AIM To investigate the relation of two different mutations to the outcome of partial external biliary diversion(PEBD)in severe bile salt export pump(BSEP) deficiency.METHODS Mutations in the gene ...encoding BSEP leading to severe BSEP deficiency in two unrelated patients were identified by genomic sequencing. Native liver biopsies and transiently transfected human embryonic kidney(HEK) 293 cells expressing either wild-type or mutated BSEP were subjected to immunofluorescence analysis to assess BSEP transporter localization. Bile acid profiles of patient and control bile samples were generated by ultra-performance liquid chromatographytandem mass spectrometry. Wild-type and mutant BSEP transport of ~3H-labeled taurocholate(TC) and taurochenodeoxycholate(TCDC) was assessed by vesicular transport assays.RESULTS A girl(at 2 mo) presented with pruritus, jaundice and elevated serum bile salts(BS). PEBD stabilized liver function and prevented liver transplantation. She was heterozygous for the BSEP deletion p.T919 del and the nonsense mutation p.R1235 X. At the age of 17 years relative amounts of conjugated BS in her bile were normal, while total BS were less than 3% as compared to controls. An unrelated boy(age 1.5 years) presenting with severe pruritus and elevated serum BS was heterozygous for the same nonsense and another missense mutation, p.G1032 R. PEBD failed to alleviate pruritus, eventually necessitating liver transplantation. BS concentration in bile was about 5% of controls. BS were mainly unconjugated with an unusual low amount of chenodeoxycholate derivatives(< 5%). The patients’ native liver biopsies showed canalicular BSEP expression. Both BSEP p.T919 del and p.G1032 R were localized in the plasma membrane in HEK293 cells. In vitro transport assays showed drastic reduction of transport by both mutations. Using purified recombinant BSEP as quantifiable reference, per-molecule transport rates for TC and TCDC were determined to be 3 and 2 BS molecules per wild-type BSEP transporter per minute, respectively.CONCLUSION In summary, our findings suggest that residual function of BSEP as well as substrate specificity influence the therapeutic effectiveness of PEBD in progressive familial intrahepatic cholestasis type 2(PFIC-2).
Novel mRNA-based vaccines have been proven to be powerful tools in combating the global pandemic caused by SARS-CoV-2, with BNT162b2 (trade name: Comirnaty) efficiently protecting individuals from ...COVID-19 across a broad age range. Still, it remains largely unknown how renal insufficiency and immunosuppressive medication affect development of vaccine-induced immunity. We therefore comprehensively analyzed humoral and cellular responses in kidney transplant recipients after the standard second vaccination dose. As opposed to all healthy vaccinees and the majority of hemodialysis patients, only 4 of 39 and 1 of 39 transplanted individuals showed IgA and IgG seroconversion at day 8 + or - 1 after booster immunization, with minor changes until day 23 + or - 5, respectively. Although most transplanted patients mounted spike-specific T helper cell responses, frequencies were significantly reduced compared with those in controls and dialysis patients and this was accompanied by a broad impairment in effector cytokine production, memory differentiation, and activation-related signatures. Spike-specific CD8.sup.+ T cell responses were less abundant than their CD4.sup.+ counterparts in healthy controls and hemodialysis patients and almost undetectable in transplant patients. Promotion of anti-HLA antibodies or acute rejection was not detected after vaccination. In summary, our data strongly suggest revised vaccination approaches in immunosuppressed patients, including individual immune monitoring for protection of this vulnerable group at risk of developing severe COVID-19.
Recently, convolutional neural networks (CNNs) systematically outperformed dermatologists in distinguishing dermoscopic melanoma and nevi images. However, such a binary classification does not ...reflect the clinical reality of skin cancer screenings in which multiple diagnoses need to be taken into account.
Using 11,444 dermoscopic images, which covered dermatologic diagnoses comprising the majority of commonly pigmented skin lesions commonly faced in skin cancer screenings, a CNN was trained through novel deep learning techniques. A test set of 300 biopsy-verified images was used to compare the classifier's performance with that of 112 dermatologists from 13 German university hospitals. The primary end-point was the correct classification of the different lesions into benign and malignant. The secondary end-point was the correct classification of the images into one of the five diagnostic categories.
Sensitivity and specificity of dermatologists for the primary end-point were 74.4% (95% confidence interval CI: 67.0–81.8%) and 59.8% (95% CI: 49.8–69.8%), respectively. At equal sensitivity, the algorithm achieved a specificity of 91.3% (95% CI: 85.5–97.1%). For the secondary end-point, the mean sensitivity and specificity of the dermatologists were at 56.5% (95% CI: 42.8–70.2%) and 89.2% (95% CI: 85.0–93.3%), respectively. At equal sensitivity, the algorithm achieved a specificity of 98.8%. Two-sided McNemar tests revealed significance for the primary end-point (p < 0.001). For the secondary end-point, outperformance (p < 0.001) was achieved except for basal cell carcinoma (on-par performance).
