177 research outputs found
Feasibility and safety of Reveal LINQ insertion in a sterile procedure room versus electrophysiology laboratory
No full textAbstract not availableGeoffrey R. Wong, Dennis H. Lau, Melissa E. Middeldorp, Judith A. Harrington, Simon Stolcman, Lauren Wilson, Darragh J. Twomey, Sharath Kumar, Dian A. Munawar, Kashif B. Khokhar, Rajiv Mahajan, Prashanthan Sander
Subclinical device-detected atrial fibrillation and stroke risk: a systematic review and meta-analysis
No full textOnline publish-ahead-of-print 10 January 2018Aims: To determine stroke risk in subclinical atrial fibrillation (AF) and temporal association between subclinical AF and stroke. Methods and results: Pubmed/Embase was searched for studies reporting stroke in subclinical AF in patients with cardiac implantable electronic devices (CIEDs). After exclusions, 11 studies were analysed. Of these seven studies reported prevalence of subclinical AF, two studies reported association between subclinical and clinical AF, seven studies reported stroke risk in subclinical AF, and five studies reported temporal relationship between subclinical AF and stroke. Subclinical AF was noted after CIEDs implant in 35% [interquartile range (IQR) 34–42] of unselected patients with pacing indication over 1–2.5 years. The definition and cut-off duration (for stroke risk) of subclinical AF varied across studies. Subclinical AF was strongly associated with clinical AF (OR 5.7, 95% CI 4.0–8.0, P defined cut-off duration was 1.89/100 person-year (95% CI 1.02–3.52) with 2.4-fold (95% CI 1.8–3.3, P < 0.001, I2 = 0%) increased risk of stroke as compared to patients with subclinical AF < cut-off duration (absolute risk was 0.93/100 person-year). Three studies provided mean CHADS2 score. In these studies, with mean CHADS2 score of 2.1 ± 0.1, subclinical AF was associated with annual stroke rate of 2.76/100 person-years (95% CI 1.46–5.23). After excluding patients without AF, only 17% strokes occurred in presence of ongoing AF. Subclinical AF was noted in 29% [IQR 8–57] within 30 days preceding stroke. Conclusion: Subclinical AF strongly predicts clinical AF and is associated with elevated absolute stroke risk albeit lower than risk described for clinical AF.Rajiv Mahajan, Tharani Perera, Adrian D. Elliott, Darragh J. Twomey, Sharath Kumar, Dian A. Munwar, Kashif B. Khokhar, Anand Thiyagarajah, Melissa E. Middeldorp, Chrishan J. Nalliah, Jeroen M. L. Hendriks, Jonathan M. Kalman, Dennis H. Lau, and Prashanthan Sander
On the dynamics of Rayleigh-Taylor mixing
No full textThe self-similar evolution of a turbulent Rayleigh-Taylor (R-T) mix is investigated through experiments and numerical simulations. The experiments consisted of velocity and density measurements using thermocouples and Particle Image Velocimetry techniques. A novel experimental technique, termed PIV-S, to simultaneously measure both velocity and density fields was developed. These measurements provided data for turbulent correlations, power spectra, and energy balance analyses. The self-similarity of the flow is demonstrated through velocity profiles that collapse when normalized by an appropriate similarity variable and power spectra that evolve in a shape-preserving form. In the self-similar regime, vertical r.m.s. velocities dominate over the horizontal r.m.s. velocities with a ratio of 2:1. This anisotropy, also observed in the velocity spectra, extends to the Taylor scales. Buoyancy forcing does not alter the structure of the density spectra, which