A Rare Case of Complete Inguinoscrotal Bladder Herniation With Ureteric Involvement: Assessing Diagnostic Challenges and Complex Surgical Management.

Inguinoscrotal hernias involving the urinary bladder are exceedingly rare, constituting a small subset of inguinal hernias. We present a case of a 47-year-old male with long-standing scrotal enlargement and obstructive uropathy due to complete herniation of the bladder with ureteric involvement. Diagnostic imaging confirmed the condition. Following an open laparotomy, the bladder was reduced, and a modified Bassini technique with orchiopexy was used for repair. Recurrence of the inguinoscrotal hernia with evidence of the bladder in the scrotal sac required additional surgery. This case underscores the rarity, diagnostic complexity, and potential complications of inguinoscrotal bladder hernias. Specialized surgical techniques and a multidisciplinary approach are crucial for successful management, especially in cases of complete bladder herniation. Future considerations should include innovative approaches to enhance primary repair outcomes for extensive hernias involving the bladder.

Bringing big data to personalized healthcare: a patient-centered framework.

Faced with unsustainable costs and enormous amounts of under-utilized data, health care needs more efficient practices, research, and tools to harness the full benefits of personal health and healthcare-related data. Imagine visiting your physician's office with a list of concerns and questions. What if you could walk out the office with a personalized assessment of your health? What if you could have personalized disease management and wellness plan? These are the goals and vision of the work discussed in this paper. The timing is right for such a research direction--given the changes in health care, reimbursement, reform, meaningful use of electronic health care data, and patient-centered outcome mandate. We present the foundations of work that takes a Big Data driven approach towards personalized healthcare, and demonstrate its applicability to patient-centered outcomes, meaningful use, and reducing re-admission rates.

Comparative protein interactomics of neuroglobin and myoglobin.

Neuroglobin is a hypoxia-inducible O(2)-binding protein with neuroprotective effects in cell and animal models of stroke and Alzheimer's disease. The mechanism underlying neuroglobin's cytoprotective action is unknown, although several possibilities have been proposed, including anti-oxidative and anti-apoptotic effects. We used affinity purification-mass spectrometry methods to identify neuroglobin-interacting proteins in normoxic and hypoxic murine neuronal (HN33) cell lysates, and to compare these interactions with those of a structurally and functionally related protein, myoglobin. We report that the protein interactomes of neuroglobin and myoglobin overlap substantially and are modified by hypoxia. In addition, neuroglobin-interacting proteins include partners consistent with both anti-oxidative and anti-apoptotic functions, as well as with a relationship to several neurodegenerative diseases.

Exploring and exploiting disease interactions from multi-relational gene and phenotype networks.

The availability of electronic health care records is unlocking the potential for novel studies on understanding and modeling disease co-morbidities based on both phenotypic and genetic data. Moreover, the insurgence of increasingly reliable phenotypic data can aid further studies on investigating the potential genetic links among diseases. The goal is to create a feedback loop where computational tools guide and facilitate research, leading to improved biological knowledge and clinical standards, which in turn should generate better data. We build and analyze disease interaction networks based on data collected from previous genetic association studies and patient medical histories, spanning over 12 years, acquired from a regional hospital. By exploring both individual and combined interactions among these two levels of disease data, we provide novel insight into the interplay between genetics and clinical realities. Our results show a marked difference between the well defined structure of genetic relationships and the chaotic co-morbidity network, but also highlight clear interdependencies. We demonstrate the power of these dependencies by proposing a novel multi-relational link prediction method, showing that disease co-morbidity can enhance our currently limited knowledge of genetic association. Furthermore, our methods for integrated networks of diverse data are widely applicable and can provide novel advances for many problems in systems biology and personalized medicine.

Network analysis of team structure in the neonatal intensive care unit.

OBJECTIVE: The goal was to examine nursing team structure and its relationship with family satisfaction. METHODS: We used electronic health records to create patient-based, 1-mode networks of nursing handoffs. In these networks, nurses were represented as nodes and handoffs as edges. For each patient, we calculated network statistics including team size and diameter, network centrality index, proportion of newcomers to care teams according to day of hospitalization, and a novel measure of the average number of shifts between repeat caregivers, which was meant to quantify nursing continuity. We assessed parental satisfaction by using a standardized survey. RESULTS: Team size increased with increasing length of stay. At 2 weeks of age, 50% of shifts were staffed by a newcomer nurse who had not previously cared for the index patient. The patterns of newcomers to teams did not differ according to birth weight. When the population was dichotomized according to median mean repeat caregiver interval value, increased reports of problems with nursing care were seen with less-consistent staffing by familiar nurses. This relationship persisted after controlling for factors including birth weight, length of stay, and team size. CONCLUSIONS: Family perceptions of nursing care quality are more strongly associated with team structure and the sequence of nursing participation than with team size. Objective measures of health care team structure and function can be examined by applying network analytic techniques to information contained in electronic health records.