Our findings show that automated classification of dermoscopic melanoma and nevi images is extendable to a multiclass classification problem, thus better reflecting clinical differential diagnoses, while still outperforming dermatologists at a significant level (p < 0.001).
•A convolutional neural network (CNN) received enhanced training with 11,444 dermoscopic images of >90% of lesions faced in a skin cancer screening setting.•The performance of 112 dermatologists from 13 university hospitals was then compared with the CNN on a test set of 300 biopsy-verified images.•The CNN achieved systematic outperformance of the dermatologists (p < 0.001) regardless of their clinical experience.
Recent studies have successfully demonstrated the use of deep-learning algorithms for dermatologist-level classification of suspicious lesions by the use of excessive proprietary image databases and ...limited numbers of dermatologists. For the first time, the performance of a deep-learning algorithm trained by open-source images exclusively is compared to a large number of dermatologists covering all levels within the clinical hierarchy.
We used methods from enhanced deep learning to train a convolutional neural network (CNN) with 12,378 open-source dermoscopic images. We used 100 images to compare the performance of the CNN to that of the 157 dermatologists from 12 university hospitals in Germany. Outperformance of dermatologists by the deep neural network was measured in terms of sensitivity, specificity and receiver operating characteristics.
The mean sensitivity and specificity achieved by the dermatologists with dermoscopic images was 74.1% (range 40.0%–100%) and 60% (range 21.3%–91.3%), respectively. At a mean sensitivity of 74.1%, the CNN exhibited a mean specificity of 86.5% (range 70.8%–91.3%). At a mean specificity of 60%, a mean sensitivity of 87.5% (range 80%–95%) was achieved by our algorithm. Among the dermatologists, the chief physicians showed the highest mean specificity of 69.2% at a mean sensitivity of 73.3%. With the same high specificity of 69.2%, the CNN had a mean sensitivity of 84.5%.
A CNN trained by open-source images exclusively outperformed 136 of the 157 dermatologists and all the different levels of experience (from junior to chief physicians) in terms of average specificity and sensitivity.
•A convolutional neural network (CNN) received enhanced training with 12,378 open-source dermoscopic images.•In a head-to-head comparison, the CNN outperformed 136 of 157 participating dermatologists.•The CNN was capable to outperform dermatologists of all hierarchical subgroups (from junior to chief physicians) in dermoscopic melanoma image classification.
In recent studies, convolutional neural networks (CNNs) outperformed dermatologists in distinguishing dermoscopic images of melanoma and nevi. In these studies, dermatologists and artificial ...intelligence were considered as opponents. However, the combination of classifiers frequently yields superior results, both in machine learning and among humans. In this study, we investigated the potential benefit of combining human and artificial intelligence for skin cancer classification.
Using 11,444 dermoscopic images, which were divided into five diagnostic categories, novel deep learning techniques were used to train a single CNN. Then, both 112 dermatologists of 13 German university hospitals and the trained CNN independently classified a set of 300 biopsy-verified skin lesions into those five classes. Taking into account the certainty of the decisions, the two independently determined diagnoses were combined to a new classifier with the help of a gradient boosting method. The primary end-point of the study was the correct classification of the images into five designated categories, whereas the secondary end-point was the correct classification of lesions as either benign or malignant (binary classification).
Regarding the multiclass task, the combination of man and machine achieved an accuracy of 82.95%. This was 1.36% higher than the best of the two individual classifiers (81.59% achieved by the CNN). Owing to the class imbalance in the binary problem, sensitivity, but not accuracy, was examined and demonstrated to be superior (89%) to the best individual classifier (CNN with 86.1%). The specificity in the combined classifier decreased from 89.2% to 84%. However, at an equal sensitivity of 89%, the CNN achieved a specificity of only 81.5%
Our findings indicate that the combination of human and artificial intelligence achieves superior results over the independent results of both of these systems.
•This article describes the first experiment on combining human and artificial intelligence for the classification of images suspicious of skin cancer.•The combination achieved a superior accuracy of 82.95% (compared to 81.59%/42.94% achieved by artificial/human intelligence alone).•The combination of human and artificial intelligence indicates superiority over a separated approach.
Driving the machinery: A biocatalytic redox‐neutral cascade for the preparation of terminal primary amines from primary alcohols at the expense of ammonia has been established in a one‐pot one‐step ...method (see picture). Applying this artificial biocatalyst network, long‐chain 1,ω‐alkanediols were converted into diamines, which are building blocks for polymers, in up to 99 % conversion.
