Esophageal adenocarcinoma (EAC) is the sixth-leading cause of cancer-related death. Although pyrimidine analogue-based neoadjuvant and adjuvant therapies are widely used, patient responses remain variable. Emerging evidence indicates that bacteria-derived metabolites influence tumor biology and therapy outcomes. To identify non-canonical plasma metabolites linked to cancer biology, we performed correlation analyses between untargeted metabolomics profiles and overall survival. This approach revealed a bacterial metabolite called L-pentahomoserine, or L-2-amino-5-hydroxypentanoic acid (L-2A5HPA), to be positively associated with overall survival. Notably, L-2A5HPA promoted cell survival under nutrient limitation by redirecting glucose metabolism towards aspartate and pyrimidine biosynthesis. In vitro, L-2A5HPA uptake varied among cell lines and was controlled by stereospecific transporters. Furthermore, metabolic profiling in mouse models of liver cancer showed different levels of L-2A5HPA and a strong correlation with pyrimidine intermediates, dihydroorotate and orotate. The link between L-2A5HPA, pyrimidine nucleotide metabolism, and cell survival provides mechanistic insight into its association with patient outcome. Our findings position L-2A5HPA as a metabolite with potential to become a prognostic biomarker for EAC and underscores its role in metabolic adaptation under nutrient-deprived conditions. ### Competing Interest Statement The authors have declared no competing interest.

The foetal plasma protein alpha-fetoprotein (AFP) harbours a high-affinity zinc binding site that is likely involved in transport and delivery of essential zinc during foetal development. Based on a recent electron microscopy structure of AFP and aided by biophysical studies on an AFP-derived peptide, we present a refined 5-coordinate model for this site.

Methodological advancement based on Artificial Intelligence (AI) has developed dramatically during the past few years in various sectors. The majority of these models are intrinsically complex and lack explanations of the decision-making process, hence the moniker “Black-Box”. Explainable AI (XAI) has emerged with significant implications for addressing the opacity of conventional black-box models. With a focus on the inscrutable AI, this study investigates the terrain of critical decision-making procedures. Commencing with a little background context, an analysis of existing techniques like Local Interpretable Model-Agnostic Explanations (LIMEs) and Shapley Additive exPlanations (SHAP) is presented. Concrete applications stem from insightful case studies that show XAI's revolutionary benefits in a variety of real-world settings. Moreover, to accentuate the delicate balance between privacy concerns and transparency, the ethical repercussions of employing XAI are examined. This work concludes with a discussion on how crucial it is for XAI to strengthen the foundation of accountability and trust in the vital decision-making process.

Lymphoma, a malignancy originating from the aberrant proliferation of lymphocytes in the lymphatic system, becomes one of the prime concerns in health care across the world. The traditional, mono-therapeutic interventions using chemotherapeutic agents and radiation showed limitations in relation to disease control, suboptimal response, and development of intractable drug resistance. This study thus opens the reasons for combination therapies in detail by explaining the intrinsic rationale and revealing the intrinsic advantages of lymphoma treatment with an initiative underpinned by scrupulous analysis. Furthermore, the broad analysis had comparisons between combination therapies, were derived from studies, clinical trials, and actual instances. Key performance metrics like as overall response rates, progression-free survival, and overall survival, together with a thorough review of safety profiles, are used to determine the efficacy and long-term tolerability of various combination regimens. Using the PROMETHEE-II technique, this study has attempted to offer a comprehensive knowledge of the complex therapeutic landscape by addressing various issues and factors such as drug-disease interactions, toxicities, cost-effectiveness, and accessibility. This study avails practitioners and researchers with indelible understanding that seeks to enhance the optimum therapeutic trajectory and evidence-based decision-making toward an improved outcome of treatment in lymphoma.

