Objective: To identify influencing factors associated with the prognosis of sepsis patients receiving blood component transfusion, and to provide a more rational and scientific transfusion strategy for clinical management. Methods: Clinical data of 232 patients with sepsis treated at the Second Xiangya Hospital of Central South University between January 2022 and December 2023 were retrospectively analyzed. These patients were categorized into the transfusion group (n=64) and the non-transfusion group (n=168) based on whether they received transfusions, and the patients in the transfusion group were further divided into non-survivor group (n=26) and survivor group (n=38) based on their survival outcome. Baseline characteristics and clinical characteristics were compared between two groups. Factors impacting the prognosis of sepsis patients undergoing blood component transfusion were identified using logistic regression. Results: Compared to the non-transfusion group, the transfusion group showed significantly higher levels of coagulation indicators (prothrombin time, activated partial thromboplastin time, international normalized ratio, D-dimer) and inflammatory markers (C-reactive protein, procalcitonin, interleukin-6), while the level of hemoglobin, platelet, lymphocyte, fibrinogen, albumin, blood glucose, and oxygen saturation were significantly lower (P<0.05). The [M(P , P )] for C-reactive protein (mg/L), hemoglobin (g/L), and platelet count (×10 /L) in the transfusion vs non-transfusion groups were 178.0(156.1-178) vs 102.7(74.0-119.6), 88.5(72.3-113.0) vs 110.5(101-121.8), and 63.0(26.5-156.5) vs 202.5(108.3-286.8), respectively (all P<0.05). Logistic regression analysis revealed that hemoglobin level, platelet count, lactate concentration, and the storage duration of transfused red blood cells were independent risk factors affecting the survival outcomes of sepsis patients receiving transfusions (P<0.05). In septic transfusion patients, the [M(P , P )] lactate concentration (mmol/L) and RBC storage time (d) in the non-survivor vs survivor groups were 3.5(1.9-7.7) vs 2.1(1.3-3.5), 18.0 (13.0-18.0) vs 12.0(9.0-14.0), respectively (both P<0.05). Conclusion: Compared to non-transfused sepsis patients, those receiving transfusions exhibited poorer baseline conditions, more severe infections, and worse survival outcomes. More importantly, the study found that the timing of transfusion decisions and the quality control of blood products (such as storage duration) may directly impact patient prognosis, providing critical evidence for optimizing transfusion strategies in septicemia patients.

Objective: To identify influencing factors associated with the prognosis of sepsis patients receiving blood component transfusion, and to provide a more rational and scientific transfusion strategy for clinical management. Methods: Clinical data of 232 patients with sepsis treated at the Second Xiangya Hospital of Central South University between January 2022 and December 2023 were retrospectively analyzed. These patients were categorized into the transfusion group (n=64) and the non-transfusion group (n=168) based on whether they received transfusions, and the patients in the transfusion group were further divided into non-survivor group (n=26) and survivor group (n=38) based on their survival outcome. Baseline characteristics and clinical characteristics were compared between two groups. Factors impacting the prognosis of sepsis patients undergoing blood component transfusion were identified using logistic regression. Results: Compared to the non-transfusion group, the transfusion group showed significantly higher levels of coagulation indicators (prothrombin time, activated partial thromboplastin time, international normalized ratio, D-dimer) and inflammatory markers (C-reactive protein, procalcitonin, interleukin-6), while the level of hemoglobin, platelet, lymphocyte, fibrinogen, albumin, blood glucose, and oxygen saturation were significantly lower (P<0.05). The [M(P , P )] for C-reactive protein (mg/L), hemoglobin (g/L), and platelet count (×10 /L) in the transfusion vs non-transfusion groups were 178.0(156.1-178) vs 102.7(74.0-119.6), 88.5(72.3-113.0) vs 110.5(101-121.8), and 63.0(26.5-156.5) vs 202.5(108.3-286.8), respectively (all P<0.05). Logistic regression analysis revealed that hemoglobin level, platelet count, lactate concentration, and the storage duration of transfused red blood cells were independent risk factors affecting the survival outcomes of sepsis patients receiving transfusions (P<0.05). In septic transfusion patients, the [M(P , P )] lactate concentration (mmol/L) and RBC storage time (d) in the non-survivor vs survivor groups were 3.5(1.9-7.7) vs 2.1(1.3-3.5), 18.0 (13.0-18.0) vs 12.0(9.0-14.0), respectively (both P<0.05). Conclusion: Compared to non-transfused sepsis patients, those receiving transfusions exhibited poorer baseline conditions, more severe infections, and worse survival outcomes. More importantly, the study found that the timing of transfusion decisions and the quality control of blood products (such as storage duration) may directly impact patient prognosis, providing critical evidence for optimizing transfusion strategies in septicemia patients.

