Internal jugular vein thrombosis presenting as paraneoplastic syndrome in benign cystic teratoma of ovary: a case report

Internal jugular vein thrombosis is a rare vascular disease that can be overlooked or misdiagnosed and is generally seen in persons with intravenous drug abuse or in patients with prolonged central venous catheterization due to iatrogenic trauma. The most common germ cell tumour of the ovary is benign (mature) cystic teratoma, occurring in adolescents and young women. We are presenting a case of a 50-year-old premenopausal woman, diagnosed to have right internal jugular vein thrombosis extending into the right subclavian and axillary vein. Her laboratory investigations revealed no predisposing cause of thrombosis. Four months later she was evaluated for menorrhagia and imaging studies showed multiple uterine fibroids with left ovarian mass (ovarian teratoma) with moderate ascites and her tumour markers levels of CA125 was elevated. She underwent staging laparotomy, total abdominal hysterectomy with bilateral salpingo-oophorectomy and pelvic lymph node dissection with infracolic omentectomy. Pathologically, ovarian cyst showed mature thyroid tissue with islands of bone, muscle tissue and fatty tissue consistent with benign cystic teratoma. Postoperatively her tumour marker CA125 level returned to normal levels and there was no reaccumulation of fluid. As there were no predisposing factors for internal jugular vein thrombosis, it was concluded to be a paraneoplastic syndrome preceding the diagnosis of benign cystic teratoma. To the best of our knowledge this is the first case report in the literature with an association between internal jugular vein thrombosis and benign cystic teratoma with raised serum tumour marker CA 125

A 3 year retrospective study on gestational trophoblastic disease in a government obstetrical tertiary care centre

Background: The aim of this study is to assess the post diagnostic outcome of Gestational Trophoblastic Disease, a heterogeneous group of disorders, in a government obstetrical tertiary care centre.Methods: The study was conducted in the Institute of Obstetrics & Gynecology, Madras Medical College as a retrospective study. A total of 75 cases were studied over a 3 year period from January 2012 to December 2015. The parameters which were studied included age group, antecedent pregnancy, beta hCG values, histopathological types and Treatment profile.Results: Of the 75 cases, 55 cases (73%) were in the 21-39 age group. The spectrum of disorders that were studied included 69 cases of complete mole, 2 cases of partial mole, 1 case of twin pregnancy with single live foetus and partial mole, 1 case of triplet pregnancy with two live foetuses and partial mole, 1 case of epithelioid trophoblastic tumour and 1 case of choriocarcinoma. Of the 75 cases, 16 cases underwent chemotherapy. No mortality was observed during the study period.Conclusions: Close monitoring and follow up with beta hCG values is of utmost importance in the management of Gestational trophoblastic disease. In cases of gestational trophoblastic neoplasia (GTN), WHO/FIGO scoring should be done and managed with chemotherapy according to the risk assessment

A rare case report of congenital high airway obstruction syndrome presenting in a 23 weeks foetus

Congenital High Airway Obstruction Syndrome or CHAOS is a blockage of the foetus’s trachea or larynx due to many factors including narrowing of the airway, a web-like membrane or even tracheal atresia. In the uterus, the foetal lungs constantly produce fluid and as a result of this airway blockage in the trachea, the lung fluid cannot escape out of the foetal mouth. Because of this the foetus’s lungs become distended with fluid and over distended lungs can put pressure on the heart and affect the heart’s ability to function. If the heart cannot function effectively hydrops or congestive heart failure can occur. We present a rare case of CHAOS diagnosed prenatally at about 23 weeks by USG in our hospital

Steroid cell tumour of the ovary: a case report with review of literature

Virilising ovarian tumours account for less than 5% of all ovarian tumours. A steroid cell tumour (SCTs) of the ovary comes under the sex cord stromal tumours and accounts for only 0.1% of all ovarian tumours. Almost 75% are functioning tumors with production of androgenic hormones causing virilisation and cushingoid features. They are usually unilateral, benign with only 25-45% malignant cases. Here authors report the incidence of steroid cell tumour in our institution and discuss about a 37-year-old woman with steroid cell tumour, not otherwise specified who presented with oligomenorrhea followed by amenorrhea, secondary infertility and signs of virilisation

Single tertiary care centre experience of ovarian granulosa cell tumour in Chennai, India: a retrospective analysis

