Tetrahydroisoquinolines - an updated patent review for cancer treatment (2016 - present).

INTRODUCTION: Cancer is a prominent cause of death globally, triggered by both non-genetic and genetic alterations in genes influenced by various environmental factors. The tetrahydroisoquinoline (THIQ), specifically 1,2,3,4-tetrahydroisoquinoline serves as fundamental element in various alkaloids, prevalent in proximity to quinoline and indole alkaloids. AREA COVERED: In this review, the therapeutic applications of THIQ derivatives as an anticancer agent from 2016 to 2024 have been examined. The patents were gathered through comprehensive searches of the Espacenet, Google patent, WIPO, and Sci Finder databases. The therapeutic areas encompassed in the patents include numerous targets of cancer. EXPERT OPINION: THIQ analogues play a crucial role in medicinal chemistry, with many being integral to pharmacological processes and clinical trials. Numerous THIQ compounds have been synthesized for therapeutic purposes, notably in cancer treatment. They show great promise for developing anticancer drugs, demonstrating strong affinity and efficacy against various cancer targets. The creation of multi-target ligands is a compelling avenue for THIQ-based anticancer drug discovery.

Four Transformer-Based Deep Learning Classifiers Embedded with an Attention U-Net-Based Lung Segmenter and Layer-Wise Relevance Propagation-Based Heatmaps for COVID-19 X-ray Scans.

Background: Diagnosing lung diseases accurately is crucial for proper treatment. Convolutional neural networks (CNNs) have advanced medical image processing, but challenges remain in their accurate explainability and reliability. This study combines U-Net with attention and Vision Transformers (ViTs) to enhance lung disease segmentation and classification. We hypothesize that Attention U-Net will enhance segmentation accuracy and that ViTs will improve classification performance. The explainability methodologies will shed light on model decision-making processes, aiding in clinical acceptance. Methodology: A comparative approach was used to evaluate deep learning models for segmenting and classifying lung illnesses using chest X-rays. The Attention U-Net model is used for segmentation, and architectures consisting of four CNNs and four ViTs were investigated for classification. Methods like Gradient-weighted Class Activation Mapping plus plus (Grad-CAM++) and Layer-wise Relevance Propagation (LRP) provide explainability by identifying crucial areas influencing model decisions. Results: The results support the conclusion that ViTs are outstanding in identifying lung disorders. Attention U-Net obtained a Dice Coefficient of 98.54% and a Jaccard Index of 97.12%. ViTs outperformed CNNs in classification tasks by 9.26%, reaching an accuracy of 98.52% with MobileViT. An 8.3% increase in accuracy was seen while moving from raw data classification to segmented image classification. Techniques like Grad-CAM++ and LRP provided insights into the decision-making processes of the models. Conclusions: This study highlights the benefits of integrating Attention U-Net and ViTs for analyzing lung diseases, demonstrating their importance in clinical settings. Emphasizing explainability clarifies deep learning processes, enhancing confidence in AI solutions and perhaps enhancing clinical acceptance for improved healthcare results.

Refinement of Risk-Stratification of Cytogenetically Normal Acute Myeloid Leukemia Adult Patients by MN1 Expression.

INTRODUCTION: Acute myeloid leukemia with normal cytogenetics (CN-AML) represents a heterogeneous group having diverse genetic mutations. Understanding the significance of each of these mutations is necessary. In this study, we evaluated the prognostic role of MN1 expression in adult CN-AML patients. METHOD: One hundred and sixty-three de-novo adult AML patients were evaluated for MN1 expression by real-time PCR. MN1 expression was correlated with the clinical characteristics of the patients and their outcomes. RESULTS: Higher MN1 expression was associated with NPM1 wild-type (p<0.0001), CD34 positivity (p=0.006), and lower clinical remission rate (p=0.027). FLT3-ITD and CEBPA mutations had no association with MN1 expression. On survival analysis, a high MN1 expression was associated with poor event-free survival (Hazard Ratio 2.47, 95% Confidence Interval: 1.42-4.3; p<0.0001) and overall survival (Hazard Ratio 4.18, 95% Confidence Interval: 2.17-8.08; p<0.0001). On multivariate analysis, the MN1 copy number emerged as an independent predictor of EFS (p<0.0001) and OS (p<0.0001). CONCLUSION: MN1 expression is an independent predictor of outcome in CN-AML.

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review.