Global transformations in the disciplines of autonomous vehicles, machine intelligence, and aerostructureshave led to an unprecedented utilizationofLithium-Ion Batteries (LIBs) due to their superlative characteristics. As electrochemicalexploration grows more significant, analytical approaches for determining the status of Li-ionbatteries are becoming more prominent. Electrochemical Impedance Spectroscopy (EIS) is a commercially efficacious non-destructive technique for analyzing LIBs due to its convenience, frequency, accuracy, and relatively inexpensiveness. EIS can anticipate the internal thermal fluctuations of LIB efficiently and precisely; no mechanical heating elements or thermal models are obligated. This study incorporates the EIS's operational model as well as techniques for estimating the temperature in LIBs such as impedance, phase shift, and intercept. In addition, EIS applications for LIBs, notably resistance of the bulk (Rb), charge transfer reaction (Rct), and interface layer (RSEI) are demonstrated, and the paper culminates with a brief discussion and challenges of EIS-based Li-ion batteries.

Robust, affordable plasma proteomic biomarker workflows are needed for large‐scale clinical studies. We evaluated aspects of sample preparation to allow liquid chromatography‐mass spectrometry (LC‐MS) analysis of more than 1500 samples from the Fenofibrate Intervention and Event Lowering in Diabetes (FIELD) trial of adults with type 2 diabetes.

Malignant cancer angiogenesis has historically attracted enormous scientific attention. Although angiogenesis is requisite for a child’s development and conducive to tissue homeostasis, it is deleterious when cancer lurks. Today, anti-angiogenic biomolecular receptor tyrosine kinase inhibitors (RTKIs) to target angiogenesis have been prolific in treating various carcinomas. Angiogenesis is a pivotal component in malignant transformation, oncogenesis, and metastasis that can be activated by a multiplicity of factors (e.g., VEGF (Vascular endothelial growth factor), (FGF) Fibroblast growth factor, (PDGF) Platelet-derived growth factor and others). The advent of RTKIs, which primarily target members of the VEGFR (VEGF Receptor) family of angiogenic receptors has greatly ameliorated the outlook for some cancer forms, including hepatocellular carcinoma, malignant tumors, and gastrointestinal carcinoma. Cancer therapeutics have evolved steadily with active metabolites and strong multi-targeted RTK inhibitors such as E7080, CHIR-258, SU 5402, etc. This research intends to determine the efficacious anti-angiogenesis inhibitors and rank them by using the Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE- II) decision-making algorithm. The PROMETHEE-II approach assesses the influence of growth factors (GFs) in relation to the anti-angiogenesis inhibitors. Due to their capacity to cope with the frequently present vagueness while ranking alternatives, fuzzy models constitute the most suitable tools for producing results for analyzing qualitative information. This research’s quantitative methodology focuses on ranking the inhibitors according to their significance concerning criteria. The evaluation findings indicate the most efficacious and idle alternative for inhibiting angiogenesis in cancer.

As the world enters the 21st century, the imperative of safeguarding cybersecurity has intensified. Considering the expanding dependence on information technology (IT) systems for a broad spectrum of military operations, the stakes have never been higher for thwarting successful cyberattacks. Conventional security protocols have become increasingly precarious in the face of evolving cyber threats. This necessitates the adoption of avant-garde and nascent technologies to bolster the military's cybersecurity stance. The paper delves into an array of incipient technologies that proffers the prospect of enhancing cybersecurity in the realm of defence. These include artificial intelligence (AI), machine learning (ML), blockchain, and quantum computing. AI can be leveraged to automate numerous cybersecurity functions, such as threat detection and response. ML can augment the precision of threat detection and response systems. Blockchain, a distributed ledger technology, can safeguard data and transactions. Quantum computing, a nascent technology, harbours the potential to revolutionize computation. It could be utilized to breach present-day encryption standards, thereby rendering data vulnerable to attackers. Moreover, the paper appraises the exigencies and possibilities in defence cyber security and scrutinizes the policy and practice ramifications of these emergent technologies and best practices.

