A fucoidan plant drink reduces Helicobacter pylori load in the stomach: a real-world study.

Background: Helicobacter pylori (Hp) infection is highly prevalent globally and is predominantly managed by antibiotics. Recently, the anti-adhesive, antioxidant, antitoxin, immunomodulatory, anti-coagulant, and anti-infective activities of fucoidan, a polysaccharide extracted from brown seaweeds, have been widely studied, and the results showed promise. Fucoidan has the potential to be utilized in Hp eradication therapy. Our present clinical study was designed to evaluate the efficiency of Lewuyou®, a fucoidan plant drink (FPD) in eradicating Hp in humans. Methods: This multi-center, clinical study was conducted between October 2020 and July 2021. Hp infection was confirmed by urea breath test (UBT). A total of 122 patients with confirmed Hp infection were enrolled; after exclusion of incomplete data, 85 eligible patients (37 males and 48 females aged 20-81 years) were included in the final analysis. FPD (50 mL per vial) was orally administered twice daily for a 4-week cycle, and 41 patients completed an 8-week cycle. Results: No adverse event (AE) was reported in all 122 participants who had consumed FPD. The Hp eradication rate and clearance rate were 77.6% (66/85) and 20.0% (17/85), respectively, after 4 weeks of FPD consumption and 80.5% (33/41) and 26.8 (11/41) , respectively, after 8 weeks of consumption. Conclusions: The 4- and 8-week protocols of FPD consumption were safe and effective at reducing Hp load on the gastric mucosa, with Hp eradicated in the majority of participants.

How much can AI see in early pregnancy: A multi-center study of fetus head characterization in week 10-14 in ultrasound using deep learning.

PURPOSE: To investigate if artificial intelligence can identify fetus intracranial structures in pregnancy week 11-14; to provide an automated method of standard and non-standard sagittal view classification in obstetric ultrasound examination METHOD AND MATERIALS: We proposed a newly designed scheme based on deep learning (DL) - Fetus Framework to identify nine fetus intracranial structures: thalami, midbrain, palate, 4th ventricle, cisterna magna, nuchal translucency (NT), nasal tip, nasal skin, and nasal bone. Fetus Framework was trained and tested on a dataset of 1528 2D sagittal-view ultrasound images from 1519 females collected from Shenzhen People's Hospital. Results from Fetus Framework were further used for standard/non-standard (S-NS) plane classification, a key step for NT measurement and Down Syndrome assessment. S-NS classification was also tested with 156 images from the Longhua branch of Shenzhen People's Hospital. Sensitivity, specificity, and area under the curve (AUC) were evaluated for comparison among Fetus Framework, three classic DL models, and human experts with 1-, 3- and 5-year ultrasound training. Furthermore, 4 physicians with more than 5 years of experience conducted a reader study of diagnosing fetal malformation on a dataset of 316 standard images confirmed by the Fetus framework and another dataset of 316 standard images selected by physicians. Accuracy, sensitivity, specificity, precision, and F1-Score of physicians' diagnosis on both sets are compared. RESULTS: Nine intracranial structures identified by Fetus Framework in validation are all consistent with that of senior radiologists. For S-NS sagittal view identification, Fetus Framework achieved an AUC of 0.996 (95%CI: 0.987, 1.000) in internal test, at par with classic DL models. In external test, FF reaches an AUC of 0.974 (95%CI: 0.952, 0.995), while ResNet-50 arrives at AUC∼0.883, 95% CI 0.828-0.939, Xception AUC∼0.890, 95% CI 0.834-0.946, and DenseNet-121 AUC∼0.894, 95% CI 0.839-0.949. For the internal test set, the sensitivity and specificity of the proposed framework are (0.905, 1), while the first-, third-, and fifth-year clinicians are (0.619, 0.986), (0.690, 0.958), and (0.798, 0.986), respectively. For the external test set, the sensitivity and specificity of FF is (0.989, 0.797), and first-, third-, and fifth-year clinicians are (0.533, 0.875), (0.609, 0.844), and (0.663, 0.781), respectively.On the fetal malformation classification task, all physicians achieved higher accuracy and F1-Score on Fetus selected standard images with statistical significance (p < 0.01). CONCLUSION: We proposed a new deep learning-based Fetus Framework for identifying key fetus intracranial structures. The framework was tested on data from two different medical centers. The results show consistency and improvement from classic models and human experts in standard and non-standard sagittal view classification during pregnancy week 11-13+6. CLINICAL RELEVANCE/APPLICATION: With further refinement in larger population, the proposed model can improve the efficiency and accuracy of early pregnancy test using ultrasound examination.

Doppler ultrasound and photoplethysmographic assessment for identifying pregnancy-induced hypertension.

The current study investigated whether placentation and systemic inflammation are associated with pregnancy-induced hypertension (PIH) or pre-eclampsia (PE), and evaluated some measurable indexes for assessment of maternal factors contributing to high-risk pregnancy. Photoplethysmographic reflection index (PPG RI), uterine artery (UtA) pulsatile index (PI) and reflection index (RI), as well as maternal serum placental growth factor (PlGF) and soluble endoglin (sEng) were measured in pregnant women with singleton pregnancy at the gestational age of 22 to 23 weeks. Study subjects were women with normal pregnancy (NP, n=24), PIH (n=14) and PE (n=16). It was found that individuals in the PIH group exhibited higher UtA RI and UtA PI values, as well as PPG RI values compared with individuals in the NP group. Individuals in the PE group had the highest UtA RI, UtA PI and PPG RI values among these 3 groups. UtA and PPG results were significantly different in PIH and PE groups compared with the NP group. Significant differences were found in both PlGF and sEng levels between PIH and PE groups. A strong inverse across-subject correlation was found between PlGF and sEng levels. A weak inverse correlation was found between PlGF and UtA RI, and PlGF and UtA PI. A moderate inverse correlation was found between PlGF and PPG RI. A moderate positive correlation was found between either sEng and UtA RI or sEng and UtA PI. A very strong positive correlation was found between sEng and PPG RI. Taken together, the current results indicated that maternal effects related to cardiovascular adaptation to placentation and systemic inflammation exhibited significant differences between NP and PIH or PE groups. Therefore, assessment of UtA and PPG could be used for identifying high-risk pregnancy.