dataesr/scientific-paragraphs-categorization huggingface dataset:
Data in the LAUS'AKI cohort were previously extracted from electronic medical records [Metavision 1 (IMD Soft, Tel Aviv, Israel) and Soarian 1 (Cerner, North Kansas City, USA)]. In particular, demographic characteristics, underlying medical conditions, admission status, and medical interventions were collected. Charlson comorbidity index and illness severity score at admission (SAPS II score corrected for creatinine) were computed. Daily serum creatinine (sCr) and hourly urinary output (UO) measurements were extracted until day 7 or ICU discharge, whichever occurred first. Patients' vital status at day-90 was assessed by cross-referencing the dataset with the Swiss national death registry.
Statistical analyses Continuous data are reported as median (interquartile range-IQR) and compared using the Wilcoxon rank sum test. Categorical variables are expressed as number (percentage) and compared using chi-square or Fisher's exact test. In the main analysis, we considered the onset day of the first AKI episode, regardless of stage and diagnostic criterion (sCr, UO, or both). We compared the incidence, characteristics, and outcomes of patients in the early and the late SA-AKI groups. We performed sensitivity analyses, using alternative definitions for early and late SA-AKI (see above) or considering only severe (stage 2 and 3) AKI or only AKI based on sCr criteria. In addition, to account for the uncertainty associated with sepsis onset, we repeated our analyses after exclusion of patients transferred from another ICU or an intermediate care unit or exclusion of those admitted to the hospital for more than 48 h prior to ICU admission.
Strengths and limitations Our study has several strengths. We used high quality and high granularity data  [13]. Indeed, urine output was documented on an hourly basis with a low rate of missing values. This allowed for precise and accurate documentation of oliguria and ultimately the use of unmodified KDIGO criteria to assess the presence of AKI and its severity. Also, we could include more than 1800 patients admitted to a tertiary ICU for sepsis over 10 years, making this study one of the largest cohorts to report on SA-AKI epidemiology. However, as a monocentric retrospective study, some limitations must be considered. First, the very small number of patients with late SA-AKI limited the relevance of a comparison with those with early SA-AKI; in particular, it did not seem appropriate to perform multivariate models.
The advantage is clear: re-encrypting a tiny chunk of the resource guarantees protection of the whole resource itself. Also, the service provider simply needs to provide storage functionality and is not required to play an active role in enforcing access control or providing user authentication. Our Mix&Slice proposal is complemented with a convenient approach for key management that, based on key regression, avoids any storage overhead for key distribution. A preliminary version of this work appeared in  [1]. In this paper, we extend our proposal with a more general approach to mixing by considering also the application of the Optimal Asymmetric Encryption Padding (OAEP), discussing two strategies for such a solution. We also extend the analysis of the effectiveness of our approach to cover erasure code attacks.
Finally, we extend the experimental evaluation, where we evaluate the performance of our approach in terms of the throughput at the client-side for the application of our protection technique, and the efficiency in policy revocation. Outline. The remainder of the paper is organized as follows. Section 2 illustrates the basic concepts of our approach, the working of mixing and slicing, and the properties they need to satisfy to enable effective revocation. Section 3 illustrates two main approaches to realize mixing, based on the iterative application of AES and on an extended 3-round OAEP, respectively. Section 4 illustrates the enforcement of access revocation. Section 5 discusses the effectiveness of our solution in providing revocation, considering its resilience against storage attacks by users who might maintain some local storage of previously accessed fragments or an erasure code on the resource.
e. , the circled E in Figure  4  and line 7 in Figure  5 ). The resulting process is illustrated in Figure  10 , where the mix function of Figure  9  has been instantiated to be the layered process in Figure  4 , and the last step of encryption has been included. The layered structure of AES-Mix guarantees complete mixing, supports macro-blocks of arbitrary size, and satisfies the no-shrinking effect. Figure  11  shows the pseudo-code of OAEP-Mix. Note that, while in the pseudocode, for simplicity we have maintained the internal call to AES-Mix (Figure  5 ), as noted the last step at each round of AES-mix is in this case the application of a cryptographic hash function, instead of AES encryption. The reason for this is that the mix function needs not be invertible, since in the OAEP approach encryption is applied at the end of the mixing process.
