Survival Prediction After Transarterial Chemoembolization for Hepatocellular Carcinoma: a Deep Multitask Survival Analysis Approach.

The accurate prediction of postoperative survival time of patients with Barcelona Clinic Liver Cancer (BCLC) stage B hepatocellular carcinoma (HCC) is important for postoperative health care. Survival analysis is a common method used to predict the occurrence time of events of interest in the medical field. At present, the mainstream survival analysis models, such as the Cox proportional risk model, should make strict assumptions about the potential random process to solve the censored data, thus potentially limiting their application in clinical practice. In this paper, we propose a novel deep multitask survival model (DMSM) to analyze HCC survival data. Specifically, DMSM transforms the traditional survival time prediction problem of patients with HCC into a survival probability prediction problem at multiple time points and applies entropy regularization and ranking loss to optimize a multitask neural network. Compared with the traditional methods of deleting censored data and strong hypothesis, DMSM makes full use of all the information in the censored data but does not need to make any assumption. In addition, we identify the risk factors affecting the prognosis of patients with HCC and visualize the importance of ranking these factors. On the basis of the analysis of a real dataset of patients with BCLC stage B HCC, experimental results on three different validation datasets show that the DMSM achieves competitive performance with concordance index of 0.779, 0.727, and 0.780 and integrated Brier score (IBS) of 0.172, 0.138, and 0.135, respectively. Our DMSM has a comparatively small standard deviation (0.002, 0.002, and 0.003) for IBS of bootstrapping 100 times. The DMSM we proposed can be utilized as an effective survival analysis model and provide an important means for the accurate prediction of postoperative survival time of patients with BCLC stage B HCC.

Achieving High Selectivity in Photocatalytic Oxidation of Toluene on Amorphous BiOCl Nanosheets Coupled with TiO2.

The inert C(sp3)-H bond and easy overoxidation of toluene make the selective oxidation of toluene to benzaldehyde a great challenge. Herein, we present that a photocatalyst, constructed with a small amount (1 mol %) of amorphous BiOCl nanosheets assembled on TiO2 (denoted as 0.01BOC/TiO2), shows excellent performance in toluene oxidation to benzaldehyde, with 85% selectivity at 10% conversion, and the benzaldehyde formation rate is up to 1.7 mmol g-1 h-1, which is 5.6 and 3.7 times that of bare TiO2 and BOC, respectively. In addition to the charge separation function of the BOC/TiO2 heterojunction, we found that the amorphous structure of BOC endows its abundant surface oxygen vacancies (Ov), which can further promote the charge separation. Most importantly, the surface Ov of amorphous BOC can efficiently adsorb and activate O2, and amorphous BOC makes the product, benzaldehyde, easily desorb from the catalyst surface, which alleviates the further oxidation of benzaldehyde, and results in the high selectivity. This work highlights the importance of the microstructure based on heterojunctions, which is conducive to the rational design of photocatalysts with high performance in organic synthesis.

Heat-Treatment-Assisted Molten-Salt Strategy to Enhance Electrochemical Performances of Li-Rich Assembled Microspheres by Tailoring Their Surface Features.

Constructing Li-rich Mn-based layered oxide (LMRO) assembled microspheres with fast kinetics and a stable surface will significantly improve discharge capacity and cyclic stability. In this work, a heat-treatment-assisted (HA) molten-salt (MS) strategy has been designed to prepare LMRO assembled microspheres HA-MS-LMRO (LMRO with heat-treatment-assisted molten-salt process). Electrochemical measurements demonstrate that HA-MS-LMRO possesses superior performance as a cathode for lithium-ion batteries. It delivers an initial discharge capacity of 181 mA h g-1 at 200 mA g-1 , which is much higher than that of the LMRO (145 mA h g-1 ). After 100 cycles, the capacity retention ratio for HA-MS-LMRO is 74.69 %, which is far larger than that of LMRO (23.06 %). Detailed analysis of the structure, valence state, and electrochemical impedance spectra shows that the heat-treatment-assisted molten-salt process plays an important role in the excellent performance of HA-MS-LMRO. The HA process enables the transition-metal ions in the synthesized samples to have stable surface valence states, which is conducive to maintaining structural stability and improving cycling performance. The following MS process facilitates the movement of lithium salt into the interior of the assembled microsphere precursors to prohibit the formation of lithium-containing amorphous compounds on the surface during the lithiation process, thus enhancing the Li-ion kinetics and increasing the initial discharge capacity. The current work provides guidance to promote the electrochemical performances of assembled microsphere cathode materials.

Elemental contents in serum of pregnant women with gestational diabetes mellitus.

Diabetes mellitus is characterized by hyperglycemia and is closely related to trace elements. Quite a few pregnant women suffer from impaired glucose tolerance (IGT) or gestational diabetes mellitus (GDM). Investigation of the changes of elemental contents in serum of the pregnant women with IGT and GDM is significant in the etiological research and cure of the diseases. In the present work, the elements Cu, Zn, Ca, Sr, Mg, P, Fe, and Al in the serum of pregnant women were determined. The elemental contents in different experimental groups were compared. Also, the correlation between elemental contents and gestational period was observed. The results showed that compared with normal pregnant women, the Cu contents in serum of pregnant women with GDM increased, but Zn contents had a decreasing trend. In addition, for all pregnant women, the Ca contents in serum had an obvious inverse correlation with gestational period.