are seen to have an inertial range with a -5/3 slope. A scaling analysis was performed to explain this behavior. Centerline velocity fluctuations drive the growth of the flow, and can hence be used to deduce the growth constant. The question of universality of this flow was addressed through 3D numerical simulations with carefully designed initial conditions. With long wavelengths present in the initial conditions, the growth constant was found to depend logarithmically on the initial amplitudes. In the opposite limit, where long wavelengths are generated purely by the nonlinear interaction of shorter wavelengths, the growth constant assumed a universal lower bound value o
Tracking Mental Health and Symptom Mentions on Twitter During COVID-19
No full textTwitter estimates of county-level mental health during COVID-19
Mental health metrics, namely psychological stress, lonely expressions, anxiety, and sentiment are measured daily using pre-trained machine learning models applied to a random 1% Twitter data. For more details, read our publication in the Journal of General Internal Medicine: http://wwbp.org/papers/jgim-2020.pdf
Data snapshot:
| group_id | feat | value | group_norm | day | cnty |
|------------------ |----------- |------- |------------------ |------------ |------- |
| 2020-04-16:01001 | lonely_score | 78 | 2.92268402613488 | 2020-04-16 | 01001 |
| 2020-04-16:01003 | lonely_score | 830 | 2.82928758282208 | 2020-04-16 | 01003 |
| 2020-04-16:01005 | lonely_score | 13 | 3.4083486715075 | 2020-04-16 | 01005 |
| 2020-04-16:01017 | lonely_score | 93 | 2.67820445675611 | 2020-04-16 | 01017 |
| 2020-04-16:01021 | lonely_score | 96 | 3.02387743147066 | 2020-04-16 | 01021 |
`cnty`: FIPS code of county
`day`: date
`group_norm`: mental health estimate; a sum of term relative frequencies weighted by their association with this mental health outcome in the pre-trained model
`value`: number of words contributing to the estimate
`feat`: descriptor of metric
`group_id`: concatenation of `day`:`cnty`
This data (aggregated to the state-level) is also used to update the Penn COVID Twitter Map https://penncovid19hub.com/twitter-map
##Citation
APA:
```
Guntuku, S. C., Sherman, G., Stokes, D. C., Agarwal, A. K., Seltzer, E., Merchant, R. M., & Ungar, L. H. (2020). Tracking Mental Health and Symptom Mentions on Twitter During COVID-19. Journal of general internal medicine, 1-3.
```
Bib:
```
@article{guntuku2020tracking,
title={Tracking Mental Health and Symptom Mentions on Twitter During COVID-19},
author={Guntuku, Sharath Chandra and Sherman, Garrick and Stokes, Daniel C and Agarwal, Anish K and Seltzer, Emily and Merchant, Raina M and Ungar, Lyle H},
journal={Journal of general internal medicine},
pages={1--3},
year={2020},
publisher={Springer}
}
```
Details of how these models were trained are described in the following papers:
Stress:
```
Guntuku, S. C., Buffone, A., Jaidka, K., Eichstaedt, J. C., & Ungar, L. H. (2019, July). Understanding and measuring psychological stress using social media. In Proceedings of the International AAAI Conference on Web and Social Media (Vol. 13, No. 01, pp. 214-225).
```
Loneliness:
```
Guntuku, S. C., Schneider, R., Pelullo, A., Young, J., Wong, V., Ungar, L., ... & Merchant, R. (2019). Studying expressions of loneliness in individuals using twitter: an observational study. BMJ open, 9(11).
```
Sentiment:
```
Mohammad, S. M., & Turney, P. D. (2013). Crowdsourcing a word–emotion association lexicon. Computational Intelligence, 29(3), 436-465.