ROAEP-Mix. Our second approach uses the 3-round OAEP structure as mix function, thus building a recursive OAEP structure. This is illustrated in Figure  12 , where the mix function of Figure  9  has been instantiated to be again the process in Figure  9  itself, and as before the last step of encryption has been included. Figure  13  shows the pseudocode of ROAEP-Mix. By using recursive OAEP mixing, the input macro-block must include b = 2 x = (Msize/bsize) blocks, with bsize the size of the output of the adopted cryptographic hash function, thus requiring x recursive applications of the 3-round OAEP structure. The main advantage of the ROAEP-Mix with respect to AES-Mix and OAEP-Mix is the flexibility in the definition of the miniblock size (msize). For AES-Mix and OAEP-Mix, the largest msize is 64 bits (half the size of the AES block) and half the size of the output of the selected hash function (e.
g. , 256 bits in our implementation with BLAKE2), respectively. For ROAEP-Mix, the largest mini-block size corresponds to the size of the output of the selected cryptographic hash function (e. g. , 512 bits in our implementation). This provides stronger protection against brute-force attacks. While in most application scenarios the use of AES-Mix (and 32 or 64 bits for the mini-block size) can be appropriate, some applications may want more flexibility, especially if having larger mini-blocks does not have a significant impact on performance. We note that block ciphers (e. g. , AES) are often implemented by dedicated circuits within many modern CPUs and the throughput exhibited by their hardware- accelerated computation is better than the software computation of cryptographic hash functions, as confirmed by our experiments (Section 6).
The client is written in Python with a C component responsible for the invocation of mixing in encrypt or decrypt mode. The experiments have been performed using, for the client, a machine with Linux Ubuntu 22. 04 LTS, Intel Xeon CPU E5-2620 v4, 8 cores. For the server, we used an Amazon EC2 m4. xlarge instance, with 4 CPUs and 16 GB of RAM. The client was connected to the Internet by a symmetric 100 Mbps connection. Our experiments evaluated the throughput for reconstructing the plaintext of the resources (i. e. , the execution of the mix process in decrypt mode, Section 6. 1) and for accessing resources at the server as well as managing policy updates (i. e. , the access to a resource and the upload/overwrite of fragments, Section 6. 2).
Mixing throughput We evaluated the cost, in terms of throughput, of the client for reconstructing the plaintext representation of a resources protected with our approach, which entails the execution of the mixing process in decrypt mode. We considered then the troughput for mixing a macro-block with the different strategies. In particular, we considered the AES-Mix (bsize=128 bits) with both the software implementation of AES and its hardware implementation AES-NI, supported by most of the current Intel x86 CPUs, and the OAEP-Mix (bsize=512 bits) with BLAKE2 as cryptographic hash function for the internal OAEP layer. The experiments have been performed over a macro-block of 256KiB (512KiB for the OAEP-Mix where the mix function operates on half of the input). Figure  18  compares the throughput obtained by the application of our mixing strategies (i.
ACKNOWLEDGMENTS This work was supported in part by the  EC  under grants  MARSAL  ( 101017171 ),  GLACIATION  ( 101070141 ),  EdgeAI  ( 101097300 ), by the  Italian MUR  under  PRIN  project  HOPE , and by project  SERICS  ( PE00000014 ) under the  MUR NRRP  funded by the  EU -NextGenerationEU.
LSECs capture viruses via lectins at their surface and, in turn, can transfer them to hepatocytes. 18  CLEC4M recognises a wide range of pathogens, among which are SARS-CoV-2, HCV, and Ebola virus. 9  In SARS-CoV, CLEC4M recognises N-glycans on the spike protein and transfers the virus to permissive cells. 19  We searched for the expression and localisation of ACE2, TMPRSS2, and CLEC4M in normal human liver and MASLD. To this end, we constructed a transcriptomic fatty liver disease meta-dataset of 243 samples including normal liver, steatosis, steatohepatitis, and MASH. To assess whether the results were specific to MASLD or related to unspecific fibroinflammatory liver changes, we explored five additional transcriptomic datasets totalling 161 patients with fibroinflammatory liver diseases.
Materials and methods Patients, samples, and datasets For fatty liver disease samples, routine formalin-fixed, paraffinembedded non-tumour liver tissues were obtained from the Anatomic Pathology Laboratory, Rennes University Hospital, after informed consent of nine patients with MASLD and five control patients undergoing partial hepatectomy (Table  S1 ). Sample collection, exploitation, and data analysis were performed within the framework of the INSERM's Institutional Review Board Approval No. 19-630. Routine cases were reviewed and selected by an experienced liver pathologist (BT). Digital slides were viewed using NDP. view (Hamamatsu Photonics). The signal was independently read by two observers (LC and OM), who were blinded to any information. Discrepancies were resolved by consensus reading.
