle progression, and cell apoptosis, etc. In summary, this review aims to introduce the dynamic modification of protein SUMOylation on various immune cells and the application of various inhibitors, thereby exploring its role in the TME. This is a challenging but hopeful field, and we look forward to future research that can bring more breakthroughs. In conclusion, the TME is a complex and dynamic environment that plays a crucial role in the development and progression of tumors. Understanding the intricate interactions within the TME and the role of PTMs, particularly SUMOylation, could provide valuable insights into the mechanisms of tumor development and potentially lead to the development of novel therapeutic strategies. The study of SUMOylation and its effects on various immune cells in the TME is an exciting and promising area of research that could significantly advance our understanding of tumor biology and potentially lead to the development of more effective treatments for cancer. This is a challenging but hopeful field, and we look forward to future research that can bring more breakthroughs.

Objective Total thyroidectomy of C57BL/6 and KM mice was performed by two different surgical methods to verify the success of mouse hypothyroidism model modeling,and compared the success rate of different surgical methods.Methods C57BL/6 and KM mice underwent total thyroidectomy by ligation(operation method Ⅰ)or hemostasis(operation method Ⅱ),and the detailed operation processes were recorded.Serum TT3,TT4 and TSH levels detected by enzyme-linked immunosorbent assay,body weight,and hematoxylin-eosin(HE)-stained neck tissue were compared before and after surgery to verify the model.Results Serum TT3 and TT4 levels were decreased(P＜0.05)and TSH was increased(P＜0.001)in both model groups.The 28-day postoperative survival rates were 40％and 60％in groups Ⅰ and Ⅱ,respectively,and 50％and 40％in KM mice.Body weights were significantly higher in both model groups compared with the sham control group.HE staining and microscopic observation showed that the cervical tissue in both strains was thyroid tissue,and the back membrane of the thyroid remained intact after isolation.Conclusions Both surgical method can induce hypothyroidism in C57BL/6 and KM mice;however,it is necessary to consider the anatomical relationship of the thyroid gland to the surrounding tissue,improve the proficiency of the surgical operation,prevent the occurrence of postoperative hypocalcemia and infection,and thus improve the survival rate of the model mice.