Molecular phenotyping of oxidative stress in diabetes mellitus with point-of-care NMR system

Machine learning-enabled high-throughput industry screening of edible oils

Long-term consumption of mixed fraudulent edible oils increases the risk of developing of chronic diseases which has been a threat to the public health globally. The complicated global supply-chain is making the industry malpractices had often gone undetected. In order to restore the confidence of consumers, traceability (and accountability) of every level in the supply chain is vital. In this work, we shown that machine learning (ML) assisted windowed spectroscopy (e.g., visible-band, infra-red band) produces high-throughput, non-destructive, and label-free authentication of edible oils (e.g., olive oils, sunflower oils), offers the feasibility for rapid analysis of large-scale industrial screening. We report achieving high-level of discriminant (AUC > 0.96) in the large-scale (n ≈ 11,500) of adulteration in olive oils. Notably, high clustering fidelity of 'spectral fingerprints' achieved created opportunity for (hypothesis-free) self-sustaining large database compilation which was never possible without machine learning. (137 words).

Machine Learning Prediction of Prediabetes in a Young Male Chinese Cohort with 5.8-Year Follow-Up

The identification of risk factors for future prediabetes in young men remains largely unexamined. This study enrolled 6247 young ethnic Chinese men with normal fasting plasma glucose at the baseline (FPGbase), and used machine learning (Mach-L) methods to predict prediabetes after 5.8 years. The study seeks to achieve the following: 1. Evaluate whether Mach-L outperformed traditional multiple linear regression (MLR). 2. Identify the most important risk factors. The baseline data included demographic, biochemistry, and lifestyle information. Two models were built, where Model 1 included all variables and Model 2 excluded FPGbase, since it had the most profound effect on prediction. Random forest, stochastic gradient boosting, eXtreme gradient boosting, and elastic net were used, and the model performance was compared using different error metrics. All the Mach-L errors were smaller than those for MLR, thus Mach-L provided the most accurate results. In descending order of importance, the key factors for Model 1 were FPGbase, body fat (BF), creatinine (Cr), thyroid stimulating hormone (TSH), WBC, and age, while those for Model 2 were BF, white blood cell, age, TSH, TG, and LDL-C. We concluded that FPGbase was the most important factor to predict future prediabetes. However, after removing FPGbase, WBC, TSH, BF, HDL-C, and age were the key factors after 5.8 years.

Investigation on Anti-diabetic Efficacy of a Cucurbitaceae Food Plant from the North-East Region of India: Exploring the Molecular Mechanism through Modulation of Oxidative Stress and Glycosylated Hemoglobin (HbA1c)

BACKGROUND

The medicinal plants of the Cucurbitaceae family, such as Solena heterophylla Lour. fruits, have significant ethnobotanical value and are readily accessible in North East India.

AIMS

We conducted a study on Solena heterophylla Lour. fruits to evaluate their anti-diabetic activity in vivo, standardize their HPTLC, and profile their metabolites using LC-QTOF-MS. We aimed to explore the molecular mechanism behind their effects on oxidative stress and glycosylated hemoglobin (HbA1c).

METHODS

Firstly, the ethyl acetate fraction of Solena heterophylla Lour. fruits was standardized using Cucurbitacin B as a standard marker by conducting HPTLC evaluation. Next, we delved into analyzing metabolite profiling. In addition, the standardized fraction was utilized in an experimental study to investigate the molecular mechanism of action in an in vivo high-fat diet and a low dose of streptozotocin-induced diabetic model.

RESULTS

We have reportedly identified 52 metabolites in the ethyl acetate fraction of Solena heterophylla (EASH). In the in vitro tests, it has been observed that this extract from plants possesses notable inhibitory properties against α-amylase and α-glucosidase. Solena heterophylla fruits with high levels of Cucurbitacin B (2.29% w/w) helped lower FBG levels in animals with EASH treatment. EASH treatment reduced HbA1c levels and normalized liver lipid peroxidation and antioxidant enzyme levels. SGOT, SGPT, and SALP serum enzyme levels also returned to normal.

CONCLUSION

Based on the current evaluation, it was found that EASH exhibited encouraging hypoglycemic effects in diabetic rats induced by a low dose of STZ and high-fat diet, which warrants further investigation.

