Diagenesis makes the impossible come true: intersecting beds in calcareous turbidites

Constructing a time-frame of the past is fundamental for any geological interpretation, and the recognition of orbital cycles preserved in the stratigraphic record has revolutionized our understanding of the global timescale, especially for the Cenozoic. In the past decades, astrochronological and cyclostratigraphic approaches based on Milankovitch cycles have become a widely used tool for precisely dating stratigraphic boundaries by calibrating sedimentary units with astronomically tuned timescales. In many cases, strikingly rhythmic limestone-marl alternations are used for such approaches. Astrochronological approaches as a basic principle rely on the fundamental assumption that such rhythmites reflect fluctuating environmental conditions caused by variations in incident solar radiation such as changes in sea level, temperature, or weathering. Nevertheless, in many cases, the crucial question as to whether such rhythmites represent primary cycles or are purely of diagenetic origin has never been positively decided. In those cases, stratigraphic dating with this method is left unreliable. Here, the ability of diagenesis to produce rhythmic bedding lacking any relation to primary sedimentary signals is demonstrated. The Miocene succession discussed here shows two cemented sets of beds with different inclinations, one parallel to the sedimentary layering, the other, coeval, but in an oblique direction. These two sets merge and do not show distinct boundaries, thus indicating synchronous cementation. It is interpreted that minor early synsedimentary tectonic stress introduced an oblique anisotropy. During early differential diagenesis, this dual anisotropy influenced the geometry of the cemented layers, giving the impression of intersecting layers

Sacral morel-lavallée lesion: a not-so-rare diagnosis

Morel-Lavallée lesions are closed soft tissue degloving injuries with a propensity to become infected, arising in the lumbosacral region or even the scalp, common anatomical locations in neuroradiological studies. The radiologist must recognize this entity, its traumatic etiology, and treatment options. Our patient's Morel-Lavallée lesion was evaluated with ultrasound and MRI, demonstrating a predominantly hemorrhagic lesion successfully managed by aspiration

Development of a Novel Ulcerative Colitis Endoscopic Mayo Score Prediction Model Using Machine Learning

Background and Aims: Endoscopic assessment is a co-primary end point in inflammatory bowel disease registration trials, yet it is subject to inter- and intraobserver variability. We present an original machine learning approach to Endoscopic Mayo Score (eMS) prediction in ulcerative colitis and report the model’s performance in differentiating key levels of endoscopic disease activity on full-length procedure videos. Methods: Seven hundred ninety-three full-length videos with centrally-read eMS were obtained from 249 patients with ulcerative colitis, who participated in a phase II trial evaluating mirikizumab (NCT02589665). A video annotation approach was established to extract mucosal features and associated eMS classification labels from each video to be used as inputs for model training. The primary objective of the model was a categorical prediction of inactive vs active endoscopic disease evaluated against 2 independent test sets: a full set with a baseline single human expert read and a consensus subset in which 2 human reads agreed. Results: On the full test set of 147 videos, the model predicted inactive vs active endoscopic disease via the eMS with an area under the curve of 89%, accuracy of 84%, positive predictive value of 80%, and negative predictive value of 85%. In the consensus test set of 94 videos, the model predicted inactive vs active endoscopic disease with an area under the curve of 92%, accuracy of 89%, positive predictive value of 87%, and negative predictive value of 90%. Conclusion: We have demonstrated that this machine learning model supervised by mucosal features and eMS video annotations accurately differentiates key levels of endoscopic disease activity

Dysregulated Levels of Circulating Autoantibodies against Neuronal and Nervous System Autoantigens in COVID-19 Patients

BACKGROUND: COVID-19 is a heterogenous disease resulting in long-term sequela in predisposed individuals. It is not uncommon that recovering patients endure non-respiratory ill-defined manifestations, including anosmia, and neurological and cognitive deficit persisting beyond recovery-a constellation of conditions that are grouped under the umbrella of long-term COVID-19 syndrome. Association between COVID-19 and autoimmune responses in predisposed individuals was shown in several studies. AIM AND METHODS: To investigate autoimmune responses against neuronal and CNS autoantigens in SARS-CoV-2-infected patients, we performed a cross-sectional study with 246 participants, including 169 COVID-19 patients and 77 controls. Levels of antibodies against the acetylcholine receptor, glutamate receptor, amyloid β peptide, alpha-synucleins, dopamine 1 receptor, dopamine 2 receptor, tau protein, GAD-65, N-methyl D-aspartate (NMDA) receptor, BDNF, cerebellar, ganglioside, myelin basic protein, myelin oligodendrocyte glycoprotein, S100-B, glial fibrillary acidic protein, and enteric nerve were measured using an Enzyme-Linked Immunosorbent Assay (ELISA). Circulating levels of autoantibodies were compared between healthy controls and COVID-19 patients and then classified by disease severity (mild [ = 74], severe [ = 65], and requiring supplemental oxygen [ = 32]). RESULTS: COVID-19 patients were found to have dysregulated autoantibody levels correlating with the disease severity, e.g., IgG to dopamine 1 receptor, NMDA receptors, brain-derived neurotrophic factor, and myelin oligodendrocyte glycoprotein. Elevated levels of IgA autoantibodies against amyloid β peptide, acetylcholine receptor, dopamine 2 receptor, myelin basic protein, and α-synuclein were detected in COVID-19 patients compared with healthy controls. Lower IgA autoantibody levels against NMDA receptors, and IgG autoantibodies against glutamic acid decarboxylase 65, amyloid β peptide, tau protein, enteric nerve, and S100-B were detected in COVID-19 patients versus healthy controls. Some of these antibodies have known clinical correlations with symptoms commonly reported in the long COVID-19 syndrome. CONCLUSIONS: Overall, our study shows a widespread dysregulation in the titer of various autoantibodies against neuronal and CNS-related autoantigens in convalescent COVID-19 patients. Further research is needed to provide insight into the association between these neuronal autoantibodies and the enigmatic neurological and psychological symptoms reported in COVID-19 patients

Kernel Analysis for Estimating the Connectivity of a Network with Event Sequences

Estimating the connectivity of a network from events observed at each node has many applications. One prominent example is found in neuroscience, where spike trains (sequences of action potentials) are observed at each neuron, but the way in which these neurons are connected is unknown. This paper introduces a novel method for estimating connections between nodes using a similarity measure between sequences of event times. Specifically, a normalized positive definite kernel defined on spike trains was used. The proposed method was evaluated using synthetic and real data, by comparing with methods using transfer entropy and the Victor-Purpura distance. Synthetic data was generated using CERM (Coupled Escape-Rate Model), a model that generates various spike trains. Real data recorded from the visual cortex of an anaesthetized cat was analyzed as well. The results showed that the proposed method provides an effective way of estimating the connectivity of a network when the time sequences of events are the only available information