Gene and metabolite expression dependence on body mass index in human myocardium

Funding Van Geest Foundation, Leicester NIHR Biomedical Research Centre, British Heart Foundation CH/12/1/29419, AA18/3/34220. Competing interests Mrs. Kumar, Prof. Murphy and Dr Woźniak received a grant from Zimmer Biomet. Dr Murphy also received grants from Terumo and Baxter. The remaining authors have disclosed that they do not have any potential conflicts of interest.Peer reviewedPublisher PD

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts. June 4-7, 2019, Szczyrk, Polan

Role of kinesin-1 and cytoplasmic dynein in endoplasmic reticulum movement in VERO cells

Generating the extended endoplasmic reticulum (ER) network depends on microtubules, which act as tracks for motor-driven ER tubule movement, generate the force to extend ER tubules by means of attachment to growing microtubule plus-ends and provide static attachment points. We have analysed ER dynamics in living VERO cells and find that most ER tubule extension is driven by microtubule motors. Surprisingly, we observe that ∼50% of rapid ER tubule movements occur in the direction of the centre of the cell, driven by cytoplasmic dynein. Inhibition of this movement leads to an accumulation of lamellar ER in the cell periphery. By expressing dominant-negative kinesin-1 constructs, we show that kinesin-1 drives ER tubule extension towards the cell periphery and that this motility is dependent on the KLC1B kinesin light chain splice form but not on KLC1D. Inhibition of kinesin-1 promotes a shift from tubular to lamellar morphology and slows down the recovery of the ER network after microtubule depolymerisation and regrowth. These observations reconcile previous conflicting studies of kinesin-1 function in ER motility in vivo. Furthermore, our data reveal that cytoplasmic dynein plays a role in ER motility in a mammalian cultured cell, demonstrating that ER motility is more complex than previously thought

A Systematic Review and Meta-Analysis of MicroRNA as Predictive Biomarkers of Acute Kidney Injury

Acute kidney injury (AKI) affects 10–15% of hospitalised patients and arises after severe infections, major surgeries, or exposure to nephrotoxic drugs. AKI diagnosis based on creatinine level changes lacks specificity and may be delayed. MicroRNAs are short non-coding RNA secreted by all cells. This review of studies measuring miRNAs in AKI aimed to verify miRNAs as diagnostic markers. The study included data from patients diagnosed with AKI due to sepsis, ischaemia, nephrotoxins, radiocontrast, shock, trauma, and cardiopulmonary bypass. Out of 71 studies, the majority focused on AKI in sepsis patients, followed by cardiac surgery patients, ICU patients, and individuals receiving nephrotoxic agents or experiencing ischaemia. Studies that used untargeted assays found 856 differentially regulated miRNAs, although none of these were confirmed by more than one study. Moreover, 68 studies measured miRNAs by qRT-PCR, and 2 studies reported downregulation of miR-495-3p and miR-370-3p in AKI patients with sepsis after the AKI diagnosis. In three studies, upregulation of miR-21 was reported at the time of the AKI diagnosis with a significant pooled effect of 0.56. MiR-21 was also measured 19–24 h after cardiac surgery in three studies. However, the pooled effect was not significant. Despite the considerable research into miRNA in AKI, there is a knowledge gap in their applicability as diagnostic markers of AKI in humans.</p

Advanced deep learning techniques for early disease prediction in cauliflower plants

Abstract Agriculture plays a pivotal role in the economies of developing countries by providing livelihoods, sustenance, and employment opportunities in rural areas. However, crop diseases pose a significant threat to both farmers’ incomes and food security. Furthermore, these diseases also show adverse effects on human health by causing various illnesses. Till date, only a limited number of studies have been conducted to identify and classify diseased cauliflower plants but they also face certain challenges such as insufficient disease surveillance mechanisms, the lack of comprehensive datasets that are properly labelled as well as are of high quality, and the considerable computational resources that are necessary for conducting thorough analysis. In view of the aforementioned challenges, the primary objective of this manuscript is to tackle these significant concerns and enhance understanding regarding the significance of cauliflower disease identification and detection in rural agriculture through the use of advanced deep transfer learning techniques. The work is conducted on the four classes of cauliflower diseases i.e. Bacterial spot rot, Black rot, Downy Mildew, and No disease which are taken from VegNet dataset. Ten deep transfer learning models such as EfficientNetB0, Xception, EfficientNetB1, MobileNetV2, EfficientNetB2, DenseNet201, EfficientNetB3, InceptionResNetV2, EfficientNetB4, and ResNet152V2, are trained and examined on the basis of root mean square error, recall, precision, F1-score, accuracy, and loss. Remarkably, EfficientNetB1 achieved the highest validation accuracy (99.90%), lowest loss (0.16), and root mean square error (0.40) during experimentation. It has been observed that our research highlights the critical role of advanced CNN models in automating cauliflower disease detection and classification and such models can lead to robust applications for cauliflower disease management in agriculture, ultimately benefiting both farmers and consumers