Automated skin lesion segmentation using multi-scale feature extraction scheme and dual-attention mechanism

Segmenting skin lesions from dermoscopic images is essential for diagnosing skin cancer. But the automatic segmentation of these lesions is complicated due to the poor contrast between the background and the lesion, image artifacts, and unclear lesion boundaries. In this work, we present a deep learning model for the segmentation of skin lesions from dermoscopic images. To deal with the challenges of skin lesion characteristics, we designed a multi-scale feature extraction module for extracting the discriminative features. Further in this work, two attention mechanisms are developed to refine the post-upsampled features and the features extracted by the encoder. This model is evaluated using the ISIC2018 and ISBI2017 datasets. The proposed model outperformed all the existing works and the top-ranked models in two competitions

A Few-Shot Approach to Dysarthric Speech Intelligibility Level Classification Using Transformers

Dysarthria is a speech disorder that hinders communication due to difficulties in articulating words. Detection of dysarthria is important for several reasons as it can be used to develop a treatment plan and help improve a person's quality of life and ability to communicate effectively. Much of the literature focused on improving ASR systems for dysarthric speech. The objective of the current work is to develop models that can accurately classify the presence of dysarthria and also give information about the intelligibility level using limited data by employing a few-shot approach using a transformer model. This work also aims to tackle the data leakage that is present in previous studies. Our whisper-large-v2 transformer model trained on a subset of the UASpeech dataset containing medium intelligibility level patients achieved an accuracy of 85%, precision of 0.92, recall of 0.8 F1-score of 0.85, and specificity of 0.91. Experimental results also demonstrate that the model trained using the 'words' dataset performed better compared to the model trained on the 'letters' and 'digits' dataset. Moreover, the multiclass model achieved an accuracy of 67%.Comment: Paper has been presented at ICCCNT 2023 and the final version will be published in IEEE Digital Library Xplor

Advanced Technologies for Oral Controlled Release: Cyclodextrins for oral controlled release

Cyclodextrins (CDs) are used in oral pharmaceutical formulations, by means of inclusion complexes formation, with the following advantages for the drugs: (1) solubility, dissolution rate, stability and bioavailability enhancement; (2) to modify the drug release site and/or time profile; and (3) to reduce or prevent gastrointestinal side effects and unpleasant smell or taste, to prevent drug-drug or drug-additive interactions, or even to convert oil and liquid drugs into microcrystalline or amorphous powders. A more recent trend focuses on the use of CDs as nanocarriers, a strategy that aims to design versatile delivery systems that can encapsulate drugs with better physicochemical properties for oral delivery. Thus, the aim of this work was to review the applications of the CDs and their hydrophilic derivatives on the solubility enhancement of poorly water soluble drugs in order to increase their dissolution rate and get immediate release, as well as their ability to control (to prolong or to delay) the release of drugs from solid dosage forms, either as complexes with the hydrophilic (e.g. as osmotic pumps) and/ or hydrophobic CDs. New controlled delivery systems based on nanotechonology carriers (nanoparticles and conjugates) have also been reviewed

Co-evolution, opportunity seeking and institutional change: Entrepreneurship and the Indian telecommunications industry 1923-2009

"This is an Author's Original Manuscript of an article submitted for consideration in Business History [copyright Taylor & Francis]; Business History is available online at http://www.tandfonline.com/." 10.1080/00076791.2012.687538In this paper, we demonstrate the importance for entrepreneurship of historical contexts and processes, and the co-evolution of institutions, practices, discourses and cultural norms. Drawing on discourse and institutional theories, we develop a model of the entrepreneurial field, and apply this in analysing the rise to global prominence of the Indian telecommunications industry. We draw on entrepreneurial life histories to show how various discourses and discursive processes ultimately worked to generate change and the creation of new business opportunities. We propose that entrepreneurship involves more than individual acts of business creation, but also implies collective endeavours to shape the future direction of the entrepreneurial field

Dizaj i statistička optimizacija liposfera s glipizidom pomoću metodologije odgovora površine

