LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 10 of total 123

Search options

  1. Article: Use of minilaparotomy in the treatment of colonic cancer (Br J Surg 2001;88:831-6).

    Somasekar, K

    The British journal of surgery

    2002  Volume 89, Issue 2, Page(s) 247; author reply 247–8

    MeSH term(s) Colonic Neoplasms/surgery ; Humans ; Laparotomy/methods ; Neoplasm Staging/methods ; Preoperative Care/methods
    Language English
    Publishing date 2002-02
    Publishing country England
    Document type Comment ; Letter
    ZDB-ID 2985-3
    ISSN 0007-1323 ; 0263-1202 ; 1355-7688
    ISSN 0007-1323 ; 0263-1202 ; 1355-7688
    DOI 10.1046/j.1365-2168.2002.t01-1-20096.x
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Uk I Zs.66: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article: Machine Learning and Image Analysis Applications in the Fight against COVID-19 Pandemic: Datasets, Research Directions, Challenges and Opportunities.

    Somasekar, J / Pavan Kumar Visulaization, P / Sharma, Avinash / Ramesh, G

    Materials today. Proceedings

    2020  

    Abstract: COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine ... ...

    Abstract COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine Learning and Image Analysis research has been making great progress in many directions in the healthcare field for providing support to subsequent medical diagnosis. In this paper, we have propose three research directions with methodologies in the fight against the pandemic namely: Chest X-Ray (CXR) images classification using deep convolution neural networks with transfer learning to assist diagnosis; Patient Risk prediction of pandemic based on risk factors such as patient characteristics, comorbidities, initial symptoms, vital signs for prognosis of disease; and forecasting of disease spread & case fatality rate using deep neural networks. Further, some of the challenges, open datasets and opportunities are discussed for researchers.
    Keywords covid19
    Language English
    Publishing date 2020-09-22
    Publishing country England
    Document type Journal Article
    ZDB-ID 2797693-2
    ISSN 2214-7853
    ISSN 2214-7853
    DOI 10.1016/j.matpr.2020.09.352
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Covid-19 Mortality in an Acute Care Hospital: Association of Patient Factors With Decision to Forego the Intensive Care Unit.

    Meisenberg, Barry R / Qureshi, Sadaf / Somasekar, Monika Thandalam / Ali, Qurat / Karpman, Mitchell / Rhule, Jane

    The American journal of hospice & palliative care

    2021  Volume 39, Issue 4, Page(s) 481–486

    Abstract: Background: Public awareness of the large mortality toll of COVID-19 particularly among elderly and frail persons is high. This public awareness represents an enhanced opportunity for early and urgent goals-of-care discussions to reduce medically ... ...

    Abstract Background: Public awareness of the large mortality toll of COVID-19 particularly among elderly and frail persons is high. This public awareness represents an enhanced opportunity for early and urgent goals-of-care discussions to reduce medically ineffective care.
    Objective: To assess the end-of-life experiences of hospitalized patients dying of COVID-19 with respect to identifying the clinical factors associated with utilization or non-utilization of the ICU.
    Methods: Retrospective cohort study of hospital outcomes using electronic medical records and individual chart review from March 15, 2020 to October 15, 2020 of every patient with a COVID-19 diagnosis who died or was admitted to hospice while hospitalized. Logistic regression multivariate analysis was used to identify the clinical and demographic factors associated with non-utilization of the ICU.
    Results: 133/749 (18%) of hospitalized COVID-19 patients died or were admitted to hospice as a result of COVID-19. Of the 133, 66 (49.6%) had no ICU utilization. In multivariate analysis, the significant patient factors associated with non-ICU utilization were increasing age, normal body mass index, and the presence of an advanced directive calling for limited life sustaining therapies. Race and residence at time of admission (home vs. facility) were significant only in the unadjusted analyses but not in adjusted. Gender was not significant in either form of analyses.
    Conclusion: Goals of care discussions performed by an augmented palliative care team and other bedside clinicians had renewed urgency during COVID-19. Large percentages of patients and surrogates, perhaps motivated by public awareness of poor outcomes, opted not to utilize the ICU.
    MeSH term(s) Aged ; COVID-19/therapy ; COVID-19 Testing ; Hospital Mortality ; Hospitals ; Humans ; Intensive Care Units ; Retrospective Studies ; SARS-CoV-2
    Language English
    Publishing date 2021-06-29
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1074344-3
    ISSN 1938-2715 ; 1049-9091
    ISSN (online) 1938-2715
    ISSN 1049-9091
    DOI 10.1177/10499091211028849
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.B 3090: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  4. Article ; Online: Computer Vision for Malaria Parasite Classification in Erythrocytes

    J.SOMASEKAR

    International Journal on Computer Science and Engineering, Vol 3, Iss 6, Pp 2251-

    2011  Volume 2256

    Abstract: In this is paper, we introduce a new approach to represent a mathematical modeling technique by means of linear programming as an efficient tool to solve problems related to medical imaging problems especially Malaria Diagnosis through Microscopy Imaging ...

