Domain and Patient Adversarial Multi-Task Learning for Arrhythmia Classification.

Manual screening of electrocardiograms (ECGs) for heart arrhythmias by clinicians is time-consuming and labor-intensive. A machine learning model for the automated diagnosis of heart arrhythmia from ECG signals can facilitate improved diagnosis, greater accessibility and earlier intervention for patients. The potential of such models is limited however by the small size of clinical datasets available for training.

Predicting Memory Score Using Paralinguistic Features.

An automated method of assessing short term memory can act as a dementia risk predictor, as poor short-term memory is strongly linked to early signs of dementia. While previous works show the feasibility of using speech to predict healthy and diagnosed dementia participants, there are still gaps in predicting 'dementia risk' and clear difficulties distinguishing early dementia with regular ageing. We extracted paralinguistic features from audio of individuals completing an over the phone episodic memory test, LOGOS.

Response of Diverse Peanut Cultivars to Nano and Conventional Calcium Forms under Alkaline Sandy Soil.

Calcium is one of the most limiting factors for the growth and reproduction of peanut, which ultimately affects pod and seed yields. A two-year field experiment was carried out to assess the impact of five calcium applications, including nano-calcium and conventional forms, on growth, leaf nutrient content, yield traits, and quality parameters of three diverse peanut cultivars (Ismailia-1, Giza-5, and Giza-6). The applied calcium applications were calcium sulfate, which is recommended for commercial peanut cultivation and commonly referred to as gypsum (coded as Ca-1), calcium nitrate (Ca-2), nano-calcium nitrate (Ca-3), 50% calcium nitrate + 50% nano-calcium (Ca-4), and 50% calcium sulfate + 50% nano-calcium (Ca-5).

Influence of Channel Selection and Subject's Age on the Performance of the Single Channel EEG-Based Automatic Sleep Staging Algorithms.

The electroencephalogram (EEG) signal is a key parameter used to identify the different sleep stages present in an overnight sleep recording. Sleep staging is crucial in the diagnosis of several sleep disorders; however, the manual annotation of the EEG signal is a costly and time-consuming process. Automatic sleep staging algorithms offer a practical and cost-effective alternative to manual sleep staging.

Roles of Exogenous α-Lipoic Acid and Cysteine in Mitigation of Drought Stress and Restoration of Grain Quality in Wheat.

Cysteine (Cys) and α-lipoic acid (ALA) are naturally occurring antioxidants (sulfur-containing compounds) that can protect plants against a wide spectrum of environmental stresses. However, up to now, there are no conclusive data on their integrative roles in mitigation of drought stress in wheat plants. Here, we studied the influence of ALA at 0.

An Automated Lexical Stress Classification Tool for Assessing Dysprosody in Childhood Apraxia of Speech.

Childhood apraxia of speech (CAS) commonly affects the production of lexical stress contrast in polysyllabic words. Automated classification tools have the potential to increase reliability and efficiency in measuring lexical stress. Here, factors affecting the accuracy of a custom-built deep neural network (DNN)-based classification tool are evaluated.

Sesame Oil-Based Nanostructured Lipid Carriers of Nicergoline, Intranasal Delivery System for Brain Targeting of Synergistic Cerebrovascular Protection.

Nicergoline (NIC) is a semisynthetic ergot alkaloid derivative applied for treatment of dementia and other cerebrovascular disorders. The efficacy of sesame oil to slow and reverse the symptoms of neurodegenerative cognitive disorders has been proven. This work aimed to formulate and optimize sesame oil-based NIC-nanostructured lipid carriers (NIC-NLCs) for intranasal (IN) delivery with expected synergistic and augmented neuroprotective properties.

Adversarial Multi-Task Learning for Robust End-to-End ECG-based Heartbeat Classification.

In clinical practice, heart arrhythmias are manually diagnosed by a doctor, which is a time-consuming process. Furthermore, this process is error-prone due to noise from the recording equipment and biological non-idealities of patients. Thus, an automated arrhythmia classifier would be time and cost-effective as well as offer better generalization across patients.

A Two Stage Approach for the Automatic Detection of Insomnia.

Chronic insomnia can significantly impair an individual's quality of life leading to a high societal cost. Unfortunately, limited automated tools exist that can assist clinicians in the timely detection of insomnia. In this paper, we propose a two stage approach to automatically detect insomnia from an overnight EEG recording.

Comparing two insomnia detection models of clinical diagnosis techniques.

Sleep disorders are becoming increasingly prevalent in society. However most of the burgeoning research on automated sleep analysis has been in the realm of sleep stage classification with limited focus on accurately diagnosing these disorders. In this paper, we explore two different models to discriminate between control and insomnia patients using support vector machine (SVM) classifiers.

Deep Learning and Insomnia: Assisting Clinicians With Their Diagnosis.

Effective sleep analysis is hampered by the lack of automated tools catering to disordered sleep patterns and cumbersome monitoring hardware. In this paper, we apply deep learning on a set of 57 EEG features extracted from a maximum of two EEG channels to accurately differentiate between patients with insomnia or controls with no sleep complaints. We investigated two different approaches to achieve this.

Cleft palate reconstruction using collagen and nanofiber scaffold incorporating bone morphogenetic protein in rats.

Background: Absorbable collagen sponge (ACS) loaded with bone morphogenetic protein-2 (BMP-2) is approved for selected clinical applications; however, burst release limits its widespread use. Therefore, nanofiber (NF)-based scaffold with ACS backbone was developed to sustain release of loaded BMP-2 to improve the outcomes of bone grafting in a rodent model of cleft palate.

