A new nickel-based co-crystal complex electrocatalyst amplified by NiO dope Pt nanostructure hybrid; a highly sensitive approach for determination of cysteamine in the presence of serotonin.

A highly sensitive electrocatalytic sensor was designed and fabricated by the incorporation of NiO dope Pt nanostructure hybrid (NiO-Pt-H) as conductive mediator, bis (1,10 phenanthroline) (1,10-phenanthroline-5,6-dione) nickel(II) hexafluorophosphate (B,1,10,P,1,10, PDNiPF6), and electrocatalyst into carbon paste electrode (CPE) matrix for the determination of cysteamine. The NiO-Pt-H was synthesized by one-pot synthesis strategy and characterized by XRD, elemental mapping analysis (MAP), and FESEM methods. The characterization data, which confirmed good purity and spherical shape with a diameter of ⁓ 30.64 nm for the synthesized NiO-Pt-H. NiO-Pt-H/B,1,10, P,1,10, PDNiPF6/CPE, showed an excellent catalytic activity and was used as a powerful tool for the determination of cysteamine in the presence of serotonin. The NiO-Pt-H/B,1,10, P,1,10, PDNiPF6/CPE was able to solve the overlap problem of the two drug signals and was used for the determination of cysteamine and serotonin in concentration ranges of 0.003-200 µM and 0.5-260 µM with detection limits of 0.5 nM and 0.1 µM, using square wave voltammetric method, respectively. The NiO-Pt-H/B,1,10,P,1,10,PDNiPF6/CPE showed a high-performance ability for the determination of cysteamine and serotonin in the drug and pharmaceutical serum samples with the recovery data of 98.1-103.06%

Refining malware analysis with enhanced machine learning algorithms using hyperparameter tuning

Many researchers address challenges and limitations inherent to machine learning algorithms to optimize classifier performance. Overfitting, a prevalent issue, arises when models are excessively complex and trained on noisy data, leading to suboptimal generalization to new data. Another concern is underfitting, where models are overly simplistic and fail to capture data complexity. This comprehensive investigation focuses on machine learning's application to malware classification, specifically targeting PE files. The study addresses these limitations using ensemble methods and pre-processing techniques, including feature selection and hyperparameter tuning. The primary objective is to augment classifier performance. Through a comparative study that aims to classify PE files as malicious or benign through analysis of machine learning methodologies such as random forests, decision trees, and gradient boosting, the study highlights the superiority of the random forests algorithm, achieving a remarkable 99% accuracy rate. Thoroughly assessing the strengths and limitations of each algorithm provides valuable insights into effectively handling diverse malware categories. This paper underscores the significance of ensemble methods, feature engineering, and pre-processing in enhancing classifier performance

Aspiration pneumonitis after seizure in a patient undergone cesarean section: a case report

Pulmonary aspiration of gastric contents during the peri-operative period is rare but with significant morbidity and mortality. A 21 years old pregnant woman with preeclampsia was scheduled for an emergency cesarean section under spinal anesthesia. After 18 hours of operation, epilepticus status was occurred. One day after control of seizure, aspiration pneumonitis was diagnosed and treated with mechanical ventilation and positive end-expiratory pressure (PEEP). In patients with the history of loss, consciousness, complication of aspiration, aspiration pneumonia and pneumonitis particularly should be considerated. In case of onset of pneumonitis, PEEP treated procedure with other mechanical ventilation is recommaded

Luminescent film: Biofouling investigation of tetraphenylethylene blended polyethersulfone ultrafiltration membrane.

Despite the huge contribution of membrane-based brine and wastewater purification systems in today's life, biofouling still affects sustainability of membrane engineering. Aimed at reducing membrane modules wastage, the need to study biofouling monitoring as one of contributory factors stemmed from the short time between initial attachment and irreversible biofoulant adhesion. Hence, a membrane for monitoring is introduced to determine the right cleaning time by using fluorescent sensing as a non-destructive and scalable approach. The classical solid-state emissive fluorophore, tetraphenylethylene (TPE), was introduced as a sustainable, safe and sensitive fluorescent indicator in order to show the potential of the method, and polyethersulfone (PES) and nonsolvent-induced phase separation method, the most popular material and method, are used to fabricate membrane in industry and academia. Since the employed filler has an aggregation-induced emission (AIE) characteristic, it can track the biofouling throughout the operation. The fabricated membranes have certain characterizations (i.e. morphology assessment, flux, antibiogram, flow cytometry, surface free energy, and protein adsorption) which indicate that hybrid membrane with 5 wt % of TPE has identical biofouling activity compared to neat PES membrane and its optimal luminescence properties make it an appropriate candidate for non-destructive and online biofouling monitoring