Heat stroke is a life-threatening condition that can be fatal if not appropriately managed. Although heat stroke has been recognized as a medical condition for centuries, the pathogenesis of heat stroke is not fully understood. This article reviewed the new advance in pathogenesis of heat stroke.

To study the relation between drug release and the drug status within curcumin-loaded microsphere, SPG (shirasu porous glass) membrane emulsification was used to prepare the curcumin-PLGA (polylactic-co-glycolic acid) microspheres with three levels of drug loading respectively, and the in vitro release was studied with high-performance liquid chromatography (HPLC). The morphology of microspheres was observed with scanning electron microscopy (SEM), and the drug status was studied with X-ray diffraction (XRD), differential scanning calorimetry (DSC) and infrared analysis (IR). The drug loading of microspheres was (5.85 ± 0.21)%, (11.71 ± 0.39)%, (15.41 ± 0.40)%, respectively. No chemical connection was found between curcumin and PLGA. According to the results of XRD, curcumin dispersed in PLGA as amorphous form within the microspheres of the lowest drug loading, while (2.12 ± 0.64)% and (5.66 ± 0.07)% curcumin crystals was detected in the other two kinds of microspheres, respectively, indicating that the drug status was different within three kinds of microspheres. In the data analysis, we found that PLGA had a limited capacity of dissolving curcumin. When the drug loading exceeded the limit, the excess curcumin would exist in the form of crystals in microspheres independently. Meanwhile, this factor contributes to the difference in drug release behavior of the three groups of microspheres.

Objective To evaluate the differentiation-inducing effect of phenylacetate on pancreatic carcinoma cells and underlying mechanism of RNA editing deaminase in cell proliferation and differentiation. Methods The effect of phenylacetate on cell proliferation and cell cycle were investigated in cultured pancreatic carcinoma BXPC-3 cell lines by methylthiazoletetrazolium( MTT) assay, and flow cytometry. The effect of phenylacetate on the expression of RNA editing deaminase (ADAR2 mRNA) in BXPC-3 cells and fresh pancreatic carcinoma specimen was evaluated by RT-PCR. Results ADAR1 mRNA expression was positive in human pancreatic carcinoma tissues and BXPC-3 cell lines. After application of 1.0 and 2.0 mmol/L phenylacetate for 24 h and 72 h, BXPC-3 cell growth inhibition rate increased, G0/G1 phase cells decreased, S phase cells increased, ADAR2 mRNA expression decreased ( P < 0.01 ). Conclusion ADAR2 mRNA played a vital role of regulation in the process of pancreatic carcinoma cells growth and differentiation. Phenylacetate could regulate the proliferation and differentiation of pancreatic carcinoma cells through the regulation of ADAR2 mRNA expression.

Objective To establish a high performance liquid chromatography method to detect the concentration of lamotrigine in blood dialysate and investigate in vitro recovery of lamotrigine and the factors.Select the microdialysis conditions that apply to the animal experiment and guide the stability study of in vivo recovery.Methods Positive dialysis and retrodialysis were used for the examination of lamotrigine in vitro recovery and the influencing factors such as flow rate, concentration, temperature and time.Filtered out the best conditions that apply to the in vivo experiment.Used the retrodialysis to determine the in vivo recovery and its stability.Results There was no significant difference between relative recovery and relative loss in the same flow rate.The concentration had no obvious effect on relative recovery.At the same condition,relative recovery decreased with the increase of the flow rate and increased with the temperature.The in vivo recovery had a good stability of 6.5 hours when the flow rate and stabilization time were set at 2μL/min and 1.5 h, respectivily.Conclusion Microdialysis technique can be used for the pharmacokinetic study of lamotrigine.Retrodialysis can be used for the determination of the lamotrigine in vivo recovery.

BACKGROUND:MRI is important for the diagnosis of early knee osteoarthritis.MRI image recognition and intelligent segmentation of knee osteoarthritis using deep learning method is a hot topic in image diagnosis of artificial intelligence. OBJECTIVE:Through deep learning of MRI images of knee osteoarthritis,the segmentation of femur,tibia,patella,cartilage,meniscus,ligaments,muscles and effusion of knee can be automatically divided,and then volume of knee fluid and muscle content were measured. METHODS:100 normal knee joints and 100 knee osteoarthritis patients were selected and randomly divided into training dataset(n=160),validation dataset(n=20),and test dataset(n=20)according to the ratio of 8:1:1.The Coarse-to-Fine sequential training method was used to train the 3D-UNET network deep learning model.A Coarse MRI segmentation model of the knee sagittal plane was trained first,and the rough segmentation results were used as a mask,and then the fine segmentation model was trained.The T1WI and T2WI images of the sagittal surface of the knee joint and the marking files of each structure were input,and DeepLab v3 was used to segment bone,cartilage,ligament,meniscus,muscle,and effusion of knee,and 3D reconstruction was finally displayed and automatic measurement results(muscle content and volume of knee fluid)were displayed to complete the deep learning application program.The MRI data of 26 normal subjects and 38 patients with knee osteoarthritis were screened for validation. RESULTS AND CONCLUSION:(1)The 26 normal subjects were selected,including 13 females and 13 males,with a mean age of(34.88±11.75)years old.The mean muscle content of the knee joint was(1 051 322.94±2 007 249.00)mL,the mean median was 631 165.21 mL,and the mean volume of effusion was(291.85±559.59)mL.The mean median was 0 mL.(2)There were 38 patients with knee osteoarthritis,including 30 females and 8 males.The mean age was(68.53±9.87)years old.The mean muscle content was(782 409.18±331 392.56)mL,the mean median was 689 105.66 mL,and the mean volume of effusion was(1 625.23±5 014.03)mL.The mean median was 178.72 mL.(3)There was no significant difference in muscle content between normal people and knee osteoarthritis patients.The volume of effusion in patients with knee osteoarthritis was higher than that in normal subjects,and the difference was significant(P<0.05).(4)It is indicated that the intelligent segmentation of MRI images by deep learning can discard the defects of manual segmentation in the past.The more accuracy evaluation of knee osteoarthritis was necessary,and the image segmentation was processed more precisely in the future to improve the accuracy of the results.

The orbitofrontal cortex (OFC) is involved in diverse brain functions via its extensive projections to multiple target regions. There is a growing understanding of the overall outputs of the OFC at the population level, but reports of the projection patterns of individual OFC neurons across different cortical layers remain rare. Here, by combining neuronal sparse and bright labeling with a whole-brain florescence imaging system (fMOST), we obtained an uninterrupted three-dimensional whole-brain dataset and achieved the full morphological reconstruction of 25 OFC pyramidal neurons. We compared the whole-brain projection targets of these individual OFC neurons in different cortical layers as well as in the same cortical layer. We found cortical layer-dependent projections characterized by divergent patterns for information delivery. Our study not only provides a structural basis for understanding the principles of laminar organizations in the OFC, but also provides clues for future functional and behavioral studies on OFC pyramidal neurons.