Metabolomics-based pharmaceutical evaluation of different parts of Swertia chirayita (Roxb.) Buch.-Ham. ex C.B. Clarke from the western Himalayas

Swertia species are common ingredients in numerous herbal remedies. It is also used to treat a wide range of illnesses and possess diverse therapeutic activities. The aim of the study is to elucidate the comprehensive metabolomics profile of Swertia chirayita and the role of various extraction methods in the phytochemical compositions of the extracts of S. chirayita, and their antioxidant and enzyme inhibitory activities. Extraction of the stems, leaves, and flowering tops of S. chirayita was performed by maceration, infusion, and soxhlation using methanol and water as solvent. Extracts were subjected to phytochemical profiling by a liquid-chromatographic system. Antioxidant and enzyme inhibitory activity was carried out. The metabolomics profiling showed that a diverse range of specialized metabolites were present in the stems and leaves & flowering tops of the plant. All the extracts showed substantial antioxidant and enzyme inhibitory activities further confirmed by molecular docking studies. This study appraised the use of S. chirayita aerial parts as a potential antioxidant and its therapeutic application in various chronic illnesses including Alzheimer's disease, diabetes, and other skin-related disorders.

What Links Chronic Kidney Disease and Ischemic Cardiomyopathy? A Comprehensive Bioinformatic Analysis Utilizing Bulk and Single-Cell RNA Sequencing Data with Machine Learning

Chronic kidney disease (CKD) emerges as a substantial contributor to various cardiovascular disorders, including ischemic cardiomyopathy (ICM). However, the underlying molecular mechanisms linking CKD and ICM remain elusive. Our study aims to unravel these connections by integrating publicly available bulk and single-cell RNA sequencing (scRNA-seq) data. Expression profiles from two ICM datasets obtained from heart tissue and one CKD with Peripheral Blood Mononuclear Cell (CKD-PBMC) dataset were collected. We initiated by identifying shared differentially expressed genes (DEGs) between ICM and CKD. Subsequent functional enrichment analysis shed light on the mechanisms connecting CKD to ICM. Machine learning algorithms enabled the identification of 13 candidate genes, including AGRN, COL16A1, COL1A2, FAP, FRZB, GPX3, ITIH5, NFASC, PTN, SLC38A1, STARD7, THBS2, and VPS35. Their expression patterns in ICM were investigated via scRNA-seq data analysis. Notably, most of them were enriched in fibroblasts. COL16A1, COL1A2, PTN, and FAP were enriched in scar-formation fibroblasts, while GPX3 and THBS2 showed enrichment in angiogenesis fibroblasts. A Gaussian naïve Bayes model was developed for diagnosing CKD-related ICM, bolstered by SHapley Additive exPlanations interpretability and validated internally and externally. In conclusion, our investigation unveils the extracellular matrix's role in CKD and ICM interplay, identifies 13 candidate genes, and showcases their expression patterns in ICM. We also constructed a diagnostic model using 13 gene features and presented an innovative approach for managing CKD-related ICM through serum-based diagnostic strategies.

A Brief Review on Medicinal Plants-At-Arms against COVID-19

COVID-19 pandemic caused by the novel SARS-CoV-2 has impacted human livelihood globally. Strenuous efforts have been employed for its control and prevention; however, with recent reports on mutated strains with much higher infectivity, transmissibility, and ability to evade immunity developed from previous SARS-CoV-2 infections, prevention alternatives must be prepared beforehand in case. We have perused over 128 recent works (found on Google Scholar, PubMed, and ScienceDirect as of February 2023) on medicinal plants and their compounds for anti-SARS-CoV-2 activity and eventually reviewed 102 of them. The clinical application and the curative effect were reported high in China and in India. Accordingly, this review highlights the unprecedented opportunities offered by medicinal plants and their compounds, candidates as the therapeutic agent, against COVID-19 by acting as viral protein inhibitors and immunomodulator in (32 clinical trials and hundreds of in silico experiments) conjecture with modern science. Moreover, the associated foreseeable challenges for their viral outbreak management were discussed in comparison to synthetic drugs.

Novel time-domain NMR-based traits for rapid, label-free Olive oils profiling

Olive oil is one of the oldest and essential edible oils in the market. The classification of olive oils (e.g. extra virgin, virgin, refined) is often influenced by factors ranging from its complex inherent physiochemical properties (e.g. fatty acid profiles) to the undisclosed manufacturing processes. Therefore, olive oils have been the target of adulteration due to its profitable margin. In this work, we demonstrate that multi-parametric time-domain NMR relaxometry can be used to rapidly (in minutes) identify and classify olive oils in label-free and non-destructive manner. The subtle differences in molecular microenvironment of the olive oils induce substantial changes in the relaxation mechanism in the time-domain NMR regime. We demonstrated that the proposed NMR-relaxation based detection (AUC = 0.95) is far more sensitive and specific than the current gold-standards in the field i.e. near-infrared spectroscopy (AUC = 0.84) and Ultraviolet-visible spectroscopy (AUC = 0.73), respectively. We further show that, albeit the inherent complexity of olive plant natural phenotypic variations, the proposed NMR-relaxation based traits may be a viable mean (AUC = 0.71) in tracing the regions of origin for olive trees, in agreement with their geographical orientation.

