Embryonic Antigens and Growth of Murine Fibrosarcomata

The amount of embryonic antigens (EA) was estimated in 13 BALB/c fibrosarcomata by in vitro cell mediated cytotoxicity of anti-embryo spleen cells and by quantitative absorption of an anti-embryo antiserum. A direct relationship between amount of EA and tumour growing capacity was found. EA were detected also on fast dividing testicular cells. It is suggested that EA expression on tumour cells is related to a cell membrane function controlling mitosis rather than to a function specifically related to the neoplastic status. Tumour take of low doses of 2 EA-bearing sarcomata was found to be enhanced in anti-embryo immune BALB/c mice in comparison with that in normal and anti-fibroblast immune mice

Quantum Circuits for the Unitary Permutation Problem

We consider the Unitary Permutation problem which consists, given $n$ unitary gates $U_1, \ldots, U_n$ and a permutation $\sigma$ of $\{1,\ldots, n\}$, in applying the unitary gates in the order specified by $\sigma$, i.e. in performing $U_{\sigma(n)}\ldots U_{\sigma(1)}$. This problem has been introduced and investigated by Colnaghi et al. where two models of computations are considered. This first is the (standard) model of query complexity: the complexity measure is the number of calls to any of the unitary gates $U_i$ in a quantum circuit which solves the problem. The second model provides quantum switches and treats unitary transformations as inputs of second order. In that case the complexity measure is the number of quantum switches. In their paper, Colnaghi et al. have shown that the problem can be solved within $n^2$ calls in the query model and $\frac{n(n-1)}2$ quantum switches in the new model. We refine these results by proving that $n\log_2(n) +\Theta(n)$ quantum switches are necessary and sufficient to solve this problem, whereas $n^2-2n+4$ calls are sufficient to solve this problem in the standard quantum circuit model. We prove, with an additional assumption on the family of gates used in the circuits, that $n^2-o(n^{7/4+\epsilon})$ queries are required, for any $\epsilon >0$. The upper and lower bounds for the standard quantum circuit model are established by pointing out connections with the permutation as substring problem introduced by Karp.Comment: 8 pages, 5 figure

Convolutional neural network-assisted recognition of nanoscale L1₂ ordered structures in face-centred cubic alloys

Nanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (gt;10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future. © 2021, The Author(s)

The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc

Knowledge of the three-dimensional structure of Galactic molecular clouds is important for understanding how clouds are affected by processes such as turbulence and magnetic fields and how this structure effects star formation within them. Great progress has been made in this field with the arrival of the Gaia mission, which provides accurate distances to $\sim10^{9}$ stars. Combining these distances with extinctions inferred from optical-IR, we recover the three-dimensional structure of 16 Galactic molecular cloud complexes at $\sim1$pc resolution using our novel three-dimensional dust mapping algorithm \texttt{Dustribution}. Using \texttt{astrodendro} we derive a catalogue of physical parameters for each complex. We recover structures with aspect ratios between 1 and 11, i.e.\ everything from near-spherical to very elongated shapes. We find a large variation in cloud environments that is not apparent when studying them in two-dimensions. For example, the nearby California and Orion A clouds look similar on-sky, but we find California to be more sheet-like, and massive, which could explain their different star-formation rates. In Carina, our most distant complex, we observe evidence for dust sputtering, which explains its measured low dust mass. By calculating the total mass of these individual clouds, we demonstrate that it is necessary to define cloud boundaries in three-dimensions in order to obtain an accurate mass; simply integrating the extinction overestimates masses. We find that Larson's relationship on mass vs radius holds true whether you assume a spherical shape for the cloud or take their true extents.Comment: accepted for publication by MNRAS, 23 pages, 9 figures, 3 table

Micro- and Nanoplastics’ Effects on Protein Folding and Amyloidosis

A significant portion of the world's plastic is not properly disposed of and, through various processes, is degraded into microscopic particles termed micro- and nanoplastics. Marine and terrestrial faunae, including humans, inevitably get in contact and may inhale and ingest these microscopic plastics which can deposit throughout the body, potentially altering cellular and molecular functions in the nervous and other systems. For instance, at the cellular level, studies in animal models have shown that plastic particles can cross the blood-brain barrier and interact with neurons, and thus affect cognition. At the molecular level, plastics may specifically influence the folding of proteins, induce the formation of aberrant amyloid proteins, and therefore potentially trigger the development of systemic and local amyloidosis. In this review, we discuss the general issue of plastic micro- and nanoparticle generation, with a focus on their effects on protein folding, misfolding, and their possible clinical implications

