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Several studies of food literacy emphasise the acquisition of critical knowledge over context. This evaluation looks at how COVID-19 impacted food literacy in a country affected by the global pandemic. TGF-beta assay To our knowledge, there has been no systematic research that would allow a better understanding of the impact of uncertainty or enhanced perceived risks generated by a global crisis on the prevalence of household food literacy. This study looks at food literacy from a perceptive of how an event that has domesticated many of them can alter knowledge and the relationship people have with food. A cross-national survey including 10,004 Canadians was conducted ten months after the start of the pandemic. Results show that Canadians have learned new recipes. Canadians have also taken up gardening and have relied on several sources to gather information. This study provides some evidence that Canadians have become more food literate because of the COVID-19 pandemic, but less significantly than anticipated. Practical and policy implications are presented as well as some future research directions.A series of novel 5-[(Z,2Z)-2-chloro-3-(4-nitrophenyl)-2-propenylidene]-thiazolidinones (Ciminalum-thiazolidinone hybrid molecules) have been synthesized. Anticancer activity screening toward the NCI60 cell lines panel, gastric cancer (AGS), human colon cancer (DLD-1), and breast cancer (MCF-7 and MDA-MB-231) cell lines allowed the identification of 3-5-[(Z,2Z)-2-chloro-3-(4-nitrophenyl)-2-propenylidene]-4-oxo-2-thioxothiazolidin-3-ylpropanoic acid (2h) with the highest level of antimitotic activity with mean GI50/TGI values of 1.57/13.3 μM and a certain sensitivity profile against leukemia (MOLT-4, SR), colon cancer (SW-620), CNS cancer (SF-539), melanoma (SK-MEL-5), gastric cancer (AGS), human colon cancer (DLD-1), and breast cancers (MCF-7 and MDA-MB-231) cell lines. The hit compounds 2f, 2i, 2j, and 2h have been found to have low toxicity toward normal human blood lymphocytes and a fairly wide therapeutic range. The significant role of the 2-chloro-3-(4-nitrophenyl)prop-2-enylidene (Ciminalum) substituent in the 5 position and the substituent's nature in the position 3 of core heterocycle in the anticancer cytotoxicity levels of 4-thiazolidinone derivatives have been established.Despite evidence that survivorship support programmes enhance physical and psychosocial wellbeing, cancer patients and survivors often do not use these supportive care services. This study investigated the utility of the Common Sense Model of Self-Regulation for predicting supportive care use following cancer, and the mediating role of coping strategies. Cancer patients and survivors (n = 336 from Australia, n = 61 from the UK; 191 males, 206 females) aged 20-83 years (Mean (M) = 62.73, Standard Deviation (SD) = 13.28) completed an online questionnaire. Predictor variables were cognitive and emotional representations of cancer, as measured by the Illness Perception Questionnaire-Revised (IPQ-R), and problem- and emotion-focused coping strategies, as measured by the Brief-Coping Orientation to Problems Experienced inventory (Brief-COPE). The outcome variable was survivorship support programme use within the preceding month. Perceived personal control over cancer predicted supportive care use, but cancer-related emotional distress did not. Coping was an inconsistent mediator of the relationships. Problem-focused coping mediated the relationship between personal control and supportive care use; emotion-focused coping did not mediate between emotional responses to cancer and the uptake of survivorship support programmes. The Common Sense Model provides a useful framework for understanding survivorship support programme use. However, more clarity around the relationship between illness beliefs and coping is required.Obesity-induced adipose tissue dysfunction and disorders of glycolipid metabolism have become a worldwide research priority. Zfp217 plays a crucial role in adipogenesis of 3T3-L1 preadipocytes, but about its functions in animal models are not yet clear. To explore the role of Zfp217 in high-fat diet (HFD)-induced obese mice, global Zfp217 heterozygous knockout (Zfp217+/-) mice were constructed. Zfp217+/- mice and Zfp217+/+ mice fed a normal chow diet (NC) did not differ significantly in weight gain, percent body fat mass, glucose tolerance, or insulin sensitivity. When challenged with HFD, Zfp217+/- mice had less weight gain than Zfp217+/+ mice. Histological observations revealed that Zfp217+/- mice fed a high-fat diet had much smaller white adipocytes in inguinal white adipose tissue (iWAT). Zfp217+/- mice had improved metabolic profiles, including improved glucose tolerance, enhanced insulin sensitivity, and increased energy expenditure compared to the Zfp217+/+ mice under HFD. We found that adipogenesis-related genes were increased and metabolic thermogenesis-related genes were decreased in the iWAT of HFD-fed Zfp217+/+ mice compared to Zfp217+/- mice. In addition, adipogenesis was markedly reduced in mouse embryonic fibroblasts (MEFs) from Zfp217-deleted mice. Together, these data indicate that Zfp217 is a regulator of energy metabolism and it is likely to provide novel insight into treatment for obesity.The benefits of early detection and classification of epileptic seizures in analysis, monitoring and diagnosis for the realization and actualization of computer-aided devices and recent internet of medical things (IoMT) devices can never be overemphasized. The success of these applications largely depends on the accuracy of the detection and classification techniques employed. Several methods have been investigated, proposed and developed over the years. This paper investigates various seizure detection algorithms and classifications in the last decade, including conventional techniques and recent deep learning algorithms. It also discusses epileptiform detection as one of the steps towards advanced diagnoses of disorders of consciousness (DOCs) and their understanding. A performance comparison was carried out on the different algorithms investigated, and their advantages and disadvantages were explored. From our survey, much attention has recently been paid to exploring the efficacy of deep learning algorithms in seizure detection and classification, which are employed in other areas such as image processing and classification.