Diabetic ketoacidosis as the presenting manifestation of pancreatic adenocarcinoma with cystic features

AbstractThe common presenting symptoms of pancreatic cancer are abdominal pain, weight loss, and jaundice. Pancreatic adenocarcinoma presenting with diabetic ketoacidosis is a very rare emergent clinical condition. However, pancreatic ductal cystadenocarcinoma presenting with diabetic ketoacidosis was not reported. We describe a 60-year-old man with pancreatic cystadenocarcinoma presenting with diabetic ketoacidosis as the initial manifestation. It must be kept in mind that in diabetic ketoacidosis cases, the precipitating factor may be pancreatic ductal cystadenocarcinoma

Adaptive Diffusion Priors for Accelerated MRI Reconstruction

Deep MRI reconstruction is commonly performed with conditional models that de-alias undersampled acquisitions to recover images consistent with fully-sampled data. Since conditional models are trained with knowledge of the imaging operator, they can show poor generalization across variable operators. Unconditional models instead learn generative image priors decoupled from the imaging operator to improve reliability against domain shifts. Recent diffusion models are particularly promising given their high sample fidelity. Nevertheless, inference with a static image prior can perform suboptimally. Here we propose the first adaptive diffusion prior for MRI reconstruction, AdaDiff, to improve performance and reliability against domain shifts. AdaDiff leverages an efficient diffusion prior trained via adversarial mapping over large reverse diffusion steps. A two-phase reconstruction is executed following training: a rapid-diffusion phase that produces an initial reconstruction with the trained prior, and an adaptation phase that further refines the result by updating the prior to minimize reconstruction loss on acquired data. Demonstrations on multi-contrast brain MRI clearly indicate that AdaDiff outperforms competing conditional and unconditional methods under domain shifts, and achieves superior or on par within-domain performance

Spindle Cell Carcinoma of the Tongue: A Rare Tumor in an Unusual Location

Spindle cell carcinoma is a rare biphasic tumor consisting of epithelial and mesenchymal components. Presence of this tumor type in the tongue has rarely been reported. Herein, a case of 55-year-old woman who presented with a polypoid lesion at her tongue has been reported. Surgery was performed and pathologic examination revealed a spindle cell carcinoma. We present this rare tumor with an unusual location to contribute in part to the better understanding and awareness of this rare malignancy

Learning Fourier-Constrained Diffusion Bridges for MRI Reconstruction

Recent years have witnessed a surge in deep generative models for accelerated MRI reconstruction. Diffusion priors in particular have gained traction with their superior representational fidelity and diversity. Instead of the target transformation from undersampled to fully-sampled data, common diffusion priors are trained to learn a multi-step transformation from Gaussian noise onto fully-sampled data. During inference, data-fidelity projections are injected in between reverse diffusion steps to reach a compromise solution within the span of both the diffusion prior and the imaging operator. Unfortunately, suboptimal solutions can arise as the normality assumption of the diffusion prior causes divergence between learned and target transformations. To address this limitation, here we introduce the first diffusion bridge for accelerated MRI reconstruction. The proposed Fourier-constrained diffusion bridge (FDB) leverages a generalized process to transform between undersampled and fully-sampled data via random noise addition and random frequency removal as degradation operators. Unlike common diffusion priors that use an asymptotic endpoint based on Gaussian noise, FDB captures a transformation between finite endpoints where the initial endpoint is based on moderate degradation of fully-sampled data. Demonstrations on brain MRI indicate that FDB outperforms state-of-the-art reconstruction methods including conventional diffusion priors

Association of serum and follicular fluid leptin and ghrelin levels with in vitro fertilization success

 Objectives: The aim of this study was to evaluate the relationship between in vitro fertilization (IVF) cycle outcomes, serum and follicular fluid (FF) levels of leptin and ghrelin. Material and methods: Forty-four women who underwent intracytoplasmic sperm injection cycles (ICSI) were enrolled in the study. On the third day (D3) of the menstrual cycle, venous blood samples were drawn for serum measurements of leptin and ghrelin. The follicular fluid (FF) and the corresponding oocyte were obtained from a single dominant preovulatory follicle at the time of oocyte pick-up. The FF and D3 serum leptin and ghrelin concentrations were measured by enzyme-linked immunosorbent assay. The relationship between pregnancy rate and serum, follicular fluid levels of leptin and ghrelin were analyzed. Results: Of the 44 cases included, nineteen achieved clinical pregnancy (43.18%). Follicular fluid ghrelin levels were significantly lower in the pregnant group than non-pregnant group (p < 0.05) With respect to FF leptin, there was no statistically significant differences between the pregnant and non-pregnant women (p > 0.05). There was no statistically significant difference in D3 serum ghrelin between pregnant and non-pregnant groups (p > 0.05). However, D3 serum leptin levels were significantly lower in pregnant women than non-pregnant women (p < 0.05). Conclusions: Lower ghrelin levels in the follicular fluid were associated with higher pregnancy rates. Also, D3 serum leptin levels were inversely correlated with clinical pregnancy rates. These findings support the potential role of these molecules on IVF outcomes