Preliminary Study of Genome-Wide Association Identified Novel Susceptibility Genes for Hemorheological Indexes in a Chinese Population

Background

Genome-wide association studies for various hemorheological characteristics have not been reported. We aimed to identify genetic loci associated with hemorheological indexes in a cohort of healthy Chinese Han individuals.

Methods

Genotyping was performed using Applied Biosystems Axiom™ Precision Medicine Diversity Array in 838 individuals, and 6,423,076 single nucleotide polymorphisms were available for genotyping. The relations were examined in an additive genetic model using mixed linear regression and combined with identical by descent matrix.

Results

We identified 38 genetic loci (p < 5 × 10^-6) related to hemorheological traits. In which, LOC102724502-OLIG2 rs28371438 was related to the levels of nd30 (p = 8.58 × 10^-07), nd300 (p = 1.89 × 10^-06), erythrocyte rigidity (p = 1.29 × 10^-06), assigned viscosity (p = 6.20 × 10^-08) and whole blood high cut relative (p = 7.30 × 10^-08). The association of STK32B rs4689231 for nd30 (p = 3.85 × 10^-06) and nd300 (p = 2.94 × 10^-06) and GTSCR1-LINC01541 rs11661911 for erythrocyte rigidity (p = 9.93 × 10^-09) and whole blood high cut relative (p = 2.09 × 10^-07) was found. USP25-MIR99AHG rs1297329 was associated with erythrocyte rigidity (p = 1.81 × 10^-06) and erythrocyte deformation (p = 1.14 × 10^-06). Moreover, the association of TMEM232-SLC25A46 rs3985087 and LINC00470-METTL4 rs9966987 for fibrinogen (p = 1.31 × 10^-06 and p = 4.29 × 10^-07) and plasma viscosity (p = 1.01 × 10^-06 and p = 4.59 × 10^-07) was found.

Conclusion

These findings may represent biological candidates for hemorheological indexes and contribute to hemorheological study.

Evaluation of the usefulness of non-invasive serum haemoglobin measurement in a perioperative setting in a prospective observational study

Patient Blood Management (PBM) programmes seek to reduce the number of missed anaemic patients in the run-up to surgery. The aim of this study was to evaluate the usefulness of haemoglobin (Hb) measured non-invasively (SpHb) in preoperative screening for anaemia. We conducted a prospective observational study in a preoperative clinic. Adult patients undergoing examination for surgery who had their Hb measured by laboratory means also had their Hb measured non-invasively by a trained health care provider. 1216 patients were recruited. A total of 109 (9.3%) patients (53 men and 56 women) was found to be anaemic by standard laboratory Hb measurement. Sensitivity for SpHb to detect anaemic patients was 0.50 (95% CI 0.37-0.63) in women and 0.30 (95% CI 0.18-0.43) in men. Specificity was 0.97 (95% CI 0.95-0.98) in men and 0.93 (95% CI 0.84-1.0) in women. The rate of correctly classified patients was 84.7% for men and 89.4% for women. Positive predictive value for SpHb was 0.50 (95% CI 0.35-0.65) in men and 0.40 (95% CI 0.31-0.50) in women; negative predictive value was 0.93 (95% CI 0.92-0.94) in men and 0.95 (95% CI 0.94-0.96) in women. We conclude that due to low sensitivity, SpHb is poorly suitable for detecting preoperative anaemia in both sexes under standard of care conditions.