The advent of quantum computing has ignited unprecedented anticipation within the realm of IT. This has transcended the limitations of classical computation and revolutionized various facets of technological advancement. At the forefront of this quantum revolution stands the Noisy Intermediate-Scale Quantum (NISQ) era, characterized by a delicate interplay of immense potential and persistent imperfections. This paper delves into the intricacies of NISQ-era quantum computing, unraveling its fundamental principles, challenges, and transformative implications for the IT landscape. Beginning with quantum computing basics-qubits, superposition, entanglement, and gates-the NISQ era's essence addresses error-prone operations and decoherence. Quantum algorithms for NISQ devices, like Grover's search, Shor's factorization, and Variational Quantum Eigensolver (VQE), are scrutinized for their computational potential. Central to NISQ's viability, quantum error correction and mitigation strategies are surveyed, stabilizing quantum computations amidst noise. Examining quantum hardware architectures and performance metrics unveils NISQ devices' potential. Applications of NISQ quantum computing in cryptography, optimization, machine learning, and more are explored, showcasing quantum advantage's real-world potential. This paper embarks on a multidimensional exploration of NISQ quantum computing, illuminating its intricate facets, applications, and ethical implications. As quantum computing evolves from a theoretical notion to a tangible paradigm, the insights gleaned herein contribute to a holistic understanding of its role in shaping the next wave of computing.

Serum albumin-Co2+ interactions are of clinical importance. They play a role in mediating the physiological effects associated with cobalt toxicity and are central to the albumin cobalt binding (ACB) assay for diagnosis of myocardial ischemia. To further understand these processes, a deeper understanding of albumin-Co2+ interactions is required. Here, we present the first crystallographic structures of human serum albumin (HSA; three structures) and equine serum albumin (ESA; one structure) in complex with Co2+. Amongst a total of sixteen sites bearing a cobalt ion across the structures, two locations were prominent, and they relate to metal-binding sites A and B. Site-directed mutagenesis and isothermal titration calorimetry (ITC) were employed to characterise sites on HSA. The results indicate that His9 and His67 contribute to the primary (putatively corresponding to site B) and secondary Co2+-binding sites (site A), respectively. The presence of additional multiple weak-affinity Co2+ binding sites on HSA was also supported by ITC studies. Furthermore, addition of 5 molar equivalents of the non-esterified fatty acid palmitate (C16:0) reduced the Co2+-binding affinity at both sites A and B. The presence of bound myristate (C14:0) in the HSA crystal structures provided insight into the fatty acid-mediated structural changes that diminish the affinity of the protein toward Co2+. Together, these data provide further support for the idea that ischemia-modified albumin corresponds to albumin with excessive fatty-acid loading. Collectively, our findings provide a comprehensive understanding of the molecular underpinnings governing Co2+ binding to serum albumin.

With the gradation of time, the world has witnessed major advancements in the field of Information Technology (IT), resulting in massive data generation. The increased use of social media and new inventions has resulted in the growth of a huge amount of data known as Big Data. This data is being collected and processed to provide crucial information if analyzed accurately. Due to the sheer increase in the generation of big data as a consequence of automation and computerization, new ways of analyzing and converting big data into valuable information and knowledge are sorely needed. Data Mining which is a blend of various techniques like Machine Learning (ML), Statistics, Data Science, pattern recognition and database theory, etc., can be effectively used for detecting patterns in huge data sets (Big Data). However, the big data revolution is becoming a threat to the survival of technological advancements that enable organizations to accumulate a massive magnitude of data. Nonetheless, data mining has proved to play a prominent role in presenting potential solutions to complex difficulties in a number of studies. This paper has provided an overview of different data mining techniques like Association Rule Learning, Classification, Regression Analysis, and clustering, it also includes big data privacy and security issues. Furthermore, this study delves into recently introduced frameworks for a variety of real-world challenges, with a particular accentuation on DDoS attacks and Suicide Bomb Attack Identification as well as presents a possibility for scientists to contemplate.

