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Abstract Background Cancer of unknown primary (CUP) is an orphan disease generally presented by undifferentiated and aggressive morphological phenotype. The treatment of CUP is solely dependent upon the origin of cancer. Despite extensive diagnostic testing, in most of the cases the primary site remains unidentifiable. Case presentation This case demonstrates a 75-year-old male patient, who initially presented with the complaints of swelling over right side of the neck since 2 months. A cervical lymph node biopsy was taken for immunohistochemistry, which revealed cytokeratin (CK) and CK7 markers to be positive. Computerized tomography (CT) of Thorax showed subcentimetric subpleural nodules in bilateral lungs fields, predominantly in lower lobes (metastatic in nature). A subsequent pulmoCORE 12 gene panel test was recommended, and patient was discharged with tablet gefitinib 250mg and capsule containing vitamins plus minerals. After one month, patient revisited with the pulmoCORE 12 gene test report which revealed polymorphism in TP53. A pathogenic variant of tumor protein p53 (TP53), i.e., p.Glu198Ter (amino acid alteration) and c.592G > T (coding) variant, was detected, which has 17.2% variant allele frequency. There are no treatment guidelines for TP53 mutation; therefore, the patient was treated with injection paclitaxel 70mg and carboplatin 100mg for 12 cycles along with palliative radiotherapy of 20 Gy for 5 fractions. The overall prognosis of patient was found to be favorable. Conclusions There is a need for development of comprehensive guidelines and new molecularly targeted therapies for treatment of CUP which can be tailored for each patient and achieve precise therapeutic outcome.

In this paper the heat transfer characteristics have been determined theoretically using heat propagation phenomena of compressed natural gas (CNG)/air premixed laminar flames impinging on flat surfaces. Effects of varied equivalence ratios (Ф), Reynolds numbers (Re), separation distances (H/d) and burner diameters (d) on stagnation point heat flux have been investigated. The simulation findings were validated with the available experimental data. The flat plate with the maximum heat flux at the stagnation position is located close to the margin of the innermost premixed reaction zone (i.e., at H/d, Re and d are 5, 1000, 8 mm). The flow of heat steadily decreases in axial direction as the separating distance from the inner reaction zone's tip increases. Greater separation lengths at the stagnation region cause a subsequent increase in heat flux. The objective of this investigation is to get an idea about the impact of existence of the plate on the flame and temperature distribution and to get clarity about the reason behind higher amount of heat flux owing to tip of the innermost reaction area at stagnation region.

Abstract In extant biology, large and complex enzymes employ low molecular weight cofactors such as dihydronicotinamides as efficient hydride transfer agents and electron carriers for the regulation of critical metabolic processes. In absence of complex contemporary enzymes, these molecular cofactors are generally inefficient to facilitate any reactions on their own. Herein, we report short peptide-based amyloid nanotubes featuring exposed arrays of cationic and hydrophobic residues that can bind small molecular weak hydride transfer agents (NaBH4) to facilitate efficient reduction of ester substrates in water. In addition, the paracrystalline amyloid phases loaded with borohydrides demonstrate recyclability, substrate selectivity and controlled reduction and surpass the capabilities of standard reducing agent such as LiAlH4. The amyloid microphases and their collaboration with small molecular cofactors foreshadow the important roles that short peptide-based assemblies might have played in the emergence of protometabolism and biopolymer evolution in prebiotic earth.

The automation of all-terrain vehicles (ATVs) through the integration of advanced technologies such as machine learning (ML) and artificial intelligence (AI) vision has significantly changed precision agriculture. This paper aims to analyse and develop trends to provide comprehensive knowledge of the current state of ATV-based precision agriculture and the future possibilities of ML and AI. A bibliometric analysis was conducted through network diagram with keywords taken from previous publications in the domain. This review comprehensively analyses the potential of machine learning and artificial intelligence in transforming farming operations through the automation of tasks and the deployment of all-terrain vehicles. The research extensively analyses how machine learning methods have influenced several aspects of agricultural activities, such as planting, harvesting, spraying, weeding, crop monitoring, and others. AI vision systems are being researched for their ability to enhance precise and prompt decision-making in ATV-driven agricultural automation. These technologies have been thoroughly tested to show how they can improve crop yield (15-20%), reduce overall investment (25-30%), and make farming more efficient (20-25%). Examples include machine learning-based seeding accuracy, AI-enabled crop health monitoring, and the use of AI vision for accurate pesticide application. The assessment examines challenges such as data privacy problems and scalability constraints, along with potential advancements and future prospects in the field. This will assist researchers and practitioners in making well-informed judgments regarding farming practices that are efficient, sustainable, and technologically robust.

