Transcriptomic profiling disclosed the role of DNA methylation and histone modifications in tumor-infiltrating myeloid-derived suppressor cell subsets in colorectal cancer

Increased numbers of myeloid-derived suppressor cells (MDSCs) are positively correlated with poor prognosis and reduced survivals of cancer patients. They play central roles in tumor immune evasion and tumor metastasis. However, limited data are available on phenotypic/transcriptomic characteristics of the different MDSCs subsets in cancer. These cells include immature (I-MDSCs), monocytic (M-MDSCs), and polymorphonuclear/granulocytic (PMN-MDSCs). Phenotypic characterization of myeloid subsets from 27 colorectal cancer (CRC) patients was assessed by flow cytometric analyses. RNA-sequencing of sorted I-MDSCs, PMN-MDSCs, and antigen-presenting cells (APCs) was also performed. We found that the levels of I-MDSCs and PMN-MDSCs were increased in tumor tissues (TT), compared with normal tissues (NT) in colorectal cancer. Our functional annotation analyses showed that genes associated with histone deacetylase (HDAC) activation- and DNA methylation-mediated transcriptional silencing were upregulated, and histone acetyl transferase (HAT)-related genes were downregulated in tumor-infiltrating I-MDSCs. Moreover, pathways implicated in cell trafficking and immune suppression, including Wnt, interleukin-6 (IL-6), and mitogen-activated protein kinase (MAPK) signaling, were upregulated in I-MDSCs. Notably, PMN-MDSCs showed downregulation in genes related to DNA methylation and HDAC binding. Using an ex vivo model, we found that inhibition of HDAC activation or neutralization of IL-6 in CRC tumor tissues downregulates the expression of genes associated with immunosuppression and myeloid cell chemotaxis, confirming the importance of HDAC activation and IL-6 signaling pathway in MDSC function and chemotaxis. This study provides novel insights into the epigenetic regulations and other molecular pathways in different myeloid cell subsets within the CRC tumor microenvironment (TME), giving opportunities to potential targets for therapeutic benefits

Transcriptomic profiling of tumor-infiltrating CD4 + TIM-3 + T Cells reveals their suppressive, exhausted, and metastatic characteristics in colorectal cancer patients

T cell immunoglobulin mucin-3 (TIM-3) is an immune checkpoint identified as one of the key players in regulating T-cell responses. Studies have shown that TIM-3 is upregulated in the tumor microenvironment (TME). However, the precise role of TIM-3 in colorectal cancer (CRC) TME is yet to be elucidated. We performed phenotypic and molecular characterization of TIM-3+ T cells in the TME and circulation of CRC patients by analyzing tumor tissues (TT, TILs), normal tissues (NT, NILs), and peripheral blood mononuclear cells (PBMC). TIM-3 was upregulated on both CD4+ and CD3+CD4− (CD8+) TILs. CD4+TIM-3+ TILs expressed higher levels of T regulatory cell (Tregs)-signature genes, including FoxP3 and Helios, compared with their TIM-3− counterparts. Transcriptomic and ingenuity pathway analyses showed that TIM-3 potentially activates inflammatory and tumor metastatic pathways. Moreover, NF-κB-mediated transcription factors were upregulated in CD4+TIM-3+ TILs, which could favor proliferation/invasion and induce inflammatory and T-cell exhaustion pathways. In addition, we found that CD4+TIM-3+ TILs potentially support tumor invasion and metastasis, compared with conventional CD4+CD25+ Tregs in the CRC TME. However, functional studies are warranted to support these findings. In conclusion, this study discloses some of the functional pathways of TIM-3+ TILs, which could improve their targeting in more specific therapeutic approaches in CRC patients

Evaluation of the Perception of Community Pharmacists Regarding their Role in Pakistan's Healthcare System: A Qualitative Approach

Purpose: To explore the perception of community pharmacists regarding their role in Pakistan's healthcare system.Methods: A qualitative study design was adopted. A semi-structured interview guide was developed and face to face interviews were conducted. The participants were community pharmacists and were recruited through one of the researcher’s personal contacts in two cities of Pakistan (Islamabad and Lahore) from April to June 2008. The interviews were conducted at the pharmacists’ work-place. Arrangements for the time and place of interview were made during initial contacts. Written consent was obtained from the participants prior to the interview.Results: Among the respondents interviewed, seven were male and three female community pharmacists aged between 25 and 50 years. All the participants regularly dispensed > 50 prescriptions daily. Thematic content analysis yielded 5 major themes: (a) provision of pharmacy services to consumers, (b) counseling at pharmacy, (c) application of Good Pharmacy Practice (GPP), (d) Pakistan Pharmacy Association (PPA) contribution towards pharmacy profession, and (e) strategies to improve community pharmacies.Conclusion: Community pharmacies in Pakistan currently face shortage of pharmacists. This has resulted in non-provision of patient counseling; rather services are more focused more on the management of pharmacies than clients. As a result, there is little public awareness of the pharmacist’s role in health care.Keywords: Perception, Community pharmacist, Patient care, Pakistan, Qualitative methodology

