[The mediating role of worker-occupation fit between occupational stress and anxiety symptoms in medical staff].

Objective: To analyze the mediating effect of work-occupation fit between occupational stress and anxiety symptoms in medical staff. Methods: Convenience sampling method was adopted to select participants of one general hospital and three specialized hospitals as respondents for a questionnaire survey in Henan Province from October 2020 to January 2021. A total of 2050 medical staff were investigated, and 1988 valid questionnaires were collected, and the effective rate of the questionnaire was 97.0% (1988/2050) . The "Depression-Anxiety-Stress Scale" and "Worker-Occupation Fit Inventory" were used to evaluate the occupational stress, anxiety symptoms and worker-occupation fit level of medical staff, and the mediation effect of work-occupation fit on the relationship between occupational stress and anxiety symptoms was analyzed using a mediating effect model. Results: The average age of the 1988 medical staff was (32.7±7.8) years old, the positive detection rates of occupational stress and anxiety symptoms were 42.5% (845/1988) and 56.7% (1127/1988) , respectively. Anxiety symptoms of medical staff were positively correlated with occupational stress, negatively correlated with worker-occupation fit (r=0.831, -0.364, P<0.001) , work-occupation fit was negatively correlated with occupational stress (r=-0.259, P<0.001) . The results of the mediation effect analysis showed that occupational stress had a direct effect on anxiety symptoms (ß=0.677, BCa 95%CI: 0.648-0.707) , and worker-occupation fit (ß=0.047, BCa 95%CI: 0.039-0.056) , characteristic fit (ß=0.089, BCa 95%CI: 0.074-0.104) , need-supply fit (ß=0.075, BCa 95%CI: 0.062-0.089) , and ability-demand fit (ß=0.035, BCa 95%CI: 0.026-0.044) mediated the association between occupational stress and anxiety symptoms in medical staff, with the mediating effect as a percentage of 6.5%, 12.3%, 10.3%, and 4.8%, respectively. Conclusion: Worker-occupation fit has a mediating effect between occupational stress and anxiety symptoms in medical staff, but mainly direct effect.

[Deliberation on the similarities and differences of the diagnosis and treatment of scars and keloids in China and Japan].

The incidence and clinical manifestations of scars and keloids are different in different races, and Asians are more likely to suffer from this disease than Caucasians. China and Japan are the representative countries for medical development in Asia. There is no comprehensive study on the similarities and differences between the academic circles in the two countries in the diagnosis and treatment of scars and keloids. By comparing and analyzing the latest expert consensus in the field of scars and keloids between the two countries, we found that the organization form of expert team and main contents of the consensus from the two countries are basically similar. However, there are obvious differences in the composition of experts, logical thinking and organizational form of consensus contents, and details of the specific schemes for scar assessment, diagnosis, and treatment. The differences in the diagnosis and treatment of scars and keloids in China and Japan may indicate the direction of future cooperative research. It is necessary for the academic circles of China and Japan to strengthen academic exchanges and work hard to cooperate in high-quality research in the field of scars and keloids.

[Research advances on the mechanism of fractional laser in treating scars].

Scars are the result of abnormal repair of skin tissue trauma. Recently, fractional laser is more and more widely used in the treatment of scars, but its mechanism is not clear. Studies have shown that fractional laser could produce multiple microthermal zones in target skin, induce wound repair responses, affect the function of epidermal and dermal cells, induce changes in blood vessels and collagens, and change the expression of heat shock proteins, microRNA, matrix metalloproteinases, cytokines such as transforming growth factor ß, basic fibroblast growth factor, and facilitate drug delivery, thus achieving the effect of treating scars. This article reviews the mechanism of fractional laser in treating scars from three aspects, including the tissue and cell mechanism, molecular mechanism, and drug delivery.

[Comparison of machine learning method and logistic regression model in prediction of acute kidney injury in severely burned patients].

Objective: To build risk prediction models for acute kidney injury (AKI) in severely burned patients, and to compare the prediction performance of machine learning method and logistic regression model. Methods: The clinical data of 157 severely burned patients in August 2nd Kunshan factory aluminum dust explosion accident conforming to the inclusion criteria were collected. Patients suffering AKI within 90 days after admission were enrolled in group AKI, while the others were enrolled in non-AKI group. Single factor analysis was used to choose independent factors associated with AKI, including sex, age, admission time, features of basic injuries, initial score on admission, treatment condition, and mortality on post injury days 30, 60, and 90. Data were processed with Mann-Whitney U test, chi-square test, and Fisher's exact test. Variables with P<0.1 in single factor analysis and those with possible clinical significance were brought into the establishment of prediction model. Logistic regression and XGBoost machine learning algorithm were used to build the prediction model of AKI. The area under receiver operating characteristic curve (AUC) was calculated, and the sensitivity and specificity for optimal threshold value were also calculated for each model. Nonparametric resampling test was used to compare the significance of difference of AUC of the two models. Results: (1) Eighty-nine (56.7%) patients developed AKI within 90 days from admission. Compared with 68 patients in non-AKI group, 89 patients in group AKI were older (Z=-2.203, P<0.05), with larger total burn area and full-thickness burn area (Z=-5.200, -6.297, P<0.01), worse acute physical and chronic health evaluation (APACHE) Ⅱ score, abbreviated burn severity index score, and sequential organ failure assessment (SOFA) score on admission (Z=-7.485, -4.739, -4.590, P<0.01), higher occurrence rate of sepsis (χ(2)=33.087, P<0.01), higher rates of accepting tracheotomy, mechanical ventilation, and continuous renal replacement therapy (χ(2)=12.373, 17.201, 43.763, P<0.01), larger first excision area (Z=-2.191, P<0.05), and higher mortality on post injury days 30, 60, and 90 (χ(2)=7.483, 37.259, 45.533, P<0.01). There were no statistically significant differences in sex, open decompression, admission time, 24-hour fluid volume after admission, 48-hour fluid volume after admission, the first 24-hour urine volume, the second 24 hour urine volume, the first excision time, and inhalation injury (χ(2)=0.529, 3.318, Z=-1.746, -0.016, -1.199, -1.824, -0.625, -1.747, P>0.05). The rates of deep vein catheterization of patients in the two groups were both 100%. (2) There were twenty possible prediction variables for preliminary establishment of model according to the difference results of single factor analysis and clinical significance of variables. (3) The logistic regression prediction model had three variables: APACHE Ⅱ score [odds ratio (OR)=1.36, 95% confidence interval (CI)=1.20-1.53, P<0.001], sepsis (OR=2.63, 95% CI=0.90-7.66, P>0.05), and the first 24-hour urine volume (OR=0.71, 95% CI=0.50-1.01, P>0.05). The AUC of the logistic regression prediction model was 0.875 (95% CI=0.821-0.930), with the specificity and sensitivity of optimal threshold value 84.4% and 77.7%, respectively. (4) XGBoost machine learning model had seven main predictive variables: APACHE Ⅱ score, full-thickness burn area, 24-hour fluid volume after admission, sepsis, the first 24-hour urine volume, SOFA score, and 48-hour fluid volume after admission. The AUC of machine learning model was 0.920 (95% CI=0.879-0.962), higher than that of logistic regression model (P<0.001), with the specificity and sensitivity of optimal threshold value 89.7% and 82.0%, respectively. Conclusions: Sepsis and fluid resuscitation are two important predictive variables that can be intervened for AKI in severely burned patients. Machine learning method has a better performance and can provide more accurate prediction for individuals than logistic regression prediction model, and therefore has good clinical application prospect.