Methods: BMP-2 was loaded on ACS scaffold and then NF hydrogel with different densities (1-2%) was added to sustain the BMP-2 release.

Polymeric micelles for pH-responsive delivery of cisplatin.

Methoxy poly(ethylene oxide)-block-poly-(α-carboxylate-ε-caprolactone) (PEO-b-PCCL) was used to develop pH-responsive polymeric micelles for the delivery of cisplatin (CDDP). Micelles were prepared through complexation of CDDP with the pendant carboxyl groups on the poly(ε-caprolactone) core, perhaps through coordinate bonding. The obtained micelles were characterized using dynamic light scattering (DLS) measurement for size and stability.

Peptide modified polymeric micelles specific for breast cancer cells.

The specific targeting ability of novel breast cancer targeting peptides as ligands coupled to polymeric micelles was evaluated in the present study. In this context, engineered breast cancer cell targeting peptides, denoted as peptide 11 (RGDPAYQGRFL) and peptide 18 (WXEAAYQRFL), were compared with the lead 12-mer p160 peptide and cyclic RGDfK peptide. All four peptides were conjugated individually to poly(ethylene oxide)-b-poly(caprolactone) (PEO-b-PCL) diblock polymeric micelles to obtain targeted carrier systems PM11, PM18, PM 160, and PM c-RGD.

Engineered breast tumor targeting peptide ligand modified liposomal doxorubicin and the effect of peptide density on anticancer activity.

This study aimed to develop actively targeted liposomal formulations of doxorubicin (DOX) using an engineered breast tumor targeting peptide ligand, p18-4 (WxEAAYQrFL). Towards this goal, stealth liposomes bearing different molar ratios of p18-4 peptide (1.5 and 0.

Engineered peptides for the development of actively tumor targeted liposomal carriers of doxorubicin.

Chemotherapy is still the treatment of choice for many types of cancer; but its effectiveness is hampered by dose limiting toxicity. Properly designed delivery systems can overcome this shortcoming by reducing the non-specific distribution and toxicity of chemotherapeutics in healthy organs and at the same time increasing drug concentrations at tumor tissue. In this study, we developed stealth liposomal formulations of doxorubicin (DOX) having a novel stable engineered peptide ligand, namely p18-4, that binds specifically to breast cancer cell line MDA-MB-435 on its surface.

Decoration of polymeric micelles with cancer-specific peptide ligands for active targeting of paclitaxel.

Polymeric micelles based on poly(ethylene oxide)-b-poly(ε-caprolactone) PEO-b-PCL or poly(ethylene oxide)-b-poly(α-benzyl carboxylate-ε-caprolactone) PEO-b-PBCL block copolymers were prepared and decorated with either c(RGDfK) or p160, a cancer cell-specific peptide ligand, on their surface. The cellular uptake of p160-decorated PEO-b-PBCL micelles containing DiI fluorescent label by MDA-MB-435 cancer cells was assessed and compared to that for c(RGDfK)-decorated micelles. The hydrophobic anticancer drug paclitaxel (PTX) was physically encapsulated into PEO-b-PCL or PEO-b-PBCL micelles (with and without peptide ligands) using a dialysis technique.

Novel jojoba oil-based emulsion gel formulations for clotrimazole delivery.

Jojoba oil-based emulgel formulations were prepared using different concentrations of various gelling agents, such as hydroxypropyl methylcellulose (HPMC) and Carbopol 934 P and combination of both. The prepared emulgels were physically evaluated for their stability after temperature cycle test, centrifugation and long-term shelf storage for 1 year at room temperature. The in vitro release at 37 °C was studied to define the effect of the concentration and type of the gelling agent.

Optimized formulation for topical administration of clotrimazole using Pemulen polymeric emulsifier.

Background: Emulgel topical formulation is a vehicle of potential for topical delivery of antifungal drugs.

Methods: The imidazole derivative antifungal drug, clotrimazole (CZ), was formulated into emulgels using two grades of hydrophobically modified co-polymers of acrylic acid, namely Pemulen TR1 and TR2. The prepared emulgels were evaluated for their rheological properties, short- and long-term stability, in vitro release at 37°C.

Novel self-associating poly(ethylene oxide)-b-poly(epsilon-caprolactone) based drug conjugates and nano-containers for paclitaxel delivery.

Poly(ethylene oxide)-block-poly(epsilon-caprolactone) (PEO-b-PCL) copolymers bearing paclitaxel (PTX) side groups on PCL (PEO-b-P(CL-PTX) were synthesized and assembled to particles of 123 nm average diameter. At 20% (w/w) PTX to polymer conjugation, PEO-b-P(CL-PTX) demonstrated only 5.0 and 6.

Disposition of drugs in block copolymer micelle delivery systems: from discovery to recovery.

Since their discovery in the early 1980s, polymeric micelles have been the subject of several studies as delivery systems that can potentially improve the therapeutic performance and modify the toxicity profile of encapsulated drugs by changing their pharmacokinetic characteristics. The efforts in this area have led in recent years to the advancement of several polymeric micellar formulations to clinical trials, some of which have shown promise in changing the biodistribution of the incorporated drug after intravenous administration as a means of tumour-targeted drug delivery. Recently, the possible benefit of polymeric micellar delivery in enhancing the absorption and bioavailability of incorporated drugs from alternative routes of drug administration has attracted interest.