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.

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.

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.

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.

Comparative quantitative analysis of fruit oil from Hippophae rhamnoides (seabuckthorn) by qNMR, FTIR and GC-MS.

Objective: To develop a qNMR method for quantitative analysis of triacylglycerols in fruit oil of Hippophae rhamnoides (seabuckthorn, SBT) and analyze commercial samples of SBT oils using GC-MS and FTIR. Methods: SBT fruit oil (IPHRFH) was extracted with hexane and the triglyceride (TAG) was isolated by vacuum liquid chromatography. Six different branded SBT oils purchased from e-commerce suppliers (Amazon) and in-house prepared SBT oil was analyzed by qNMR and fatty acyl composition of TAGs determined by using NMR. In-house oil was also analysed by GC-MS and FTIR spectroscopy. Results: The qNMR results showed that the oil contained 80.3% of triacylglycerol (TAG). The SBT oil TAGs comprised of linolenate 6.6%, palmitoleate/oleate 65.4%, and total saturated fatty acyl chain including palmitate 28% as determined by qNMR. GC-MS analysis revealed that the major acyl functionalities present in the TAG were palmitoleic acid 36.5%, oleic acid 12.9%, palmitic acid 21.2%, and linoleic acid 18%. Of the six commercial samples analyzed, samples from only one supplier (SW) were fruit oil; All others were the seed oils or mix of fruit oil and seed oil. The labels for samples except for the SW did not indicate whether it was fruit oil or seed oil. Conclusion: The results suggest that SBT oil should be analyzed by combination of GC-MS, FTIR and qNMR for factual content of free fatty acid or TAGs, which are chemically different in nature and affect the quality of oil. GC-MS showed the content of omega free fatty acids after hydrolysis, while qNMR and FTIR showed the content of TAGs. The major acyl functionalities found in SBT fruit oil TAGs are palmitoleate/palmitate/oleate, while linoleate and linonelate make up a minor fraction. Furthermore, analysis of commercial samples showed discrepancies between label claims and actual content.

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.

Polygenic Risk Score for Cardiovascular Diseases in Artificial Intelligence Paradigm: A Review.

Cardiovascular disease (CVD) related mortality and morbidity heavily strain society. The relationship between external risk factors and our genetics have not been well established. It is widely acknowledged that environmental influence and individual behaviours play a significant role in CVD vulnerability, leading to the development of polygenic risk scores (PRS). We employed the PRISMA search method to locate pertinent research and literature to extensively review artificial intelligence (AI)-based PRS models for CVD risk prediction. Furthermore, we analyzed and compared conventional vs. AI-based solutions for PRS. We summarized the recent advances in our understanding of the use of AI-based PRS for risk prediction of CVD. Our study proposes three hypotheses: i) Multiple genetic variations and risk factors can be incorporated into AI-based PRS to improve the accuracy of CVD risk predicting. ii) AI-based PRS for CVD circumvents the drawbacks of conventional PRS calculators by incorporating a larger variety of genetic and non-genetic components, allowing for more precise and individualised risk estimations. iii) Using AI approaches, it is possible to significantly reduce the dimensionality of huge genomic datasets, resulting in more accurate and effective disease risk prediction models. Our study highlighted that the AI-PRS model outperformed traditional PRS calculators in predicting CVD risk. Furthermore, using AI-based methods to calculate PRS may increase the precision of risk predictions for CVD and have significant ramifications for individualized prevention and treatment plans.

DermAI 1.0: A Robust, Generalized, and Novel Attention-Enabled Ensemble-Based Transfer Learning Paradigm for Multiclass Classification of Skin Lesion Images.

