Objective To construct nomogram models to predict the risk factors for early death in patients with metastatic melanoma(MM).Methods The study covered 2138 cases from the Surveillance,Epidemiology,and End Results Program(SEER)database and all these patients were diagnosed with MM between 2010 and 2015.Logistic regression was performed to identify independent risk factors affecting early death in MM patients.These risk factors were then used to construct nomograms of all-cause early death and cancer-specific early death.The efficacy of the model was assessed with receiver operating characteristic(ROC)curves,calibration curves,and decision curve analysis(DCA).In addition,external validation of the model was performed with clinicopathologic data of 105 patients diagnosed with MM at Sichuan Cancer Hospital between January 2015 and January 2020.Results According to the results of logistic regression,marital status,the primary site,N staging,surgery,chemotherapy,bone metastases,liver metastases,lung metastases,and brain metastases could be defined as independent predictive factors for early death.Based on these factors,2 nomograms were plotted to predict the risks of all-cause early death and cancer-specific early death,respectively.For the models for all-cause and cancer-specific early death,the areas under the curve(AUCs)for the training group were 0.751(95%confidence interval[CI]:0.726-0.776)and 0.740(95%CI:0.714-0.765),respectively.The AUCs for the internal validation group were 0.759(95%CI:0.722-0.797)and 0.757(95%CI:0.718-0.780),respectively,while the AUCs for the external validation group were 0.750(95%CI:0.649-0.850)and 0.741(95%CI:0.644-0.838),respectively.The calibration curves showed high agreement between the predicted and the observed probabilities.DCA analysis indicated high clinical application value of the models.Conclusion The nomogram models demonstrated good performance in predicting early death in MM patients and can be used to help clinical oncologists develop more individualized treatment strategies.

Heart failure is a disease that seriously threatens human health and has become a global public health problem. Diagnostic and prognostic analysis of heart failure based on medical imaging and clinical data can reveal the progression of heart failure and reduce the risk of death of patients, which has important research value. The traditional analysis methods based on statistics and machine learning have some problems, such as insufficient model capability, poor accuracy due to prior dependence, and poor model adaptability. In recent years, with the development of artificial intelligence technology, deep learning has been gradually applied to clinical data analysis in the field of heart failure, showing a new perspective. This paper reviews the main progress, application methods and major achievements of deep learning in heart failure diagnosis, heart failure mortality and heart failure readmission, summarizes the existing problems and presents the prospects of related research to promote the clinical application of deep learning in heart failure clinical research.
Humans ; Artificial Intelligence ; Deep Learning ; Heart Failure/diagnosis* ; Machine Learning ; Diagnostic Imaging

Abstract In recent years, the incidence of allergic diseases in children and adolescents has been on the rise globally, which has become an important public health problem. It is important to identify modifiable risk factors for allergic diseases in this group. In this paper, through research review on the association between sleep behavior and allergic diseases in children and adolescents, it is suggested that sleep deficiency, sleep disorder and sleep rhythm disturbance are closely related to children s allergic diseases, which provides a new concept for prevention of allergic diseases through sleep behavioral improvement.

Bone homeostasis is a relative balance between bone formation and resorption. Signal transducer and activator of transcription 3 (STAT3), which is closely related to bone homeostasis, takes part in multiple intracellular and extracellular signal pathways. STAT3 participates in the process of osteoblast differentiation regulated by several factors. It can also maintain bone homeostasis by regulating the recruitment, differentiation and activation of osteoclasts. In addition, STAT3 is involved in the interaction between osteoblasts and osteoclasts. Patients with STAT3 mutations can have several inherited bone metabolism diseases. Furthermore, STAT3 plays a critical role in load-driven bone remodeling. Mechanical stimulation promotes osteoblast differentiation and bone formation through activating or enhancing STAT3 expression during bone remodeling process. This review summarizes the participation of STAT3 in maintaining bone homeostasis together with its possible mechanisms and discusses the connection between STAT3 and mechanical stimulation in bone remodeling, so as to provide a potential pharmacological target for the treatment of bone diseases.

Objective:To evaluate a new type of draw-bar skin stretcher in repair of full-thickness skin defects.Methods:From May 2015 to January 2019, 52 patients with full-thickness skin defects were repaired with a new type of draw-bar skin stretcher at Daping Hospital, Army Medical University. They were 40 males and 12 females, aged from 4 to 61 years (average, 37.1 years). Their skin was stretched for primary wound closure. When primary wound closure failed, skin stretching was performed again to close the wound depending on the wound condition. When the Pinch test was negative after skin stretching, the wound was sutured directly. In cases of positive Pinch test, a skin graft or flap was used to repair the remaining wound. At 12 months after surgery, scar contracture and size of skin graft or flap were observed and wound healing after skin stretching was evaluated in comparison with the original wound.Results:After skin stretching, one-stage wound closure was achieved in 36 cases and multi-stage wound closure in 8 cases; of the remaining 8 cases, 2 were repaired by skin graft and 6 by skin flap after their wounds were reduced by skin stretching. In one-stage closed wounds, infection occurred in 3 cases and marginal necrosis in 5 cases; in the wounds repaired by skin graft or flap, no infection or necrosis was observed. The 12-month follow-up for all the patients showed fine healing of all the wounds after one-stage or multi-stage closure, linear scar, absence of scar contracture, and smaller wound sizes than the original ones after skin graft or flap repair.Conclusions:Skin stretching using our new type of draw-bar skin stretcher is an effective treatment for skin wounds. It can replace traditional skin grafting and flap surgery in some cases, but its indications should be strictly followed to avoid related complications.

A novel coronavirus pneumonia (NCP) epidemic has occurred in Wuhan, Hubei Province since December 2019, caused by a novel coronavirus (2019-nCoV) never been seen previously in human. China has imposed the strictest quarantine and closed management measures in history to control the spreading of the disease. However, severe trauma can still occur in the NCP patients. In order to standardize the emergency treatment and the infection prevention and control of severe trauma patients with hidden infection, suspected or confirmed infection of 2019-nCoV, Trauma Surgery Branch of Chinese Medical Doctors' Association organized this expert consensus. The consensus illustrated the classification of the NCP patients, severe trauma patients in need of emergency surgery, emergency surgery type, hierarchical protection for medical personnel and treatment places. Meanwhile, the consensus standardized the screening, injury severity evaluation, emergency surgical treatment strategy and postoperative management strategy of severe trauma patients during the epidemic period of NCP, providing a basis for the clinical treatment of such kind of patients.