The use of sirolimus in the treatment of giant cystic lymphangioma : Four case reports and update of medical therapy

Rationale: Lymphatic malformations (LMs) are rare and benign anomalies resulting from the defective embryological development of the primordial lymphatic structures. Due to their permeative growth throughout all tissue layers, treatment is often challenging. Small asymptomatic lesions can be conservatively managed, while symptomatic lesions require active management. Surgery has been historically considered the treatment of choice, but today less invasive therapeutic options are preferred (sclerotherapy, laser therapy, oral medications). However, there are not uniform therapeutic protocols. Sirolimus is an oral medication that has been reported to be effective in the recent literature. Here we present the case of 4 newborns with giant multicystic lymphangioma treated with oral sirolimus after surgical resection had failed. Patient concerns: At birth the LMs were clinically appreciated as giant masses involving different organs and structures. Diagnoses: All patients had a prenatal diagnosis of giant multicystic lymphangioma confirmed at histological and cytological analysis. Interventions: Patients were treated with oral sirolimus after unsuccessful surgical resection. Outcomes: In all patients, sirolimus determined an overall reduction of the mass and a global involution from the macro-to the microcystic composition. Sirolimus was safe and poor disadvantages had been observed. The main and isolated adverse effect at laboratory analysis was progressive dyslipidemia, with increasing levels of total cholesterol and triglycerides. Lessons: To date, our experience with sirolimus in the management of LMs is favorable. We recommend the use of sirolimus after unsuccessful surgical excision have been tried or when the surgical approach is not feasible. A multidisciplinary follow-up is needed to monitor disease evolution

A maChine and deep Learning Approach to predict pulmoNary hyperteNsIon in newbornS with congenital diaphragmatic Hernia (CLANNISH): Protocol for a retrospective study

Introduction Outcome predictions of patients with congenital diaphragmatic hernia (CDH) still have some limitations in the prenatal estimate of postnatal pulmonary hypertension (PH). We propose applying Machine Learning (ML), and Deep Learning (DL) approaches to fetuses and newborns with CDH to develop forecasting models in prenatal epoch, based on the integrated analysis of clinical data, to provide neonatal PH as the first outcome and, possibly: Favorable response to fetal endoscopic tracheal occlusion (FETO), need for Extracorporeal Membrane Oxygenation (ECMO), survival to ECMO, and death. Moreover, we plan to produce a (semi)automatic fetus lung segmentation system in Magnetic Resonance Imaging (MRI), which will be useful during project implementation but will also be an important tool itself to standardize lung volume measures for CDH fetuses. Methods and analytics Patients with isolated CDH from singleton pregnancies will be enrolled, whose prenatal checks were performed at the Fetal Surgery Unit of the Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico (Milan, Italy) from the 30th week of gestation. A retrospective data collection of clinical and radiological variables from newborns' and mothers' clinical records will be performed for eligible patients born between 01/01/2012 and 31/12/2020. The native sequences from fetal magnetic resonance imaging (MRI) will be collected. Data from different sources will be integrated and analyzed using ML and DL, and forecasting algorithms will be developed for each outcome. Methods of data augmentation and dimensionality reduction (feature selection and extraction) will be employed to increase sample size and avoid overfitting. A software system for automatic fetal lung volume segmentation in MRI based on the DL 3D U-NET approach will also be developed. Ethics and dissemination This retrospective study received approval from the local ethics committee (Milan Area 2, Italy). The development of predictive models in CDH outcomes will provide a key contribution in disease prediction, early targeted interventions, and personalized management, with an overall improvement in care quality, resource allocation, healthcare, and family savings. Our findings will be validated in a future prospective multicenter cohort study

Validation of cytoplasmic-to-nuclear ratio of survivin as an indicator of improved prognosis in breast cancer