Skin lesion classification plays a crucial role in dermatology, aiding in the early detection, diagnosis, and management of life-threatening malignant lesions. However, standalone transfer learning (TL) models failed to deliver optimal performance. In this study, we present an attention-enabled ensemble-based deep learning technique, a powerful, novel, and generalized method for extracting features for the classification of skin lesions. This technique holds significant promise in enhancing diagnostic accuracy by using seven pre-trained TL models for classification. Six ensemble-based DL (EBDL) models were created using stacking, softmax voting, and weighted average techniques. Furthermore, we investigated the attention mechanism as an effective paradigm and created seven attention-enabled transfer learning (aeTL) models before branching out to construct three attention-enabled ensemble-based DL (aeEBDL) models to create a reliable, adaptive, and generalized paradigm. The mean accuracy of the TL models is 95.30%, and the use of an ensemble-based paradigm increased it by 4.22%, to 99.52%. The aeTL models' performance was superior to the TL models in accuracy by 3.01%, and aeEBDL models outperformed aeTL models by 1.29%. Statistical tests show significant p-value and Kappa coefficient along with a 99.6% reliability index for the aeEBDL models. The approach is highly effective and generalized for the classification of skin lesions.

Artificial intelligence-based preventive, personalized and precision medicine for cardiovascular disease/stroke risk assessment in rheumatoid arthritis patients: a narrative review.

The challenges associated with diagnosing and treating cardiovascular disease (CVD)/Stroke in Rheumatoid arthritis (RA) arise from the delayed onset of symptoms. Existing clinical risk scores are inadequate in predicting cardiac events, and conventional risk factors alone do not accurately classify many individuals at risk. Several CVD biomarkers consider the multiple pathways involved in the development of atherosclerosis, which is the primary cause of CVD/Stroke in RA. To enhance the accuracy of CVD/Stroke risk assessment in the RA framework, a proposed approach involves combining genomic-based biomarkers (GBBM) derived from plasma and/or serum samples with innovative non-invasive radiomic-based biomarkers (RBBM), such as measurements of synovial fluid, plaque area, and plaque burden. This review presents two hypotheses: (i) RBBM and GBBM biomarkers exhibit a significant correlation and can precisely detect the severity of CVD/Stroke in RA patients. (ii) Artificial Intelligence (AI)-based preventive, precision, and personalized (aiP3) CVD/Stroke risk AtheroEdge™ model (AtheroPoint™, CA, USA) that utilizes deep learning (DL) to accurately classify the risk of CVD/stroke in RA framework. The authors conducted a comprehensive search using the PRISMA technique, identifying 153 studies that assessed the features/biomarkers of RBBM and GBBM for CVD/Stroke. The study demonstrates how DL models can be integrated into the AtheroEdge™-aiP3 framework to determine the risk of CVD/Stroke in RA patients. The findings of this review suggest that the combination of RBBM with GBBM introduces a new dimension to the assessment of CVD/Stroke risk in the RA framework. Synovial fluid levels that are higher than normal lead to an increase in the plaque burden. Additionally, the review provides recommendations for novel, unbiased, and pruned DL algorithms that can predict CVD/Stroke risk within a RA framework that is preventive, precise, and personalized.

Prognostic relevance of surface expression of cytokine receptor-like factor 2 in pediatric B-lineage acute lymphoblastic leukemia.

Overexpression of cytokine receptor-like factor 2 (CRLF2) resulting from its genomic rearrangement is the most frequent genetic alteration found in Philadelphia chromosome-like (Ph-like) B-cell acute lymphoblastic leukemia (B-ALL), a high-risk leukemia. Detection of CRLF2 expression by multiparameter flow cytometry has been proposed as a screening tool for the identification of Ph-like B-ALL. However, the prognostic relevance of flow cytometric expression of CRLF2 in pediatric B-ALL is not very clear. Additionally, its association with common copy number alterations (CNA) has not been studied in detail. Hence, in this study, we prospectively evaluated the flow cytometric expression of CRLF2 in 256 pediatric B-ALL patients and determined its association with molecular features such as common CNAs detected using Multiplex ligation-dependent probe amplification and mutations in CRLF2, JAK2 and IL7RA genes. Further, its association with clinicopathological features including patient outcome was assessed. We found that 8.59% (22/256) pediatric B-ALL patients were CRLF2-positive at diagnosis. Among CNAs, CRLF2 positivity was associated with presence of PAX5 alteration (P=0.041). JAK2 and IL-7R mutations were found in 9% and 13.6% CRLF2-positive patients, respectively. IGH::CRLF2 or P2RY8::CRLF2 fusions were each found in 1/22 individuals. CRLF2-positive patients were found to have inferior overall (hazard ratio (HR) =4.39, P=0.006) and event free survival (HR=2.62, P=0.045), independent to other clinical features. Furthermore, concomitant CNA of IKZF1 in CRLF2 positive patients was associated with a greater hazard for poor overall and event free survival, compared to patients without these alterations or presence of any one of them. Our findings demonstrate that the surface CRLF2 expression in association with IKZF1 copy number alteration can be used to risk stratify pediatric B-ALL patients.

