Probabilistic graphlets capture biological function in probabilistic molecular networks

Motivation: Molecular interactions have been successfully modeled and analyzed as networks, where nodes represent molecules and edges represent the interactions between them. These networks revealed that molecules with similar local network structure also have similar biological functions. The most sensitive measures of network structure are based on graphlets. However, graphlet-based methods thus far are only applicable to unweighted networks, whereas real-world molecular networks may have weighted edges that can represent the probability of an interaction occurring in the cell. This information is commonly discarded when applying thresholds to generate unweighted networks, which may lead to information loss. Results: We introduce probabilistic graphlets as a tool for analyzing the local wiring patterns of probabilistic networks. To assess their performance compared to unweighted graphlets, we generate synthetic networks based on different well-known random network models and edge probability distributions and demonstrate that probabilistic graphlets outperform their unweighted counterparts in distinguishing network structures. Then we model different real-world molecular interaction networks as weighted graphs with probabilities as weights on edges and we analyze them with our new weighted graphlets-based methods. We show that due to their probabilistic nature, probabilistic graphlet-based methods more robustly capture biological information in these data, while simultaneously showing a higher sensitivity to identify condition-specific functions compared to their unweighted graphlet-based method counterparts.This work was supported by the European Research Council (ERC) Consolidator Grant 770827, the Serbian Ministry of Education and Science. Project III44006, the Slovenian Research Agency project J1-8155 and The Prostate Project.Peer ReviewedPostprint (author's final draft

Rare adrenal cavernous hemangioma: a case report highlighting diagnostic challenges

IntroductionAdrenal cavernous hemangiomas are rare benign vascular tumors that pose significant diagnostic challenges. Despite their benign nature, features overlapping with malignancies often complicate management decisions.Case presentationA 64-year-old male presented with a 4.4 cm necrotic left adrenal mass discovered incidentally on imaging. His medical history included papillary thyroid carcinoma, with subsequent thyroidectomy and radioactive iodine ablation. Evaluations for hiccups revealed multiple lung nodules, hypertrophic cardiomyopathy, and anemia. Given the patient’s previous cancer history, elevated aldosterone/renin ratio, and mass size, our multidisciplinary tumor board decided to proceed with a left adrenalectomy. Post-surgical pathology confirmed a diagnosis of adrenal cavernous hemangioma.ConclusionThe occurrence of ambiguous adrenal mass with other pathologies, such as our patient’s papillary thyroid carcinoma, complicates the diagnostic and therapeutic landscape. As demonstrated in our case, opting for surgery remains a viable solution for adrenal cavernous hemangiomas, especially for masses greater than 4 cm. Interdisciplinary collaboration, exemplified by our tumor board’s decision-making process, is crucial for optimal management. This case underscores the need for a multifaceted approach when confronting adrenal masses with such diagnostic ambiguity

KNOW Health, No COVID

Compliance with CDC guidelines by the public may impact the extent that COVID-19 may be damaging to our community. Due to the lack of data in the literature on this devastating virus, much electronic information is being released by various sources, many of which are unreliable, not peer reviewed, and may possibly include false information regarding the virus. This study is focused to determine the extent that college-level students from the University of South Florida, in the Judy Genshaft Honors College have a clear understanding of the COVID-19 virus. The population consisted of survey responses from students enrolled within the Judy Genshaft Honors College. A quantitative causal comparative approach was utilized. Initially a MANOVA was conducted to identify .038 significant trends across groups. The independent variable was the insurance status of the participants. The dependent variables were the participant responses to the survey questions regarding: compliance with social distancing, handwashing knowledge, cough/sneeze etiquette, mask compliance, and compliance with handwashing in public. There was one statistically significant finding as evidence by Wilk’s Lambda 0.96 (8,804) p\u3c 0.038. Overall, 99% of participants knew about handwashing for at least 20 seconds or cleaning hands thoroughly. The presence of insurance may potentially offer an insight into the understanding of COVID-19 precautionary measures as well as a method to potentially avoid the virus. This study implies that the Judy Genshaft Honors college students understand the COVID-19 guidelines as well as the extent that their compliance will keep the students at the university free from COVID-19