Objective: To construct a prediction model for sleep disorders in shift workers of a chemical fiber enterprise, so as to provide the basis for early identification and prevention of sleep disorders in shift workers. Methods: Shift workers were sampled from a chemical fiber enterprise in Tongxiang City, Zhejiang Province using a cluster sampling method from August 2022 to July 2024. Demographic information, length of service and average weekly working hours were collected through questionnaire surveys. Depressive symptoms, anxiety symptoms and sleep disorders were evaluated using the Pittsburgh Sleep Quality Index, Patient Health Questionnaire and Generalized Anxiety Disorder Questionnaire, respectively. The shift workers were randomly divided into a training set and a validation set at a ratio of 7∶3. Predictive factors were selected using a multivariable logistic regression model based on the training set, and a nomograph model for prediction of sleep disorders in shift workers was established. The predictive values of the model were evaluated using the receiver operating characteristic (ROC) curve and calibration curve based on the training set and validation set. Results: Totally 673 shift workers were included, with a median age of 32 (interquartile range, 12) years. There were 493 males, accounting for 73.25%. There were 471 (69.99%) workers in the training set and 202 (30.01%) workers in the validation set. There were 274 workers with sleep disorders, accounting for 40.71%. The equation for the prediction model was ln[p/(1-p)]=-8.391+1.906×average weekly working hours+1.822×depressive symptoms+1.667×anxiety symptoms. The area under the ROC curve was 0.769 (95%CI: 0.661-0.835) for the training set and 0.655 (95%CI: 0.593-0.737) for the validation set, and Hosmer-Lemeshow test showed a good fitting effect (both P>0.05). Conclusion The nomograph model constructed by average weekly working hours, depressive symptoms and anxiety symptoms can be used to predict the risk of sleep disorders in shift workers of a chemical fiber enterprise.

Objectives:To compare the diagnostic efficacy of non-ST-segment elevation myocardial infarction (NSTEMI) and the predictive value for major adverse cardiovascular events (MACE) of the 0/3-hour algorithm for high-sensitivity cardiac troponin (hs-cTn) recommended by the 2015 European Society of Cardiology (ESC) guidelines for the management of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) and the 2021 "Chinese Expert Consensus on Laboratory Testing and Clinical Application of Cardiac Troponin" in suspected acute coronary syndrome (ACS) patients in the Chinese population. Methods:This is a multicenter prospective observational study,including 1527 patients with suspected ACS from three clinical centers from January 2017 to September 2020.Plasma hs-cTnI levels were measured using the ARCHITECT assay at the time of presentation and 3 hours later in patients with suspected ACS (test determination).Clinical judgment (independent clinical judgment by cardiac experts,independent of the test results) was used as the gold standard to compare the sensitivity,specificity,and consistency of the two diagnostic algorithms,and to analyze their predictive value for MACE at 30 days and 180 days.MACE in this study was defined as a composite event of cardiovascular death,myocardial infarction,and unplanned coronary revascularization. Results:According to clinical judgment,there were 400 patients with NSTEMI and 1127 patients without NSTEMI.The 0/3-hour algorithm recommended by the 2021 Chinese Expert Consensus showed higher sensitivity in diagnosing NSTEMI than the 2015 ESC guidelines (91.50％[95％ CI:88.32％-94.04％]vs.87.75％[95％ CI:84.13％-90.80％]),but slightly lower specificity (93.88％[95％ CI:92.32％-95.21％]vs.95.56％[95％ CI:94.19％-96.69％]),with both differences being statistically significant (both P<0.001).In the follow-up at 30 days and 180 days,the incidence of MACE in patients diagnosed with NSTEMI by both algorithms was higher than in those without NSTEMI (P<0.001).The incidence of MACE at 30 days and 180 days for the group excluded from the diagnosis of NSTEMI by 2015 ESC guidelines was 0.19％ and 1.120％,respectively,and for the NSTEMI group was 2.89％ and 3.68％,respectively;for the group excluded from NSTEMI by the 2021 Chinese Expert Consensus,the incidence was 0.096％ and 0.770％,respectively,and for the NSTEMI group was 2.91％ and 4.36％,respectively.Cox analysis showed that the HR ratio for MACE at 180 days in the NSTEMI group diagnosed by both algorithms was 3.418 and 5.892,respectively,significantly higher than the group excluded from NSTEMI. Conclusions:The 0/3-hour algorithm recommended by the 2021 Chinese Expert Consensus has superior diagnostic sensitivity compared to the 2015 ESC NSTE-ACS guidelines,at the cost of slightly lower specificity.Both algorithms can effectively predict MACE within 180 days,but based on the data from this study,the algorithm recommended by the 2021 Chinese Expert Consensus is more sensitive in predicting the risk of MACE,and patients excluded from the diagnosis of NSTEMI by this method have a lower incidence of MACE,suggesting that its application in clinical practice may be more helpful in terms of long-term safe management of patients.