Until now, the clinical differentiation between a nevus and a melanoma is still challenging in some cases. Line-field confocal optical coherence tomography (LC-OCT) is a new tool with the aim to ...change that. The aim of the study was to evaluate LC-OCT for the discrimination between nevi and melanomas. A total of 84 melanocytic lesions were examined with LC-OCT and 36 were also imaged with RCM. The observers recorded the diagnoses, and the presence or absence of the 18 most common imaging parameters for melanocytic lesions, nevi, and melanomas in the LC-OCT images. Their confidence in diagnosis and the image quality of LC-OCT and RCM were evaluated. The most useful criteria, the sensitivity and specificity of LC-OCT vs. RCM vs. histology, to differentiate a (dysplastic) nevus from a melanoma were analyzed. Good image quality correlated with better diagnostic performance (Spearman correlation: 0.4). LC-OCT had a 93% sensitivity and 100% specificity compared to RCM (93% sensitivity, 95% specificity) for diagnosing a melanoma (vs. all types of nevi). No difference in performance between RCM and LC-OCT was observed (McNemar's
value = 1). Both devices falsely diagnosed dysplastic nevi as non-dysplastic (43% sensitivity for dysplastic nevus diagnosis). The most significant criteria for diagnosing a melanoma with LC-OCT were irregular honeycombed patterns (92% occurrence rate; 31.7 odds ratio (OR)), the presence of pagetoid spread (89% occurrence rate; 23.6 OR) and the absence of dermal nests (23% occurrence rate, 0.02 OR). In conclusion LC-OCT is useful for the discrimination between melanomas and nevi.
Microglia, the immunocompetent cells of the CNS, rapidly respond to brain injury and disease by altering their morphology and phenotype to adopt an activated state. Microglia can exist broadly ...between two different states, namely the classical (M1) and the alternative (M2) phenotype. The first is characterized by the production of pro-inflammatory cytokines/chemokines and reactive oxygen and/or nitrogen species. In contrast, alternatively activated microglia are typified by an anti-inflammatory phenotype supporting wound healing and debris clearance. The objective of the present study was to determine the outcome of lysophosphatidic acid (LPA)-mediated signaling events on microglia polarization.
LPA receptor expression and cyto-/chemokine mRNA levels in BV-2 and primary murine microglia (PMM) were determined by qPCR. M1/M2 marker expression was analyzed by Western blotting, immunofluorescence microscopy, or flow cytometry. Cyto-/chemokine secretion was quantitated by ELISA.
BV-2 cells express LPA receptor 2 (LPA2), 3, 5, and 6, whereas PMM express LPA1, 2, 4, 5, and 6. We show that LPA treatment of BV-2 and PMM leads to a shift towards a pro-inflammatory M1-like phenotype. LPA treatment increased CD40 and CD86 (M1 markers) and reduced CD206 (M2 marker) expression. LPA increased inducible nitric oxide synthase (iNOS) and COX-2 levels (both M1), while the M2 marker Arginase-1 was suppressed in BV-2 cells. Immunofluorescence studies (iNOS, COX-2, Arginase-1, and RELMα) extended these findings to PMM. Upregulation of M1 markers in BV-2 and PMM was accompanied by increased cyto-/chemokine transcription and secretion (IL-1β, TNFα, IL-6, CCL5, and CXCL2). The pharmacological LPA5 antagonist TCLPA5 blunted most of these pro-inflammatory responses.
LPA drives BV-2 and PMM towards a pro-inflammatory M1-like phenotype. Suppression by TCLPA5 indicates that the LPA/LPA5 signaling axis could represent a potential pharmacological target to interfere with microglia polarization in disease.
The RNA-binding proteins Roquin-1 and Roquin-2 redundantly control gene expression and cell-fate decisions. Here, we show that Roquin not only interacts with stem-loop structures, but also with a ...linear sequence element present in about half of its targets. Comprehensive analysis of a minimal response element of the Nfkbid 3'-UTR shows that six stem-loop structures cooperate to exert robust and profound post-transcriptional regulation. Only binding of multiple Roquin proteins to several stem-loops exerts full repression, which redundantly involved deadenylation and decapping, but also translational inhibition. Globally, most Roquin targets are regulated by mRNA decay, whereas a small subset, including the Nfat5 mRNA, with more binding sites in their 3'-UTRs, are also subject to translational inhibition. These findings provide insights into how the robustness and magnitude of Roquin-mediated regulation is encoded in complex cis-elements.
The RNase Regnase-1 is a master RNA regulator in macrophages and T cells that degrades cellular and viral RNA upon NF-κB signaling. The roles of its family members, however, remain largely unknown. ...Here, we analyzed
-deficient mice, which develop hypertrophic lymph nodes. We used various mice with immune cell-specific deletions of
to demonstrate that Regnase-3 acts specifically within myeloid cells.
deficiency systemically increased IFN signaling, which increased the proportion of immature B and innate immune cells, and suppressed follicle and germinal center formation. Expression analysis revealed that Regnase-3 and Regnase-1 share protein degradation pathways. Unlike Regnase-1, Regnase-3 expression is high specifically in macrophages and is transcriptionally controlled by IFN signaling. Although direct targets in macrophages remain unknown, Regnase-3 can bind, degrade, and regulate mRNAs, such as
(
), in vitro. These data indicate that Regnase-3, like Regnase-1, is an RNase essential for immune homeostasis but has diverged as key regulator in the IFN pathway in macrophages.
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