Object class recognition using combination of colour dense SIFT and texture descriptors

Object class recognition has recently become one of the most popular research fields. This is due to its importance in many applications such as image classification, retrieval, indexing, and searching. The main aim of object class recognition is determining how to make computers understand and identify automatically which object or scene is being displayed on the image. Despite a lot of efforts that have been made, it still considered as one of the most challenging tasks, mainly due to inter-class variations and intra-class variations like occlusion, background clutter, viewpoint changes, pose, scale and illumination. Feature extraction is one of the important steps in any object class recognition system. Different image features are proposed in the literature review to increase categorisation accuracy such as appearance, texture, shape descriptors. In this paper, we propose to combine different descriptors which are dense colour scale-invariant feature transform (dense colour SIFT) as appearance descriptors with different texture descriptors. The colour completed local binary pattern (CCLBP) and completed local ternary pattern (CLTP) are integrated with dense colour SIFT due to the importance of the texture information in the image. Using different pattern sizes to extract the CLTP and CCLBP texture descriptors will help to find dense texture information from the image. Bag of features is also used in the proposed system with each descriptor while the late fusion strategy is used in the classification stage. The proposed system achieved high recognition accuracy rate when applied in some datasets, namely SUN-397, OT4N, OT8, and Event sport datasets, which accomplished 38.9%, 95.9%, 89.02%, and 88.167%, respectively

How colonization bottlenecks, tissue niches, and transmission strategies shape protozoan infections

Protozoan pathogens such as Plasmodium spp., Leishmania spp., Toxoplasma gondii, and Trypanosoma spp. are often associated with high-mortality, acute and chronic diseases of global health concern. For transmission and immune evasion, protozoans have evolved diverse strategies to interact with a range of host tissue environments. These interactions are linked to disease pathology, yet our understanding of the association between parasite colonization and host homeostatic disruption is limited. Recently developed techniques for cellular barcoding have the potential to uncover the biology regulating parasite transmission, dissemination, and the stability of infection. Understanding bottlenecks to infection and the in vivo tissue niches that facilitate chronic infection and spread has the potential to reveal new aspects of parasite biology

A Comparative Study of Single and Multi-Stage Forecasting Algorithms for the Prediction of Electricity Consumption Using a UK-National Health Service (NHS) Hospital Dataset

Data Availability Statement: Restrictions apply to the availability of the electricity consumption data. The data belong to Medway NHS Foundation Trust but were collected using systems provided by EnergyLogix. Data, however, can be made available with the approval of the corresponding author (A.T.), Medway NHS Foundation Trust, and EnergyLogix. As for the weather data, they were obtained from [24].Copyright © 2023 by the authors. Accurately looking into the future was a significantly major challenge prior to the era of big data, but with rapid advancements in the Internet of Things (IoT), Artificial Intelligence (AI), and the data availability around us, this has become relatively easier. Nevertheless, in order to ensure high-accuracy forecasting, it is crucial to consider suitable algorithms and the impact of the extracted features. This paper presents a framework to evaluate a total of nine forecasting algorithms categorised into single and multistage models, constructed from the Prophet, Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and the Least Absolute Shrinkage and Selection Operator (LASSO) approaches, applied to an electricity demand dataset from an NHS hospital. The aim is to see such techniques widely used in accurately predicting energy consumption, limiting the negative impacts of future waste on energy, and making a contribution towards the 2050 net zero carbon target. The proposed method accounts for patterns in demand and temperature to accurately forecast consumption. The Coefficient of Determination (R2), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE) were used to evaluate the algorithms’ performance. The results show the superiority of the Long Short-Term Memory (LSTM) model and the multistage Facebook Prophet model, with R2 values of 87.20% and 68.06%, respectively.Engineering and Physical Sciences Research Council (EPSRC) grants, EP/T517896/1

Stimulatory Effects of Gamma Irradiation on Phytochemical Properties, Mitotic Behaviour, and Nutritional Composition of Sainfoin ( Onobrychis viciifolia