Objective:To investigate the dosimetric characteristics of intensity modulated proton therapy (IMPT) and intensity modulated radiation therapy (IMRT) for lung cancers.Methods:Three lung cancer patients (central-lower, central, and peripheral types) admitted to Shandong Cancer Hospital and Institute from January 2024 to May 2024 were selected as the research subjects. IMPT and IMRT plans were designed for each case based on the anatomical location of the clinical target volume and the dose constraints for organs at risk (OARs). Dosimetric parameters, including conformity index (CI), homogeneity index (HI), and gradient index (GI) for target coverage, as well as OARs dosimetric parameters were evaluated. The volume of additional dose deposition in the body was compared by assessing regions receiving 10%, 30%, and 50% of the prescription dose.Results:For all three cases, IMRT plans demonstrated higher CI values (0.80, 0.60, and 0.79) compared to IMPT plans (0.61, 0.57, and 0.34). IMPT plans yielded lower HI values (0.07, 0.06, and 0.06) than IMRT plans (0.09, 0.15, and 0.09) and lower GI values (2.84, 2.47, and 4.56 vs. 4.91, 3.09, and 4.99 for IMRT plans). Compared with the IMRT plans, the low-dose region in the ipsilateral lung was significantly reduced in IMPT plans (V 5 of the IMPT plans were 20.59%, 46.29%, 10.94%, respectively; V 5 of the IMRT plans were 48.91%, 60.63%, 19.92%, respectively), but there was no significant advantage in the high-dose region compared to IMRT plans (V 20 of the IMPT plans were 12.88%, 34.75%, 5.21%, respectively; V 20 of the IMRT plans were 21.70%, 36.50%, 5.31%, respectively). The dose to the contralateral lung and heart was significantly reduced in IMPT plans [the D mean of the contralateral lung in the IMPT plans were 0.08, 0.04, and 0.00 Gy (RBE), respectively, and those in the IMRT plans were 3.25, 1.18, and 0.55 Gy, respectively; the heart D mean in the IMPT plans were 6.23, 7.04, and 0.00 Gy (RBE), respectively, while those of the IMRT plans were 18.33, 10.27, and 0.08 Gy, respectively). IMPT plans significantly reduced the volumes receiving 10% of the prescription dose by 65.94%, 25.57% and 72.47%, respectively, compared to IMRT plans. The volumes IMPT plans occupied by 30% of the prescription dose area in the body were reduced by 54.97%, 26.47% and 39.04%, respectively, compared to the IMRT plans. The volumes IMPT plans occupied by 50% of the prescription dose area in the body were reduced by 54.49%, 30.43% and 28.89%, respectively, compared to the IMRT plans. Conclusions:IMPT plan significantly reduces the V 5 of the ipsilateral lung, the D mean of the contralateral lung and the heart, while maintaining target coverage compared with IMRT plan for lung cancers. However, IMPT plan does not show much more advantage than IMRT plan in the ipsilateral lung V 20. IMPT can reduce the additional exposure volume within the body.

Objective: To compare the effectiveness of qualitative and quantitative fecal immunochemical tests (FIT) in identifying colorectal cancer, so as to provide insights into perfecting screening strategies for colorectal cancer. Methods: Participants in the Colorectal Cancer Screening Program for Key Populations in Zhejiang Province from May 2020 to December 2021 were recruited, and their demographic information, lifestyle and disease history were collected through a questionnaire survey. Qualitative or quantitative FIT along with a questionnaire-based risk assessment were employed as the initial screening tests. Individuals who were positive in any FIT or had high-risk assessment results were required to attend a subsequent colonoscopy examination. The positive rate, detection rate of colorectal cancer, positive predictive value and number of colonoscopies required were compared between qualitative and quantitative FITs, and stratified analyses by gender and age were conducted. Results: Totally 4 099 769 participants were included. The qualitative FIT group included 3 574 917 individuals, yielding a positive rate of 11.35%, a detection rate of 1.19%, a positive predictive value of 0.48% and 83.84 colonoscopies required to detect one cancer case. The quantitative FIT group involved 524 852 individuals, yielding a positive rate of 6.70%, a detection rate of 2.31%, a positive predictive value of 1.01% and 43.23 colonoscopies required to detect one cancer case. The quantitative FIT group showed significantly higher detection rate of colorectal cancer, higher positive predictive value and less number of colonoscopies required compared to the qualitative FIT group (all P<0.05). The same results were obtained after stratification by gender and age. Conclusion Compared to qualitative FIT, quantitative FIT improves the detection of colorectal cancer and reduces the workload of colonoscopy examinations, making it more suitable for colorectal cancer screening in large-scale populations.

