An Analysis of Resting-State Functional Transcranial Doppler Recordings from Middle Cerebral Arteries

Functional transcrannial Doppler (fTCD) is used for monitoring the hemodynamics characteristics of major cerebral arteries. Its resting-state characteristics are known only when considering the maximal velocity corresponding to the highest Doppler shift (so called the envelope signals). Significantly more information about the resting-state fTCD can be gained when considering the raw cerebral blood flow velocity (CBFV) recordings. In this paper, we considered simultaneously acquired envelope and raw CBFV signals. Specifically, we collected bilateral CBFV recordings from left and right middle cerebral arteries using 20 healthy subjects (10 females). The data collection lasted for 15 minutes. The subjects were asked to remain awake, stay silent, and try to remain thought-free during the data collection. Time, frequency and time-frequency features were extracted from both the raw and the envelope CBFV signals. The effects of age, sex and body-mass index were examined on the extracted features. The results showed that the raw CBFV signals had a higher frequency content, and its temporal structures were almost uncorrelated. The information-theoretic features showed that the raw recordings from left and right middle cerebral arteries had higher content of mutual information than the envelope signals. Age and body-mass index did not have statistically significant effects on the extracted features. Sex-based differences were observed in all three domains and for both, the envelope signals and the raw CBFV signals. These findings indicate that the raw CBFV signals provide valuable information about the cerebral blood flow which can be utilized in further validation of fTCD as a clinical tool. © 2013 Sejdić et al

Thermal diffusivity measurements of metastable austenite during continuous cooling

The thermal diffusivity of the metastable undercooled austenite is relevant for the quantitative analysis of the carbon and low-alloy steel quench. The standard laser-flash method requires prior thermal equilibrium between the sample and the furnace, which may not be possible to achieve without allowing the metastable phase to transform. Nevertheless, depending upon the steel's hardenability, the thermal transient due to a laser pulse may be much shorter than a cooling transient sufficiently steep to prevent the transformation of the austenite. In one such case, flash measurements were performed during continuous sample cooling and the thermal diffusivity of the metastable austenite was determined by using an extension of the standard analytical model. The adopted analytical model and data reduction procedure are described and the limitations and uncertainties of this method are discussed, also with the aid of a non-linear numerical simulation. The measured thermal diffusivity of the under cooled low-alloy austenite decreases linearly from 5.4•10−6 m2 s−1 at 1133 K to 4.3•10−6 m2 s−1 at 755 K; this trend is in broad agreement with one previous set of measurements upon a low-alloy undercooled austenite and with a large number of previous standard measurements upon stable (high-alloy) austenitic stainless steels

Ten years of Ana: lessons from a transdisciplinary body of literature on online pro-eating disorder websites

This paper offers a methodical review of the scientific literature of the last decade that concerns itself with online services offering supportive advocacy for anorexia nervosa and bulimia nervosa (‘pro-ana’ and ‘pro-mia’). The main question is whether these studies reproduce the traditional divide in the study of eating disorders, between clinical and social science perspectives, with limited mutual exchanges. Having first identified a specific body of literature, the authors investigate its content, methods and approaches, and analyse the network of cross-citations the components generate and share. On this basis, the authors argue that the scientific literature touching on pro-ana websites can be regarded as a single transdisciplinary body of knowledge. What’s more, they show that the literature on computermediated sociabilities centred on eating disorders displays different structural characteristics with respect to the traditional, non-Web-related research on eating disorders. In the latter, the social sciences have usually provided a critical counterpoint to the development of a health sciences mainstream. In the case of Web-related research, however, the social sciences have taken the lead role in defining the field, with the health sciences following suit

Vesicular and non-vesicular transport feed distinct glycosylation pathways in the Golgi.

Newly synthesized proteins and lipids are transported across the Golgi complex via different mechanisms whose respective roles are not completely clear. We previously identified a non-vesicular intra-Golgi transport pathway for glucosylceramide (GlcCer)--the common precursor of the different series of glycosphingolipids-that is operated by the cytosolic GlcCer-transfer protein FAPP2 (also known as PLEKHA8) (ref. 1). However, the molecular determinants of the FAPP2-mediated transfer of GlcCer from the cis-Golgi to the trans-Golgi network, as well as the physiological relevance of maintaining two parallel transport pathways of GlcCer--vesicular and non-vesicular--through the Golgi, remain poorly defined. Here, using mouse and cell models, we clarify the molecular mechanisms underlying the intra-Golgi vectorial transfer of GlcCer by FAPP2 and show that GlcCer is channelled by vesicular and non-vesicular transport to two topologically distinct glycosylation tracks in the Golgi cisternae and the trans-Golgi network, respectively. Our results indicate that the transport modality across the Golgi complex is a key determinant for the glycosylation pattern of a cargo and establish a new paradigm for the branching of the glycosphingolipid synthetic pathwa

Enhancing Breast Cancer Risk Prediction with Machine Learning: Integrating BMI, Smoking Habits, Hormonal Dynamics, and BRCA Gene Mutations—A Game-Changer Compared to Traditional Statistical Models?

The association between genetics and lifestyle factors is crucial when determining breast cancer susceptibility, a leading cause of deaths globally. This research aimed to compare the body mass index, smoking behavior, hormonal influences, and BRCA gene mutations between affected patients and healthy individuals, all with a family history of cancer. All these factors were then utilized as features to train a machine learning (ML) model to predict the risk of breast cancer development. Between 2020 and 2023, a total of 1389 women provided detailed lifestyle and risk factor data during visits to a familial cancer center in Italy. Descriptive and inferential statistics were assessed to explore the differences between the groups. Among the various classifiers used, the ensemble of decision trees was the best performer, with a 10-fold cross-validation scheme for training after normalizing the features. The performance of the model was evaluated using the receiver operating characteristic (ROC) curve and its area under the curve (AUC), alongside the accuracy, sensitivity, specificity, precision, and F1 score. Analysis revealed that individuals in the tumor group exhibited a higher risk profile when compared to their healthy counterparts, particularly in terms of the lifestyle and genetic markers. The ML model demonstrated predictive power, with an AUC of 81%, 88% sensitivity, 57% specificity, 78% accuracy, 80% precision, and an F1 score of 0.84. These metrics significantly outperformed traditional statistical prediction models, including the BOADICEA and BCRAT, which showed an AUC below 0.65. This study demonstrated the efficacy of an ML approach in identifying women at higher risk of breast cancer, leveraging lifestyle and genetic factors, with an improved predictive performance over traditional methods

Engineering Polymeric Nanosystems against Oral Diseases

Nanotechnology and nanoparticles (NPs) are at the forefront of modern research, particularly in the case of healthcare therapeutic applications. Polymeric NPs, specifically, hold high promise for these purposes, including towards oral diseases. Careful optimisation of the production of polymeric NPs, however, is required to generate a product which can be easily translated from a laboratory environment to the actual clinical usage. Indeed, considerations such as biocompatibility, biodistribution, and biodegradability are paramount. Moreover, a pre-clinical assessment in adequate in vitro, ex vivo or in vivo model is also required. Last but not least, considerations for the scale-up are also important, together with an appropriate clinical testing pathway. This review aims to eviscerate the above topics, sourcing at examples from the recent literature to put in context the current most burdening oral diseases and the most promising polymeric NPs which would be suitable against them