Heart disease is one of the key contributors to human death. Each year, several people die due to this disease. According to the WHO, 17.9 million people die each year due to heart disease. With the various technologies and techniques developed for heart-disease detection, the use of image classification can further improve the results. Image classification is a significant matter of concern in modern times. It is one of the most basic jobs in pattern identification and computer vision, and refers to assigning one or more labels to images. Pattern identification from images has become easier by using machine learning, and deep learning has rendered it more precise than traditional image classification methods. This study aims to use a deep-learning approach using image classification for heart-disease detection. A deep convolutional neural network (DCNN) is currently the most popular classification technique for image recognition. The proposed model is evaluated on the public UCI heart-disease dataset comprising 1050 patients and 14 attributes. By gathering a set of directly obtainable features from the heart-disease dataset, we considered this feature vector to be input for a DCNN to discriminate whether an instance belongs to a healthy or cardiac disease class. To assess the performance of the proposed method, different performance metrics, namely, accuracy, precision, recall, and the F1 measure, were employed, and our model achieved validation accuracy of 91.7%. The experimental results indicate the effectiveness of the proposed approach in a real-world environment.

Lung cancer starts and spreads in the tissues of the lungs, more specifically, in the tissue that forms air passages. This cancer is reported as the leading cause of cancer deaths worldwide. In addition to being the most fatal, it is the most common type of cancer. Nearly 47,000 patients are diagnosed with it annually worldwide. This article proposes a fully automated and practical system to identify and classify lung cancer. This system aims to detect cancer in its early stage to save lives if possible or reduce the death rates. It involves a deep convolutional neural network (DCNN) technique, VGG-19, and another deep learning technique, long short-term memory networks (LSTMs). Both tools detect and classify lung cancers after being customized and integrated. Furthermore, image segmentation techniques are applied. This system is a type of computer-aided diagnosis (CAD). After several experiments on MATLAB were conducted, the results show that this system achieves more than 98.8% accuracy when using both tools together. Various schemes were developed to evaluate the considered disease. Three lung cancer datasets, downloaded from the Kaggle website and the LUNA16 grad challenge, were used to train the algorithm, test it, and prove its correctness. Lastly, a comparative evaluation between the proposed approach and some works from the literature is presented. This evaluation focuses on the four performance metrics: accuracy, recall, precision, and F-score. This system achieved an average of 99.42% accuracy and 99.76, 99.88, and 99.82% for recall, precision, and F-score, respectively, when VGG-19 was combined with LSTMs. In addition, the results of the comparison evaluation show that the proposed algorithm outperforms other methods and produces exquisite findings. This study concludes that this model can be deployed to aid and support physicians in diagnosing lung cancer correctly and accurately. This research reveals that the presented method has functionality, competence, and value among other implemented models.

Poly(N-isopropylacrylamide/2-acrylamido-2-methylpropane sulfonic acid) microgel was prepared and fabricated with silver nanoparticles to design a material for dual functions of catalyst and sensor.

Selenium is an indispensable trace element for all living organisms. It is an essential structural component of several selenium-dependent enzymes, which support the human body’s defense mechanism. Recently, the significance of selenium in preventing/treating COVID-19 has been documented in the literature. This review highlights the clinical studies, compositions, and patent literature on selenium to prevent/treat COVID-19. Selenium exerts its anti-COVID-19 action by reducing oxidative stress, declining the expression of the ACE-2 receptor, lowering the discharge of pro-inflammatory substances, and inhibiting the 3CL_Pro (main protease) and PL_pro enzyme of SARS-CoV-2. The data of clinical studies, inventive compositions, and patent literature revealed that selenium monotherapy and its compositions with other nutritional supplements/drugs (vitamin, iron, zinc, copper, ferulic acid, resveratrol, spirulina, N-acetylcysteine, fish oil, many herbs, doxycycline, azithromycin, curcumin, quercetin, etc.,) might be practical to prevent/treat COVID-19. The studies have also suggested a correlation between COVID-19 and selenium deficiency. This indicates that adequate selenium supplementation may provide promising treatment outcomes in COVID-19 patients. The authors foresee the development and commercialization of Selenium-based compositions and dosage forms (spray, inhalers, control release dosage forms, etc.) to battle COVID-19. We also trust that numerous selenium-based compositions are yet to be explored. Accordingly, there is good scope for scientists to work on developing novel and inventive selenium-based compositions to fight against COVID-19. However, there is also a need to consider the narrow therapeutic window and chemical interaction of selenium before developing selenium-based compositions.

Decaprenyl-phosphoryl-ribose 2'-epimerase (DprE1) inhibitors are an innovative and futuristic orally active group of antituberculosis agents. A few DprE1 inhibitors are in the clinical trial for tuberculosis (TB), including macozinone. This review highlights the discovery, developmental status, clinical studies, patents, and prospects of macozinone (MCZ). The patent and non-patent literature search was done by entering keywords such as macozinone; MCZ; PBTZ169; PBTZ-169 in Pubmed, Espacenet, Patentscope, and the USPTO databases. However, data on Sci-Finder was searched using CAS registry number: 1377239-83-2. MCZ clinical trial studies were retrieved from the clinicaltrials.gov database using the exact keywords. The chemical structure of MCZ was disclosed in 2009. Accordingly, patents/patent applications published from 2009 to June 12, 2022, have been discussed herein. MCZ and MCZ hydrochloride salt patents were granted in 2014 and 2019, respectively, in the USA. The patent literature and the clinical trial studies suggest capsule, tablet, and suspension formulations of crystalline MCZ and its hydrochloride salt as the possible and prospective dosage forms to treat TB. Some combinations of MCZ with other drugs (chloroquine, telacebec, tafenoquine, TBI-166, and sanfetrinem) with improved anti-TB efficacy have been documented. Based on the literature covered in this review article on the clinical studies and patents applied/granted to MCZ, it can be inferred that MCZ seems to be a promising DprE1 inhibitor and could help to tackle the emerging dilemma of drug-resistant either as a monotherapy or in combination with additional anti-TB agents. Furthermore, the authors anticipate the development of new combinations, salts, and polymorphs of MCZ as anti-TB agents shortly. This review article might prove beneficial to the scientific community as it summarizes chemistry, pharmacology and provides an update on the clinical studies and patents/patent applications of one of the emerging anti-TB drugs in one place.

COVID-19 has had an impact on human quality of life and economics. Scientists have been identifying remedies for its prevention and treatment from all possible sources, including plants. Nigella sativa L. (NS) is an important medicinal plant of Islamic value. This review highlights the anti-COVID-19 potential, clinical trials, inventions, and patent literature related to NS and its major chemical constituents, like thymoquinone. The literature was collected from different databases, including Pubmed, Espacenet, and Patentscope. The literature supports the efficacy of NS, NS oil (NSO), and its chemical constituents against COVID-19. The clinical data imply that NS and NSO can prevent and treat COVID-19 patients with a faster recovery rate. Several inventions comprising NS and NSO have been claimed in patent applications to prevent/treat COVID-19. The patent literature cites NS as an immunomodulator, antioxidant, anti-inflammatory, a source of anti-SARS-CoV-2 compounds, and a plant having protective effects on the lungs. The available facts indicate that NS, NSO, and its various compositions have all the attributes to be used as a promising remedy to prevent, manage, and treat COVID-19 among high-risk people as well as for the therapy of COVID-19 patients of all age groups as a monotherapy or a combination therapy. Many compositions of NS in combination with countless medicinal herbs and medicines are still unexplored. Accordingly, the authors foresee a bright scope in developing NS-based anti-COVID-19 composition for clinical use in the future.