Cardiovascular disease/stroke risk stratification in deep learning framework: a review.

The global mortality rate is known to be the highest due to cardiovascular disease (CVD). Thus, preventive, and early CVD risk identification in a non-invasive manner is vital as healthcare cost is increasing day by day. Conventional methods for risk prediction of CVD lack robustness due to the non-linear relationship between risk factors and cardiovascular events in multi-ethnic cohorts. Few recently proposed machine learning-based risk stratification reviews without deep learning (DL) integration. The proposed study focuses on CVD risk stratification by the use of techniques mainly solo deep learning (SDL) and hybrid deep learning (HDL). Using a PRISMA model, 286 DL-based CVD studies were selected and analyzed. The databases included were Science Direct, IEEE Xplore, PubMed, and Google Scholar. This review is focused on different SDL and HDL architectures, their characteristics, applications, scientific and clinical validation, along with plaque tissue characterization for CVD/stroke risk stratification. Since signal processing methods are also crucial, the study further briefly presented Electrocardiogram (ECG)-based solutions. Finally, the study presented the risk due to bias in AI systems. The risk of bias tools used were (I) ranking method (RBS), (II) region-based map (RBM), (III) radial bias area (RBA), (IV) prediction model risk of bias assessment tool (PROBAST), and (V) risk of bias in non-randomized studies-of interventions (ROBINS-I). The surrogate carotid ultrasound image was mostly used in the UNet-based DL framework for arterial wall segmentation. Ground truth (GT) selection is vital for reducing the risk of bias (RoB) for CVD risk stratification. It was observed that the convolutional neural network (CNN) algorithms were widely used since the feature extraction process was automated. The ensemble-based DL techniques for risk stratification in CVD are likely to supersede the SDL and HDL paradigms. Due to the reliability, high accuracy, and faster execution on dedicated hardware, these DL methods for CVD risk assessment are powerful and promising. The risk of bias in DL methods can be best reduced by considering multicentre data collection and clinical evaluation.

Simultaneous quantitation of nine carbazole alkaloids from Murraya koenigii (L.) Spreng by 1H qNMR spectroscopy.

Murraya koenigii leaves are widely used as a spice and also have several biological activities. The major active constituents are carbazole alkaloids. Quantitation by HPLC or HPTLC requires pure marker compounds, whereas nuclear magnetic resonance spectroscopy can be used as a quantitative technique without the requirement of a pure marker compound. An alkaloid-rich fraction was prepared from the leaves and a validated qNMR method was developed for the quantitation of nine carbazole alkaloids, namely mahanimbine, girinimbine, koenimbine, koenine, kurrayam, mukonicine, isomahanimbine, euchristine B and bismahanine. One of the major compounds, koenimbine, was isolated and quantified by HPTLC to compare the results. The results obtained by qNMR were compared with the reported yields of these compounds.

Ensemble Deep Learning Derived from Transfer Learning for Classification of COVID-19 Patients on Hybrid Deep-Learning-Based Lung Segmentation: A Data Augmentation and Balancing Framework.