Background: Granulosa cell tumours of ovary are rare sex-cord stromal tumours characterized by long natural history and favourable prognosis. The present study was done to evaluate the clinical presentation, treatment, outcome, and prognostic factors for patients diagnosed as granulosa cell tumours.Methods: A Retrospective study of Granulosa cell tumour of the ovary was done for a period of five years from January 2011 to December 2015 at a tertiary care centre, Institute of Obstetrics and Gynaecology, Madras Medical College, Chennai. The clinical data and the treatment details were retrieved from the records of medical oncology department and the data were analysed.Results: Twenty five patients were diagnosed as granulose cell tumours of ovary during the study period. The median patient age was 48 years. The most common clinical presentation at diagnosis was vaginal bleeding (76%) followed by abdominal pain (40%). Mean tumor size was 9.6cm. The majority of patients were diagnosed in FIGO stage Ia (84%, n = 21). Thirteen patients (52%) underwent complete staging laparotomy. Twenty three patients (92%) had Adult Granulosa cell tumour. Two patients (8%)had juvenile Granulosa cell tumour. After surgery, all patients were put on observation except two patients who received adjuvant chemotherapy (EP: Etoposide, Cisplatin). The median followup period was 48 months. Five patients (20%) had recurrence; The average time to relapse was 29.6 months. Patients who had tumour size more than 9.7cm had more recurrence events (Hazard Ratio(HR):1.058), but their association is not significant (P value-0.839). The association between menopausal status, torsion of tumour mass, tumour stage with recurrence rate were not significant. The estimated mean overall survival was 84.8 months. Following univariate Cox regression modeling, survival appeared to be independent of age range, post operative residual tumour and the FIGO stage.Conclusions: Granulosa cell tumours of ovary are rare, often diagnosed in early stage. Patients who had tumour size of more than 9.7cm had more recurrence events. A prolonged post therapeutic follow-up is necessary to pick up the late relapses

In-silico identification of phenotype-biased functional modules

<p>Abstract</p> <p>Background</p> <p>Phenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.</p> <p>Results</p> <p>In this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.</p> <p>Conclusion</p> <p>Thus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (<url>http://freescience.org/cs/phenotype-biased-biclusters/</url>).</p

DENSE: efficient and prior knowledge-driven discovery of phenotype-associated protein functional modules

<p>Abstract</p> <p>Background</p> <p>Identifying cellular subsystems that are involved in the expression of a target phenotype has been a very active research area for the past several years. In this paper, <it>cellular subsystem </it>refers to a group of genes (or proteins) that interact and carry out a common function in the cell. Most studies identify genes associated with a phenotype on the basis of some statistical bias, others have extended these statistical methods to analyze functional modules and biological pathways for phenotype-relatedness. However, a biologist might often have a specific question in mind while performing such analysis and most of the resulting subsystems obtained by the existing methods might be largely irrelevant to the question in hand. Arguably, it would be valuable to incorporate biologist's knowledge about the phenotype into the algorithm. This way, it is anticipated that the resulting subsytems would not only be related to the target phenotype but also contain information that the biologist is likely to be interested in.</p> <p>Results</p> <p>In this paper we introduce a fast and theoretically guranteed method called <it>DENSE </it>(Dense and ENriched Subgraph Enumeration) that can take in as input a biologist's <it>prior </it>knowledge as a set of query proteins and identify all the dense functional modules in a biological network that contain some part of the query vertices. The density (in terms of the number of network egdes) and the enrichment (the number of query proteins in the resulting functional module) can be manipulated via two parameters γ and <it>μ</it>, respectively.</p> <p>Conclusion</p> <p>This algorithm has been applied to the protein functional association network of <it>Clostridium acetobutylicum </it>ATCC 824, a hydrogen producing, acid-tolerant organism. The algorithm was able to verify relationships known to exist in literature and also some previously unknown relationships including those with regulatory and signaling functions. Additionally, we were also able to hypothesize that some uncharacterized proteins are likely associated with the target phenotype. The DENSE code can be downloaded from <url>http://www.freescience.org/cs/DENSE/</url></p

Complex biomarker discovery in neuroimaging data: Finding a needle in a haystack

AbstractNeuropsychiatric disorders such as schizophrenia, bipolar disorder and Alzheimer's disease are major public health problems. However, despite decades of research, we currently have no validated prognostic or diagnostic tests that can be applied at an individual patient level. Many neuropsychiatric diseases are due to a combination of alterations that occur in a human brain rather than the result of localized lesions. While there is hope that newer imaging technologies such as functional and anatomic connectivity MRI or molecular imaging may offer breakthroughs, the single biomarkers that are discovered using these datasets are limited by their inability to capture the heterogeneity and complexity of most multifactorial brain disorders. Recently, complex biomarkers have been explored to address this limitation using neuroimaging data. In this manuscript we consider the nature of complex biomarkers being investigated in the recent literature and present techniques to find such biomarkers that have been developed in related areas of data mining, statistics, machine learning and bioinformatics

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http://freescience.org/cs/NIBBS