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.

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.

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.

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.

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.

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.

Cisplatin conjugation with an exopolysaccharide extracted from Lactobacillus gasseri potentiates its efficacy and attenuates its toxicity.

The development of newer cisplatin analogs is constantly being investigated owing to its low solubility, poor pharmacokinetics, and dose-related toxicity. In order to address the limitations of current cisplatin therapy, the present study was undertaken. Cisplatin conjugation with an exopolysaccharide extracted from Lactobacillus gasseri (LG-EPS) showed remarkably enhanced and selective anticancer activity by targeting tumor cells overexpressing glucose transporter 1 (GLUT1). The EPS-cisplatin complex exhibited a 600-fold increase in aqueous solubility with a better pharmacokinetic profile (longer half-life) in comparison to cisplatin. Cell viability assay and western blotting demonstrated a strong correlation between the cytotoxicity profile and GLUT1 expressions in different cell lines. The concentration of DNA-bound platinum was also found to be significantly higher in EPS-cisplatin-treated cells. Quercetin, a competitive inhibitor of GLUTs, was shown to prevent this selective uptake of EPS-cisplatin complex. Surprisingly, EPS-cisplatin complex showed an exceptionally safer profile (4 times the maximum tolerated dose of cisplatin) in the acute toxicity study and was also more efficacious against the xenograft mice model. The study suggests that this green glycoconjugation can be an effective and safer strategy to broaden the therapeutic potential of anti-cancer drugs in general and cisplatin in particular.

Economics of Artificial Intelligence in Healthcare: Diagnosis vs. Treatment.

Motivation: The price of medical treatment continues to rise due to (i) an increasing population; (ii) an aging human growth; (iii) disease prevalence; (iv) a rise in the frequency of patients that utilize health care services; and (v) increase in the price. Objective: Artificial Intelligence (AI) is already well-known for its superiority in various healthcare applications, including the segmentation of lesions in images, speech recognition, smartphone personal assistants, navigation, ride-sharing apps, and many more. Our study is based on two hypotheses: (i) AI offers more economic solutions compared to conventional methods; (ii) AI treatment offers stronger economics compared to AI diagnosis. This novel study aims to evaluate AI technology in the context of healthcare costs, namely in the areas of diagnosis and treatment, and then compare it to the traditional or non-AI-based approaches. Methodology: PRISMA was used to select the best 200 studies for AI in healthcare with a primary focus on cost reduction, especially towards diagnosis and treatment. We defined the diagnosis and treatment architectures, investigated their characteristics, and categorized the roles that AI plays in the diagnostic and therapeutic paradigms. We experimented with various combinations of different assumptions by integrating AI and then comparing it against conventional costs. Lastly, we dwell on three powerful future concepts of AI, namely, pruning, bias, explainability, and regulatory approvals of AI systems. Conclusions: The model shows tremendous cost savings using AI tools in diagnosis and treatment. The economics of AI can be improved by incorporating pruning, reduction in AI bias, explainability, and regulatory approvals.

Cardiovascular/Stroke Risk Stratification in Diabetic Foot Infection Patients Using Deep Learning-Based Artificial Intelligence: An Investigative Study.

A diabetic foot infection (DFI) is among the most serious, incurable, and costly to treat conditions. The presence of a DFI renders machine learning (ML) systems extremely nonlinear, posing difficulties in CVD/stroke risk stratification. In addition, there is a limited number of well-explained ML paradigms due to comorbidity, sample size limits, and weak scientific and clinical validation methodologies. Deep neural networks (DNN) are potent machines for learning that generalize nonlinear situations. The objective of this article is to propose a novel investigation of deep learning (DL) solutions for predicting CVD/stroke risk in DFI patients. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) search strategy was used for the selection of 207 studies. We hypothesize that a DFI is responsible for increased morbidity and mortality due to the worsening of atherosclerotic disease and affecting coronary artery disease (CAD). Since surrogate biomarkers for CAD, such as carotid artery disease, can be used for monitoring CVD, we can thus use a DL-based model, namely, Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) for CVD/stroke risk prediction in DFI patients, which combines covariates such as office and laboratory-based biomarkers, carotid ultrasound image phenotype (CUSIP) lesions, along with the DFI severity. We confirmed the viability of CVD/stroke risk stratification in the DFI patients. Strong designs were found in the research of the DL architectures for CVD/stroke risk stratification. Finally, we analyzed the AI bias and proposed strategies for the early diagnosis of CVD/stroke in DFI patients. Since DFI patients have an aggressive atherosclerotic disease, leading to prominent CVD/stroke risk, we, therefore, conclude that the DL paradigm is very effective for predicting the risk of CVD/stroke in DFI patients.

Quantitative analysis of tiliroside and other flavonoid glycosides in Hippophae salicifolia D. Don leaves by HPLC-PDA.

A new HPLC-PDA method was developed and validated for simultaneous determination of five phenolic compounds (trans-and cis- isomers of tiliroside, quercetin-3-O-ß-D-glucoside, ellagic acid, kaempferol-3-O-ß-D-glucoside and isorhamnetin-3-O-glucoside) in the leaves of Hippophae salicifolia D. Don. Of the five compounds, three (tiliroside, quercetin-3-O-ß-D-glucoside and ellagic acid) were isolated and characterised by spectroscopy techniques. The developed HPLC method provided a selective, sensitive and rapid analysis with good linearity (r2> 0.999), accuracy and precision. Also, the leaves of H. salicifolia were extracted by three different extraction techniques viz. reflux, microwave and ultrasound. Methanolic extracts prepared by reflux method showed the highest content of all the five compounds.