Objective To explore the prognostic value of hs-cTnⅠ in patients with suspected acute coronary syndrome(ACS)in emergency department.Methods A large-scale,prospective observa-tional study was conducted on totally 966 patients with suspected ACS admitted in Fuwai Hospi-tal from January 2017 to October 2020.Their baseline serum/plasma hs-cTnⅠ level was detected at admission,conventional treatment was performed,and relevant data were collected.Logistic regression analysis was used to predict the risk of primary and secondary endpoint events within 30 d by hs-TnⅠ concentration,and subgroup analysis was performed.Results Among the 966 patients,the time from chest pain to visit was 5.0(2.5,13.0)h,and 284 patients had primary end-point events within 30 d,including 283 cases of myocardial infarction(99.6％)at the first visit,1 case of recurrent myocardial infarction(0.4％),5 cases of cardiovascular death(1.8％),and 1 case of unplanned revascularization(0.4％).When hs-cTnⅠ was at the minimum detection limit of 2 ng/L,the incidence of adverse events was 5.8％,when the limit of 70 ng/L,the incidence was 49.2％,and when of 316 ng/L,the incidence reached 100％.The model could correctly classify 92.3％of the patients.Conclusion The hs-cTn sequence has a good predictive effect for the risk of short-term cardiovascular adverse events in Chinese population.

Objective To simulate the microflow field environment between the anastomotic nail surface and intestinal wall tissue after implantation and to study the effect of hydrophobic surfaces on the flow rate of extracellular fluid and the fluid shear force on the wall to regulate bacterial adhesion through changes in the flow field.Methods The microstructure of shark skin was observed,and a simplified two-dimensional(2D)movement model of bacteria in a microflow field was established.Using computational fluid dynamics(CFD)numerical simulation,the movement of bacteria on a smooth surface and micro-textured surface in a static and dynamic flow field were simulated.The flow field characteristics around bacteria and the magnitude of fluid shear force under the two surface environments were compared,and the internal mechanism of the fluid shear force affecting bacterial adhesion was analyzed.Results The addition of the biomimetic microtexture enhanced the flow rate of the extracellular fluid in the microflow field,and the fluid had little viscous effect on the bacteria in the static flow field.The fluid in the dynamic flow field had a stronger pushing effect on the bacteria.The fluid shear force on the microtextured wall increased when the pit width was within a specific range.Conclusions The bionic micro-textured surface of the anastomotic nail can accelerate the flow rate of extracellular fluid,increase the fluid shear force of micro-textured walls and bacteria,and influence bacterial adhesion.These result provide a theoretical basis for studying bacteriostatic surfaces of anastomotic nails.

Pancreatic cancer is a common malignant tumor of the digestive system,with a low early diagnosis rate,a high surgical mortality rate,a low cure rate,and a poor overall prognosis.In recent years,with the continuous development of artificial intelligence in the medical field,artificial intelligence techniques,such as machine learning and deep learning,have been widely used in medical research.This article reviews the application of artificial intelligence techniques in the screening,diagnosis,treatment,complications,and prognosis prediction of pancreatic cancer,so as to provide a basis and new ideas for the application of artificial intelligence in the diagnosis and treatment of pancreatic cancer.