BACKGROUND AND MOTIVATION: Lung computed tomography (CT) techniques are high-resolution and are well adopted in the intensive care unit (ICU) for COVID-19 disease control classification. Most artificial intelligence (AI) systems do not undergo generalization and are typically overfitted. Such trained AI systems are not practical for clinical settings and therefore do not give accurate results when executed on unseen data sets. We hypothesize that ensemble deep learning (EDL) is superior to deep transfer learning (TL) in both non-augmented and augmented frameworks. METHODOLOGY: The system consists of a cascade of quality control, ResNet-UNet-based hybrid deep learning for lung segmentation, and seven models using TL-based classification followed by five types of EDL's. To prove our hypothesis, five different kinds of data combinations (DC) were designed using a combination of two multicenter cohorts-Croatia (80 COVID) and Italy (72 COVID and 30 controls)-leading to 12,000 CT slices. As part of generalization, the system was tested on unseen data and statistically tested for reliability/stability. RESULTS: Using the K5 (80:20) cross-validation protocol on the balanced and augmented dataset, the five DC datasets improved TL mean accuracy by 3.32%, 6.56%, 12.96%, 47.1%, and 2.78%, respectively. The five EDL systems showed improvements in accuracy of 2.12%, 5.78%, 6.72%, 32.05%, and 2.40%, thus validating our hypothesis. All statistical tests proved positive for reliability and stability. CONCLUSION: EDL showed superior performance to TL systems for both (a) unbalanced and unaugmented and (b) balanced and augmented datasets for both (i) seen and (ii) unseen paradigms, validating both our hypotheses.

The Influence of Social Media on Alcohol Consumption of Mothers of Children and Adolescents: A Scoping Review of the Literature.

Alcohol misuse is a common problem in many countries, where alcohol is often portrayed as a fun and interactive coping strategy for mothers to manage the demands of motherhood. Social media platforms have established themselves as a popular forum for mothers to share information and create an environment in which mothers may be exposed to and influenced by alcohol-related content. Given the increased social acceptance and normalization of drinking among mothers, especially during the recent pandemic, a critical analysis of social media influences on alcohol behaviours and consumption is warranted. A scoping review mapped the evidence on social media influences and alcohol consumption among mothers of children and teenagers younger than eighteen years old. Several databases were consulted, and the evidence was collated into two themes and seven subthemes. Factors related to alcohol consumption in motherhood include (1) community and social support, (2) coping and mental health, (3) motherhood expectations and identity, (4) alcohol consumption, (5) marketing strategies, (6) everyday issues, and (7) social media influence. Numerous social, economic, and health problems are associated with alcohol misuse. The current literature suggests that social media is a powerful tool to disseminate messages about alcohol and normalize mothers' drinking behaviours.

Prognostic relevance of NPM1, CEBPA, and FLT3 mutations in cytogenetically normal adult AML patients.

BACKGROUND: Acute myeloid leukemia with normal cytogenetics (CN-AML) is the largest group of AML patients with very heterogenous patient outcomes. The revised World Health Organization classification of the hematolymphoid tumours, 2022, has incorporated AML with Nucleophosphmin1 (NPM1) and CCAAT/enhancer binding protein-alpha (CEBPA) mutations as distinct entities. Despite the existing evidence of the prognostic relevance of FMS-like tyrosine kinase-3 internal tandem duplication (FLT3-ITD) in AML, it has not been included in the revised classification. METHOD: In this prospective study, we determined the prevalence of NPM1, CEBPA, and FLT3 gene mutations in 151 de novo CN-AML adult patients (age ≥18 years) in a tertiary care hospital in north India. Additionally, the prognostic relevance of these mutations was also evaluated. RESULTS: NPM1, FLT3-ITD, and CEBPA mutations were found in 33.11%, 23.84%, and 15.77% of CN-AML patients, respectively. CEBPA mutations were found at 3 domains: transactivation domain 1 (TAD1) in 10 (6.62%), transactivation domain 2 (TAD2) in 5 (3.31%), and basic leucine zipper domain (bZIP) in 11 (7.82%) patients. Patients with NPM1 mutation had better clinical remission rate (CR) (P=0.003), event-free survival (P=0.0014), and overall survival (OS) (P=0.0017). However, FLT3-ITD and CEBPA mutations did not show any association with CR (P=0.404 and 0.92, respectively). Biallelic CEBPA mutations were found in 12 (7.95%) patients and were associated with better OS (P=0.043). CONCLUSIONS: These findings indicate that NPM1 and CEBPA mutations can be precisely used for risk stratification in CN-AML patients.