Recurrent fibrous solitary tumor of the parotid gland with satellite location: imaging, clinical and histological findings of rare entity.

Fibrous Solitary Tumors are infrequent neoplasms originating from mesenchymal tissues, most commonly arising from the visceral pleura and frequently exhibiting a benign behavior. Extra-pleural localization is unusual and the site of origin of these tumors from the parenchyma of the parotid gland is considered extremely rare. We report the case of a 66-years old woman with non-painful slow-growing left latero-cervical mass, who underwent a gadolinium-enhanced Magnetic Resonance Imaging showing a mass originating from the deep lobe of the parotid gland extending into the retro-pharyngeal space. After a total parotidectomy with tumor excision, a diagnosis of histologically proven fibrous solitary tumor of the parotid gland was made. Two years later, CT scan showed post-operative recurrence and further satellite localization in the neck, distant from the initial mass. We performed a literature review of the published similar cases, in order to clinicopathological and imaging features of this rare entity.

Clinical value of radiomics and machine learning in breast ultrasound: a multicenter study for differential diagnosis of benign and malignant lesions.

OBJECTIVES: We aimed to assess the performance of radiomics and machine learning (ML) for classification of non-cystic benign and malignant breast lesions on ultrasound images, compare ML's accuracy with that of a breast radiologist, and verify if the radiologist's performance is improved by using ML. METHODS: Our retrospective study included patients from two institutions. A total of 135 lesions from Institution 1 were used to train and test the ML model with cross-validation. Radiomic features were extracted from manually annotated images and underwent a multistep feature selection process. Not reproducible, low variance, and highly intercorrelated features were removed from the dataset. Then, 66 lesions from Institution 2 were used as an external test set for ML and to assess the performance of a radiologist without and with the aid of ML, using McNemar's test. RESULTS: After feature selection, 10 of the 520 features extracted were employed to train a random forest algorithm. Its accuracy in the training set was 82% (standard deviation, SD, ± 6%), with an AUC of 0.90 (SD ± 0.06), while the performance on the test set was 82% (95% confidence intervals (CI) = 70-90%) with an AUC of 0.82 (95% CI = 0.70-0.93). It resulted in being significantly better than the baseline reference (p = 0.0098), but not different from the radiologist (79.4%, p = 0.815). The radiologist's performance improved when using ML (80.2%), but not significantly (p = 0.508). CONCLUSIONS: A radiomic analysis combined with ML showed promising results to differentiate benign from malignant breast lesions on ultrasound images. KEY POINTS: • Machine learning showed good accuracy in discriminating benign from malignant breast lesions • The machine learning classifier's performance was comparable to that of a breast radiologist • The radiologist's accuracy improved with machine learning, but not significantly.

Artificial intelligence applied to neuroimaging data in Parkinsonian syndromes: Actuality and expectations.

Idiopathic Parkinson's Disease (iPD) is a common motor neurodegenerative disorder. It affects more frequently the elderly population, causing a significant emotional burden both for the patient and caregivers, due to the disease-related onset of motor and cognitive disabilities. iPD's clinical hallmark is the onset of cardinal motor symptoms such as bradykinesia, rest tremor, rigidity, and postural instability. However, these symptoms appear when the neurodegenerative process is already in an advanced stage. Furthermore, the greatest challenge is to distinguish iPD from other similar neurodegenerative disorders, "atypical parkinsonisms", such as Multisystem Atrophy, Progressive Supranuclear Palsy and Cortical Basal Degeneration, since they share many phenotypic manifestations, especially in the early stages. The diagnosis of these neurodegenerative motor disorders is essentially clinical. Consequently, the diagnostic accuracy mainly depends on the professional knowledge and experience of the physician. Recent advances in artificial intelligence have made it possible to analyze the large amount of clinical and instrumental information in the medical field. The application machine learning algorithms to the analysis of neuroimaging data appear to be a promising tool for identifying microstructural alterations related to the pathological process in order to explain the onset of symptoms and the spread of the neurodegenerative process. In this context, the search for quantitative biomarkers capable of identifying parkinsonian patients in the prodromal phases of the disease, of correctly distinguishing them from atypical parkinsonisms and of predicting clinical evolution and response to therapy represent the main goal of most current clinical research studies. Our aim was to review the recent literature and describe the current knowledge about the contribution given by machine learning applications to research and clinical management of parkinsonian syndromes.

Currarino syndrome in an elderly man: Multimodality imaging findings.