Objective:To investigate the effects and mechanisms of copper transporter 1 (CTR1) in radiation induced intestinal injury in vitro. Methods:Human small intestinal epithelial cells (HIEC) were irradiated with 2, 4, 6, 8 Gy of X-rays and rat intestinal epithelial cells (IEC-6) were irradiated with 5, 10, 15, 20 Gy of X-rays. At 2, 4, 8, 24, and 48 h after irradiation, the expression of CTR1 was detected by Western blot assay. In some experiments, HIEC and IEC-6 cells were transfected with CTR1 shRNA and then exposed to X-rays. Copper levels were detected by Inductively Coupled Plasma Mass Spectrometry (ICP-MS). The radiosensitivity of cells was verified by colonogenic assay, the cellular reactive oxygen species (ROS) level and DNA damage were detected to further explore the related mechanism. In addition, Western blot was applied to detect the expressions of antioxidants and cuproptosis associated proteins in enterocytes after silencing CTR1 or irradiation.Results:The expression of CTR1 was increased by X-ray irradiation in a dose-dependent manner ( t=3.53, 3.45, 6.37, 11.11, 11.13, P<0.05). CTR1 expression was successfully diminished by CTR1 shRNA adenovirus vectors. According to the survival curves, the enhancement ratios of the radiosensitivity of HIEC and IEC-6 cells with CTR1 knocking-down were 1.146 and 1.201, respectively. Radiation-induced copper accumulation was alleviated after CTR1 silencing in IEC-6 cells ( t=3.10, P<0.05). At 0.5 h after irradiation, the ROS production in the CTR1 knockdown group was significantly lower than that in the control group ( t=5.23, 2.96, P<0.05). At 1 h after irradiation, the protein expression of γ-H2AX in the CTR1 knockdown group was obviously lower than that in the control group ( t=7.50, 4.29, P<0.05). The expressions of Nrf2 and HO-1 were increased after irradiation, which could be further increased after CTR1 silencing. In addition, cuproptosis associated protein DLAT, LIAS and FDX1 were reduced post-irradiation, which were recovered after CTR1 silencing. Conclusions:The radioresistance of HIEC and IEC-6 cells was enhanced after CTR1 silencing, possibly through the intracellular ROS and cuproptosis pathway.

Objective:To investigate clinical and imaging parameters to predict blood loss and cranial nerve injury (CNI) following carotid body paraganglioma (CBP) resection.Methods:A retrospective examination of clinical and imaging data was conducted on 63 patients who underwent CBP resection at Xiangya Hospital of Central South University from January 2016 to December 2022, including 23 males and 40 females, aged 26-87 years old. Three imaging parameters including tumor volume, the angle of contact with the internal carotid artery (ICA), and the distance to the base of skull (DTBOS) were gauged using the IMEDPACS software on CTA and MR imaging. The predictive efficacies of age, gender, Shamblin classification, and three imaging parameters for blood loss and CNI following surgery were analysed. Logistic composite parameter models were constructed and their predictive validity was assessed.Results:Multivariate logistic regression analysis underscored that only tumor volume ( OR=1.381,95% CI:1.167-1.507, P=0.001) showed significant statistical correlations with blood loss following surgery. Area under curve (AUC) values of 0.910 for receiver operating characteristic (ROC) curves showed a sensitivity of 1.000 and a specificity of 0.694. Tumor volume ( OR=1.126,95% CI:1.030-1.231, P=0.002) and DTBOS ( OR=0.225,95% CI:0.081-0.630, P=0.005) were significantly associated with postoperative CNI. The analysis of logistic composite model showed AUC values for tumor volume, DTBOS and combination of the two parameters were 0.858, 0.788, and 0.872, respectively. The model for combination of tumor volume and DTBOS also proved superior in predicting postoperative CNI ( Z=3.106, P<0.001), with a sensitivity of 0.833 and a specificity of 0.769. Conclusions:Tumor volume and DTBOS emerged as effective predictors for blood loss and/or CNI in patients with CBP resection. Moreover, the logistic composite parameter model outclassed single-parameter models in terms of their predictive clinical value.