    Abstract In this is paper, we introduce a new approach to represent a mathematical modeling technique by means of linear programming as an efficient tool to solve problems related to medical imaging problems especially Malaria Diagnosis through Microscopy Imaging problems .Two applications are approached: formulation of a linear programming based on the given data and solving the given problem using graphical method approach for detecting parasite. Also we applied some image processing techniques namely image segmentation, morphological operations. In the first application, just we have to develop mathematical model from the collected information and in second approach we have to solve problem by Graphical approach. We markregion infected with malaria from the original image leads to identifying parasite and also we classified different species of malaria by using graphical approach. By observation of graph we can predict whether the blood is infected by parasite or not. We can also classify the number of species of parasite infected the erythrocytes and parasite identification by labeling the infected area.
    Keywords Classification in Erythrocytes ; Electronic computers. Computer science ; QA75.5-76.95 ; Instruments and machines ; QA71-90 ; Mathematics ; QA1-939 ; Science ; Q ; DOAJ:Computer Science ; DOAJ:Technology and Engineering
    Subject code 000
    Language English
    Publishing date 2011-06-01T00:00:00Z
    Publisher Engg Journals Publications
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  5. Article ; Online: Machine learning and image analysis applications in the fight against COVID-19 pandemic

    Somasekar, J. / Pavan Kumar, P. / Sharma, Avinash / Ramesh, G.

    Materials Today: Proceedings ; ISSN 2214-7853

    Datasets, research directions, challenges and opportunities

    2020  

    Keywords covid19
    Language English
    Publisher Elsevier BV
    Publishing country us
    Document type Article ; Online
    DOI 10.1016/j.matpr.2020.09.352
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

    Full text online

    More links

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  6. Article: Machine Learning and Image Analysis Applications in the Fight against COVID-19 Pandemic: Datasets, Research Directions, Challenges and Opportunities

    Somasekar, J / Pavan Kumar Visulaization, P / Sharma, Avinash / Ramesh, G

    Abstract: COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine ... ...

    Abstract COVID-19 pandemic has become the most devastating disease of the current century and spread over 216 countries around the world. The disease is spreading through outbreaks despite the availability of modern sophisticated medical treatment. Machine Learning and Image Analysis research has been making great progress in many directions in the healthcare field for providing support to subsequent medical diagnosis. In this paper, we have propose three research directions with methodologies in the fight against the pandemic namely: Chest X-Ray (CXR) images classification using deep convolution neural networks with transfer learning to assist diagnosis; Patient Risk prediction of pandemic based on risk factors such as patient characteristics, comorbidities, initial symptoms, vital signs for prognosis of disease; and forecasting of disease spread & case fatality rate using deep neural networks. Further, some of the challenges, open datasets and opportunities are discussed for researchers.
    Keywords covid19
    Publisher WHO
    Document type Article
    Note WHO #Covidence: #779379
    Database COVID19

    Kategorien

    Inter-library loan at ZB MED

    Your chosen title can be delivered directly to ZB MED Cologne location if you are registered as a user at ZB MED Cologne.

  7. Article: A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images

    Somasekar, J. / Ramesh, G. / Ramu, Gandikota / Dileep Kumar Reddy, P. / Eswara Reddy, B. / Lai, Ching-Hao

    Data in Brief. 2019 Dec., v. 27

    2019  

    Abstract: In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of ... ...

    Abstract In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging.
    Keywords blood ; data collection ; malaria
    Language English
    Dates of publication 2019-12
    Publishing place Elsevier Inc.
    Document type Article
    ZDB-ID 2786545-9
    ISSN 2352-3409
    ISSN 2352-3409
    DOI 10.1016/j.dib.2019.104643
    Database NAL-Catalogue (AGRICOLA)

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  8. Article ; Online: A dataset for automatic contrast enhancement of microscopic malaria infected blood RGB images.

    Somasekar, J / Ramesh, G / Ramu, Gandikota / Dileep Kumar Reddy, P / Eswara Reddy, B / Lai, Ching-Hao

    Data in brief

    2019  Volume 27, Page(s) 104643

    Abstract: In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of ... ...

    Abstract In this article we introduce a malaria infected microscopic images dataset for contrast enhancement which assist for malaria diagnosis more accurately. The dataset contains around two hundred malaria infected, normal, species and various stages of microscopic blood images. We propose and experimentally demonstrate a contrast enhancement technique for this dataset. This simple technique increases the contrast of an image and hence, reveals significant information about malaria infected cells. Experiments on the dataset show the superior performance of our proposed method for contrast enhancement of malaria microscopic imaging.
    Language English
    Publishing date 2019-10-12
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 2786545-9
    ISSN 2352-3409 ; 2352-3409
    ISSN (online) 2352-3409
    ISSN 2352-3409
    DOI 10.1016/j.dib.2019.104643
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  9. Article ; Online: Breastfeeding in Coronavirus Disease 2019 (COVID-19): Position Statement of Indian Academy of Pediatrics and Infant and Young Child Feeding Chapter.

