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Despite strides in diagnostic and therapeutic approaches for ESCC, patient survival rates remain relatively low. Recent studies highlight the pivotal role of long non-coding RNAs (lncRNAs) in regulating diverse cellular activities in humans. Dysregulated lncRNAs have emerged as potential diagnostic indicators across various cancers, including ESCC. However, further research is necessary to effectively leverage ESCC-associated lncRNAs in clinical settings. Understanding their clinical significance for ESCC diagnosis and their mechanisms can pave the way for more effective therapeutic strategies. Our qRT-PCR analysis revealed significant downregulation of SOX2-OT (~ 2.02-fold) and NEAT1 (~ 1.53-fold) in ESCC blood samples. These lncRNAs show potential as biomarkers for distinguishing ESCC patients from healthy individuals, with ROC curves and AUC values of 0.736 for SOX2-OT and 0.621 for NEAT1 . Further analysis examined the correlation between SOX2-OT and NEAT1 expression and various clinicopathological factors, including age, gender, smoking, alcohol use, hot beverage intake, tumor grade, and TNM stages. In-silico studies highlighted their roles in miRNA sponging via mTOR and MAPK pathways, while co-expression network analysis identified associated genes. This research paves the way for future studies on ESCC prognosis using SOX2-OT and NEAT1 as predictive markers. By thoroughly investigating the functions of these lncRNAs, we aim to deepen our understanding of their potential as diagnostic markers and their role in facilitating effective therapeutic interventions for esophageal squamous cell carcinoma (ESCC) within clinical contexts.

Lung cancer is a severe challenge to the health care system with intrinsic resistance to first and second-line chemo/radiotherapies. In view of the sterile environment of lung cancer, several immunotherapeutic drugs including nivolumab, pembrolizumab, atezolizumab, and durvalumab are currently being used in clinics globally with the intention of releasing exhausted T-cells back against refractory tumor cells. Immunotherapies have a limited response rate and may cause immune-related adverse events (irAEs) in some patients. Hence, a deeper understanding of regulating immune checkpoint interactions could significantly enhance lung cancer treatments. In this review, we explore the role of miRNAs in modulating immunogenic responses against tumors. We discuss various aspects of how manipulating these checkpoints can bias the immune system’s response against lung cancer. Specifically, we examine how altering the miRNA profile can impact the activity of various immune checkpoint inhibitors, focusing on the PD-1/PD-L1 pathway within the complex landscape of lung cancer. We believe that a clear understanding of the host’s miRNA profile can influence the efficacy of checkpoint inhibitors and significantly contribute to existing immunotherapies for lung cancer patients. Additionally, we discuss ongoing clinical trials involving immunotherapeutic drugs, both as standalone treatments and in combination with other therapies, intending to advance the development of immunotherapy for lung cancer.

This paper proposes a multi-input single-output (MISO) third order voltage mode (VM) universal filter using only one operational transresistance amplifier (OTRA). The proposed circuit realizes low-pass, high-pass, all-pass, band-pass, and notch responses from the same topology. The PSPICE Simulation results using 0.5 μm CMOS technology agree well with the theoretical design.

An experiment was conducted to develop a plant extracts based quality indicator for monitoring aerobically packed chicken meat quality during storage at room temperature (25±1°C) until spoiled. Ripen black mulberry (Morus nigra) fruit extracts containing anthocyanins was used for developing quality indicator. Filter paper based indicator was exposed to the closed fresh meat environment. Changes in the indicator color and different quality parameters of chicken meat viz., total volatile basic nitrogen (TVBN), ammonia level, pH, sensory attributes and instrumental color values were evaluated and were correlated with color changed of indicator. During storage meat become visibly spoiled on 5th day of storage where as the color of the indicator changed gradually from red on 1st day to light green between 2nd-4th day and finally dark yellow on 5th. Both Total volatile basic nitrogen (TVBN) and ammonia level increased significantly (P<0.5) during storage. The pH value of meat increased significantly (P<0.5) from 5.4 to 6.1 during storage. Sensory attributes score for color, general appearance and acceptability decreased significantly (P<0.5) throughout the storage period. Lovibond Tinto meter color value for redness, yellowness, hue and chroma decreased during storage period. On the basis of results observed it might be concluded that the developed quality indicator is expected to provide a convenient aid to monitor the quality and shelf life of chicken meat during storage at room temperature.