Research backgroundThere is considerable diversity in newly developed pummelo × sweet orange citrus hybrids. Most hybrids showed lower peel thickness and high juice yield but there is a lack of information on fruit quality parameters and molecular characterization. Therefore, the aim of the current study is to determine the content of antioxidants and properties of the fresh juice of 24 new pummelo × sweet orange citrus hybrids (Citrus maxima [Burm. f.] Osbeck × Citrus sinensis [L.] Osbeck) and the parental genotypes along with molecular characteristics determined using acidity specific markers. Experimental approach. The correlation and estimate of inheritance of the fruit juice properties: ascorbic acid, total phenol, total flavonoid, total antioxidant, total soluble solid and sugar contents, pH, titratable acidity, along with sensory evaluation was performed. Molecular characterization of these hybrids was carried out using de novo generated acidity specific simple sequence repeat (SSR) markers. Results and conclusions. The main constituents of the fruit juice of pummelo × sweet orange hybrids were observed in the range of w(ascorbic acid)=40.00–58.13 mg/100 g, total phenols expressed as gallic acid equivalents w(GAE)=40.67–107.33 mg/100 g, total antioxidants expressed as Trolox equivalents b(Trolox)=2.03–5.49 µmol/g, total flavonoids expressed as quercetin equivalents w(QE)=23.67–59.33 mg/100 g, along with other properties: total soluble solids=7.33–11.33 %, w(total sugar)=2.10–5.76 %, w(reducing sugar)=1.69–2.78 %, w(non-reducing sugar)=0.39–3.17 % and titratable acidity 1.00–2.11 %. The above parameters differed significantly in the fruit juice of the evaluated pummelo × sweet orange hybrids. Considering these parameters, the hybrids SCSH 17-9, SCSH 13-13, SCSH 11-15 and SCSH 3-15 had superior antioxidant properties in terms of these parameters. A higher heritability (≥80 %) was also observed for all juice properties. Molecular characterization of pummelo × sweet orange hybrids showed that >50 % of the hybrids were grouped with medium acidity parents. Both molecular and biochemical parameter-based clustering showed that interspecific hybrids exhibit transgressive segregation with increased antioxidants that help alleviate the health problems. Novelty and scientific contribution. These newly developed pummelo × sweet orange citrus hybrids are a valuable source of high-quality antioxidants for a healthy diet. The identification of trait markers that enable selection at the seedling stage is of great benefit to citrus breeders, as the characteristic features of a mature tree are not yet visible at the juvenile stage.

Present study was carried out during 2021–23 (Ambe and Mrig bahar season) at the research farm of ICAR- Indian Agricultural Research Institute, New Delhi to analyse the morphological diversity of 24 orangelo (Citrus spp.) hybrids and their 4 parents based on 17 quantitative traits such as leaves, flowers, fruits and seed characters. Cluster analysis based on 17 morphological traits divided 28 genotypes of citrus into two main groups. A higher range of variation with average Euclidean distance coefficients ranging from 0.02–0.26 was observed between groups as well as clusters, thereby indicating that genotypes within and among clusters were highly diverse. The PCA for morphological quantitative traits showed that five components leaf lamina length (LLL); leaf lamina width (LLW); LLL: LLW; leaf lamina thickness and pedicel length, of the 17 principal component axis had Eigen-values>1 and all together accounted for over 77.76% of the total variability. Analysing the genetic relationships among the hybrids yielded important insights for potential use in future breeding programmes as valuable sources of desirable traits.

Abstract Background Cisplatin resistance is one of the major contributors to the poor survival rate among head and neck cancer (HNC) patients. Focusing on the protein–protein interaction rather than a single protein could provide a better understanding of drug resistance. Thus, this study aimed to identify hub genes in a complex network of cisplatin resistance associated genes in HNC chemotherapy via a series of bioinformatic tools. Methods The genes involved in cisplatin resistance were retrieved from the NCBI gene database using “head and neck cancer” and “cisplatin resistance” as key words. The human genes retrieved were analyzed for their interactions and enriched using the STRING database. The interaction between KEGG pathways and genes was visualized in Cytoscape 3.7.2. Further, the hub gene was identified using the Cytohubba plugin of Cytoscape and validated using UALCAN and Human Protein Atlas database. Validated genes were investigated for the drug–gene interaction using the DGIbd database. Results Out of 137 genes obtained using key words, 133 were associated with cisplatin resistance in the human species. A total of 150 KEGG pathways, 82 cellular components, 123 molecular functions, and 1752 biological processes were modulated on enrichment analysis. Out of 37 hub genes, CCND1, AXL, CDKN2A, TERT, and EXH2 genes were found to have significant (p

In this study machine learning models are used that forecast the compressive strength of self-compacting concrete (SCC) depending on percentage replacement of cement by supplementary cementitious material. In order to predict the mechanical property of SCC, a hybrid artificial neural network (ANN) along with metaheuristic optimization techniques and two traditional models were employed which comprised of 300 datasets. Several input factors are employed in the modelling process, including cement, the water-binder ratio, coarse aggregate, fine aggregate, fly ash (FA) and the superplasticizer. ANN hybridised with RUNge Kutta optimizer (RUN) was chosen as one of the top predictive models based on coefficient of determination ((R2 = 0.933 (in training) and R2 = 0.9203 (in testing))) findings and model validation. According to the rank analysis, the ANN-RUN surpassed other models with a score of 128. Permutation feature importance, statistical analysis, and comparisons amongst regression models are used to determine the models' efficacy. The best input parameters were selected based on person correlation and density plot. Also, the use of Taylor diagrams and accuracy matrices was also examined for the visual comprehension of the data which has shown similar results as discussed above. The results show that the suggested machine learning models attained consistent accuracy and can be used as a robust technique to predict the compressive strength of SCC.