Moxibustion for Chemotherapy-Induced Nausea and Vomiting: A Systematic Review and Meta-Analysis

Nausea and vomiting are distressing symptoms for patients receiving chemotherapy. Moxibustion, which involves the use of burning moxa to generate heat and stimulate acupoints, has been reported to potentially ameliorate chemotherapy-induced side effects, particularly nausea and vomiting. This systematic review evaluated current evidence on the effectiveness of moxibustion against chemotherapy-induced nausea and vomiting (CINV). We searched eight online databases and two trial registries for relevant trials. The random-effects model was used to conduct a meta-analysis. Furthermore, the risk ratio (RR) and mean difference (MD) were used to explain dichotomous and continuous outcomes, respectively; the outcomes were within 95% confidence intervals (CIs). The results revealed that moxibustion might more favorably relieve the severity and frequency of CINV, compared with no treatment (RR: 2.04, 95% CI: 1.42–2.93); moxibustion might have stronger effects than antiemetic drugs (RR: 1.87, 95% CI: 1.27–2.76). There is no robust result that moxibustion could enhance the effects of antiemetic drugs administered as a complementary treatment. Actual moxibustion (8.10±10.98) may have more favorable effects than placebo moxibustion (46.67±23.32). However, the evidence obtained is not sufficient because of the lack of strict clinical trials. Protocol Registration. This trial is registered with PROSPERO CRD42016030037

Triggering receptor expressed on myeloid cells (TREM) like transcript-1 (TLT-1) reveals platelet activation in preeclampsia

Triggering receptor expressed on myeloid cells (TREM) like transcript-1 (TLT-1) is a membrane protein receptor found in α-granules of megakaryocytes and platelets. Upon platelet activation TLT-1 is rapidly relocated to the surface of platelets. In plasma, a soluble form of TLT-1 (sTLT-1) is present. Plasma levels of sTLT-1 are significantly elevated in thrombotic diseases. In the present study, we investigated to whether TLT-1 reflects platelet activation in pregnant women with preeclampsia. We studied 30 preeclamptic patients who were matched with 30 normotensive pregnant women and 30 non-pregnant controls. Basal TLT-1, P-selectin, and CD63 expressions on platelets were analyzed with the use of flow-cytometry (FCM). Platelet reactivity was induced by thrombin receptor activation peptide and determined by FCM. Plasma concentrations of sTLT-1 and soluble P-selectin (sP-selectin) were measured by an enzyme-linked immunosorbent assay. Results show that basal platelet expression of TLT-1, P-selectin and CD63 were increased in women with preeclampsia (PE) compared with normotensive pregnant women (NP). Platelets from PE women and NP women were more responsive compared to from nonpregnant women controls (NC), and which was demonstrated by increased expression of TLT-1, P-selectin, and CD63 upon stimulation in vitro. Plasma concentration of sTLT-1 was greater in PE women compared to NP women and NC women. Plasma sP-selectin level was higher in pregnant women than in nonpregnant women, but there were no significant differences between PE and NP women. In summary, our results revealed that platelet activation is prominent in preeclampsia, TLT-1 reflects platelet activation and may be a useful indicator for preeclampsia

Preparation of Carbon Aerogel Electrode for Electrosorption of Copper Ions in Aqueous Solution

Carbon aerogel (CA) has a rich porous structure, in which micropores and mesopores provide a huge specific surface area to form electric double layers. This property can be applied to the application of capacitive deionization (CDI). The adsorption effect of CA electrode on Cu2+ in an aqueous solution was explored for solving heavy metal water pollution. The CAs were synthesized by a sol-gel process using an atmospheric drying method. The structure of CAs was characterized by scanning in an electron microscope (SEM) and nitrogen adsorption/desorption techniques. The adsorption system was built using Cu2+ solution as the simulation of heavy metal pollution solution. The control variate method was used to investigate the effect of the anion species in copper solution, the molar ratio of resorcinol to catalyst (R/C) of CA, and the applied voltage and concentration of copper ion on the adsorption results

Facial-Sketch Synthesis: A New Challenge

This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high costs of obtaining hand-drawn sketch datasets, there lacks a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2,104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS investigation by reviewing 89 classical methods, including 25 handcrafted feature-based facial-sketch synthesis approaches, 29 general translation methods, and 35 image-to-sketch approaches. Besides, we elaborate comprehensive experiments on the existing 19 cutting-edge models. Third, we present a simple baseline for FSS, named FSGAN. With only two straightforward components, i.e., facial-aware masking and style-vector expansion, FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin. Finally, we conclude with lessons learned over the past years and point out several unsolved challenges. Our code is available at https://github.com/DengPingFan/FSGAN.Comment: Accepted to Machine Intelligence Research (MIR

Facial-sketch Synthesis: A New Challenge

This paper aims to conduct a comprehensive study on facial-sketch synthesis (FSS). However, due to the high cost of obtaining hand-drawn sketch datasets, there is a lack of a complete benchmark for assessing the development of FSS algorithms over the last decade. We first introduce a high-quality dataset for FSS, named FS2K, which consists of 2 104 image-sketch pairs spanning three types of sketch styles, image backgrounds, lighting conditions, skin colors, and facial attributes. FS2K differs from previous FSS datasets in difficulty, diversity, and scalability and should thus facilitate the progress of FSS research. Second, we present the largest-scale FSS investigation by reviewing 89 classic methods, including 25 handcrafted feature-based facial-sketch synthesis approaches, 29 general translation methods, and 35 image-to-sketch approaches. In addition, we elaborate comprehensive experiments on the existing 19 cutting-edge models. Third, we present a simple baseline for FSS, named FSGAN. With only two straightforward components, i.e., facial-aware masking and style-vector expansion, our FSGAN surpasses the performance of all previous state-of-the-art models on the proposed FS2K dataset by a large margin. Finally, we conclude with lessons learned over the past years and point out several unsolved challenges. Our code is available at https://github.com/DengPingFan/FSGAN.ISSN:2731-538XISSN:2731-539