Loading of anticancer drug anastrozole using Fe3O4@SiO2

Anastrozole is a prescription drug that is used to treat hormone-dependent breast cancer, mostly in women who have gone through menopause. Once a day, it is taken by mouth. Anastrozole stops the activity of an enzyme called aromatase, which changes androgens into oestrogens. But taking the drug often comes with side effects that depend on how much you take, such as tiredness, diarrhea, hot flashes, nausea, headaches, muscle and joint pain, and so on. Anastrozole has also been linked to other side effects and more bone loss. To overcome the side effects of anastrozole and for their efficient delivery anastrozole must be loaded on the surfaces which is biocompatible and stable towards human body. So, the co-precipitation method was used to make iron oxide nanoparticles, which were then covered with silica using the Stober method. The made Fe3O4@SiO2 nanocomposite was taken out as a black powder and studied using FTIR, EDX, and SEM. The SEM picture showed that the Fe3O4 and Fe3O4@SiO2 nanoparticles size ranges were between 30 and 45 nm and 55 to 70 nm respectively. We also looked at how contact time, pH, and the amount of nanocomposite affected the loading of the drug. The best adsorption (85.6%) happened when the reaction lasted 12 h, the pH was 4, and the adsorbent dose was 10 mg

Elucidation of Seismic Soil Liquefaction Significant Factors

The paper develops a framework to analyze the interactions among seismic soil liquefaction significant factors using the interpretive structural model (ISM) approach based on cone penetration test. To identify the contextual relationships among the significant factors, systematic literature review approach was used bearing in mind the selection principle. Since multiple factors influence seismic soil liquefaction, determining all factors in soil liquefaction would be extremely difficult, as even a few seismic soil liquefaction factors are not easy to deal with. This study highlighted two main characteristics of seismic soil liquefaction factors. First, the seismic soil liquefaction factors–peak ground acceleration F2 (amax), equivalent clean sand penetration resistance F5 (qc1Ncs), and thickness of soil layer F11 (Ts) influenced soil liquefaction directly and were located at level 2 (top level) in the ISM model, meaning they require additional seismic soil liquefaction factors except thickness of soil layer F11 (Ts) to collaboratively impact on soil liquefaction potential. The multilevel hierarchy reveals that depth of soil deposit F10 (Ds) is formed the base of ISM hierarchy. Secondly, Matrice d’impacts croisés multiplication appliqués à un classement (MICMAC) analysis has been employed for evaluating these identified factors in accordance with driving power and dependence power. Factors with a higher driving power should be given special consideration. Autonomous soil liquefaction factors have no reliance on other soil liquefaction factors and interfere less. In order to identify the significant factors that affect seismic soil liquefaction susceptibility, the model built in this study clearly illustrates the complex relationships between factors and demonstrates the direct and indirect relationships

Parametric analysis of wax printing technique for fabricating microfluidic paper-based analytic devices (µPAD) for milk adulteration analysis

Accurate prediction of hydrophobic–hydrophilic channel barriers is essential in the fabrication of paper-based microfluidic devices. This research presents a detailed parametric analysis of wax printing technique for fabricating µPADs. Utilizing commonly used Grade 1 filter paper, experimental results show that the wax spreading in the paper porous structure depends on the initially deposited wax line thickness, a threshold melting temperature and melting time. Initial width of the printed line has a linear relationship with the final width of the barrier; however, a less pronounced effect of temperature was observed. Based on the spreading behavior of the molten wax at different parameters, a generalized regression model has been developed and validated experimentally. The developed model accurately predicts wax spreading in Whatman filter paper: a non-uniform distribution of pores and fibers. Finally, tests were carried out for calorimetric detection of commonly used adulterants present in milk samples

Seed priming with sorghum extracts and benzyl aminopurine improves the tolerance against salt stress in wheat (Triticum aestivum L.)