Purpose: This study aimed to identify prognostic factors for patients with distant lymph node-involved gastric cancer (GC) using a machine learning algorithm, a method that offers considerable advantages and new prospects for high-dimensional biomedical data exploration. Materials and Methods: This study employed 79 features of clinical pathology, laboratory tests, and therapeutic details from 289 GC patients whose distant lymphadenopathy was presented as the first episode of recurrence or metastasis. Outcomes were measured as anycause death events and survival months after distant lymph node metastasis. A prediction model was built based on possible outcome predictors using a random survival forest algorithm and confirmed by 5×5 nested cross-validation. The effects of single variables were interpreted using partial dependence plots. A contour plot was used to visually represent survival prediction based on 2 predictive features. Results: The median survival time of patients with GC with distant nodal metastasis was 9.2 months. The optimal model incorporated the prealbumin level and the prothrombin time (PT), and yielded a prediction error of 0.353. The inclusion of other variables resulted in poorer model performance. Patients with higher serum prealbumin levels or shorter PTs had a significantly better prognosis. The predicted one-year survival rate was stratified and illustrated as a contour plot based on the combined effect the prealbumin level and the PT. Conclusions Machine learning is useful for identifying the important determinants of cancer survival using high-dimensional datasets. The prealbumin level and the PT on distant lymph node metastasis are the 2 most crucial factors in predicting the subsequent survival time of advanced GC.Trial Registration: ChiCTR Identifier: ChiCTR1800019978

Objective:To explore the effect of Internet + technical liaison service mode on the postoperative continuing nursing of elderly patients with osteoporotic hip fractures.Methods:A total of 92 elderly patients with osteoporotic hip fractures who were admitted into in Orthopaedics Department of the First Affiliated Hospital of Soochow University from July 2018 to December 2019 were selected, and randomly divided into intervention group and control group by random number table method, with 46 cases in each group. The control group was given routine follow-up outside hospital, while the intervention group was given the continuing nursing led by the orthopedic specialist nurses based on the Internet + Technology home orthopedic care platform. All patients were followed up to 6 months after operation, and the differences of Harris Hip Score (Harris), Numeric Rating Scales (NRS), Barthel Index, Short Form 36-item Health Survey (SF-36) between the two groups were compared.Results:The Harris scores were 49.74 ± 4.28, 76.59 ± 4.33 and 90.78 ± 2.61 in the intervention group, and 46.17 ± 3.85, 74.26 ± 4.24 and 88.65 ± 2.17 in the control group in 1, 3 and 6 months after operation, respectively. The differences were statistically significant ( t=-4.20, -2.60, -4.26, all P<0.05). The NRS scores in the intervention group were 1.33 ± 0.47, 0.83 ± 0.38 and 0.76±0.43 in 1, 3 and 6 months after operation respectively, while the NRS scores in the control group were 1.61 ± 0.54, 0.96 ± 0.42 and 0.84 ± 0.38, respectively. The difference in pain scores between the two groups was statistically significant only at 1 month after operation ( t=2.68, P<0.05). The Barthel Index in the intervention group were 61.74 ± 8.90, 93.80 ± 5.29 and 98.26 ± 2.83 in 1, 3 and 6 months after operation respectively, while those of the control group were 60.33 ± 5.81, 91.09 ± 7.52 and 97.83 ± 3.10, respectively. The difference in Barthel Index between the two groups was statistically significant only in 3 months after operation ( t=-2.00, P<0.05). The scores of SF-36 in the intervention group were 93.73 ± 3.89, 100.26 ± 3.77 and 107.50 ± 3.56 at 1, 3 and 6 months after operation respectively, while those of the control group were 92.67±3.42, 97.71±2.67 and 103.68±2.83, respectively. The difference in SF-36 scores between the two groups was statistically significant only at 3 and 6 months after operation ( t=-3.74, -5.71, P<0.05). Conclusions:The continuing nursing based on multi-disciplinary team cooperation in hospital + Internet + Technology home orthopaedic nursing platform led by orthopaedic specialist nurses can improve joint function, relieve pain and improve quality of life of elderly patients with osteoporotic hip fractures after operation.

With the rapid development of biopharmaceuticals, therapeutic monoclonal antibodies (mAbs) are widely accepted due to their low side-effects and high therapeutic efficacy. Individual differences in the response to mAb drugs put forward new requirements for therapeutic drug monitoring of antibody drugs.Therefore, the need for accurate and robust bioanalytical methods is increasing.Recently, LC-MS/MS has been gaining increasing interest in the field of large molecules.In this article, the recent advances in this emerging field are reviewed, along with common issues and analytical approaches.Thus, this review article is helpful for better understanding the advance of LC-MS/MS technique in the field of therapeutic drug monitoring for mAbs.