Scoring of MASLD and MASH activities was done as described on a fourpoint scale. 23, 24  A human fatty liver disease transcriptomic meta-dataset of 243 samples was constructed by merging three microarray datasets: GSE33814  25  (12 normal livers, 19 steatoses, and 12 steatohepatitides), GSE48452  26  (14 normal livers, 27 livers from obese patients, 14 steatoses, and 18 non-alcoholic steatohepatitides [MASHs]), and GSE83452 27 (231 samples from which 126 MASHs were extracted for study). Altogether, the meta-dataset includes 27 normal livers, 27 livers from obese patients, 33 steatoses, 12 steatohepatitides, and 144 MASHs. Batch effect was removed using the ComBat algorithm (sva R package), as we previously described. 28  Raw expression data were quantilenormalised and log 2 -transformed.
S1A ), the intestine (Fig. S1B ), and cholangiocytes (Fig. 1A-D ) and of TMPRSS2 in the human prostate (Fig. S1C  and D ). Of note, our analysis of a transcriptomic microarray meta-dataset assembling 745 primary cell samples from more than 100 studies  22  revealed that ACE2 and TMPRSS2 mRNAs were detected in hepatocytes and the bronchial epithelium at similar levels (Fig. S2A ). These findings were consistent with the reported detection of ACE2 protein in human livers  33  and the detection of low but homogenously distributed ACE2 enzymatic activity by in situ autoradiography in normal rat livers. 34  We analysed ACE2 and TMPRSS2 expression and localisation in nine cases of fatty liver disease and five controls by immunohistochemistry. Available clinical and biological data, as well as scores for steatosis, ballooning, inflammation, MASLD, and MASH are shown in Table  S1.
ACE2 was located mainly at the sinusoidal and biliary poles of hepatocyte cords. In summary, cholangiocytes, sinusoidal endothelial cells, and hepatocytes are the major cell sources of ACE2 protein in the liver. Resident liver immune cells do not appear to express detectable levels of ACE2 protein in steatohepatitis, as observed using the highly sensitive tyramide signal amplification method. Liver ACE2 mRNA expression increases proportionally with age, inflammation, and fibrogenesis in patients with fatty liver disease We assessed the regulation of ACE2 and TMPRSS2 expression in MASLD after merging three independent microarray datasets  [25] [26] [27]  into a meta-dataset consisting of 243 liver samples and 20,941 mRNA transcripts. Batch effect correction and normalisation were applied to minimise experimental bias (Fig.
4C ). In turn, the mRNA level of these extracellular matrix components was directly proportional to the inflammation scores (Fig. 4D ). Altogether, these data reveal upregulation of ACE2 mRNA expression in MASLD. The expression of ACE2, TMPRSS2, and DPP4 mRNAs in fatty liver tissues correlates with markers of immune cell infiltration The absence of detectable ACE2 protein in liver immune cells by immunohistochemistry, despite the use of the highly sensitive tyramide-based signal amplification technology, led us to search for mRNA expression of ACE2, TMPRSS2, and DPP4 in immune cell subsets isolated from circulating leucocytes. To this end, we analysed the Immune Response in Silico (IRiS) microarray dataset, composed of 228 immune cell samples that had been isolated by cell-specific antibody pull-down before transcriptomic analyses.
36  First, we confirmed detection of immune cell subset marker mRNAs specific to the cell types analysed (Fig. S9 ). Next, we detected ACE2 in B lymphocytes (naive B cells, and IgA and IgM/G memory and plasma cells) and neutrophils. In turn, TMPRSS2 was detected at variable levels in all cell types except in plasma cells. By contrast, DPP4 was restricted to CD4 + T, CD8 + T, and natural killer lymphocytes (Fig. S10A ). Of note, although ACE2 and TMPRSS2 mRNAs were detected in purified immune cells from circulating leucocytes, the mRNA abundance was 100fold lower than that of cell surface cluster differentiation immune cell markers (compare Figs. S9  and S10A ). To infer whether expression of ACE2, TMPRSS2, and DPP4 mRNAs in fatty liver disease tissues was associated with inflammatory infiltration, we explored the GSE33814 dataset composed of 13 normal livers, 19 steatoses, and 12 steatohepatitides  25  and searched for correlations with immune cell subsetspecific markers, according to the IRiS immune cell repository  36  (Fig.