Background: The field of precision medicine endeavors to transform the healthcare industry by advancing individualised strategies for diagnosis, treatment modalities, and predictive assessments. This is achieved by utilizing extensive multidimensional biological datasets encompassing diverse components, such as an individual's genetic makeup, functional attributes, and environmental influences. Artificial intelligence (AI) systems, namely machine learning (ML) and deep learning (DL), have exhibited remarkable efficacy in predicting the potential occurrence of specific cancers and cardiovascular diseases (CVD). Methods: We conducted a comprehensive scoping review guided by the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) framework. Our search strategy involved combining key terms related to CVD and AI using the Boolean operator AND. In August 2023, we conducted an extensive search across reputable scholarly databases including Google Scholar, PubMed, IEEE Xplore, ScienceDirect, Web of Science, and arXiv to gather relevant academic literature on personalised medicine for CVD. Subsequently, in January 2024, we extended our search to include internet search engines such as Google and various CVD websites. These searches were further updated in March 2024. Additionally, we reviewed the reference lists of the final selected research articles to identify any additional relevant literature. Findings: A total of 2307 records were identified during the process of conducting the study, consisting of 564 entries from external sites like arXiv and 1743 records found through database searching. After 430 duplicate articles were eliminated, 1877 items that remained were screened for relevancy. In this stage, 1241 articles remained for additional review after 158 irrelevant articles and 478 articles with insufficient data were removed. 355 articles were eliminated for being inaccessible, 726 for being written in a language other than English, and 281 for not having undergone peer review. Consequently, 121 studies were deemed suitable for inclusion in the qualitative synthesis. At the intersection of CVD, AI, and precision medicine, we found important scientific findings in our scoping review. Intricate pattern extraction from large, complicated genetic datasets is a skill that AI algorithms excel at, allowing for accurate disease diagnosis and CVD risk prediction. Furthermore, these investigations have uncovered unique genetic biomarkers linked to CVD, providing insight into the workings of the disease and possible treatment avenues. The construction of more precise predictive models and personalised treatment plans based on the genetic profiles of individual patients has been made possible by the revolutionary advancement of CVD risk assessment through the integration of AI and genomics. Interpretation: The systematic methodology employed ensured the thorough examination of available literature and the inclusion of relevant studies, contributing to the robustness and reliability of the study's findings. Our analysis stresses a crucial point in terms of the adaptability and versatility of AI solutions. AI algorithms designed in non-CVD domains such as in oncology, often include ideas and tactics that might be modified to address cardiovascular problems. Funding: No funding received.

Deep learning approach for cardiovascular disease risk stratification and survival analysis on a Canadian cohort.

The quantification of carotid plaque has been routinely used to predict cardiovascular risk in cardiovascular disease (CVD) and coronary artery disease (CAD). To determine how well carotid plaque features predict the likelihood of CAD and cardiovascular (CV) events using deep learning (DL) and compare against the machine learning (ML) paradigm. The participants in this study consisted of 459 individuals who had undergone coronary angiography, contrast-enhanced ultrasonography, and focused carotid B-mode ultrasound. Each patient was tracked for thirty days. The measurements on these patients consisted of maximum plaque height (MPH), total plaque area (TPA), carotid intima-media thickness (cIMT), and intraplaque neovascularization (IPN). CAD risk and CV event stratification were performed by applying eight types of DL-based models. Univariate and multivariate analysis was also conducted to predict the most significant risk predictors. The DL's model effectiveness was evaluated by the area-under-the-curve measurement while the CV event prediction was evaluated using the Cox proportional hazard model (CPHM) and compared against the DL-based concordance index (c-index). IPN showed a substantial ability to predict CV events (p < 0.0001). The best DL system improved by 21% (0.929 vs. 0.762) over the best ML system. DL-based CV event prediction showed a ~ 17% increase in DL-based c-index compared to the CPHM (0.86 vs. 0.73). CAD and CV incidents were linked to IPN and carotid imaging characteristics. For survival analysis and CAD prediction, the DL-based system performs superior to ML-based models.

Wound-healing effect of topical nanoemulsion-loaded cream and gel formulations of Hippophae rhamnoides L. (sea buckthorn) fruit oil and their acute dermal toxicity study on female SD rats.

OBJECTIVE: The objective of this study was to evaluate the efficacy and safety of topical nanoemulsion (NE)-loaded cream and gel formulations of Hippophae rhamnoides L. (sea buckthorn [SBT]) fruit oil for wound healing. MATERIALS AND METHODS: The NE-loaded cream and gel formulations of H. rhamnoides L. (SBT) fruit oil (IPHRFH) were prepared and evaluated for their wound-healing activity on female Sprague-Dawley (SD) rats. They were further divided into groups (seven) and the wound-healing activity was determined by measuring the area of the wound on the wounding day and on the 0th, 4th, 8th, and 10th days. The acute dermal toxicity of the formulations was assessed by observing the erythema, edema, and body weight (BW) of the rats. RESULTS: The topical NE cream and gel formulations of H. rhamnoides L. (SBT) fruit oil showed significant wound-healing activity in female SD rats. The cream formulation of IPHRFH showed 78.96%, the gel showed 72.59% wound contraction on the 8th day, whereas the positive control soframycin (1% w/w framycetin) had 62.29% wound contraction on the 8th day. The formulations also showed a good acute dermal toxicity profile with no changes significantly affecting BW and dermal alterations. CONCLUSIONS: The results of this study indicate that topical NE-loaded cream and gel formulation of H. rhamnoides L. (SBT) fruit oil are safe and effective for wound healing. The formulations showed no signs of acute dermal toxicity in female SD rats.