Sepsis is one of the common severe diseases caused by the dysregulated host response to infection， which seriously threatens the life and health of human beings all over the world. The incidence and mortality of the disease are extremely high， and it has always been an urgent problem to be solved in the field of acute and critical diseases. At present， anti-infection， fluid resuscitation， mechanical ventilation and other programs are most used in clinic to treat sepsis， but their poor prognosis and high cost and other issues remain to be resolved. Therefore， it is necessary to explore a new， efficient， safe and inexpensive drug and treatment model at this stage. The treatment of traditional Chinese medicine （TCM） is based on syndrome differentiation and holistic concept. It can effectively regulate the progression of sepsis， maintain the homeostasis of the body， and has fewer adverse reactions. It has achieved good clinical results. In recent years， a large number of studies have shown that TCM can reduce the inflammatory response by regulating the Toll-like receptor 4（TLR4） signaling pathway， thereby reducing the severity and mortality of sepsis patients. However， there is still a lack of systematic exposition of TCM regulating TLR4 signaling pathway in the treatment of sepsis. Therefore， this article summarizes the relationship between TLR4 signaling pathway and sepsis and the mechanism of TCM in the disease by searching and consulting relevant literature in recent years. It is found that some Chinese medicine monomers and active ingredients， Chinese medicine compounds and Chinese medicine preparations can effectively reduce systemic inflammatory response， repair organ damage and improve the prognosis of sepsis by inhibiting the activation of TLR4 signaling pathway. However， due to various limitations， some studies have directly focused on the differential expression and function of TLR4， ignoring the downstream molecular expression and phenotypic effects of TLR4. The alternative mechanism， relationship and specific molecular mechanism of the pathway are still unclear. There are problems such as unclear pharmacokinetics and unclear mechanism in the pro- and anti-inflammatory balance， which need to be further studied and explored in order to provide new ideas for the potential treatment and drug development for sepsis.

Objective To observe the correlations of chest CT quantitative parameters in patients with acute exacerbation of chronic obstructive pulmonary disease(AECOPD)with blood eosinophil(EOS)level.Methods Chest CT data of 162 AECOPD patients with elevated eosinophils were retrospectively analyzed.The patients were divided into low EOS group(n=105)and high EOS group(n=57)according to the absolute counting of blood EOS.The quantitative CT parameters,including the number of whole lung bronchi and the volume of blood vessels,low-attenuation area percentage(LAA％)of whole lung,of left/right lung and each lobe of lung,as well as the luminal diameter(LD),wall thickness(WT),wall area(WA)and WA percentage of total bronchial cross-section(WA％)of grade 3 to 8 bronchi were compared between groups.Spearman correlations were performed to analyze the correlations of quantitative CT parameters with blood EOS level.Results LAA％of the whole lung,of the left/right lung and each lobe of lung,as well as of the upper lobe of right lung LDgrade4,middle lobe of right lung WTgrade5,upper lobe of right lung WAgrade4,middle lobe of right lung WAgrade5 and lower lobe of left lung WAgrade3 in low EOS group were all higher than those in high EOS group(all P＜0.05).Except for the upper lobe of right lung LDgrade4,the above quantitative CT indexes being significant different between groups were all weakly and negatively correlated with blood EOS level(r=-0.335 to-0.164,all P＜0.05).Conclusion Chest CT quantitative parameters of AECOPD patients were correlated with blood EOS level,among which LAA％,a part of WT and WA were all weakly negatively correlated with blood EOS level.

The condylar movement trajectories in healthy people usually have relatively consistent characteristics,while the change of the trajectory of the temporomandibular condyle often indicates the occurrence of various oral diseases.An in-depth understanding of the condylar movement can help physicians diagnose and evaluate the efficacy of oral diseases.This article reviews the development of con-dylar motion tracing technique,and introduces the research progress and clinical application status of condylar motion tracing analysis in various oral diseases.At the same time,the limitations of the current condylar motion tracing technique and the prospect of future clinical application are proposed.