Salt stress impedes the productivity of wheat (L.) in many parts of the world. This study evaluated the potential role of benzyl aminopurine (BAP) and sorghum water extract (SWE) in improving the wheat performance under saline conditions. Seeds were primed with BAP (5\ua0mg\ua0L), SWE (5% v/v), BAP\ua0+\ua0SWE, and distilled water (hydropriming). Soil filled pots maintained at the soil salinity levels of 4 and 10\ua0dS\ua0mwere used for the sowing of primed and non-primed seeds. Salt stress suppressed the wheat growth; seed priming treatments significantly improved the wheat growth under optimal and suboptimal conditions. Total phenolics, total soluble sugars and proteins, α-amylase activity, chlorophyll contents, and tissue potassium ion (K) contents were increased by seed priming under salt stress; while, tissue sodium ion (Na) contents were decreased. Seed priming with SWE\ua0+\ua0BAP was the most effective in this regard. Under salt stress, the tissue Nacontents were reduced by 5.78, 28.3, 32.2, 36.7% by hydropriming, seed priming with SWE, seed priming with BAP, and seed priming with SWE\ua0+\ua0BAP, respectively over the non-primed control. Effectiveness of seed priming techniques followed the order SWE\ua0+\ua0BAP\ua0>\ua0BAP\ua0>\ua0SWE\ua0>\ua0Hydropriming. In conclusion, seed priming with SWE\ua0+\ua0BAP may be opted to improve the tolerance against salt stress in wheat

Brait-Fahn-Schwartz Disease: A Unique Co-Occurrence of Parkinson’s Disease and Amyotrophic Lateral Sclerosis

The Parkinson’s disease-amyotrophic lateral sclerosis (ALS) complex typically manifests as levodopa-responsive parkinsonism, followed by ALS. It is extremely rare for Parkinson’s disease and ALS to coexist without other neurological disorders. Named after the scientists who first described this overlap of two neurodegenerative conditions, it is referred to as Brait-Fahn-Schwartz disease. Given its variable presentation, increasing rarity, and lack of any diagnostic test, it poses a diagnostic challenge for physicians. We present a case of a 55-year-old Pakistani male experiencing progressive quadriparesis with spastic lower limbs and flaccid upper limbs, in addition to the cardinal features of idiopathic Parkinson’s disease. Since there is currently no cure available for either Parkinson’s disease or ALS, all available treatment focuses on improving quality of life, which we achieved in our patient. This case is unique in being the first incidence of Parkinson’s disease-ALS complex in a novel geographic region such as Pakistan, where genetic testing and cost constraints limit the diagnosis of rare disorders. The coexistence of extrapyramidal symptoms and pyramidal symptoms is uncommon. In such situations, physicians may overlook one group of symptoms, potentially leading to a misdiagnosis. This case highlights the value of a thorough physical examination and electrodiagnostic studies and suggests the association between Parkinson’s disease and ALS. This case demonstrates the significance of understanding when Parkinson’s disease symptoms start to appear in patients with ALS and the need to start dopaminergic therapy in those who had Parkinson’s disease features before ALS to alleviate the suffering of an individual and enhance quality of life

Impact of invasive plant species on the livelihoods of farming households: evidence from Parthenium hysterophorus invasion in rural Punjab, Pakistan

Invasive plant species often have negative impacts on agriculture and society in addition to their detrimental effects on biodiversity and environment. It is important to assess such impacts to devise effective management plans. A field survey study was carried out to assess the socio-economic effects of a highly invasive plant species, parthenium weed (Parthenium hysterophorus L.) across the three different cropping regions in Punjab province of Pakistan. The farming communities of different cropping regions reported significant effects of parthenium weed on their crop and livestock production, health and social well-being. The mixed cropping region was heavily infested and most affected region, whereas the cotton–wheat region was least affected. Farmers were well-aware of parthenium weed presence, its biology, habitat, and mode of dispersal across the landscape. All the major crops cultivated were infested by varying degrees of weed densities with potato, sugarcane and maize being the most infested crops. Farmers were generally good at managing the weed in crops which cost them significant amounts of money (ca. $205 per household). Parthenium weed also infested the fodder collection and grazing sites affecting the livestock production negatively. Each farmer lost an additional ca.$935 annually due to the weed infestations on fodder collection sites. A significant proportion of farmers also reported negative effects of the weed on animal health (22–36%) and human health (14–24%). The average annual costs associated with animal health and human health were ca. $2031 and$73 per household, respectively. Despite acknowledging the value of weed management in non-cropped areas, fewer farmers managed it practically in such areas. Most farmers reported parthenium weed as a very difficult-to-manage weed. About 37% of farmers were willing while 60% were likely to participate in a potential management program in future. A comprehensive management strategy is urgently needed to address the looming crisis of parthenium weed invasion across the province and similar approach must be implemented at the national and international level

