ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique.

Unlabelled: In this study, attention deficit hyperactivity disorder (ADHD), a childhood neurodevelopmental disorder, is being studied alongside its comorbidity, conduct disorder (CD), a behavioral disorder. Because ADHD and CD share commonalities, distinguishing them is difficult, thus increasing the risk of misdiagnosis. It is crucial that these two conditions are not mistakenly identified as the same because the treatment plan varies depending on whether the patient has CD or ADHD.

Application of explainable artificial intelligence for healthcare: A systematic review of the last decade (2011-2022).

Background And Objectives: Artificial intelligence (AI) has branched out to various applications in healthcare, such as health services management, predictive medicine, clinical decision-making, and patient data and diagnostics. Although AI models have achieved human-like performance, their use is still limited because they are seen as a black box. This lack of trust remains the main reason for their low use in practice, especially in healthcare.

Effects of different delivery modes on teaching biomedical science practical skills in higher education during the 2021 pandemic measures.

The COVID-19 pandemic related measures had augmented the rise of online education. While online teaching had mitigated the negative impacts from educational institutional closures, it was unable to displace hands-on biomedical laboratory practical lessons effectively. Without practical sessions, there was concern over the imparting of laboratory skills even with video demonstrations.

Automated detection of ADHD: Current trends and future perspective.

Attention deficit hyperactivity disorder (ADHD) is a heterogenous disorder that has a detrimental impact on the neurodevelopment of the brain. ADHD patients exhibit combinations of inattention, impulsiveness, and hyperactivity. With early treatment and diagnosis, there is potential to modify neuronal connections and improve symptoms.

CGP17Pat: Automated Schizophrenia Detection Based on a Cyclic Group of Prime Order Patterns Using EEG Signals.

Background And Purpose: Machine learning models have been used to diagnose schizophrenia. The main purpose of this research is to introduce an effective schizophrenia hand-modeled classification method.

Method: A public electroencephalogram (EEG) signal data set was used in this work, and an automated schizophrenia detection model is presented using a cyclic group of prime order with a modulo 17 operator.

Application of photoplethysmography signals for healthcare systems: An in-depth review.

Background And Objectives: Photoplethysmography (PPG) is a device that measures the amount of light absorbed by the blood vessel, blood, and tissues, which can, in turn, translate into various measurements such as the variation in blood flow volume, heart rate variability, blood pressure, etc. Hence, PPG signals can produce a wide variety of biological information that can be useful for the detection and diagnosis of various health problems. In this review, we are interested in the possible health disorders that can be detected using PPG signals.

Automated classification of attention deficit hyperactivity disorder and conduct disorder using entropy features with ECG signals.

Background: The most prevalent neuropsychiatric disorder among children is attention deficit hyperactivity disorder (ADHD). ADHD presents with a high prevalence of comorbid disorders such as conduct disorder (CD). The lack of definitive confirmatory diagnostic tests for ADHD and CD make diagnosis challenging.

Role of Artificial Intelligence in COVID-19 Detection.

The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem.

Application of Deep Learning Models for Automated Identification of Parkinson's Disease: A Review (2011-2021).

Parkinson's disease (PD) is the second most common neurodegenerative disorder affecting over 6 million people globally. Although there are symptomatic treatments that can increase the survivability of the disease, there are no curative treatments. The prevalence of PD and disability-adjusted life years continue to increase steadily, leading to a growing burden on patients, their families, society and the economy.

Automated interpretation of biopsy images for the detection of celiac disease using a machine learning approach.

Background And Objectives: Celiac disease is an autoimmune disease occurring in about 1 in 100 people worldwide. Early diagnosis and efficient treatment are crucial in mitigating the complications that are associated with untreated celiac disease, such as intestinal lymphoma and malignancy, and the subsequent high morbidity. The current diagnostic methods using small intestinal biopsy histopathology, endoscopy, and video capsule endoscopy (VCE) involve manual interpretation of photomicrographs or images, which can be time-consuming and difficult, with inter-observer variability.

Automated detection of conduct disorder and attention deficit hyperactivity disorder using decomposition and nonlinear techniques with EEG signals.

