Smoking behaviour in post-acute myocardial infarction patients: cross-sectional study.

Introduction: Heart disease remains the leading cause of death in developed countries, and cigarette smoking contributes to a significant proportion of cardiovascular-related deaths. Abstaining from tobacco use is associated with a significant reduction in the risk of recurrent myocardial infarctions. Methodology: In this cross-sectional study, 384 participants post-acute myocardial infarction (MI) were recruited through random sampling to explore the associations between smoking status and intention to quit smoking. Data collection took place over a 6-month period at a tertiary care hospital, Islamabad, Pakistan. Results: The majority of participants were male (59.9%) and fell into the age category of 46-50 years (37.5%). Heavy daily smokers comprised the largest smoking group (41.6%), and non-ST-elevated MI was the most common subtype (40.1%). Intention to quit smoking varied among participants, with the pre-contemplation stage having the highest representation (19.3%), followed by contemplation (25.8%). Notably, a significant proportion of participants expressed no intention to quit smoking (35.4%). Conclusion: Multinomial logistic regression analysis identified current smoking as a significant predictor of intention to quit in the preparation and contemplation stages. Overall, this study underscores the importance of considering smoking behaviour when evaluating the intention to quit smoking post-MI and highlights the need for tailored interventions and support strategies to address smoking cessation in this population. These findings offer valuable insights for the development of effective strategies aimed at reducing persistent smoking following MI and improving patient outcomes.

Heavy metal toxicity arising from the industrial effluents repercussions on oxidative stress, liver enzymes and antioxidant activity in brain homogenates of Oreochromis niloticus.

Industrial effluents reaching to the aquatic ecosystem is one of the major causes of environmental pollution and exposure to industrial effluents containing harmful substances may be a serious threat to human health. Therefore, the present study aimed to determine the sub-lethal (1/5th of predetermined LC50) impact of industrial effluents from Sundar Industrial Estate on Oreochromis niloticus with proper negative control. The physicochemical analysis of industrial effluents showed enormous loads of inorganic pollutants and exhibited high mean levels of heavy metals, Mn, Fe, Pb, Ni, Cr, Hg, As, Zn and Fe with statistically significant differences at p < 0.05. Highest level of Mn and Fe was detected in effluent's samples as 147.36 ± 80.91 mg/L and 90.52 ± 32.08 mg/L, respectively. Exposure led to increase in serum biochemical parameters alanine aminotransferase + 25%, aspartate aminotransferase + 20% and alkaline phosphatase + 7% over control although superoxide dismutase, catalase, glutathione peroxidase and reduced glutathione significantly increased as 3.42, 2.44, 4.8 and 8 folds, respectively in metabolically active tissue brain which indicated stress caused by industrial effluents. The results concluded that industrial effluent has potent oxidative stress inducers on one hand whereas histoarchitectural and physiological toxicity causing contaminants on the other. This condition may adversely affect the health of aquatic organisms, the fish and ultimately the human beings.

Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.

The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.

A cell-based high-throughput screen identifies inhibitors that overcome P-glycoprotein (Pgp)-mediated multidrug resistance.

Multidrug resistance (MDR) to chemotherapeutic drugs remains one of the major impediments to the treatment of cancer. Discovery and development of drugs that can prevent and reverse the acquisition of multidrug resistance constitute a foremost challenge in cancer therapeutics. In this work, we screened a library of 1,127 compounds with known targets for their ability to overcome Pgp-mediated multidrug resistance in cancer cell lines. We identified four compounds (CHIR-124, Elesclomol, Tyrphostin-9 and Brefeldin A) that inhibited the growth of two pairs of parental and Pgp-overexpressing multidrug-resistant cell lines with similar potency irrespective of their Pgp status. Mechanistically, CHIR-124 (a potent inhibitor of Chk1 kinase) inhibited Pgp activity in both multidrug-resistant cell lines (KB-V1 and A2780-Pac-Res) as determined through cell-based Pgp-efflux assays. Other three inhibitors on the contrary, were effective in Pgp-overexpressing resistant cells without increasing the cellular accumulation of a Pgp substrate, indicating that they overcome resistance by avoiding efflux through Pgp. None of these compounds modulated the expression of Pgp in resistant cell lines. PIK-75, a PI3 Kinase inhibitor, was also determined to inhibit Pgp activity, despite being equally potent in only one of the two pairs of resistant and parental cell lines. Strong binding of both CHIR-124 and PIK-75 to Pgp was predicted through docking studies and both compounds inhibited Pgp in a biochemical assay. The inhibition of Pgp causes accumulation of these compounds in the cells where they can modulate the function of their target proteins and thereby inhibit cell proliferation. In conclusion, we have identified compounds with various cellular targets that overcome multidrug resistance in Pgp-overexpressing cell lines through mechanisms that include Pgp inhibition and efflux evasion. These compounds, therefore, can avoid challenges associated with the co-administration of Pgp inhibitors with chemotherapeutic or targeted drugs such as additive toxicities and differing pharmacokinetic properties.

Synthesis and evaluation of novel α-substituted chalcones with potent anti-cancer activities and ability to overcome multidrug resistance.

A series of forty α-substituted chalcones were synthesized and screened for their antiproliferative activities against HCT116 (colorectal) and HCC1954 (breast) cancer cell lines. Compounds 5a and 5e were found to be the most potent compounds with GI50 values of 0.63 µM and 0.725 µM in HCC1954 cell line and 0.69 µM and 1.59 µM in HCT116 cell line, respectively. Both compounds induced a G2/M cell cycle arrest and caused apoptotic cell death in HCT116 cells as shown by the induction of PARP cleavage. The compounds also stabilized p53 in a dose-dependent manner in HCT116 cells following 24-hour treatment. Furthermore, both 5a and 5e were able to overcome multidrug resistance in two MDR-1 overexpressing multidrug resistant cell lines.