Comparison of landslide susceptibility models and their robustness analysis: a case study from the NW Himalayas, Pakistan

Machine learning methods are considered as most effective approaches to accomplish landslide susceptibility analysis around the globe. Landslide susceptibility maps (LSMs) have been frequently executed by statistical models in NW Himalaya. However, the comparison and applications of the statistical models with modern machine learning techniques has not been fully explored in this region. Hence, this study aims to compare the predicted performance of statistical and popular machine learning models to explore robust landslide prediction model in the landslide-prone area of NW Himalaya and investigate the compensations and limitations of these models to grasp a more precise and consistent result. This study presented machine learning approaches based on the artificial neural network (ANN), support vector machine (SVM) and logistic regression (LR) and the statistical methods based on the frequency ratio (FR), information value (InfoV) and weight of evidence (WoE). For this purpose, first an inventory map of 1507 landslides was prepared and randomly divided into training (70%) and testing (30%) dataset. Furthermore, 12 landslide conditioning factors (LCFs) were extracted from geospatial dataset to prepare thematic layers in ArcGIS. Thereafter, factor analysis was performed to eliminate colinear and least important variables which can mislead the results. The results showed that all selected LCFs are noncolinear and have significant contribution on landslides initiation, however, lithology, slope angle, annual rainfall and landuse were most influential factors. For modeling purpose, landslide inventory was correlated against all LCFs and trained into six models to produce respective LSMs. Finally, the performance of produced LSM models was validated and compared through area under receiver operating characteristic curve (AUROC), Accuracy, Recall, F1-score and Cohen’s Kappa coefficients to assess the robustness of employed models. The results exhibit that the performance scores of machine learning models were considerably superior than statistical models. While, the AUROC values based on validation dataset indicate that LR (0.89) has better prediction ability followed by SVM (0.86), ANN (0.84), FR (0.83), InfoV (0.82) and WoE (0.81) in this study. Therefore, it is reasoned out that the machine learning methods are more reliable in generating adequate LSMs. However, the LR is recommended as most efficient model for predicting landslide susceptible zones in study region and thus can be considered as robust model for landslide susceptibility assessment in similar geo-environmental regimes

Label‑free imaging flow cytometry for analysis and sorting of enzymatically dissociated tissues

Biomedical research relies on identification and isolation of specific cell types using molecular biomarkers and sorting methods such as fluorescence or magnetic activated cell sorting. Labelling processes potentially alter the cells’ properties and should be avoided, especially when purifying cells for clinical applications. A promising alternative is the label-free identification of cells based on physical properties. Sorting real-time deformability cytometry (soRT-DC) is a microfluidic technique for label-free analysis and sorting of single cells. In soRT-FDC, bright-field images of cells are analyzed by a deep neural net (DNN) to obtain a sorting decision, but sorting was so far only demonstrated for blood cells which show clear morphological differences and are naturally in suspension. Most cells, however, grow in tissues, requiring dissociation before cell sorting which is associated with challenges including changes in morphology, or presence of aggregates. Here, we introduce methods to improve robustness of analysis and sorting of single cells from nervous tissue and provide DNNs which can distinguish visually similar cells. We employ the DNN for image-based sorting to enrich photoreceptor cells from dissociated retina for transplantation into the mouse eye