S10B ). Liver ACE2 mRNA expression was most highly correlated with liver infiltration by T and B lymphocytes, macrophages, and dendritic cells. TMPRSS2 mRNA was associated with macrophage and neutrophil markers, whereas DPP4 was associated with macrophage, neutrophil, and T-cell markers. Validating the above findings, we found correlation of ACE2 with T-cell, B-cell, and dendritic cell markers; TMPRSS2 with macrophage and neutrophil markers; and DPP4 with T-cell markers in an independent dataset (GSE48452; Fig. S11 ). 26  These results show that upregulation of ACE, TMPRSS2, and DPP4 mRNA expression correlates with immune cell infiltration in steatohepatitis. They match with the higher levels of ACE2 protein expression seen in steatohepatitis with respect to control livers and with the association of ACE2 mRNA levels with fibroinflammation in fatty liver disease.
Steatohepatitis shows enhanced immune reactivity To appraise the functional significance of immune cell infiltrates in MASLD, we constructed immunophenograms (i. e. phenetic diagrams) that classify immune functions into four families, namely, major histocompatibility complex, effector cells, suppressor cells, and checkpoint inhibitors and immune modulators (Fig. 5A ), applying deconvolution algorithms. 37  Immunophenograms were obtained from 13 controls, 19 steatoses, and 12 steatohepatitides (Fig. S12 ) from the GSE33814 transcriptomic   (D-F ) Bile canaliculi, identified with the specific marker ABCC2 (also known as MRP2) (green), contain ACE2 (red). Digital slides were acquired using a 40× objective in a confocal scanner. The image shows a Z-stack of four 500-nm focusing steps. ABCC2, ATP binding cassette subfamily C member 2; ACE2, angiotensinconverting enzyme 2; MRP2, multidrug resistance-associated protein 2.
dataset. 25  Data are summarised in a spider web chart (Fig. 5A ). We observed enhanced immune reactivity in steatohepatitis, with increase in major histocompatibility complex markers involved in antigen processing and presentation (HLA-F, HLA-DPA1, HLA-B, HLA-A, and TAP2) and positive immunomodulation (CD27 and ICOS), with downregulation of the negative   checkpoint TIGIT and activation of CD4 T cells. We confirmed these data using two different approaches. First, using the IRiS tool,  36  we found that steatohepatitides were enriched in neutrophils, macrophages, dendritic cells, natural killer lymphocytes, B lymphocytes, and T lymphocytes (Fig. 5B ). Second, we constructed an unsupervised gene co-expression network to identify densely connected sub-networks or modules relating biological functions with the patient groups.
Applying weighted gene co-expression network analysis to the meta-dataset consisting of 243 human liver samples, we identified three modules highly associated with steatohepatitis (Figs. S13-16 and Table  S7 ). Gene ontology analysis confirmed functions involved in antigen processing and presentation, lymphocyte chemotaxis, inflammation, fibrogenesis, and typical hepatocyte metabolism. These findings confirm an enhanced immune reactivity in steatohepatitis leading to a chronic fibroinflammatory microenvironment. ACE2 expression is upregulated in chronic fibroinflammatory liver diseases in a context of patient overweight We tested whether the upregulation of ACE2 was specific to steatohepatitis or whether this was also seen in other fibroinflammatory liver diseases. Analysis of four transcriptomic datasets totalling 120 patients did not reveal significant ACE2 mRNA changes in the liver in alcoholic hepatitis (GSE28619), blood mononuclear cells (GSE119117), and the liver in HCV infection (GSE48445) or in the liver in HBV infection (GSE54747), except that treatment of patients with HCV with pegylated interferon alpha 2 (IFNA2) was associated with decreased ACE2 expression with respect to untreated patients (Fig.
29  Treatment of human microvascular endothelial cells with IL-6, IL-1B, TNFA, or LPS did not significantly change ACE2, TMPRSS2, or FURIN mRNA expression (Fig. S19 ). Thus, we hypothesised that ACE2 upregulation could result from the synergy between chronic inflammation and fibrogenesis in a context of patient overweight. We analysed the mRNA expression of ACE2 and the markers of fibrogenesis COL1A1, COL3A1, and VCAN in 41 non-tumour livers from TCGA dataset according to the BMI and the staging of inflammatory activity and fibrosis, as assessed by METAVIR scoring 40 on digital slides. Table  S8  shows METAVIR scores, as well as anonymised IDs; BMI; ACE2, COL1A1, COL3A1, and VCAN mRNA levels; and aetiologies. MET-AVIR inflammation scores were dichotomised as low (0-1) and high (2-3). METAVIR fibrosis scores were dichotomised as low (0-1-2) and high (3-4).