A 32 factorial design was employed to produce glipizide lipospheres by the emulsification phase separation technique using paraffin wax and stearic acid as retardants. The effect of critical formulation variables, namely levels of paraffin wax (X1) and proportion of stearic acid in the wax (X2) on geometric mean diameter (dg), percent encapsulation efficiency (% EE), release at the end of 12 h (rel12) and time taken for 50% of drug release (t50), were evaluated using the F-test. Mathematical models containing only the significant terms were generated for each response parameter using the multiple linear regression analysis (MLRA) and analysis of variance (ANOVA). Both formulation variables studied exerted a significant influence (p < 0.05) on the response parameters. Numerical optimization using the desirability approach was employed to develop an optimized formulation by setting constraints on the dependent and independent variables. The experimental values of dg, % EE, rel12 and t50 values for the optimized formulation were found to be 57.54 ± 1.38 µm, 86.28 ± 1.32 %, 77.23 ± 2.78 % and 5.60 ± 0.32 h, respectively, which were in close agreement with those predicted by the mathematical models. The drug release from lipospheres followed first-order kinetics and was characterized by the Higuchi diffusion model. The optimized liposphere formulation developed was found to produce sustained anti-diabetic activity following oral administration in rats.32 faktorijalni dizajn primijenjen je za pripravu liposfera s glipizidom metodom separacije pomoću emulzija koristeći parafinski vosak i starinsku kiselinu kao tvari za usporavanje. Pomoću F-testa praćen je učinak kritičnih varijabli tijekom formuliranja, tj. količina parafinskog voska (X1) i udio stearinske kiseline (X2) na srednji promjer liposfera (dg), postotak inkapsulirane ljekovite tvari (% EE), oslobađanje ljekovite tvari nakon 12 h (rel12) te vrijeme potrebno za oslobađanje 50% ljekovite tvari (t50). Pomoću multiple linearne regresijske analize (MLRA) i analize varijabli (ANOVA) za svaki su parametar načinjeni matematički modeli koji sadrže samo značajne varijable. Proučavanje varijabli na oba načina ukazalo je na njihov značajan utjecaj (p < 0,05) na parametre liposfera. Postavljanjem ograničenja na zavisne i nezavisne varijable provedena je numerička optimizacija na principu poželjnosti. Eksperimentalne vrijednosti dg, % EE, rel12 i t50 optimiziranih formulacija bile su 57,54 ± 1,38 µm, 86,28 ± 1,32%, 77,23 ± 2,78% i 5,60 ± 0,32 h. Dobivene eksperimentalne vrijednosti iznosile su vrlo slične vrijednostima predviđenim matematičkim modelima. Oslobađanje glipizida iz liposfera slijedio je kinetiku prvog reda i okarakterizirano je Higuchijevim difuzijskim modelom. Optimizirane liposfere su nakon peroralne primjene na štakorima pokazale produljeni antidijabetički učinak

Viral Mediated Redirection of NEMO/IKKγ to Autophagosomes Curtails the Inflammatory Cascade

The early host response to viral infections involves transient activation of pattern recognition receptors leading to an induction of inflammatory cytokines such as interleukin-1β (IL-1β) and tumor necrosis factor α (TNFα). Subsequent activation of cytokine receptors in an autocrine and paracrine manner results in an inflammatory cascade. The precise mechanisms by which viruses avert an inflammatory cascade are incompletely understood. Nuclear factor (NF)-κB is a central regulator of the inflammatory signaling cascade that is controlled by inhibitor of NF-κB (IκB) proteins and the IκB kinase (IKK) complex. In this study we show that murine cytomegalovirus inhibits the inflammatory cascade by blocking Toll-like receptor (TLR) and IL-1 receptor-dependent NF-κB activation. Inhibition occurs through an interaction of the viral M45 protein with the NF-κB essential modulator (NEMO), the regulatory subunit of the IKK complex. M45 induces proteasome-independent degradation of NEMO by targeting NEMO to autophagosomes for subsequent degradation in lysosomes. We propose that the selective and irreversible degradation of a central regulatory protein by autophagy represents a new viral strategy to dampen the inflammatory response

SlimPLS: A Method for Feature Selection in Gene Expression-Based Disease Classification

A major challenge in biomedical studies in recent years has been the classification of gene expression profiles into categories, such as cases and controls. This is done by first training a classifier by using a labeled training set containing labeled samples from the two populations, and then using that classifier to predict the labels of new samples. Such predictions have recently been shown to improve the diagnosis and treatment selection practices for several diseases. This procedure is complicated, however, by the high dimensionality if the data. While microarrays can measure the levels of thousands of genes per sample, case-control microarray studies usually involve no more than several dozen samples. Standard classifiers do not work well in these situations where the number of features (gene expression levels measured in these microarrays) far exceeds the number of samples. Selecting only the features that are most relevant for discriminating between the two categories can help construct better classifiers, in terms of both accuracy and efficiency. In this work we developed a novel method for multivariate feature selection based on the Partial Least Squares algorithm. We compared the method's variants with common feature selection techniques across a large number of real case-control datasets, using several classifiers. We demonstrate the advantages of the method and the preferable combinations of classifier and feature selection technique