Background And Objectives: Attention deficit hyperactivity disorder (ADHD) is often presented with conduct disorder (CD). There is currently no objective laboratory test or diagnostic method to discern between ADHD and CD, and diagnosis is further made difficult as ADHD is a common neuro-developmental disorder often presenting with other co-morbid difficulties; and in particular with conduct disorder which has a high degree of associated behavioural challenges. A novel automated system (AS) is proposed as a convenient supplementary tool to support clinicians in their diagnostic decisions.

A novel hybrid approach for automated detection of retinal detachment using ultrasound images.

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes.

5-Fluorouracil encapsulated HA/PLGA composite microspheres for cancer therapy.

5-Fluorouracil (5FU) was successfully entrapped within poly(lactide-co-glycolide) (PLGA) and hydroyapatite (HA) composite microspheres using the emulsification/solvent extraction technique. The effects of HA to PLGA ratio, solvent ratio as well as polymer inherent viscosity (IV) on encapsulation efficiency were investigated. The degradation and drug release rates of the microspheres were studied for 5 weeks in vitro in phosphate buffered solution of pH 7.

Fabrication of cisplatin-loaded poly(lactide-co-glycolide) composite microspheres for osteosarcoma treatment.

Purpose: To reduce the toxicity and achieve a sustainable and controllable release of cisplatin (CDDP).

Methods: CDDP was loaded onto Fe5 (Fe(3+) doped hydroxyapatite at atomic ratio of Fe(added)/Ca(added) = 5%) nanoparticles through surface adsorption. Subsequently, CDDP-loaded Fe5 nanoparticles (CDDP-Fe5) and/or CDDP were encapsulated into poly(lactide-co-glycolide) (PLGA) microspheres using oil-in-water single emulsion.

Hydrolytic degradation and drug release properties of ganciclovir-loaded biodegradable microspheres.

The in vitro hydrolytic degradation of ganciclovir (GCV)-loaded biodegradable microspheres of poly(D,L-lactide) and poly(D,L-lactide-co-glycolide) polymers were studied. Microspheres of size 120+/-40 microm were prepared using an oil-in-water emulsification/solvent evaporation technique. The effects of polymer molecular weight, lactide (LA) to glycolide (GA) ratio and GCV payload on the degradation and drug release profiles were investigated in vitro in phosphate-buffered solution (pH 7.

Fabrication and characterization of porous poly(L-lactide) scaffolds using solid-liquid phase separation.

Freeze-extraction, which involves phase separation principle, gave highly porous scaffolds without the time and energy consuming freeze-drying process. The presented method eliminates the problem of formation of surface skin observed in freeze-drying methods. The effects of different freezing temperature (-80 and -24 degrees C), medium (dry ice/ethanol bath and freezer) and polymer concentrations (1, 3, and 5 wt.

Hydrolytic degradation of electron beam irradiated high molecular weight and non-irradiated moderate molecular weight PLLA.

The purpose of this study is to examine the hydrolytic degradation of electron beam irradiated ring-opening polymerized (ROP) poly(l-lactide) (PLLA-ir) and non-irradiated melt polycondensation polymerized poly(l-lactic acid) (PLLA-pc). It was observed that irradiation increases the hydrolytic degradation rate constant for ROP PLLA. This was due to a more hydrophilic PLLA-ir, as a result of irradiation.

Influence of electron-beam radiation on the hydrolytic degradation behaviour of poly(lactide-co-glycolide) (PLGA).

The purpose of this study is to examine the effect of electron-beam (e-beam) radiation on the hydrolytic degradation of poly(lactide-co-glycolide) (PLGA) films. PLGA films were irradiated and observed to undergo radiation-induced degradation through chain scission, as observed from a drop in its average molecular weight with radiation dose. Irradiated (5, 10 and 20 Mrad) and non-irradiated (0 Mrad) samples of PLGA were subsequently hydrolytically degraded in phosphate-buffered saline solution at 37.

Effect of isothermal annealing on the hydrolytic degradation rate of poly(lactide-co-glycolide) (PLGA).

Isothermal crystallization through annealing at 115 degrees C was conducted to increase the degree of crystallinity of poly (lactide-co-glycolide) (PLGA). The maximum increase in the degree of crystallinity (approximately 21%) was achieved after 60 min of annealing. The crystal size/perfection was observed to increase with annealing time.