Cross-sectional assessment of perception and attitude of pharmacy students towards pharmaceutical promotion: a study from developing country, Pakistan.

Background: The pharmaceutical promotion is "all informational and persuasive activities by manufacturers and distributors, the effect of which is to induce the prescription, supply, purchase and/or use of medicinal drugs. These promotional activities affect the dispensing behavior of physicians and pharmacists and influence begins from educational institutes.

Objective: Our study's main aim was to evaluate opinion and attitude of pharmacy students towards pharmaceutical promotion.

Multimodal CNN-DDI: using multimodal CNN for drug to drug interaction associated events.

Drug-to-drug interaction (DDIs) occurs when a patient consumes multiple drugs. Therefore, it is possible that any medication can influence other drugs' effectiveness. The drug-to-drug interactions are detected based on the interactions of chemical substructures, targets, pathways, and enzymes; therefore, machine learning (ML) and deep learning (DL) techniques are used to find the associated DDI events.

Improving Monocular Facial Presentation-Attack-Detection Robustness with Synthetic Noise Augmentations.

We present a synthetic augmentation approach towards improving monocular face presentation-attack-detection (PAD) robustness to real-world noise additions. Face PAD algorithms secure authentication systems against spoofing attacks, such as pictures, videos, and 2D-inspired masks. Best-in-class PAD methods typically use 3D imagery, but these can be expensive.

Deep learning ensemble 2D CNN approach towards the detection of lung cancer.

In recent times, deep learning has emerged as a great resource to help research in medical sciences. A lot of work has been done with the help of computer science to expose and predict different diseases in human beings. This research uses the Deep Learning algorithm Convolutional Neural Network (CNN) to detect a Lung Nodule, which can be cancerous, from different CT Scan images given to the model.

Machine learning techniques for identification of carcinogenic mutations, which cause breast adenocarcinoma.

Breast adenocarcinoma is the most common of all cancers that occur in women. According to the United States of America survey, more than 282,000 breast cancer patients are registered each year; most of them are women. Detection of cancer at its early stage saves many lives.

Toward Realigning Automatic Speaker Verification in the Era of COVID-19.

The use of face masks has increased dramatically since the COVID-19 pandemic started in order to to curb the spread of the disease. Additionally, breakthrough infections caused by the Delta and Omicron variants have further increased the importance of wearing a face mask, even for vaccinated individuals. However, the use of face masks also induces attenuation in speech signals, and this change may impact speech processing technologies, e.

Comparative analysis of the serum microbiome of HIV infected individuals.

HIV infects the CD4 cells which marks the suppression of our immune system. DNA from serum of healthy, treated and untreated HIV infected individuals was extracted. The DNA was subjected to 16S metagenomic sequencing and analyzed using QIIME2 pipeline.

Radar Data Integrity Verification Using 2D QIM-Based Data Hiding.

The modern-day vehicle is evolved in a cyber-physical system with internal networks (controller area network (CAN), Ethernet, etc.) connecting hundreds of micro-controllers. From the traditional core vehicle functions, such as vehicle controls, infotainment, and power-train management, to the latest developments, such as advanced driver assistance systems (ADAS) and automated driving features, each one of them uses CAN as their communication network backbone.

Nature of Complex Network of Dengue Epidemic as a Scale-Free Network.

Objectives: Dengue epidemic is a dynamic and complex phenomenon that has gained considerable attention due to its injurious effects. The focus of this study is to statically analyze the nature of the dengue epidemic network in terms of whether it follows the features of a scale-free network or a random network.

Methods: A multifarious network of is addressed keeping the viewpoint of a complex system and modelled as a network.

Bis[bis-(2-methyl-phen-yl)phosphan-yl]methane.

In the title compound, C(29)H(30)P(2), the dihedral angles between the two substituted benzene rings to the same P atom are 88.39 (7) and 83.88 (9)°.

Bis{[μ-bis-(diphenyl-phosphan-yl)methane-1:2κP:P']nona-carbonyl-1κC,2κC,3κC-[(4-methyl-sulfanylphen-yl)diphenyl-phosphane-3κP]-triangulo-triruthen-ium(0)} dichloro-methane monosolvate.

The asymmetric unit of the title triangulo-triruthenium compound, 2[Ru(3)(C(25)H(22)P(2))(C(19)H(17)PS)(CO)(9)]·CH(2)Cl(2), contains one triangulo-triruthenium complex mol-ecule and one half-mol-ecule of the dichloro-methane solvent. The dichloro-methane solvent mol-ecule lies across a crystallographic inversion center leading to the mol-ecule being disordered over two positions of equal occupancy. The bis-(diphenyl-phosphan-yl)methane ligand bridges an Ru-Ru bond and the monodentate phosphane ligand bonds to the third Ru atom.

[μ-Bis(di-o-tolyl-phosphan-yl)methane-1:2κP:P']deca-carbonyl-1κC,2κC,3κC-triangulo-triruthenium(0)-methanol (8/1).

The asymmetric unit of the title compound, [Ru(3)(C(29)H(30)P(2))(CO)(10)].1/8CH(3)OH, contains two triangulo-triruthenium mol-ecules with similar configurations and a methanol solvent mol-ecule (fractional site occupancy for the latter = 0.25).

[μ-Bis(di-o-tolyl-phosphan-yl)methane-1:2κP:P']nona-carbonyl-1κC,2κC,3κC-[diphen-yl(phenyl-sulfanylmeth-yl)phosphane-3κP]-triangulo-triruthenium(0) dichloro-methane 0.25-solvate.

In the title compound, [Ru(3)(C(29)H(30)P(2))(C(19)H(17)PS)(CO)(9)]·0.25CH(2)Cl(2), the atoms of the dichloro-methane solvent mol-ecule have a fractional site occupancy of 0.25; the dichloro-methane mol-ecule is disordered about an inversion centre.

[μ-Bis(diphenyl-arsanyl)methane-1:2κAs:As']nona-carbonyl-1κC,2κC,3κC-[(4-methyl-sulfanylphen-yl)diphenyl-phosphane-3κP]-triangulo-triruthenium(0).

In the title triangulo-triruthenium compound, [Ru(3)(C(25)H(22)As(2))(C(19)H(17)PS)(CO)(9)], the bis-(diphenyl-arsanyl)methane ligand bridges an Ru-Ru bond and the monodentate phosphane ligand bonds to the third Ru atom. Both arsine and phosphane ligands are equatorial with respect to the Ru(3) triangle. In addition, each Ru atom carries one equatorial and two axial terminal carbonyl ligands.