Objectives: To compare simple conventional treatment with the addition of hyperbaric oxygen therapy (HBOT) to conventional therapies in the treatment of Fournier's gangrene (FG). Methods: A retrospective study of clinical data was performed by reviewing 28 cases of FG from January 2004 to December 2013 at Xiangya Hospital, Central South University. Among them, 12 patients were treated with the conventional therapy (non-HBOT group) and the other 16 cases were combined with hyperbaric oxygen therapy besides conventional therapy (HBOT group). All patients were followed up for 2 months to assess the therapeutic effect. The analyzed data included age, Fournier gangrene severity index (FGSI) score, number of surgical debridement, indwelling drainage tube time, length of stay (LOS), effective time, and curative time. Results: The mortality rate was lower in the HBOT group at 12.5% (2/16) compared to the non-HBOT group, which was 33.3% (4/12). The difference in the number of surgical debridement, indwelling drainage tube time, and curative time between were significantly lower in the HBOT group compared to the non-HBOT group. Conclusions: Our preliminary research suggests that the effect of combining hyperbaric oxygen therapy with conventional therapy offers considerable advantage in the management of Fournier's gangrene. Multicenter studies with a larger sample size are required to confirm these observations.

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Urinary angiotensinogen levels were remarkably high in the acute phase in the patients with proteinuric HSP, suggesting increased UAGT may indicate a series of functional changes in the kidney and it may be used as a potential biomarker of severity of HSP to monitor the progression of HSP with renal involvement.

Effective control of the thickness of the hot-rolled oxide scale on the surface of the steel strip is very vital to ensure the surface quality of steel products. Hence, terahertz nondestructive technology was proposed to measure the thickness of thin oxide scale. The finite difference time domain (FDTD) numerical simulation method was employed to obtain the terahertz time-domain simulation data of oxide scale with various thickness (0–15 μm). Added Gaussian white noise with a Signal Nosie Reduction (SNR) of 10 dB was used when simulating real test signals, using four wavelet denoising methods to reduce noise and to compare their effectiveness. Two machine learning algorithms were adopted to set up models to achieve this goal, including the classical back-propagation (BP) neural network algorithm and the novel extreme learning machine (ELM) algorithm. The principal component analysis (PCA) algorithm and particle swarm optimization (PSO) algorithm were combined to reduce the dimensions of the terahertz time-domain data and improve the robustness of the machine learning model. It could be clearly seen that the novel hybrid PCA-PSO-ELM model possessed excellent prediction performance. Finally, this work proposed a novel, convenient, online, nondestructive, noncontact, safety and high-precision thin oxide scale thickness measuring method that could be employed to improve the surface quality of iron and steel products.