GeneAI 3.0: powerful, novel, generalized hybrid and ensemble deep learning frameworks for miRNA species classification of stationary patterns from nucleotides.

Due to the intricate relationship between the small non-coding ribonucleic acid (miRNA) sequences, the classification of miRNA species, namely Human, Gorilla, Rat, and Mouse is challenging. Previous methods are not robust and accurate. In this study, we present AtheroPoint's GeneAI 3.0, a powerful, novel, and generalized method for extracting features from the fixed patterns of purines and pyrimidines in each miRNA sequence in ensemble paradigms in machine learning (EML) and convolutional neural network (CNN)-based deep learning (EDL) frameworks. GeneAI 3.0 utilized five conventional (Entropy, Dissimilarity, Energy, Homogeneity, and Contrast), and three contemporary (Shannon entropy, Hurst exponent, Fractal dimension) features, to generate a composite feature set from given miRNA sequences which were then passed into our ML and DL classification framework. A set of 11 new classifiers was designed consisting of 5 EML and 6 EDL for binary/multiclass classification. It was benchmarked against 9 solo ML (SML), 6 solo DL (SDL), 12 hybrid DL (HDL) models, resulting in a total of 11 + 27 = 38 models were designed. Four hypotheses were formulated and validated using explainable AI (XAI) as well as reliability/statistical tests. The order of the mean performance using accuracy (ACC)/area-under-the-curve (AUC) of the 24 DL classifiers was: EDL > HDL > SDL. The mean performance of EDL models with CNN layers was superior to that without CNN layers by 0.73%/0.92%. Mean performance of EML models was superior to SML models with improvements of ACC/AUC by 6.24%/6.46%. EDL models performed significantly better than EML models, with a mean increase in ACC/AUC of 7.09%/6.96%. The GeneAI 3.0 tool produced expected XAI feature plots, and the statistical tests showed significant p-values. Ensemble models with composite features are highly effective and generalized models for effectively classifying miRNA sequences.

An adamantyl-caffeoyl-anilide exhibits broad-spectrum antibacterial activity by inhibiting FtsZ assembly and Z-ring formation.

Several harmful bacteria have evolved resistance to conventional antibiotics due to their extensive usage. FtsZ, a principal bacterial cell division protein, is considered as an important drug target to combat resistance. We identified a caffeoyl anilide derivative, (E)-N-(4-(3-(3,4-dihydroxyphenyl)acryloyl)phenyl)-1-adamantylamide (compound 11) as a new antimicrobial agent targeting FtsZ. Compound 11 caused cell elongation in Mycobacterium smegmatis, Bacillus subtilis, and Escherichia coli cells, indicating that it inhibits cell partitioning. Compound 11 inhibited the assembly of Mycobacterium smegmatis FtsZ (MsFtsZ), forming short and thin filaments in vitro. Interestingly, the compound increased the rate of GTP hydrolysis of MsFtsZ. Compound 11 also impeded the assembly of Mycobacterium tuberculosis FtsZ. Fluorescence and absorption spectroscopic analysis suggested that compound 11 binds to MsFtsZ and produces conformational changes in FtsZ. The docking analysis indicated that the compound binds at the interdomain cleft of MsFtsZ. Further, it caused delocalization of the Z-ring in Mycobacterium smegmatis and Bacillus subtilis without affecting DNA segregation. Notably, compound 11 did not inhibit tubulin polymerization, the eukaryotic homolog of FtsZ, suggesting its specificity on bacteria. The evidence indicated that compound 11 exerts its antibacterial effect by impeding FtsZ assembly and has the potential to be developed as a broad-spectrum antimicrobial agent.

Alternatives to Topical Glaucoma Medication for Glaucoma Management.

Topical glaucoma medications have favorable safety and efficacy, but their use is limited by factors such as side effects, nonadherence, costs, ocular surface disease, intraocular pressure fluctuations, diminished quality of life, and the inherent difficulty of penetrating the corneal surface. Although traditionally these limitations have been accepted as an inevitable part of glaucoma treatment, a rapidly-evolving arena of minimally invasive surgical and laser interventions has initiated the beginnings of a reevaluation of the glaucoma treatment paradigm. This reevaluation encompasses an overall shift away from the reactive, topical-medication-first default and a shift toward earlier intervention with laser or surgical therapies such as selective laser trabeculoplasty, sustained-release drug delivery, and micro-invasive glaucoma surgery. Aside from favorable safety, these interventions may have clinically important attributes such as consistent IOP control, cost-effectiveness, independence from patient adherence, prevention of disease progression, and improved quality of life.