Differentiation of internet addiction risk level based on autonomic nervous responses: the internetaddiction hypothesis of autonomic activity

Abstract How high-risk Internet addiction (IA) abusers respond to different autonomic nervous activities compared with low-risk subjects may be a critical research goal with prevention and treatment implications. The aim of the present study was to address this issue by observing differences between high-and low-risk IA abusers in four physiological assessments when surfing the Internet: blood volume pulse (BVP), skin conductance (SC), peripheral temperature (PTEMP), and respiratory response (RESPR). Forty-two male and ten female participants aged 18-24 years were screened with the Chen Internet Addiction Scale (CIAS, 2003), and then separated into high-and lowrisk IA groups. Using psychophysiology equipment, participants encountered a 3-minute adaptation period followed by a 6-minute testing period for surfing the Internet on baseline and testing phases. The present results indicate that: (a) the CIAS scores were positively and negatively correlated with the RESPR and the PTEMP; (b) the PTEMP and RESPR of high-risk IA abusers were respectively weaker and stronger than those of low-risk IA abusers; the BVP and SC of high-risk IA abusers were respectively augmented and decreased relative to low-risk IA abusers. Thus we suggest that four autonomic responses may be differentially sensitive to abusers' potency in terms of the IA hypothesis of autonomic activity. The stronger BVP and RESPR responses and the weaker PTEMP reactions of the high-risk IA abusers indicate the sympathetic nervous system was heavily activated in these individuals. However, SC activates parasympathetic responses at the same time in the high-risk IA abusers. The paradoxical responses between the sympathetic and parasympathetic actions are addressed in the discussion

An atypical case of Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC)-associated renal cell carcinoma identified by next-generation sequencing

Germline mutations in the fumarate hydratase (FH) gene classically lead to Hereditary Leiomyomatosis and Renal Cell Cancer (HLRCC) syndrome. Patients with HLRCC typically exhibit multiple cutaneous and uterine leiomyomas at a young age. They also display a 20–30% lifetime risk for renal carcinomas, which commonly present before 40 years of age, have a distinct papillary morphology, and an aggressive phenotype. However, the clinical presentation of HLRCC and the morphology of HLRCC-associated renal cell carcinomas (RCCs) can be variable and thereby evade diagnosis. Here, we present two cases of HLRCC-associated RCC to emphasize this point. The first case is typical of HLRCC, involving a 29-year-old man with multiple cutaneous leiomyomas and a renal tumor with characteristic papillary morphology. Next, we describe a 48-year-old man presenting with metastatic cancer of unknown primary origin and no skin findings. Interestingly, next-generation sequencing of his metastatic tumor identified two unique FH mutations. In both cases, FH mutations were confirmed as germline. These cases highlight the variable presentations of HLRCC-associated RCC and underscore the importance of screening tumors of unknown origin for FH mutations using next-generation sequencing. Keywords: Renal cell carcinoma, HLRCC, Fumarate hydratase, Next-generation sequencin

Effect of salinity on the growth performance, osmolarity and metabolism-related gene expression in white shrimp Litopenaeus vannamei

An 8-week feeding trial was conducted to study the effect of long-term low-salinity stress on the growth performance, and expression of osmolarity and metabolism-related genes (Na+-K+-ATPase α-subunit and in gills, trypsin and chymotypsin in hepatopancreas) in white shrimp (L. vannamei). Four groups of the white shrimp (mean initial weight, 0.31 ± 0.02 g) were cultivated at salinity of 2, 10, 20 and 30 psu for 8-week. All treatments were conducted in triplicate of 40 each. The results indicated that shrimps reared at salinity 20 psu and 30 psu were significantly higher in final weight, weight gain and specific growth ratio than the other treatments (p < 0.05 when compared to 2 and 10), and those reared at salinity 2 psu were significantly lower than the other treatments in growth performance and survival (p < 0.05 when compared to other treatments). Quantitative real-time PCR (qRT-PCR) results indicated that Na+-K+-ATPase α-subunit and carbonic anhydrase mRNA levels at salinity 2 psu and 10 psu were increased significantly 1.79-, 1.65-fold and 3.22-, 2.31-fold respectively according to salinity 20 psu, chymotrypsin and trypsin mRNA level at salinity 10 psu and 2 psu decreased significantly 15%, 36% and 72%, 45% respectively according to salinity 30 psu. In conclusion, low salinity could, to some extent, reduce growth performance and survival significantly, and influence transcript levels of Na+-K+-ATPase α-subunit, carbonic anhydrase in gills and chymotrypsin, trypsin in hepatopancreas