```
For any queries, please reach out at `sharathg at cis dot upenn dot edu` or `garricks at sas dot upenn dot edu`
A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA
No full textIn this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone\u27s life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone\u27s personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can\u27t apply directly to the structured complex graph  data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner.Â
The grenz zone
No full textThe grenz zone is a narrow area of the papillary dermis uninvolved by underlying pathology. Historically believed to be a feature unique to granuloma faciale, this feature has also been observed in other cutaneous inflammatory conditions, infectious entities, and neoplastic benign and malignant tumors. This review attempts to enumerate cutaneous entities commonly displaying a grenz zone with an emphasis on histopathological features that help in their differentiation. It also attempts to answer the obvious question of why select entities have this histopathologic feature by ascertaining the defining structure of a grenz zone. Copyright © 2013 by Lippincott Williams and Wilkins.Arai E, 2005, HUM PATHOL, V36, P505, DOI 10.1016-j.humpath.2005.02.012; Baldassano MF, 1999, AM J SURG PATHOL, V23, P88, DOI 10.1097-00000478-199901000-00010; BHAWAN J, 1976, J CUTAN PATHOL, V3, P5, DOI 10.1111-j.1600-0560.1976.tb00841.x; BOERSMA D, 1951, AMA ARCH DERM SYPH, V63, P520; Britton WJ, 2004, LANCET, V363, P1209, DOI 10.1016-S0140-6736(04)15952-7; Buchner SA, 1985, AM J DERMATOPATHO, V7, P109; Burg G, 2005, J CUTAN PATHOL, V32, P647, DOI 10.1111-j.0303-6987.2005.00495.x; Chaudhry IH, 2005, HISTOPATHOLOGY, V47, P179, DOI 10.1111-j.1365-2559.2005.02192.x; Cheng L, 1997, AM J SURG PATHOL, V21, P711, DOI 10.1097-00000478-199706000-00012; Chimenti S, 1999, J CUTAN PATHOL, V26, P379, DOI 10.1111-j.1600-0560.1999.tb01861.x; Cho-Vega JH, 2008, AM J CLIN PATHOL, V129, P130, DOI 10.1309-WYACYWF6NGM3WBRT; Colli C, 2004, J CUTAN PATHOL, V31, P232, DOI 10.1111-j.0303-6987.2003.00167.x; Dayrit JF, 2011, J CUTAN PATHOL, V38, P475, DOI 10.1111-j.1600-0560.2011.01680.x; de Almeida LS, 2008, AM J DERMATOPATH, V30, P207, DOI 10.1097-DAD.0b013e3181716e6b; Gadelha AR, 1983, DERMATOPATOLOGIA, P125; Gauger A, 2005, BRIT J DERMATOL, V153, P454, DOI 10.1111-j.1365-2133.2005.06752.x; GERSTEIN W, 1963, J INVEST DERMATOL, V41, P445, DOI 10.1038-jid.1963.139; Grabbe J, 2000, BRIT J DERMATOL, V143, P415, DOI 10.1046-j.1365-2133.2000.03673.x; Han TY, 2011, ANN DERMATOL, V23, P185, DOI 10.5021-ad.2011.23.2.185; Hantschke M, 2010, AM J SURG PATHOL, V34, P216, DOI 10.1097-PAS.0b013e3181c7d8b2; HAY ED, 1963, DEV BIOL, V7, P152, DOI 10.1016-0012-1606(63)90114-3; Job CK, 1999, INT J LEPROSY, V67, P164; Kaddu S, 1999, J AM ACAD DERMATOL, V40, P966, DOI 10.1016-S0190-9622(99)70086-1; Kaddu S, 2002, AM J SURG PATHOL, V26, P35, DOI 10.1097-00000478-200201000-00004; Kaur I, 2009, BRIT J DERMATOL, V160, P305, DOI 10.1111-j.1365-2133.2008.08899.x; Klemke CD, 2003, J AM ACAD DERMATOL, V49, pS233, DOI 10.1067-S0190-9622(03)00037-9; Lazar AJF, 2005, AM J SURG PATHOL, V29, P927, DOI 10.1097-01.pas.0000157294.55796.d3; LEBOIT PE, 1991, AM J SURG PATHOL, V15, P48; Loser Karin, 2007, Adv Dermatol, V23, P307, DOI 10.1016-j.yadr.2007.07.014; Luzar B, 2010, J CUTAN PATHOL, V37, P301, DOI 10.1111-j.1600-0560.2009.01425.x; Ly Hoang, 2005, Australas J Dermatol, V46, P44, DOI 10.1111-j.1440-0960.2005.00137.x; Mahalingam M, 2001, AM J DERMATOPATH, V23, P299, DOI 10.1097-00000372-200108000-00004; Malhotra Purnima, 2010, Indian J Dermatol, V55, P337, DOI 10.4103-0019-5154.74535; Marcoval