Objective:To investigate the feasibility of classifying imaging patterns of dermatomyositis/polymyositis-related interstitial lung disease (DM/PM-ILD) into subtypes based on chest CT radiomics features and a model was constructed by machine learning algorithms.Methods:From November 2011 to November 2020, 107 patients diagnosed with PM/DM-ILD at the First Affiliated Hospital of Xi′an Jiaotong University were retrospectively analyzed. A total of 315 cases with chest CT were collected. Doctors pre-classified image patterns, including 105 cases with non-specific interstitial pneumonia (NSIP), 90 cases with organizing pneumonia (OP), and 66 cases with non-specific interstitial pneumonia combined with organizing pneumonia (NSIP+OP), 35 cases with common interstitial pneumonia (UIP), and 19 cases with diffuse alveolar damage (DAD), ANOVA was used to test the difference of baseline clinical information among the imaging classification groups. All images were divided into the training set and the est set by stratified random sampling at a ratio of 4∶1. In each CT scan, 3D slicer was used to segment each lung lobe, and then reconstructed into 3 mm 3 of voxels, and Pyradiomics library was used to extract the radiomic features of the whole lung and each lobe. The multi-classification goal was achieved by constructing random forest base classifiers for each of the five groups and then voting as the final model. In the process of constructing the base classifier, firstly, the balance between sample groups was achieved by SMOTETomek comprehensive sampling, and the optimal feature set was selected by independent sample t test and L1 regularized least absolute shrinkage and selection operator (LASSO) regression. In this study, the Radiomics model was constructed based on chest CT radiomics features, and the Radiomics + model was constructed by introducing gender and age information. The base classifier and the integration model use the mean accuracy and the area under the receiver operator characteristics analysis curve (AUC) to evaluate the performance, respectively. Results:There was a statistically significant difference ( P<0.05) between the ages of the NSIP, OP, NSIP+OP, UIP, and DAD groups [(57±13),(53±8),(54±10),(44±11), and (46±8)years old, respectively], F=11.82, P<0.001. In the Radiomics model, for each group of NSIP, OP, NSIP+OP, UIP, and DAD, the AUCs of the training set were 0.87, 0.91, 0.91, 0.96, and 0.99, respectively, and the AUC of the test set were 0.81, 0.82, 0.79, 0.93, 0.89. In the final Radiomics + model, for each group of NSIP, OP, NSIP+OP, UIP, and DAD, the AUCs of the training set were 0.89, 0.91, 0.92, 0.97, and 0.99, respectively, and the AUCs of the test set were 0.84, 0.82, 0.78, 0.94, 0.90. Conclusion:Based on chest CT radiomics features and key clinical features (sex, age), the Radiomics + model constructed by machine learning has good classification performance for the imaging patterns of PM/DM-LD.

Objective:To systematically summarize and assess risk prediction models for occurrence of cervical cancer and to provide evidence for selecting the most reliable model for practice, and guide cervical cancer screening.Methods:Two groups of keywords related to cervical cancer and risk prediction model were searched on Chinese databases (CNKI, and Wanfang) and English databases (PubMed, Embase, and Cochrane Library). Original articles that developed or validated risk prediction models and published before November 21, 2019, were selected. Information form was created based on the CHARMS checklist. The PROBAST was used to assess the risk of bias.Results:12 eligible articles were identified, describing 15 prediction models, of which five were established in China. The predicted outcomes included multiple stages from cervical precancerous lesions to cancer occurrence, i.e., abnormal Pap smear (1), occurrence or recurrence of CIN (9), and occurrence of cervical cancer (5), etc. The most frequently used predictors were HPV infection (12), age (7), smoking (5), and education (5). There were two models using machine learning to develop models. In terms of model performance, the discrimination ranged from 0.53 to 0.87, while only two models assessed the calibration correctly. Only two models were externally validated in Taiwan of China, using people in different periods. All of the models were at high risk of bias, especially in the analysis domain. The problems were concentrated in the improper handling of missing data (13), preliminary evaluation of model performance (13), improper use of internal validation (12), and insufficient sample size (11). In addition, the problems of inconsistency measurements of predictors and outcomes (8) and the flawed report of the use of blindness for outcome measures (8) were also severe. Compared with the other models, the Rothberg (2018) model had relatively high quality. Conclusions:There are a certain number of cervical cancer risk prediction models, but the quality is poor. It is urgent to improve the measurement of predictors and outcomes, the statistical analysis details such as handling missing data and evaluation of model performance and externally validate existing models to better guide screening.

The research of central mechanism of internet addiction disorder ( IAD) has become a trend gradually .Mostly the researches are available on the executive function of IAD based on experimental paradigms .This paper is a brief review of the main ex-perimental paradigms of executive function on the research of IAD .