    Bharadva, Ketan / Bellad, Roopa M / Tiwari, Satish / Somasekar, R / Phadke, Mrudula / Bodhankar, Uday / Bang, Akash / Kinikar, Aarti Avinash / Mallikarjuna, H B / Shah, Jayant / Khurana, Omesh / Gunasingh, D / Basavaraja, G V / Kumar, Remesh / Gupta, Piyush

    Indian pediatrics

    2021  Volume 59, Issue 1, Page(s) 58–62

    Abstract: Justification: Recent research has provided evidence for lack of transmission of SARS-CoV-2 through human milk and breastfeeding. Updating the practice guidelines will help in providing appropriate advice and support regarding breastfeeding during the ... ...

    Abstract Justification: Recent research has provided evidence for lack of transmission of SARS-CoV-2 through human milk and breastfeeding. Updating the practice guidelines will help in providing appropriate advice and support regarding breastfeeding during the coronavirus 2019 (COVID-19) pandemic.
    Objectives: To provide evidence-based guidelines to help the healthcare professionals to advise optimal breastfeeding practices during the COVID-19 pandemic.
    Process: Formulation of key questions was done under the chairmanship of President of the IAP. It was followed by review of literature and the recommendations of other international and national professional bodies. Through Infant and Young child (IYCF) focused WhatsApp group opinion of all members was taken. The final document was prepared after the consensus and approval by all members of the committee.
    Recommendations: The IYCF Chapter of IAP strongly recommends unabated promotion, protection and support to breastfeeding during the COVID-19 pandemic with due precautions.
    MeSH term(s) Breast Feeding ; COVID-19 ; Child ; Female ; Humans ; Infant ; Pandemics ; Pediatrics ; SARS-CoV-2
    Language English
    Publishing date 2021-11-22
    Publishing country India
    Document type Journal Article
    ZDB-ID 402594-5
    ISSN 0974-7559 ; 0019-6061
    ISSN (online) 0974-7559
    ISSN 0019-6061
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 859: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  10. Article ; Online: Ophthatome™: an integrated knowledgebase of ophthalmic diseases for translating vision research into the clinic.

    Raj, Praveen / Tejwani, Sushma / Sudha, Dandayudhapani / Muthu Narayanan, B / Thangapandi, Chandrasekar / Das, Sankar / Somasekar, J / Mangalapudi, Susmithasane / Kumar, Durgesh / Pindipappanahalli, Narendra / Shetty, Rohit / Ghosh, Arkasubhra / Kumaramanickavel, Govindasamy / Chaudhuri, Amitabha / Soumittra, Nagasamy

    BMC ophthalmology

    2020  Volume 20, Issue 1, Page(s) 442

    Abstract: Background: Medical big data analytics has revolutionized the human healthcare system by introducing processes that facilitate rationale clinical decision making, predictive or prognostic modelling of the disease progression and management, disease ... ...

    Abstract Background: Medical big data analytics has revolutionized the human healthcare system by introducing processes that facilitate rationale clinical decision making, predictive or prognostic modelling of the disease progression and management, disease surveillance, overall impact on public health and research. Although, the electronic medical records (EMR) system is the digital storehouse of rich medical data of a large patient cohort collected over many years, the data lack sufficient structure to be of clinical value for applying deep learning methods and advanced analytics to improve disease management at an individual patient level or for the discipline in general. Ophthatome™ captures data contained in retrospective electronic medical records between September 2012 and January 2018 to facilitate translational vision research through a knowledgebase of ophthalmic diseases.
    Methods: The electronic medical records data from Narayana Nethralaya ophthalmic hospital recorded in the MS-SQL database was mapped and programmatically transferred to MySQL. The captured data was manually curated to preserve data integrity and accuracy. The data was stored in MySQL database management system for ease of visualization, advanced search functions and other knowledgebase applications.
    Results: Ophthatome™ is a comprehensive and accurate knowledgebase of ophthalmic diseases containing curated clinical, treatment and imaging data of 581,466 ophthalmic subjects from the Indian population, recorded between September 2012 and January 2018. Ophthatome™ provides filters and Boolean searches with operators and modifiers that allow selection of specific cohorts covering 524 distinct ophthalmic disease types and 1800 disease sub-types across 35 different anatomical regions of the eye. The availability of longitudinal data for about 300,000 subjects provides additional opportunity to perform clinical research on disease progression and management including drug responses and management outcomes. The knowledgebase captures ophthalmic diseases in a genetically diverse population providing opportunity to study genetic and environmental factors contributing to or influencing ophthalmic diseases.
    Conclusion: Ophthatome™ will accelerate clinical, genomic, pharmacogenomic and advanced translational research in ophthalmology and vision sciences.
    MeSH term(s) Electronic Health Records ; Eye Diseases/diagnosis ; Eye Diseases/epidemiology ; Eye Diseases/therapy ; Humans ; Knowledge Bases ; Ophthalmology ; Retrospective Studies
    Language English
    Publishing date 2020-11-10
    Publishing country England
    Document type Journal Article
    ZDB-ID 2050436-6
    ISSN 1471-2415 ; 1471-2415
    ISSN (online) 1471-2415
    ISSN 1471-2415
    DOI 10.1186/s12886-020-01705-5
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top