J, 2004, J AM ACAD DERMATOL, V51, P269, DOI 10.1016-j.jaad.2003.11.071; MARTENS U, 1984, INT J LEPROSY, V52, P55; Miller DD, 2011, MODERN PATHOL, DOI [10.1038-modpathol.2011.196, DOI 10.1038-M0DPATHOL.2011.196]; Miranda MFR, 2010, AN BRAS DERMATOL, V85, P39, DOI 10.1590-S0365-05962010000100005; Ortonne N, 2005, J AM ACAD DERMATOL, V53, P1002, DOI 10.1016-j.jaad.2005.08.021; Pande S, 2007, DERMATOPATHOL PRACT, V13, P28; Philip H, 1995, AUSTRIAN ARMY 1740 8, P13; Plaza JA, 2011, AM J DERMATOPATH, V33, P649, DOI 10.1097-DAD.0b013e3181eeb433; Plaza JA, 2010, AM J DERMATOPATH, V32, P129, DOI 10.1097-DAD.0b013e3181b34a19; Rastogi R, 2011, ROM J MORPHOL EMBRYO, V52, P165; Rathi Sanjay K, 2005, Indian J Dermatol Venereol Leprol, V71, P250; Sangueza OP, 1997, AM J DERMATOPATH, V19, P214, DOI 10.1097-00000372-199706000-00003; Sehgal VN, 2009, AM J DERMATOPATH, V31, P268, DOI 10.1097-DAD.0b013e318185d1d0; Servitje O, 2002, BRIT J DERMATOL, V147, P1147, DOI 10.1046-j.1365-2133.2002.04961.x; Shafer D, 2008, ARCH DERMATOL, V144, P1155, DOI 10.1001-archderm.144.9.1155; Sharath Kumar BC, 2011, INDIAN J DERMATOL VE, V77, P498; Singh N, 1998, J CUTAN PATHOL, V25, P95, DOI 10.1111-j.1600-0560.1998.tb01696.x; Smith JG JR, 1965, J SOC COSMET CHEM, V16, P527; SMOLLER BR, 1993, J CUTAN PATHOL, V20, P442, DOI 10.1111-j.1600-0560.1993.tb00668.x; Swetter SM, 2004, ARCH DERMATOL, V140, P99, DOI 10.1001-archderm.140.1.99; Twersky JM, 2004, J AM ACAD DERMATOL, V51, P123, DOI 10.1016-j.jaad.2003.12.027; Wahl CE, 2005, AM J DERMATOPATH, V27, P397, DOI 10.1097-01.dad.0000175526.89249.be; WAYNER EA, 1993, J CELL BIOL, V121, P1141, DOI 10.1083-jcb.121.5.1141; YIANNIAS JA, 1992, J AM ACAD DERMATOL, V26, P38, DOI 10.1016-0190-9622(92)70003-X; Ziemer M, 2011, J CUTAN PATHOL, V38, P876, DOI 10.1111-j.1600-0560.2011.01760.x1
A SURVEY ON PRIVACY PRESERVING TECHNIQUES FOR SOCIAL NETWORK DATA
No full textIn this era of 20th century, online social network like Facebook, twitter, etc. plays a very important role in everyone’s life. Social network data, regarding any individual organization can be published online at any time, in which there is a risk of information leakage of anyone’s personal data. So preserving the privacy of individual organizations and companies are needed before data is published online. Therefore the research was carried out in this area for many years and it is still going on. There have been various existing techniques that provide the solutions for preserving privacy to tabular data called as relational data and also social network data represented in graphs. Different techniques exists for tabular data but you can’t apply directly to the structured complex graph data,which consists of vertices represented as individuals and edges representing some kind of connection or relationship between the nodes. Various techniques like K-anonymity, L-diversity, and T-closeness exist to provide privacy to nodes and techniques like edge perturbation, edge randomization are there to provide privacy to edges in social graphs. Development of new techniques by Integration to exiting techniques like K-anonymity ,edge perturbation, edge randomization, L-Diversity are still going on to provide more privacy to relational data and social network data are ongoingin the best possible manner. </jats:p
Outcomes of persistent and long-standing persistent atrial fibrillation ablation: a systematic review and meta-analysis