Cluster-based text mining for extracting drug candidates for the prevention of COVID-19 from the biomedical literature

الملخص: أهداف البحث: جعلت الأزمة الصحية كوفيد-19 التي بدأت في نهاية عام 2019 الباحثين من جميع أنحاء العالم يتسابقون بسرعة لإيجاد حلول فعالة حتى الآن. كثرت الأبحاث ذات الصلة وكان من المحتم أن تكون هناك حاجة إلى نهج آلي للعثور على معلومات مفيدة ، وبالتحديد التنقيب عن النص ، للتغلب على كوفيد-19، لا سيما فيما يتعلق باكتشاف مرشح العلاج. بينما تحاول طرق التنقيب عن النص للعثور على الأدوية المرشحة في الغالب استخراج ارتباطات حيوية من ''بابميد''، إلا أن القليل جدا منها يستخدم أسلوب التجميع. الغرض من البحث هو إثبات فعالية نهجنا في تحديد الأدوية للوقاية من كوفيد-19 من خلال مراجعة الأبحاث وتحليل الكتلة وحسابات إرساء الأدوية وبيانات التجارب السريرية. طريقة البحث: تم إجراء هذا البحث في أربع مراحل رئيسية. أولا، تم تنفيذ مرحلة التنقيب عن النص من خلال إشراك ''بايوبيرت'' للحصول على تمثيل متجه لكل كلمة في الجملة من النصوص. كانت المرحلة التالية هي إنشاء روابط دوائية للأمراض يتم الحصول عليها من المراسلات بين المرض والعقار. بعد ذلك ، جمعت مرحلة التجميع القواعد من خلال تشابه الأمراض من خلال استخدام ''تي إف-آي دي إف'' كميزات لها. أخيرا، تتم معالجة مرحلة استخراج مرشح الدواء من خلال الاستفادة من قواعد بيانات ''بابكيم'' و ''بنك الدواء''. كما استخدمنا حزمة إرساء الأدوية ''أوتودوك فينا'' في برنامج ''بي واي آر إكس'' للتحقق من النتائج. النتائج: أظهر التحليل المقارن الذي تم إجراؤه أن النسبة المئوية للنتائج المستخدمة في التعدين مع العنقودية تفوقت على التعدين دون التجميع في جميع البيئات التجريبية. بالإضافة إلى ذلك ، اقترحنا أن أفضل ثلاثة أدوية / مواد كيميائية نباتية من خلال تحليل الالتحام بالعقاقير قد تكون فعالة في الوقاية من كوفيد-19. الاستنتاجات: تعد الطريقة المقترحة لتعدين النص باستخدام طريقة التجميع واعدة للغاية في اكتشاف الوقاية من الأدوية المرشحة لكوفيد-19 من خلال الأدبيات الطبية الحيوية. Abstract: Objective: The coronavirus disease 2019 (COVID-19) health crisis that began at the end of 2019 made researchers around the world quickly race to find effective solutions. Related literature exploded and it was inevitable that an automated approach was needed to find useful information, namely text mining, to overcome COVID-19, especially in terms of drug candidate discovery. While text mining methods for finding drug candidates mostly try to extract bioentity associations from PubMed, very few of them mine with a clustering approach. The purpose of this study was to demonstrate the effectiveness of our approach to identify drugs for the prevention of COVID-19 through literature review, cluster analysis, drug docking calculations, and clinical trial data. Methods: This research was conducted in four main stages. First, the text mining stage was carried out by involving Bidirectional Encoder Representations from Transformers for Biomedical to obtain vector representation of each word in the sentence from texts. The next stage generated the disease-drug associations, which were obtained from the correlation between disease and drug. Next, the clustering stage grouped the rules through the similarity of diseases by utilizing Term Frequency-Inverse Document Frequency as its feature. Finally, the drug candidate extraction stage was processed through leveraging PubChem and DrugBank databases. We further used the drug docking package AUTODOCK VINA in PyRx software to verify the results. Results: Comparative analyses showed that the percentage of findings using mining with clustering outperformed mining without clustering in all experimental settings. In addition, we suggest that the top three drugs/phytochemicals by drug docking analysis may be effective in preventing COVID-19. Conclusions: The proposed method for text mining utilizing the clustering method is quite promising in the discovery of drug candidates for the prevention of COVID-19 through the biomedical literature

Book of Abstracts: 2019 Health Equity Summer Research Summit Organized by the Center of Excellence in Health Equity, Training and Research, Baylor College of Medicine, Houston, Texas 77030, USA on June 18th, 2019

Copyright © 2020 Harris. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited