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1,2,3,4-Tetrahydroquinolines (THQs) are present in molecules with significant biological importance and pharmaceuticals with diverse biological activities. Various synthetic routes have been developed to tetrahydroquinolines, such as the hydrogenation of quinolines, domino reactions, intramolecular cyclization, and notably, the Povarov reaction. Organocatalysts are small, chiral organic molecules that can successfully catalyze organic transformations. This review examines the use of organocatalysts in the asymmetric synthesis of tetrahydroquinoline derivatives using aminocatalysis, NHC catalysis, and H-bonding catalysis covering the literature between 2000 and early 2024. 1 Introduction 2 Aminocatalysis 3 NHC Catalysis 4 H-Bonding Catalysis 5 Miscellaneous 6 Conclusion [ABSTRACT FROM AUTHOR]

Lung cancer is the most common causes of death among all cancer‐related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X‐ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image‐processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k‐nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi‐layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f‐score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. Research Highlights: Lung cancer is a leading cause of cancer‐related death. Imaging (MRI, CT, and X‐ray) aids diagnosis.Automated classification of lung cancer faces challenges due to complex imaging steps.This study proposes human lung cancer classification using diverse machine learning techniques.Input images from lung cancer dataset undergo image processing and machine learning.Classifiers like k‐nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi‐layer perceptron (MLP) classify cancer types; MLP excels in accuracy. [ABSTRACT FROM AUTHOR]

Background: We retrospectively evaluated radiomics as a predictor of the tumor microenvironment (TME) and efficacy with anti‐PD‐1 mAb (IO) in R/M HNSCC. Methods: Radiomic feature extraction was performed on pre‐treatment CT scans segmented using 3D slicer v4.10.2 and key features were selected using LASSO regularization method to build classification models with XGBoost algorithm by incorporating cross‐validation techniques to calculate accuracy, sensitivity, and specificity. Outcome measures evaluated were disease control rate (DCR) by RECIST 1.1, PFS, and OS and hypoxia and CD8 T cells in the TME. Results: Radiomics features predicted DCR with accuracy, sensitivity, and specificity of 76%, 73%, and 83%, for OS 77%, 86%, 70%, PFS 82%, 75%, 89%, and in the TME, for high hypoxia 80%, 88%, and 72% and high CD8 T cells 91%, 83%, and 100%, respectively. Conclusion: Radiomics accurately predicted the efficacy of IO and features of the TME in R/M HNSCC. Further study in a larger patient population is warranted. [ABSTRACT FROM AUTHOR]

The synthesis of 1,3-enyne has widespread appeal in organic synthesis due to their proven adaptability as intermediates in routes to compounds of significant biological and material interest. A variety of methods have been designed to formulate 1,3-enynes from diverse substrates, such as alkynes, 1,3-diynes, alkynyl-substituted cyclopropanes, and propargyl alcohols. This review covers the synthesis of 1,3-enynes utilizing the homo- and cross-coupling of alkynes, nucleophilic metal/acid-induced cyclopropane ring opening, and rearrangement/dehydration of propargyl alcohols. A key concern in procedures starting from alkynes and 1,3-diynes is the management of regio-, stereo-, and, where fitting, chemoselectivity. In contrast, in cyclopropyl ring opening nucleophile orientation determines the 1,3-enynes formed. Efficient methods for the broad and selective synthesis of 1,3-enynes are highlighted and specific examples are given to demonstrate the efficacy of these processes. 1 Introduction and Scope 2 Synthesis 2.1 Synthesis of 1,3-Enynes from Alkynes 2.1.1 Metal-Catalyzed Cross-Coupling/Additions of Alkynes with Alkenes or Vinyl or Aryl Halides 2.1.1.1 Palladium Catalysis 2.1.1.2 Rhodium Catalysis 2.1.1.3 Copper Catalysis 2.1.1.4 I ron Catalysis 2.1.1.5 Nickel Catalysis 2.1.1.6 Miscellaneous 2.2 Synthesis of Enynes from Propargyl Alcohols 2.3 Metal/Acid-Catalyzed Ring Opening of Cyclopropanes 3 Conclusion [ABSTRACT FROM AUTHOR]

Hydrochar (HC), a carbon (C)-rich solid material produced using a hydrothermal technique at relatively low temperatures and pressures, has gained little attention regarding its use as a nanocatalyst in multiple wastewater treatment technologies. These HC-based nanocatalysts, catalyst support materials, and nano-adsorbents have the advantage of being cost-effective and chemically stable, having a porous structure, semiconductive properties, and oxygen (O)-rich surface functionality. Laboratory-scale trials that have successfully used HC catalysts to accelerate the kinetics of advanced oxidation processes (AOPs) (photocatalysis, electrochemical oxidation, Fenton catalysis) are here reviewed in depth, with a special focus on the mechanisms involved. The potential of HC catalysts in energy and valuables recovery using anaerobic digestion, bioelectrochemical systems, metal air batteries, transesterification, isomerization, and reforming processes are highlighted. In addition, the synthesis of HC from waste biomass (a zero-cost C precursor) would reduce the cost of expensive electrocatalysts, reduce greenhouse gas (GHG) emissions, ensure C capture, offer a low-C economy with less of a C footprint, and enhance environmental sustainability. This review provides a unique and comprehensive sustainability related perspective on HC and HC-based nanocatalysts that can be used to positively impact the United Nations' Sustainable Development Goals (SDGs), especially SDGs 3, 6, 7, 11, and 13. Finally, a circular economy approach that includes HC and HC catalyst use in wastewater treatment and waste management systems is discussed, along with future perspectives and challenges. [ABSTRACT FROM AUTHOR]

A Differential Difference Current Conveyor Transconductance Amplifier (DDCCTA) based resistor tunable memristor emulator has been proposed in this work. The emulator can be used in both grounded and floating circumstances. The proposed design incorporates only one active block along with few passive components. Moreover, the circuit can operate in both incremental and decremental modes, by simply changing the input ports. The circuit demonstrates all the characteristics of an ideal memristor up to 6 MHz. The proposed model has been simulated using TSMC 0.18 $\mu $ μ m process parameter and occupies an area of 51 × 42.5 $\mu $ μ m2 chip-area, excluding capacitor. The circuit's reliability has been verified by studying non-ideal, non-volatile, Monte-Carlo, process corner variation analysis. The circuit applicability has been tested through series/parallel combinations. To validate the experimental demonstration, AD844AN and CA3080 have been used to make a prototype, which shows good agreement with theoretical and simulation results. [ABSTRACT FROM AUTHOR]