The friction stir welding computational fluid dynamics (CFD) model in three dimensions (FSW) for quantitatively study to effect heat generation on the work piece material at different tool pin profile are revealed in this study. The simulated thermal parameter are studies and compare with experimental results. The FSW process is modeled the laminar flow and transient condition for various welding condition. The rotating tool geometry such as cylindrical, tapered, triangular and square are used for FSW. Analysis of the thermomechanical behavior of various pin profiles at spindle speeds of 1000 rpm and feed rates of 16–63 mm/min. The tapered tool pin profile is effective as compared to other tool pin because it generates maximum temperature and strain rate. The tapered pin profile has lower surface area and therefore gives better performance due to maximum heat generation of 695 K and high strain rate of 0.9 S −1 as compared to other. On the other hand, modelling results for the tapered pin profile reveal an increase in temperature of around 80% of the base materials' melting point.

Chicken is one of the most preferred protein sources for nonvegetarian consumers around the world. Due to the huge demand for quality chicken products, the processors and the suppliers need to maintain the optimum chicken quality during the supply chain. Therefore, this study was aimed to develop a suitable device to monitor chicken quality in supply chain. In the present study, a natural dye extracted from coleus blumei leaves ( Plectranthus scutellarioides ) has been used to develop a chicken quality monitoring sensor. This sensor has been tested for its color-changing ability by attaching inside the packet of fresh chicken at different storage temperatures viz., 4 ± 1, 10 ± 1, 15 ± 1, 25 ± 1, and 37 ± 1 °C. During the storage of chicken the changes in quality parameters (physicochemical, microbiological, and sensory attributes) were measured at regular intervals. The sensor showed a color change during the progress of the storage period and this was well in concurrence with the changes in the quality parameters of the chicken. The results showed the potentiality of the coleus blumei leaves dye-based sensor as a monitoring device to detect the quality of chicken during its storage at various temperatures.

Among various nonconventional energy sources, wind energy is a noteworthy and suitable source with the ability to generate electricity continuously and sustainably. However, there are a number of drawbacks to wind energy, including high basic utilization costs, the static nature of wind farms, and the challenge of locating energy that is wind-efficient, regions. Using five machine learning methods, long-term wind power prediction was done in this study using daily wind speed data. We suggested an effective way to forecast wind power values using machine learning techniques. To demonstrate how machine learning algorithms, perform, we earned out a number of case studies. The outcomes demonstrated that long-term wind power values might be predicted using machine learning algorithms in relation to past wind speed data. Additionally, the consequences show that machine learning-based Models could be used in places other than those where they were taught. This study showed that, by employing a model of a base site, machine learning algorithms could be applied frequently prior to the development of wind plants in an undisclosed environmental region, provided that it makes sense.

The advent of material technology witnessed an enormous application of aluminium alloys in day-to-day life. The Aluminum Alloy 6061 is one such alloy that finds immense application in engineering field. However, joining process of such aluminium alloys is difficult task through conventional techniques due to occurrence of high thermal conductivity. Friction stir welding (FSW) turns out to be an innovative welding technique used for joining such alloys and comparatively less hazard. The FSW process required to controlled several working parameters for strengthen the mechanical properties. It becomes very important to optimize these process parameters to obtain a good weld with enhanced mechanical properties. The current article describes the experimental procedure for welding AA6061 alloy at different operating parameters. Taguchi method and regression analysis which is widely acceptable methodology implemented to optimize different FSW parameters using L16 orthogonal array. The present study implemented the ANOVA table to examine the influence of tool geometry, rotational speed and welding speed on tensile strength, percentage elongation and harness respectively. The percent contributions of factors i.e., tool geometry, rotation speed and welding speed to the tensile strength is found to be of 33.4%, 4.69% and 58.39% respectively. It is observed that welding speed (58.39%) plays significant role influencing the tensile strength. Similarly, the percentage contributions of tool geometry, rotation speed and welding speed on percentage elongation is found to be 35.08%, 14.29% and 38.28% respectively. The observation concluded that welding speed is the most influential factor for percentage elongation. In addition, the percent contributions of the tool geometry, rotation speed and welding speed on hardness reported as 50.1%, 19.36% and 20.49% respectively. This concluded that tool geometry is the most effective factor for hardness. The predicted results are validated with experimental data’s depicted a good convergence with optimization techniques for controlled operating parameters.