Digitalization and intelligent systems have engulfed all dimensions and vicinity of existence. The intense fusion of hodiernal information technology and archaic agriculture has ushered in the epoch of agriculture 4.0, invariably reputed as smart agriculture. Variegated operational or forthcoming technologies reckon in agriculture 4.0, e.g., algae feedstock, automation, hydroponics, nanotechnology, genome editing, cognitive computing, blockchain, and machine learning, may have far-reaching implications for future agriculture and food systems and radical rudimentary potential. Although the profusion benefits of this Fourth Revolution abound, many unresolved issues and stumbling blocks must be conquered to make this paradigm shift feasible. Future solutions will coerce data availability and accuracy as vital factors to assist farmers, also security will become indispensable for constructing vigorous and efficacious systems. Security deals with difficulties like compatibility, scarce funds, and gargantuan data as smart agriculture incorporate a huge gamut and volume of resources. In this frame of reference, the authors opine that the inclusion and exclusion impact of Agriculture 4.0 technologies require ancillary scrutiny. This paper articulates the core technologies of agriculture 4.0 with an explicit focus on its applications in veritable contexts whilst delving into security issues (chiefly cyber-security). This research is envisaged to have a pragmatic influence on agriculture 4.0, emphasizing paradigms and security measures to aid farm stakeholders to navigate the firm's transition to digitization.

Industry executives in the Architecture, Engineering, and Construction (AEC) business have deliberately escalated the need for precise, unambiguous, and intuitive knowledge. Industry 4.0 is of paramount importance towards the formation of a virtual logistics system in AEC that facilitates automation in manufacturing operations and acts as a link between distinct stakeholders and construction initiatives. Immersive Virtual and Augmented Reality (VAR) technologies are rolling out in many entertainment and scientific areas as consumer-based Head-Mounted Displays (HMDs) become more ubiquitous. Furthermore, VAR innovations have great practical utility in the AEC industry due to improvements in Building Information Modeling (BIM). Although the AEC business has been reluctant to adopt VAR innovations in comparison to certain other sectors, mainly due to a paucity of reliable research contrasting the rise in benefits versus the unit price of deployment. This study aims to explore the dynamics of the industry in VAR technology adoption and addresses AEC strategic initiatives. Moreover, this paper discusses the VAR use cases in the AEC industry, along with its challenges and immersive VAR technology for BIM.

Nowadays, Blockchain technology has also drawn mammoth attention as it is currently triggering multiple projects. This technology is the prevalent technology that allows Bitcoin to operate. Bitcoin has already cemented itself as a popular virtual currency, and it will soon become a pressing requirement for citizens. Furthermore, with digital currencies proliferating all over the world, security must not be overlooked, especially when there have been numerous attacks and security vulnerabilities on bitcoin's blockchain. However, the lucrative system has drawn opponents and attackers who are continually attempting to exploit flaws in the technology in order to get profit. This paper contemplates the bitcoin's transaction process, with a focus on obscuring the true identities of clients and their coins prior to transacting, and then elucidating on the following: SHA-256 function, the primary threats to Bitcoin's security, along with some countermeasures to be undertaken.

Oesophageal adenocarcinoma (OAC) is an aggressive cancer with a five-year survival of <15%. Current chemotherapeutic strategies only benefit a minority (20-30%) of patients and there are no methods available to differentiate between responders and non-responders. We performed quantitative proteomics using Sequential Window Acquisition of all THeoretical fragment-ion spectra-Mass Spectrometry (SWATH-MS) on albumin/IgG-depleted and non-depleted plasma samples from 23 patients with locally advanced OAC prior to treatment. Individuals were grouped based on tumour regression (TRG) score (TRG1/2/3 vs TRG4/5) after chemotherapy, and differentially abundant proteins were compared. Protein depletion of highly abundant proteins led to the identification of around twice as many proteins. SWATH-MS revealed significant quantitative differences in the abundance of several proteins between the two groups. These included complement c1q subunit proteins, C1QA, C1QB and C1QC, which were of higher abundance in the low TRG group. Of those that were found to be of higher abundance in the high TRG group, GSTP1 was found to exhibit the lowest p-value and highest classification accuracy and Cohen’s kappa value. Concentrations of these proteins were further examined using ELISA-based assays. This study provides quantitative information relating to differences in the plasma proteome that underpin response to chemotherapeutic treatment in oesophageal cancers.