Sainfoin (Onobrychis viciifolia Scop. Syn. Onobrychis sativa L.) is a bloat-safe forage crop with high levels of tannins, which is renowned for its medicinal qualities in grazing animals. Mutagenesis technique was applied to investigate the influence of gamma irradiation at 30, 60, 90, and 120 Gy on mitotic behavior, in vitro growth factors, phytochemical and nutritional constituents of sainfoin. Although a percentage of plant necrosis and non-growing seed were enhanced by irradiation increment, the germination speed was significantly decreased. It was observed that gamma irradiated seeds had higher value of crude protein and dry matter digestibility compared to control seeds. Toxicity of copper was reduced in sainfoin irradiated seeds at different doses of gamma rays. Anthocyanin content also decreased in inverse proportion to irradiation intensity. Accumulation of phenolic and flavonoid compounds was enhanced by gamma irradiation exposure in leaf cells. HPLC profiles differed in peak areas of the two important alkaloids, Berberine and Sanguinarine, in 120 Gy irradiated seeds compared to control seeds. There were positive correlations between irradiation dose and some abnormality divisions such as laggard chromosome, micronucleus, binucleated cells, chromosome bridge, and cytomixis. In reality, radiocytological evaluation was proven to be essential in deducing the effectiveness of gamma irradiation to induce somaclonal variation in sainfoin

Ten years after ImageNet: a 360° perspective on artificial intelligence

It is 10 years since neural networks made their spectacular comeback. Prompted by this anniversary, we take a holistic perspective on artificial intelligence (AI). Supervised learning for cognitive tasks is effectively solved—provided we have enough high-quality labelled data. However, deep neural network models are not easily interpretable, and thus the debate between blackbox and whitebox modelling has come to the fore. The rise of attention networks, self-supervised learning, generative modelling and graph neural networks has widened the application space of AI. Deep learning has also propelled the return of reinforcement learning as a core building block of autonomous decision-making systems. The possible harms made possible by new AI technologies have raised socio-technical issues such as transparency, fairness and accountability. The dominance of AI by Big Tech who control talent, computing resources, and most importantly, data may lead to an extreme AI divide. Despite the recent dramatic and unexpected success in AI-driven conversational agents, progress in much-heralded flagship projects like self-driving vehicles remains elusive. Care must be taken to moderate the rhetoric surrounding the field and align engineering progress with scientific principles

The association between airborne pollen monitoring and sensitization in the hot desert climate

Background: Pollen is a major cause of allergic respiratory diseases. In Qatar, data on the presence and prevalence of allergenic airborne types of pollen is quite limited. / Methods: The study aimed to determine and correlate the most frequently implicated airborne pollen detected by aerobiological monitoring samplers in respiratory allergy symptoms. An aerobiological survey was started on May 8, 2017. Airborne pollen was collected using two Hirst type seven-day recorder volumetric traps. Skin prick test in patients attending allergy clinics in Doha using commercial extracts was conducted. / Results: Twenty-five pollen types representing the native, as well as the introduced plants, with a relatively low daily mean concentration were observed from May 2017 to May 2019. The highest pollen concentrations were reached by Amaranthaceae (58.9%), followed by Poaceae (21.7%). SPT revealed a comparatively higher degree of sensitization to pollen. Among 940 patients, 204 were sensitized to pollen (54% female) with 135 (66.2%) and 114 (55.8%) to Amaranthaceae and Poaceae, respectively. Some patients had polysensitization. There was a statistically significant association between Amaranthaceae, and asthma (r = 0.169, P = 0.016) and allergic rhinitis (r = 0.177, P = 0.012). / Conclusions: This is the first study to monitor airborne pollen in the state of Qatar. The main pollen detected were Amaranthaceae and Poaceae. Pollen may represent a possible exacerbating factor in adult patients with allergic diseases such as asthma and allergic rhinitis

Estimation of Thermal & Epithermal Neutron Flux and Gamma Dose Distribution in a Medical Cyclotron Facility for Radiation Protection Purposes Using Gold Foils and Gate 9

The aim of this study is to characterise the neutron flux generated directly behind targets used in medical cyclotrons. The characterisation process aims at determining the feasibility of using the generated neutrons for research purposes in neutron activation analysis. The study was performed by activating gold foils placed directly behind the cyclotron targets. The thermal and epithermal neutron flux were found to be 4.5E+05 ± 8.78E+04 neutrons cm-2 s-1 and 2.13E+06 ± 8.59E+04 neutrons cm-2 s-1, respectively. The flux value is the same order of magnitude listed in the manual produced by the cyclotron manufacturer. The results are encouraging and show high potential for using the cyclotron facility as a thermal neutron source for research purposes. However, it is important radiation protection procedures be followed to ensure the safety of researchers due to the high gamma dose rate measured directly behind the target at 2.46 Sv/h using an OSL chip during the beam on time