Currarino syndrome is a rare congenital disorder characterized by the triad of anorectal anomalies, sacrococcygeal dysgenesis and presacral mass. Because of the anorectal anomalies, the extrinsic compression due to the presacral mass and neurologic deficits, patients usually present with gastrointestinal symptoms, most commonly chronic constipation. Most cases of Currarino syndromes are diagnosed in childhood, at birth or in the pre-birth period and, even if adult presentation has been reported in few sporadic case reports, the diagnosis in the late stages of life remains extremely rare. In this paper, we describe the imaging findings of an elderly man with a past medical history of megacolon surgically treated in his childhood, who was diagnosed with Currarino syndrome at the age of 72.

Rare causes of early-onset dystonia-parkinsonism with cognitive impairment: a de novo PSEN-1 mutation.

Mutations in PSEN1 are responsible for familial Alzheimer's disease (FAD) inherited as autosomal dominant trait, but also de novo mutations have been rarely reported in sporadic early-onset dementia cases. Parkinsonism in FAD has been mainly described in advanced disease stages. We characterized a patient presenting with early-onset dystonia-parkinsonism later complicated by dementia and myoclonus. Brain MRI showed signs of iron accumulation in the basal ganglia mimicking neurodegeneration with brain iron accumulation (NBIA) as well as fronto-temporal atrophy. Whole exome sequencing revealed a novel PSEN1 mutation and segregation within the family demonstrated the mutation arose de novo.We suggest considering PSEN1 mutations in cases of dystonia-parkinsonism with positive DAT-Scan, later complicated by progressive cognitive decline and cortical myoclonus even without a dominant family history.

Physicochemical characterization of a thermostable alcohol dehydrogenase from Pyrobaculum aerophilum.

In this work we characterize an alcohol dehydrogenase (ADH) from the hyperthermophilic archaeon Pyrobaculum aerophilum (PyAeADHII). We have previously found that PyAeADHII has no activity when standard ADH substrates are used but is active when α-tetralone is used as substrate. Here, to gain insights into enzyme function, we screened several chemical libraries for enzymatic modulators using an assay employing α-tetralone. The results indicate that PyAeADHII activity in the presence of α-tetralone was inhibited by compounds such as flunarizine. We also examined metal coordination of the enzyme in solution by performing metal substitution of the enzyme-bound zinc (Zn²âº) with cobalt. The solution-based absorption spectra for cobalt substituted PyAeADHII supports substitution at the structural Zn²âº site. To gain structural insight, we obtained the crystal structure of both wild-type and cobalt-substituted PyAeADHII at 1.75 Å and 2.20 Å resolution, respectively. The X-ray data confirmed one metal ion per monomer present only at the structural site with otherwise close conservation to other ADH enzymes. We next determined the co-crystal structure of the NADPH-bound form of the enzyme at 2.35 Å resolution to help define the active site region of the enzyme and this data shows close structural conservation with horse ADH, despite the lack of a catalytic Zn²âº ion in PyAeADHII. Modeling of α-tetralone into the NADPH bound structure suggests an arginine as a possible catalytic residue. The data presented here can yield a better understanding of alcohol dehydrogenases lacking the catalytic zinc as well as the structural features inherent to thermostable enzymes.

Alcohol dehydrogenase from the hyperthermophilic archaeon Pyrobaculum aerophilum: stability at high temperature.

The structural and stability properties of a novel zinc-dependent alcohol dehydrogenase from the hyperthermophilic archaeon Pyrobaculum aerophilum (PyAeADHII) were investigated by Fourier transformed infrared spectroscopy (FTIR). This enzyme is a thermostable homo-tetramer belonging to the GroES-fold motif proteins characterized by an irregular ß-barrel conformation. Our results revealed a protein with a secondary structure rich in ß-sheet (32% of the total secondary elements) and it showed a three-step thermal unfolding pathway. The complete enzyme denaturation was preceded by the formation of a relaxed tertiary/quaternary structure and previously by an excited native-like conformation. Two-dimensional correlation spectroscopy analysis (2D-COS) and differential scanning calorimetry (DSC) experiments supported these data and allowed us to determine the protein melting temperature at 96.9 °C as well as to suggest the sequence of the events that occurred during the protein denaturation process.

Properties and evolution of an alcohol dehydrogenase from the Crenarchaeota Pyrobaculum aerophilum.

