Objective To study the cumulative radiation dose of multiple trauma patients during their hospitalization and to analyze the dose influece factors.Methods The DLP for CT and DR were retrospectively collected from the patients during June,2009 and April,2011 at a university affiliated hospital.The cumulative radiation doses were calculated by summing typical effective doses of the anatomic regions scanned.Results The cumulative radiation doses of 113 patients were collected.The maximum,minimum and the mean values of cumulative effective doses were 153.3,16.48 mSv and(52.3 ± 26.6) mSv.Conclusions Multiple trauma patients have high cumulative radiation exposure.Therefore,the management of cumulative radiation doses should be enhanced.To establish the individualized radiation exposure archives will be helpful for the clinicians and technicians to make decision wheather to image again and how to select the imaging parameters.

Objective To investigate the effect of different medical standards for the urinary system on the enlistment of pilots between PLAAF and USAF , and offer support to amendment .Methods Data on pilots′final enlistment between 2012 and 2015 were analyzed , and results of physical examinations of the urinary system were compared according to differ -ent medical standards .Results One hundred and twenty teenagers enrolled in this examination were disqualifiied due to defects of the urinary system while 97 applicants were qualified after comprehensive assessment of the urinary system .The main problems with the urinary system that affected results of pilots′enlistment were nephroptosia , renal cyst , microscopic hematuria , renal calcified foci and calculus .Conclusion Medical standards for the urinary system are different between PLAAF and USAF, especially those for nephroptosia , renal cyst and microscopic hematuria .The medical standards directo-ry about the urinary system approved by USAF can serve as a reference during our revision of current medical enlistment standards.

Objective To explore the feasibility of detecting the cytokeratin 20 (CK-20) mRNA in exfoliated urothelial cells for the diagnosis of bladder carcinoma. Methods Reverse transcriptase-polymerase chain reaction (RT-PCR) was used to detect the expression of CK-20 mRNA in cells collected from the urine of 45 cases of bladder cancer, 15 cases of cystitis accompanied by hematuria, 10 healthy volunteers, and 7 different cell lines, including bladder cancer cell line T24, kidney cancer 786-0 and GRC-1, breast cancer MCF-7 and MDA-MB-435, and ovary cancer SKOV 3 and 3AO. Results CK-20 mRNA expression was detected in 36 of 41 cases of bladder transitional cell carcinoma (87.80%), in 18 of the 21 GⅠ patients (85.71%), in 11 of the 13 GⅡ patients (84.62%), in 7 of the 7 GⅢ patients (100%), in 20 of the 22 T a-1 patients (90.91%), and in 16 of the 19T 2-4 patients (84.21%). Sensitivity of the method was found to be 87.80%, whereas specificity was 73.33%. In 15 patients with hematuria, there were 4 cases of false positive: 1 case of BPH, 1 case of atypical hyperplasia, 1 case of chronic inflammation, and 1 case undergoing TURP previously. CK-20 amplification band was also obtained in all of 19 cases of bladder transitional cell tumor tissues and bladder cancer cell line T24, but not in 4 patients with non-transitional cell carcinoma and 6 other tumor cell lines. No false positive cases were found in the healthy control group. Conclusion These results suggest that CK-20 might be a useful tumor marker for early noninvasive diagnosis and follow-up of bladder cancer by detecting CK-20 mRNA expression of uroepithelial cells from the voided urine specimen by RT-PCR.

Since the outbreak of COVID-19, the hospital is faced with such challenges as sustained input in patient screening, referral and rescue, supportive medical supplies including the management of personal protective supplies and the maintenance of medical equipment. In the face of increasing workload, the medical materials support team of the hospital took the following measures in the principles of " discovering new sources of supplies and minimizing consumption" . These measures taken include proactive supply procurement strategy, prioritizing allocations based on risk exposure, uniform distribution of supplies based on inventory and usage, and traceable management for donated supplies; optimal and central deployment of equipments, emergency installation, pre-maintenance and emergency repairs for equipments; efficient and safe sterilization of reusable materials and equipments using sterilizers; enhanced personnel training and management to keep updating the medical supplies capacity of the team. All these efforts have contributed significantly to the orderly work of epidemic control.