Oleic and stearic acids induce steatosis in normal human hepatocytes and lead to increased ACE2 mRNA expression in vitro The above results led us to compare the impact of two statistical models of the variance of the MASLD meta-dataset by combining ACE2 mRNA expression with either inflammatory cytokines or markers of fatty liver disease. Both models had similar impact on Wilk's Lambda scores and confirmed the differences in ACE2 expression between 27 control patients and 30 patients with steatohepatitis (Fig. 7A  and B ). These results are in line with a hypothetical interaction between fatty liver disease and chronic inflammation on ACE2 upregulation. To experimentally test this hypothesis, we challenged primary cultures of normal human hepatocytes from three patients by in vitro modelling the metabolic effects of steatohepatitis (Fig.
Discussion We show that the expression of ACE2 is upregulated in chronic fibroinflammatory liver diseases in a context of patient overweight. In particular, the basal and apical poles of hepatocyte chords display an increased density of ACE2 receptors in patients with steatohepatitis. High liver ACE2 expression is related to the percentage of fat area, enhanced liver immune reactivity, and fibrogenesis. In vitro modelling of steatohepatitis by treating primary normal human hepatocytes with the long-chain fatty acids oleic and stearic acids leads to increased ACE2 mRNA expression, confirming the molecular data obtained from clinical samples. ACE2 was also detected in CLEC4M + sinusoidal endothelial cells and in CD34 + portal capillaries in steatohepatitis. CLEC4M may facilitate the transfer of viruses from the sinusoidal endothelium to ACE2-positive cells.
19  Although TMPRSS2 protein was not detected in normal liver, it was co-detected with ACE2 in hepatocytes in steatohepatitis. Therefore, ACE2, TMPRSS2, and CLEC4M could support viral infection through the space of Disse. In consistency with these findings, SARS-CoV-2 has been detected within endothelial cells in portal veins  42  and sinusoidal microthrombi  43  and associated with vascular dysfunction, such as occlusive thrombosis, in patients who died from COVID-19. 11, 44  In contrast with our findings, a recent report  45  concluded that there was no evidence for an increased ACE2 expression in MASLD. The authors found no difference in ACE2 mRNA expression between MASLD, steatosis, and MASH after analysis of the GSE48452 26 transcriptomic microarray dataset. First, the GSE48452 dataset (n = 54) is included within our meta-dataset consisting of 243 patients with fatty liver disease.
Second, we agree that, in this dataset, there is no difference between lean patients without MASLD and the other groups. However, ACE2 mRNA levels were higher in patients with MASH than in obese patients without MASLD and in patients with simple steatosis (Wilcoxon test adjusted for multiple testing using Benjamini and Hochberg's correction; Fig. S20 ). In consistency with our results, ACE2 and TMPRSS2 upregulation in MASH correlates with activity score. 32  Moreover, international studies including over 60 million patients revealed a higher risk for COVID-19 in metabolic syndrome. In particular, patients with MASH,  5  MASLD,  46  and NAFLD  4  are at high risk of severe COVID-19 and liver injury. Our data suggested that a background of fatty liver disease is important for upregulation of ACE2 to occur.
Relative mRNA expression ACE2 Low High Inflammation ACE2 Low High Fibrosis COL1A1 Low High Inflammation COL1A1 Low High Fibrosis COL3A1 Low High Inflammation COL3A1 Low High Fibrosis VCAN Low High Inflammation VCAN Low High An alternative mechanism leading to ACE2 upregulation could result from the antiviral IFN response. Type 1 IFNs, specifically IFNA2, whose levels are increased in patients with severe COVID-19, increase ACE2 expression and viral copy number in vitro. 38  The same mechanism could explain higher ACE2 expression in primary hepatocytes infected with HCV in vitro. 30  However, our meta-analysis of 30 HCV-infected patients from GSE48445 (Fig. S17 ) revealed that treatment with pegylated IFNA2 resulted in decreased ACE2 expression. This raises questions about the interactions between HCV viral load and the expression of endogenous IFNA2 and ACE2 across the timeline of antiviral treatment in the era of direct antiviral agents against HCV.