Retrospective Analysis of Pancreas Transplants in Poland in Years 1998-2015
Authors: Kaja Śmigielska
Wojciech Lisik
Paweł Skrzypek
Maciej Kosieradzki
Paweł Ziemiański
Journal: Transplantation proceedings (2020)
Discipline: CLINICAL MEDICINE
Keywords: Pancreas transplant; Diabetes mellitus; Immunosuppression; Organ donors; Recipients
Affiliations: Department of General and Transplantation Surgery, Medical University of Warsaw, Poland., Department of General, Gastroenterologic and Oncologic Surgery, Medical University of Warsaw, Poland., Department of General and Transplantation Surgery, Medical University of Warsaw, Poland. Electronic address: .

Abstract:  Transplantation of the pancreas is an established method for the treatment of complicated diabetes mellitus. As the numbers of diabetic patients increase so does the need for efficient treatment methods. Despite significant perioperative risk and complications related to immunosuppression, pancreas transplant remains the best therapeutic option for selected patients. Methods: The analysis was based on the comparison of characteristics of all organ donors and recipients in years 1998 to 2015. The collected data were divided into 2 periods to facilitate identification of populational changes. Results: The total number of pancreas transplants in Poland was 139 in years 1998 to 2006 and 268 in years 2007 to 2015. The largest differences revealed by the comparison of donor-related variables in both periods were those related to the doses of pressor amines, duration of circulatory arrest, and duration of stay at the intensive care unit. The critical finding consisted in the improvement of short-term survival of recipients and organs being observed in contrast to the surprising lack of improvement in long-term survival. Reduced likelihood of transplantation success was observed already in overweight patients (body mass index 25-29.99 kg/m2). Conclusions: No significant changes were observed with regard to pancreas transplant outcomes over the period of many years. Transplantation success is determined by 1-year survival of the organ, and the therapeutic success is measured by long-term disease-free survival of the patient. In the era of rapid advances in numerous areas of medicine, the lack of significant extension of patient survival times warrants a closer look of our knowledge on pancreas transplants. 

