Superacid-Mediated Late-Stage Aromatic Polydeuteration of Pharmaceuticals.

The field of medicinal chemistry is currently witnessing a deuterium rush owing to the remarkable properties of this element as bioisoster of hydrogen atom. Aromatic hydrogen isotope exchange (HIE) is one of the most studied strategies nowadays as it promises to access deuterium-modified drugs directly from their non-labeled parents. While most of the recent studies focus on metal-catalyzed C-H activation strategy, the use of superacidic conditions has been largely overlooked. This study shows that the use of TfOD as reaction medium allows the late-stage polydeuteration of a broad library of pharmaceuticals bearing a wide array of functional groups, complementing existing procedures.

Complementary Site-Selective Halogenation of Nitrogen-Containing (Hetero)Aromatics with Superacids.

Site-selective functionalization of arenes that is complementary to classical aromatic substitution reactions remains a long-standing quest in organic synthesis. Exploiting the generation of halenium ion through oxidative process and the protonation of the nitrogen containing function in HF/SbF5 , the chlorination and iodination of classically inert Csp2 -H bonds of aromatic amines occurs. Furthermore, the superacid-promoted (poly)protonation of the molecules acts as a protection, favoring the late-stage selective halogenation of natural alkaloids and active pharmaceutical ingredients.

Fluorinated pyrrolidines and piperidines incorporating tertiary benzenesulfonamide moieties are selective carbonic anhydrase II inhibitors.

A series of substituted pyrrolidines and piperidines were synthesized using superacid HF/SbF5 chemistry. Investigated as inhibitors of several human carbonic anhydrase (hCA, EC 4.2.1.1) isoforms, i.e. the cytosolic hCA I and II as well as the tumor-associated transmembrane isoforms hCA IX and XII, these compounds showed a never yet reported selectivity toward the human carbonic anhydrase hCA II. In the tertiary benzenesulfonamide family, this class of inhibitors points out a new mechanism of action for human carbonic anhydrase II inhibition.

Using machine learning algorithms to enhance IoT system security.

The term "Internet of Things" (IoT) refers to a system of networked computing devices that may work and communicate with one another without direct human intervention. It is one of the most exciting areas of computing nowadays, with its applications in multiple sectors like cities, homes, wearable equipment, critical infrastructure, hospitals, and transportation. The security issues surrounding IoT devices increase as they expand. To address these issues, this study presents a novel model for enhancing the security of IoT systems using machine learning (ML) classifiers. The proposed approach analyzes recent technologies, security, intelligent solutions, and vulnerabilities in ML IoT-based intelligent systems as an essential technology to improve IoT security. The study illustrates the benefits and limitations of applying ML in an IoT environment and provides a security model based on ML that manages autonomously the rising number of security issues related to the IoT domain. The paper proposes an ML-based security model that autonomously handles the growing number of security issues associated with the IoT domain. This research made a significant contribution by developing a cyberattack detection solution for IoT devices using ML. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. The study used seven ML algorithms to identify the most accurate classifiers for their AI-based reaction agent's implementation phase, which can identify attack activities and patterns in networks connected to the IoT. Compared to previous research, the proposed approach achieved a 99.9% accuracy, a 99.8% detection average, a 99.9 F1 score, and a perfect AUC score of 1. The study highlights that the proposed approach outperforms earlier machine learning-based models in terms of both execution speed and accuracy. The study illustrates that the suggested approach outperforms previous machine learning-based models in both execution time and accuracy.

Deep learning model for fully automated breast cancer detection system from thermograms.

Breast cancer is one of the most common diseases among women worldwide. It is considered one of the leading causes of death among women. Therefore, early detection is necessary to save lives. Thermography imaging is an effective diagnostic technique which is used for breast cancer detection with the help of infrared technology. In this paper, we propose a fully automatic breast cancer detection system. First, U-Net network is used to automatically extract and isolate the breast area from the rest of the body which behaves as noise during the breast cancer detection model. Second, we propose a two-class deep learning model, which is trained from scratch for the classification of normal and abnormal breast tissues from thermal images. Also, it is used to extract more characteristics from the dataset that is helpful in training the network and improve the efficiency of the classification process. The proposed system is evaluated using real data (A benchmark, database (DMR-IR)) and achieved accuracy = 99.33%, sensitivity = 100% and specificity = 98.67%. The proposed system is expected to be a helpful tool for physicians in clinical use.

A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms.

Breast cancer is the second most frequent cancer worldwide, following lung cancer and the fifth leading cause of cancer death and a major cause of cancer death among women. In recent years, convolutional neural networks (CNNs) have been successfully applied for the diagnosis of breast cancer using different imaging modalities. Pooling is a main data processing step in CNN that decreases the feature maps' dimensionality without losing major patterns. However, the effect of pooling layer was not studied efficiently in literature. In this paper, we propose a novel design for the pooling layer called vector pooling block (VPB) for the CCN algorithm. The proposed VPB consists of two data pathways, which focus on extracting features along horizontal and vertical orientations. The VPB makes the CNNs able to collect both global and local features by including long and narrow pooling kernels, which is different from the traditional pooling layer, that gathers features from a fixed square kernel. Based on the novel VPB, we proposed a new pooling module called AVG-MAX VPB. It can collect informative features by using two types of pooling techniques, maximum and average pooling. The VPB and the AVG-MAX VPB are plugged into the backbone CNNs networks, such as U-Net, AlexNet, ResNet18 and GoogleNet, to show the advantages in segmentation and classification tasks associated with breast cancer diagnosis from thermograms. The proposed pooling layer was evaluated using a benchmark thermogram database (DMR-IR) and its results compared with U-Net results which was used as base results. The U-Net results were as follows: global accuracy = 96.6%, mean accuracy = 96.5%, mean IoU = 92.07%, and mean BF score = 78.34%. The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.

Substitution effect on the hydrofluorination reaction of unsaturated amines in superacid HF/SbF(5).

This paper describes the scope and limitations of the hydrofluorination reaction in superacid HF/SbF(5). On the basis of experimental studies of polyfunctional substrates' behaviour, the dramatic effect of substitution on the superelectrophilic character of ammonium-carbenium dications was emphasized. This reaction was applied to the synthesis of novel fluorinated key building blocks. Furthermore, the hydrofluorination reaction and the discovered homodimerization/fluorination reaction were applied to the synthesis of highly valued fluorinated diamines.

A novel, facile route to beta-fluoroamines by hydrofluorination using superacid HF-SbF(5).

A range of unsaturated amines and sulfonamides were converted to beta-fluoro nitrogen analogues after hydrofluorination in superacid HF-SbF(5), based on the formation of highly reactive electrophilic intermediates.