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The objectives of this study were to investigate the presence and annual cycle of sex steroids in scleractinian coral, Euphyllia ancora. The free and conjugated forms of sex steroids in coral and spawning seawater were investigated, and aromatase activity in the coral tissue was identified. Polyps collected from corals and seawater were extracted with diethyl ether, and purified by alumina column and reversed-phase HPLC; testosterone and estradiol-17beta (E2) was measured by a validated RIA. E2 and testosterone in their free and glucuronide forms were consistently detected in coral tissue throughout the year. Peak concentrations of free E2, E2 glucuronide, and testosterone glucuronide were obtained in the coral tissue just prior to spawning. The presence of specific aromatase activity was demonstrated in coral tissue. Free E2 and E2 glucuronide concentrations were higher than androgen (testosterone and testosterone glucuronide) in coral tissue and spawning seawater. Higher concentrations of free E2 than E2 glucuronide were detected in coral tissues throughout the year. In contrast, higher concentrations of E2 glucuronide than free E2 and testosterone glucuronide were found in seawater during mass coral spawning. No steroid sulfate could be detected in the coral tissue and seawater. We suggest that the release of E2 glucuronide may play an important role in coral mass spawning.

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Bacteria associated with eight field-collected and five cultured soft corals of Briareum sp., Sinularia sp., Sarcophyton sp., Nephtheidae sp., and Lobophytum sp. were screened for their abilities in producing antimicrobial metabolites. Field-collected coral samples were collected from Nanwan Bay in southern Taiwan. Cultured corals were collected from the cultivating tank at National Museum of Marine Biology and Aquarium. A total of 1,526 and 1,138 culturable, heterotrophic bacteria were isolated from wild and cultured corals, respectively; seawater requirement and antimicrobial activity were then assessed. There is no significant difference between the ratio of seawater-requiring bacteria on the wild and cultured corals. The ratio of antibiotic-producing bacteria within the seawater-requiring bacteria did not differ between the corals. Nineteen bacterial strains that showed high antimicrobial activity were selected for 16S rDNA sequencing. Three strains could be assigned at the family level (Rhodobacteraceae). The remaining 16 strains belong to eight genera: Marinobacterium (2 strains), Pseudoalteromonas (1), Vibrio (5), Enterovibrio (1), Tateyamaria (1), Labrenzia (2), and Pseudovibrio (4). The crude extract from bacteria strains CGH2XX was found to have high cytotoxicity against the cancer cell line HL-60 (IC(50) = 0.94 μg/ml) and CCRF-CEM (IC(50) = 1.19 μg/ml). Our results demonstrate that the marine bacteria from corals have great potential in the discovery of useful medical molecules.

The objectives of this study were to investigate the presence of immunoreactive GnRH (irGnRH) in scleractinian coral, Euphyllia ancora, study its seasonal variation, and evaluate its biological activity. irGnRH was detected and quantified in coral polyps. The biological activity of coral irGnRH was tested on pituitary cells from black porgy by evaluating its ability to stimulate LH release. Coral extracts (10(-9)-10(-5) M irGnRH) as well as mammalian (m) GnRH agonist (10(-10)-10(-6) M) had a similar dose-dependent effect on LH release. Furthermore, GnRH receptor antagonist dose-dependently inhibited the stimulation of LH release in response to coral extracts (10(-5) M irGnRH) and mGnRH agonist (10(-6) M). Peak levels of irGnRH (10-fold increase) were observed during the spawning period in a 3-yr investigation. Significantly higher aromatase activity and estradiol (E2) levels were also detected during the period of spawning compared with the nonreproductive season. In in vivo experiments, mGnRH agonist time- and dose-dependently stimulated aromatase activity as well as the concentrations of testosterone and E2 in free and glucuronided forms in coral. In conclusion, our data indicate that irGnRH does exist in coral, with its ability to stimulate LH release in fish. Seasonal variations of coral irGnRH, with a dramatic increase during the spawning period, concomitant to that in aromatase and E2, as well as the ability of mGnRH agonist to stimulate coral aromatase, steroidogenesis, and steroid glucuronization suggest that irGnRH plays an important role in the control of oocyte growth and mass spawning in corals.

Two norcembranoidal diterpenes, 5-episinuleptolide acetate (1) and scabrolide D (2), were isolated from a Formosan octocoral identified as Sinularia sp. The structures of norcembranoids 1 and 2 were established by spectroscopic methods and by comparison of the spectral data with those of known analogues and 1 was proven to be a new natural product. Norcembranoid 1 was found to exhibit cytotoxicity toward a panel of tumor cells.

Anesthesia assessment is most important during surgery. Anesthesiologists use electrocardiogram (ECG) signals to assess the patient’s condition and give appropriate medications. However, it is not easy to interpret the ECG signals. Even physicians with more than 10 years of clinical experience may still misjudge. Therefore, this study uses convolutional neural networks to classify ECG image types to assist in anesthesia assessment. The research uses Internet of Things (IoT) technology to develop ECG signal measurement prototypes. At the same time, it classifies signal types through deep neural networks, divided into QRS widening, sinus rhythm, ST depression, and ST elevation. Three models, ResNet, AlexNet, and SqueezeNet, are developed with 50% of the training set and test set. Finally, the accuracy and kappa statistics of ResNet, AlexNet, and SqueezeNet in ECG waveform classification were (0.97, 0.96), (0.96, 0.95), and (0.75, 0.67), respectively. This research shows that it is feasible to measure ECG in real time through IoT and then distinguish four types through deep neural network models. In the future, more types of ECG images will be added, which can improve the real-time classification practicality of the deep model.

Single photon emission computed tomography (SPECT) has been employed to detect Parkinson’s disease (PD). However, analysis of the SPECT PD images was mostly based on the region of interest (ROI) approach. Due to limited size of the ROI, especially in the multi-stage classification of PD, this study utilizes deep learning methods to establish a multiple stages classification model of PD. In the retrospective study, the 99mTc-TRODAT-1 was used for brain SPECT imaging. A total of 202 cases were collected, and five slices were selected for analysis from each subject. The total number of images was thus 1010. According to the Hoehn and Yahr Scale standards, all the cases were divided into healthy, early, middle, late four stages, and HYS I~V six stages. Deep learning is compared with five convolutional neural networks (CNNs). The input images included grayscale and pseudo color of two types. The training and validation sets were 70% and 30%. The accuracy, recall, precision, F-score, and Kappa values were used to evaluate the models’ performance. The best accuracy of the models based on grayscale and color images in four and six stages were 0.83 (AlexNet), 0.85 (VGG), 0.78 (DenseNet) and 0.78 (DenseNet).