Retracted: No

PubPeer comments:
 Liu and colleagues provide evidence demonstrating that the putatively u201ctask-negativeu201d posterior DMN (pDMN) plays an active role in narrative speech comprehension. The authors rally a handful of related fMRI results in support of this claim. First, two u201csanity checksu201d replicate the task-negative profile pDMN: (a) the ICA-based pDMN defined here is shown to be anticorrelated with executive control (ECN) and language (LN) networks; (b) pDMN also displays a task-negative contrast in both story-listening vs. a brief fixation baseline and in an independent (visual) task; next, two speaker-listener ISC analyses suggest a positive role for pDMN in speech comprehension: (c) speaker-listener lagged ISC in pDMN is correlated with behavioral comprehension scores; and (d) the degree of anticorrelation between pDMN and ECN was correlated with speaker-listener ISC in pDMN. The study includes a handful of control analyses (e.g. control story stimulus, control ICA network, etc), as well as a dynamic causal modeling (DCM) analysis positioning anterior DMN (aDMN) between the ECN and LN networks and pDMN. This study replicates and expands on the relationship between speaker-listener ISC and comprehension (focusing in particular on pDMN) with a considerably larger sample size (N u2248 60) than early studies on this topic (e.g. Stephens et al., 2010; N u2248 11u2014yikes). My main complaint in reading this manuscript is that itu2019s fairly difficult for the reader to synthesize all these results as currently written. I have a few questions suggestions (that hopefully donu2019t inflate the already wide array of analyses) as well as some wording suggestions or clarity. *Major comments:* While reading, I found it difficult to synthesize each of the results presented in this study into a coherent story about the DMNu2019s active role in narrative comprehension. The results sometimes read as a catalogue of findings. Any effort the authors could make to streamline the interpretation and guide the reader through the results would be much appreciated! For example, when introducing a new Results section, it might be worth spending an additional sentence providing (more) conceptual motivation for why this analysis is necessary (i.e. what outstanding question concern does it address?) and linking it to the preceding analyses. The interpretation felt a little convoluted despite attempts to pull everything together in the Discussion. In one of the initial analyses, the authors contrast a brief (20-second) fixation baseline with roughly 10 minutes of story listening to show that pDMN is deactivated during story-listening relative to baseline whereas LN is activated during story-listening relative to baseline. This doesnu2019t seem like a particularly meaningful analysis, given the short duration of the baseline (and much longer and more variable activity during story-listening). Authors use a separate task to validate pDMN deactivation, and also demonstrate that pDMN is anticorrelated with LN and ECN. These latter results provide a more convincing u201csanity checku201d for the classical u201ctask-negativeu201d character of DMN, and the u201cstory-listening vs. 20-second baselineu201d analysis could probably be deemphasized. Related to the previous comment, you describe contrasting baseline with story-listening as u201c[(task-baseline) baseline]u201d where u201ctask and baseline denote the mean of time points within the task phase and baseline phase.u201d Iu2019m curious why you divide by mean baseline activity in this example rather than simply subtracting baseline from task (maybe thereu2019s a precedent for this that Iu2019m not aware of). Iu2019m curious whether the anticorrelations observed between e.g. pDMN and LN in Figure 2B would hold up in an intersubject functional connectivity analysis (among listeners; ISFC; Simony et al., 2016; Nastase et al., 2019). Within-subject functional connectivity is notoriously susceptible to head motion and other confounds. On the other hand, ISFC analysis effectively filters out subject-specific idiosyncratic fluctuations and yields stimulus-driven connectivities. I donu2019t think this analysis is strictly necessary or that it would meaningfully change the story, but it might even strengthen the relative network relationships observed with within-subject connectivity. The observation that greater anticorrelation between pDMN and ECN was correlated with increased speaker-listener coupling in pDMN was difficult for me to interpret. Does the degree of pDMN and ECN anticorrelation also correlate with comprehension? The argument that u201cneural coupling between a speaker-listener pair is likely less susceptible to the influences of task difficulty, since speech production is typically more cognitively demanding than speech comprehensionu201d was not very convincing to me. Even if speech is generally more cognitively demanding than listening, as you admit (line 526), there may be parts of the spoken narrative that are concurrently more or less demanding for both the speaker and listeneru2014which would undermine this argument. On the other hand, I donu2019t find the original argument that DMN ISC may reflect fluctuations in generic u201ctask difficultyu201d during language processing very compelling either. Real-world language probably *does* naturally vary in cognitive demand; and this varying cognitive demand may properly be a part of language comprehension. Using experimental manipulations to try to rule out this putative u201cconfoundu201d will by necessity introduce an artificiality to the linguistic paradigm that limits generalizability to real-world language. Some of the terminology is confusing here. For example, in the Introduction paragraph starting at line 108, the text seems to contrast intersubject correlation (ISC) with u201ccouplingu201du2014but coupling here is defined as u201cthe correlation of brain activities over time,u201d which is the typical definition of ISC. ISC computed between listeners, or between speaker and listener, is still ISC. I understand the authors want to use u201ccouplingu201d to refer specifically to the listener-speaker correlation, but this should be made explicit at each usage. Another bit of terminology I found hard to follow: the u201csuccessfulu201d and u201cfailedu201d terminology for communication conditions with the Chinese and Mongolian speakers should be introduced early on when the stimuli design are described. This also made terms like the u201cchange (successful failed) of pDMN-ECN functional connectivityu201d (line 467) hard to parse at first. *Minor comments:* Line 57: u201cdespite they have masteredu201d u201cdespite having masteredu201d Line 124: u201cThe group Independent...u201d u201cGroup Independent...u201d Line 161: u201cTo make the listeners attendu201d u201cTo encourage listeners to attendu201d Line 207: u201csuccessful and failed communication conditionsu201du2014make sure to introduce these condition labels early on with reference to the Chinese and Mongolian speakers Line 213: Iu2019m not very familiar with the pipeline for group ICA and dual regression, so maybe this is my ignoranceu2014but the description here wasnu2019t entirely clear. Are the individually-defined PCs aligned prior to concatenating data across subjects? Or is this not necessary? Is the u201cgroup dimension reductionu201d mentioned at line 215 referring to ICA, or a second group-level PCA step? At line 219, maybe specify that 20 components might be appropriate for naturalistic data (as opposed to e.g. resting-state data). Figure 1: the orange text doesnu2019t seem particularly well-aligned here Line 310: u201cliedu201d u201clieu201d Figure 2: panel B may benefit from a correlation matrix to depict network pairs (although no error bars in that case) Line 385: u201clagged by that in the speakeru2019su201d u201clagged relative to the speakeru2019su201d Lines 422 and 425: why was the 6-second lag chosen here? (presumably the peak lag) 548: u201cand notu201d u201cand was notu201d References: A couple references jumped out to me as missing, but take with a grain of saltu2014my reading list is biased and many of these are papers published by my current lab (donu2019t feel pressured to cite). For example, when discussing brain-to-brain coupling as a system or transmitting linguistic and social information between brains, Hasson et al., 2012, came to mind. When describing the DMN as the interface between high-level semantic content and internal content, a recent review by Yeshurun et al. (2020) came to mind. When describing the DMNu2019s role in narrative comprehension, Baldassano et al., 2017, Chen et al., 2017, and Zadbood et al., 2017, came to mind (although these studies rely on intersubject pattern similarity rather than time-series ISC). I encourage the authors to share the code associated with this paper (e.g. via GitHub). This seems like a very rich and useful dataset, so I hope ultimately the authors will share the data as well! *References:* Baldassano, C., Chen, J., Zadbood, A., Pillow, J. W., Hasson, U., Norman, K. A. (2017). Discovering event structure in continuous narrative perception and memory. *Neuron*, *95*(3), 709u2013721. https: doi.org 10.1016 j.neuron.2017.06.041 Chen, J., Leong, Y. C., Honey, C. J., Yong, C. H., Norman, K. A., Hasson, U. (2017). Shared memories reveal shared structure in neural activity across individuals. *Nature Neuroscience*, *20*(1), 115u2013125. https: doi.org 10.1038 nn.4450 Hasson, U., Ghazanfar, A. A., Galantucci, B., Garrod, S., Keysers, C. (2012). Brain-to-brain coupling: a mechanism for creating and sharing a social world. *Trends in Cognitive Sciences*, *16*(2), 114u2013121. https: doi.org 10.1016 j.tics.2011.12.007 Nastase, S. A., Gazzola, V., Hasson, U., Keysers, C. (2019). Measuring shared responses across subjects using intersubject correlation. *Social Cognitive and Affective Neuroscience*, *14*(6), 667u2013685. https: doi.org 10.1093 scan nsz037 Yeshurun, Y., Nguyen, M., Hasson, U. (2021). The default mode network: where the idiosyncratic self meets the shared social world. *Nature Reviews Neuroscience*, *22*(3), 181u2013192. https: doi.org 10.1038 s41583-020-00420-w Zadbood, A., Chen, J., Leong, Y. C., Norman, K. A., Hasson, U. (2017). How we transmit memories to other brains: constructing shared neural representations via communication. *Cerebral Cortex*, *27*(10), 4988u20135000. https: doi.org 10.1093 cercor bhx202 ,