<p>Abstract</p> <p>Background</p> <p>Conflicting data exist regarding the prognostic and predictive impact of survivin (BIRC5) in breast cancer. We previously reported survivin cytoplasmic-to-nuclear ratio (CNR) as an independent prognostic indicator in breast cancer. Here, we validate survivin CNR in a separate and extended cohort. Furthermore, we present new data suggesting that a low CNR may predict outcome in tamoxifen-treated patients.</p> <p>Methods</p> <p>Survin expression was assessed using immunhistochemistry on a breast cancer tissue microarray (TMA) containing 512 tumours. Whole slide digital images were captured using an Aperio XT scanner. Automated image analysis was used to identify tumour from stroma and then to quantify tumour-specific nuclear and cytoplasmic survivin. A decision tree model selected using a 10-fold cross-validation approach was used to identify prognostic subgroups based on nuclear and cytoplasmic survivin expression.</p> <p>Results</p> <p>Following optimisation of the staining procedure, it was possible to evaluate survivin protein expression in 70.1% (n = 359) of the 512 tumours represented on the TMA. Decision tree analysis predicted that nuclear, as opposed to cytoplasmic, survivin was the most important determinant of overall survival (OS) and breast cancer-specific survival (BCSS). The decision tree model confirmed CNR of 5 as the optimum threshold for survival analysis. Univariate analysis demonstrated an association between a high CNR (>5) and a prolonged BCSS (HR 0.49, 95% CI 0.29-0.81, p = 0.006). Multivariate analysis revealed a high CNR (>5) was an independent predictor of BCSS (HR 0.47, 95% CI 0.27-0.82, p = 0.008). An increased CNR was associated with ER positive (p = 0.045), low grade (p = 0.007), Ki-67 (p = 0.001) and Her2 (p = 0.026) negative tumours. Finally, a high CNR was an independent predictor of OS in tamoxifen-treated ER-positive patients (HR 0.44, 95% CI 0.23-0.87, p = 0.018).</p> <p>Conclusion</p> <p>Using the same threshold as our previous study, we have validated survivin CNR as a marker of good prognosis in breast cancer in a large independent cohort. These findings provide robust evidence of the importance of survivin CNR as a breast cancer biomarker, and its potential to predict outcome in tamoxifen-treated patients.</p

Cerebellar ataxia, neuropathy, vestibular areflexia syndrome due to RFC1 repeat expansion

Ataxia, causing imbalance, dizziness and falls, is a leading cause of neurological disability. We have recently identified a biallelic intronic AAGGG repeat expansion in replication factor complex subunit 1 (RFC1) as the cause of cerebellar ataxia, neuropathy, vestibular areflexia syndrome (CANVAS) and a major cause of late onset ataxia. Here we describe the full spectrum of the disease phenotype in our first 100 genetically confirmed carriers of biallelic repeat expansions in RFC1 and identify the sensory neuropathy as a common feature in all cases to date. All patients were Caucasian and half were sporadic. Patients typically reported progressive unsteadiness starting in the sixth decade. A dry spasmodic cough was also frequently associated and often preceded by decades the onset of walking difficulty. Sensory symptoms, oscillopsia, dysautonomia and dysarthria were also variably associated. The disease seems to follow a pattern of spatial progression from the early involvement of sensory neurons, to the later appearance of vestibular and cerebellar dysfunction. Half of the patients needed walking aids after 10 years of disease duration and a quarter were wheelchair dependent after 15 years. Overall, two-thirds of cases had full CANVAS. Sensory neuropathy was the only manifestation in 15 patients. Sixteen patients additionally showed cerebellar involvement, and six showed vestibular involvement. The disease is very likely to be underdiagnosed. Repeat expansion in RFC1 should be considered in all cases of sensory ataxic neuropathy, particularly, but not only, if cerebellar dysfunction, vestibular involvement and cough coexist

Differential diagnosis of neurodegenerative dementias with the explainable MRI based machine learning algorithm MUQUBIA

Biomarker-based differential diagnosis of the most common forms of dementia is becoming increasingly important. Machine learning (ML) may be able to address this challenge. The aim of this study was to develop and interpret a ML algorithm capable of differentiating Alzheimer’s dementia, frontotemporal dementia, dementia with Lewy bodies and cognitively normal control subjects based on sociodemographic, clinical, and magnetic resonance imaging (MRI) variables. 506 subjects from 5 databases were included. MRI images were processed with FreeSurfer, LPA, and TRACULA to obtain brain volumes and thicknesses, white matter lesions and diffusion metrics. MRI metrics were used in conjunction with clinical and demographic data to perform differential diagnosis based on a Support Vector Machine model called MUQUBIA (Multimodal Quantification of Brain whIte matter biomArkers). Age, gender, Clinical Dementia Rating (CDR) Dementia Staging Instrument, and 19 imaging features formed the best set of discriminative features. The predictive model performed with an overall Area Under the Curve of 98%, high overall precision (88%), recall (88%), and F1 scores (88%) in the test group, and good Label Ranking Average Precision score (0.95) in a subset of neuropathologically assessed patients. The results of MUQUBIA were explained by the SHapley Additive exPlanations (SHAP) method. The MUQUBIA algorithm successfully classified various dementias with good performance using cost-effective clinical and MRI information, and with independent validation, has the potential to assist physicians in their clinical diagnosis