Objective:To develop and evaluate a nomogram prediction model for the 3-month mortality risk of patients with sepsis-associated acute kidney injury (S-AKI).Methods:Based on the American Medical Information Mart for Intensive Care-Ⅳ (MIMIC-Ⅳ), clinical data of S-AKI patients from 2008 to 2021 were collected.Initially, 58 relevant predictive factors were included, with all-cause mortality within 3 months as the outcome event. The data were divided into training and testing sets at a 7∶3 ratio. In the training set, univariate Logistic regression analysis was used for preliminary variable screening. Multicollinearity analysis, Lasso regression, and random forest algorithm were employed for variable selection, combined with the clinical application value of variables, to establish a multivariable Logistic regression model, visualized using a nomogram. In the testing set, the predictive value of the model was evaluated through internal validation. The receiver operator characteristic curve (ROC curve) was drawn, and the area under the curve (AUC) was calculated to evaluate the discrimination of nomogram model and Oxford acute severity of illness score (OASIS), sequential organ failure assessment (SOFA), and systemic inflammatory response syndrome score (SIRS). The calibration curve was used to evaluate the calibration, and decision curve analysis (DCA) was performed to assess the net benefit at different probability thresholds.Results:Based on the survival status at 3 months after diagnosis, patients were divided into 7?768 (68.54%) survivors and 3?566 (31.46%) death. In the training set, after multiple screenings, 7 variables were finally included in the nomogram model: Logistic organ dysfunction system (LODS), Charlson comorbidity index, urine output, international normalized ratio (INR), respiratory support mode, blood urea nitrogen, and age. Internal validation in the testing set showed that the AUC of nomogram model was 0.81 [95% confidence interval (95% CI) was 0.80-0.82], higher than the OASIS score's 0.70 (95% CI was 0.69-0.71) and significantly higher than the SOFA score's 0.57 (95% CI was 0.56-0.58) and SIRS score's 0.56 (95% CI was 0.55-0.57), indicating good discrimination. The calibration curve demonstrated that the nomogram model's calibration was better than the OASIS, SOFA, and SIRS scores. The DCA curve suggested that the nomogram model's clinical net benefit was better than the OASIS, SOFA, and SIRS scores at different probability thresholds. Conclusions:A nomogram prediction model for the 3-month mortality risk of S-AKI patients, based on clinical big data from MIMIC-Ⅳ and including seven variables, demonstrates good discriminative ability and calibration, providing an effective new tool for assessing the prognosis of S-AKI patients.

Road traffic accidents (RTA) can cause a large number of casualties and property losses. Driving fatigue is one of the important factors leading to RTA. Electrophysiological signals, as a kind of information feedback for the nervous system to regulate body functions, can reflect drivers’ fatigue state. However, there is a lack of systematic reviews on the current research on electrophysiological signals as information input of machine learning methods for driving fatigue recognition. By investigating fatigue-related literature, the current paper summarized the neural regulation mechanism of fatigue, clarified that driving fatigue is caused by both psychological and physiological loads, recognized inducing factors related to driving fatigue, and summed up electrophysiological signals now in use of driving fatigue recognition, as well as their physiological mechanisms and related indicators. Machine learning algorithms are widely used in identifying driving fatigue. Based on existing studies that used electrophysiological signals as information input source and applied various machine learning algorithms to build driving fatigue identification models, this paper compared the effectiveness of various machine learning algorithms, and described the advantages and disadvantages of supervised machine learning. It is pointed out that suitable classification algorithms should be selected according to sample conditions and model eigenvalues when applied to driving fatigue recognition. In addition, a variety of electrophysiological signals as information sources can help improve the accuracy of a fatigue recognition model, but the increase of model input eigenvalues cannot. Finally, the research progress of identification methods based on electrophysiological signals provided new opportunities for identifying driving fatigue.

In this study, we presented the isolation and characterization of eight novel seco-guaianolide sesquiterpenoids (1-8) and two known guaianolide derivatives (9 and 10), from the aerial part of Achillea alpina L.. Compounds 1-3 were identified as guaianolides bearing an oxygen insertion at the 2, 3 position, while compounds 4-8 belonged to a group of special 3-nor guaianolide sesquiterpenoids. The structural elucidation of 1-8, including their absolute configurations, were accomplished by a combination of spectroscopic data analysis and quantum electronic circular dichroism (ECD) calculations. To evaluate the potential antidiabetic activity of compounds 1-10, we investigated their effects on glucose consumption in palmitic acid (PA)-mediated HepG2-insulin resistance (IR) cells. Among the tested compounds, compound 7 demonstrated the most pronounced ability to reverse IR. Moreover, a mechanistic investigation revealed that compound 7 exerted its antidiabetic effect by reducing the production of the pro-inflammatory cytokine IL-1β, which was achieved through the suppression of the NLRP3 pathway.
Humans ; Hypoglycemic Agents/pharmacology* ; Circular Dichroism ; Cytokines ; Glucose ; Hep G2 Cells ; Insulin Resistance