Oesophageal adenocarcinoma (OAC) is a disease with an incredibly poor survival rate and a complex makeup. The growth and spread of OAC tumours are profoundly influenced by their surrounding microenvironment and the properties of the tumour itself. Constant crosstalk between the tumour and its microenvironment is key to the survival of the tumour and ultimately the death of the patient. The tumour microenvironment (TME) is composed of a complex milieu of cell types including cancer associated fibroblasts (CAFs) which make up the tumour stroma, endothelial cells which line blood and lymphatic vessels and infiltrating immune cell populations. These various cell types and the tumour constantly communicate through environmental cues including fluctuations in pH, hypoxia and the release of mitogens such as cytokines, chemokines and growth factors, many of which help promote malignant progression. Eventually clusters of tumour cells such as tumour buds break away and spread through the lymphatic system to nearby lymph nodes or enter the circulation forming secondary metastasis. Collectively, these factors need to be considered when assessing and treating patients clinically. This review aims to summarise the ways in which these various factors are currently assessed and how they relate to patient treatment and outcome at an individual level.

Angelica glauca Edgew is an important medicinal and aromatic herb (family Apiaceae). The roots of A. glauca commonly used as spices by local peoples. In the present study, the phytochemical constituents, biochemical parameters and, in-vitro antioxidant activity of A. glauca roots collected from the Himalayan region have been studied. For preliminary phytochemical analysis, the hydroalcoholic and aqueous root extract of Angelica glauca were screened for the presence of carbohydrates, protein, alkaloids, glycosides, sterols, triterpenes, saponin, tannins, phenols, flavonoids, and coumarin. The biochemical parameter and in-vitro antioxidant potential of Angelica glauca were analysed by using standard methods. Phytochemical analysis of extract showed major classes of phytochemicals constituents such as carbohydrates, protein, alkaloids, glycosides, sterols, triterpenes, saponin, tannins, phenols, flavonoids, and coumarin. The results from the current study demonstrated that A.glauca roots contained carbohydrate (21±0.72 %), crude protein (12.7±0.31 %), total ash (3.86±0.034 %), dietary fiber (18.9±0.14 %), total fat (4.5±0.38 %) and ascorbic acid (68.5±0.19mg/100g). The hydroalcoholic extract showed the highest quantity of total phenol, total flavonoids, and total tannin content compared to aqueous extract. The hydroalcoholic extract exhibited high DPPH radical scavenging activity (IC50=68.1±0.34 µg/ml). The result showed that A.glauca roots have high nutritional and antioxidant potential. Hence the plant can be used as a nutraceutical and natural antioxidant.

Insulin is stored within the pancreas in an inactive Zn2+ -bound hexameric form prior to release. Similarly, clinical insulins contain Zn2+ and form multimeric complexes. Upon release from the pancreas or upon injection, insulin only becomes active once Zn2+ disengages from the complex. In plasma and other extracellular fluids, the majority of Zn2+ is bound to human serum albumin (HSA), which plays a vital role in controlling insulin pharmacodynamics by enabling removal of Zn2+ . The Zn2+ -binding properties of HSA are attenuated by non-esterified fatty acids (NEFAs) also transported by HSA. Elevated NEFA concentrations are associated with obesity and type 2 diabetes. Here we present the hypothesis that higher NEFA levels in obese and/or diabetic individuals may contribute to insulin resistance and affect therapeutic insulin dose-response profiles, through modulation of HSA/Zn2+ dynamics. We envisage this novel concept to have important implications for personalized treatments and management of diabetes-related conditions in the future.