The gene encoding a novel alcohol dehydrogenase (ADH) that belongs to the medium chain dehydrogenase/reductase (MDR) superfamily was identified in the hyperthermophilic archaeon, Pyrobaculum aerophilum. The P. aerophilum ADH gene (Pae2687) was over-expressed in Escherichia coli, and the protein (PyAeADHII) was purified to homogeneity and characterized. The PyAeADHII belongs to a medium chain class because its monomer size is 330 residues and even if it is structurally similar to other enzymes belonging to MDR superfamily, it lacks key residues involved in the coordination of the catalytic Zn ion and in the binding of alcoholic substrates typical of other ADHs. Consistently, PyAeADHII does not show activity on a large number of alcohols, aldheydes or ketones. It is active only when alpha-tetralone is used as a substrate. The enzyme has a strict requirement for NADP(H) as the coenzyme and has remarkable thermophilicity, displaying activity at temperatures up to 95 degrees C. The study of the metabolic pathways of P. aerophilum can provide information on the evolution of genes and enzymes and may be crucial for understanding the evolution of eukaryotic cells.

Pressure effects on the structure and stability of the hyperthermophilic trehalose/maltose-binding protein from Thermococcus litoralis.

In this work, we investigated the effect of pressure on the structure and stability of the recombinant D-trehalose/D-maltose-binding protein isolated from the hyperthermophilic archaeon Thermococcus litoralis (TMBP). The spectroscopic results obtained both in the absence and in the presence of maltose or trehalose revealed that the TMBP-Mal complex exhibits a larger structural stability under high pressure values than TMBP-Tre complex. In addition, the results also pointed out that pressure induces reversible denaturation transitions of the protein structure. By combining the fluorescence results obtained with 8-anilino-1-naphtalene sulfonate as extrinsic probe and the intrinsic indolic fluorescence of TMBP, it is evident that the protein structural changes above 400 MPa that involve the exposure to the solvent of a large portion of the hydrophobic protein domains are preceded by a partially unfolded protein structural state. The spectroscopic results have been interpreted and discussed by taking into account the X-ray structure of the protein and, in particular, the interactions of maltose and trehalose within the three-dimensional structure of TMBP.

Enzymes and proteins from extremophiles as hyperstable probes in nanotechnology: the use of D-trehalose/D-maltose-binding protein from the hyperthermophilic archaeon Thermococcus litoralis for sugars monitoring.

The D-trehalose/D-maltose-binding protein (TMBP), a monomeric protein of 48 kDa, is one component of the trehalose and maltose (Mal) uptake system. In the hyperthermophilic archaeon Thermococcus litoralis, this is mediated by a protein-dependent ATP-binding cassette system transporter. The gene coding for a thermostable TMBP from the archaeon T. litoralis has been cloned, and the recombinant protein has been expressed in E. coli. The recombinant TMBP has been purified to homogeneity and characterized. It exhibits the same functional and structural properties as the native one. In fact, it is highly thermostable and binds sugars, such as maltose, trehalose and glucose, with high affinity. In this work, we have immobilized TMBP on a porous silicon wafer. The immobilization of TMBP to the chip was monitored by reflectivity and Fourier Transformed Infrared spectroscopy. Furthermore, we have tested the optical response of the protein-Chip complex to glucose binding. The obtained data suggest the use of this protein for the design of advanced optical non-consuming analyte biosensors for glucose detection.

Temperature modulates binding specificity and affinity of the d-trehalose/d-maltose-binding protein from the hyperthermophilic archaeon Thermococcus litoralis.

We investigated the effect of temperature on the binding specificity of the recombinant d-trehalose/d-maltose-binding protein from the hyperthermophilic archaeon Thermococcus litoralis (TMBP). Importantly, we found that TMBP can bind d-glucose (Glc). The Glc binding was characterized by means of fluorescence spectroscopy in the temperature range of 25 degrees C-85 degrees C. Our results show that at 25 degrees C the binding of Glc to TMBP is well represented by a bimodal model with apparent K(d) of 20 muM and approximately 3-8 mM for the first and the second binding step, respectively. At 60 degrees C the binding of Glc to TMBP is represented by a simple hyperbolic model with an apparent K(d) value of about 40 muM. Finally, at 85 degrees C Glc did not bind to TMBP. Molecular dynamics (MD) simulations were used to shed light on the molecular mechanism of the Glc binding. Our results suggest that after proper fluorescent labeling TMBP can be used as a highly thermostable and non-consuming analyte biosensor for monitoring the level of glucose in fluids (e.g. human blood) where other sugars are not present.

D-galactose/D-glucose-binding Protein from Escherichia coli as Probe for a Non-consuming Glucose Implantable Fluorescence Biosensor.

D-Galactose/D-glucose-binding protein from E. coli (GGBP) is a monomer thatbinds glucose with high affinity. The protein structure of GGBP is organized in twoprincipal domains linked by a hinge region that form the sugar-binding site. In this workwe show that the mutant form of GGBP at the amino acid position 182 can be utilized as aprobe for the development of a non-consuming analyte fluorescence biosensor to monitorthe glucose level in diabetes health care.