No full textAims: Several techniques have been utilized for the ablation of persistent (P) and long-standing persistent (LsP) atrial fibrillation (AF); however, the best approach of substrate ablation remains poorly defined. This study aims to examine the impact of ablation approach on outcomes associated with P or LsP AF ablation by conducting a meta-analysis and regression on contemporary literature. Methods and Results: A systematic literature review was conducted up to 29 July 2015 for scientific literature reporting on outcomes associated with P or LsP AF ablation. One hundred and thirteen studies reported outcomes in a total of 18 657 patients undergoing various ablation approaches for the treatment of P-LsP AF between 2001 and 2015. The point efficacy estimate of a single-AF ablation procedure without the use of anti-arrhythmic drugs was 43% (95% CI; 39-47%). Multiple procedures and/or the use of anti-arrhythmic drugs increase success to 69% (95% CI; 66-71%). Meta-regression revealed that ablation technique (P < 0.001) and left atrial size (P = 0.02) were predictive of single procedure, drug-free success. The addition of extra-pulmonary substrate approaches was associated with declining efficacy when compared to a pulmonary vein ablation alone. Conclusion: The efficacy of a single-AF ablation procedure for P or LsP AF is 43%; however, can be increased to 69% with the use of multiple procedures and/or anti-arrhythmic drugs. Current literature supports the finding that pulmonary vein antrum ablation/isolation is at least equivalently efficacious to other contemporary P-LsP ablation strategies.Jock A. Clarnette, Anthony G. Brooks, Rajiv Mahajan, Adrian D. Elliott, Darragh J. Twomey, Rajeev K. Pathak, Sharath Kumar, Dian A. Munawar, Glenn D. Young, Jonathan M. Kalman, Dennis H. Lau, and Prashanthan Sander
Characterization of major zinc containing myonecrotic and procoagulant metalloprotease 'malabarin' from non lethal trimeresurus malabaricus snake venom with thrombin like activity: its neutralization by chelating agents
No full textA major myonecrotic zinc containing metalloprotease 'malabarin' with thrombin like activity was purified by the combination of gel permeation and anion exchange chromatography from T. malabaricus snake venom. MALDI-TOF analysis of malabarin indicated a molecular mass of 45.76 kDa and its N-terminal sequence was found to be Ile-Ile-Leu- Pro(Leu)-Ile-Gly-Val-Ile-Leu(Glu)-Thr-Thr. Atomic absorption spectral analysis of malabarin raveled the association of zinc metal ion. Malabarin is not lethal when injected i.p. or i.m. but causes extensive hemorrhage and degradation of muscle tissue within 24 hours. Sections of muscle tissue under light microscope revealed hemorrhage and congestion of blood vessel during initial stage followed by extensive muscle fiber necrosis with elevated levels of serum creatine kinase and lactate dehydrogenase activity. Malabarin also exhibited strong procoagulant action and its procoagulant action is due to thrombin like activity; it hydrolyzes fibrinogen to form fibrin clot. The enzyme preferentially hydrolyzes A? followed by B subunits of fibrinogen from the N-terminal region and the released products were identified as fibrinopeptide A and fibrinopeptide B by MALDI. The myonecrotic, fibrinogenolytic and subsequent procoagulant activities of malabarin was neutralized by specific metalloprotease inhibitors such as EDTA, EGTA and 1, 10-phenanthroline but not by PMSF a specific serine protease inhibitor. Since there is no antivenom available to neutralize local toxicity caused by T. malabaricus snakebite, EDTA chelation therapy may have more clinical relevance over conventional treatment
Thermally Developing Region of a Parallel Plate Channel Partially Filled with a Porous Material with the Effect of Axial Conduction and Viscous Dissipation: Uniform Wall Heat Flux
No full text- …