Transient Polyhydramnios during Pregnancy Complicated with Gestational Diabetes Mellitus: Case Report and Systematic Review

Polyhydramnios is an obstetrical condition defined as a pathological increase in the amniotic fluid and is associated with a high risk of maternal-fetal complications. Common causes of polyhydramnios include fetal anatomical and genetic abnormalities, gestational diabetes mellitus, and fetal viral infections. We present the case of a 30-year-old Caucasian woman with transient polyhydramnios associated with gestational diabetes mellitus and obstetric complications. The diagnosis was based on the ultrasound assessment of amniotic fluid volume during a common examination at 26 weeks. Two weeks prior, the patient had been diagnosed with gestational diabetes mellitus. After 4 days, the patient was examined, and the amniotic fluid index returned to normal values. At 38 weeks, the patient presented to the emergency room due to lack of fetal active movement. Ultrasound revealed polyhydramnios, the patient was admitted for severe fetal bradycardia, and fetal extraction through emergency cesarian section was performed. Six weeks after birth, the patient underwent an oral glucose tolerance test with normal values, confirming gestational diabetes mellitus. We performed a systematic review of the literature on polyhydramnios, from January 2016 to April 2022, to analyze all recent published cases and identify the most common etiological causes and important aspects related to maternal-fetal outcomes.

Review of Microdevices for Hemozoin-Based Malaria Detection

Despite being preventable and treatable, malaria still puts almost half of the world's population at risk. Thus, prompt, accurate and sensitive malaria diagnosis is crucial for disease control and elimination. Optical microscopy and immuno-rapid tests are the standard malaria diagnostic methods in the field. However, these are time-consuming and fail to detect low-level parasitemia. Biosensors and lab-on-a-chip devices, as reported to different applications, usually offer high sensitivity, specificity, and ease of use at the point of care. Thus, these can be explored as an alternative for malaria diagnosis. Alongside malaria infection inside the human red blood cells, parasites consume host hemoglobin generating the hemozoin crystal as a by-product. Hemozoin is produced in all parasite species either in symptomatic and asymptomatic individuals. Furthermore, hemozoin crystals are produced as the parasites invade the red blood cells and their content relates to disease progression. Hemozoin is, therefore, a unique indicator of infection, being used as a malaria biomarker. Herein, the so-far developed biosensors and lab-on-a-chip devices aiming for malaria detection by targeting hemozoin as a biomarker are reviewed and discussed to fulfil all the medical demands for malaria management towards elimination.

Panoramic tongue imaging and deep convolutional machine learning model for diabetes diagnosis in humans

Diabetes is a serious metabolic disorder with high rate of prevalence worldwide; the disease has the characteristics of improper secretion of insulin in pancreas that results in high glucose level in blood. The disease is also associated with other complications such as cardiovascular disease, retinopathy, neuropathy and nephropathy. The development of computer aided decision support system is inevitable field of research for disease diagnosis that will assist clinicians for the early prognosis of diabetes and to facilitate necessary treatment at the earliest. In this research study, a Traditional Chinese Medicine based diabetes diagnosis is presented based on analyzing the extracted features of panoramic tongue images such as color, texture, shape, tooth markings and fur. The feature extraction is done by Convolutional Neural Network (CNN)—ResNet 50 architecture, and the classification is performed by the proposed Deep Radial Basis Function Neural Network (RBFNN) algorithm based on auto encoder learning mechanism. The proposed model is simulated in MATLAB environment and evaluated with performance metrics—accuracy, precision, sensitivity, specificity, F1 score, error rate, and receiver operating characteristics (ROC). On comparing with existing models, the proposed CNN based Deep RBFNN machine learning classifier model outperformed with better classification performance and proving its effectiveness.

Visualization and Measurements of Blood Cells Flowing in Microfluidic Systems and Blood Rheology: A Personalized Medicine Perspective

Hemorheological alterations in the majority of metabolic diseases are always connected with blood rheology disturbances, such as the increase of blood and plasma viscosity, cell aggregation enhancement, and reduction of the red blood cells (RBCs) deformability. Thus, the visualizations and measurements of blood cells deformability flowing in microfluidic devices (point-of-care devices) can provide vital information to diagnose early symptoms of blood diseases and consequently to be used as a fast clinical tool for early detection of biomarkers. For instance, RBCs rigidity has been correlated with myocardial infarction, diabetes mellitus, hypertension, among other blood diseases. In order to better understand the blood cells behavior in microfluidic devices, rheological properties analysis is gaining interest by the biomedical committee, since it is strongly dependent on the interactions and mechanical cells proprieties. In addition, the development of blood analogue fluids capable of reproducing the rheological properties of blood and mimic the RBCs behavior at in vitro conditions is crucial for the design, performance and optimization of the microfluidic devices frequently used for personalized medicine. By combining the unique features of the hemorheology and microfluidic technology for single-cell analysis, valuable advances in personalized medicine for new treatments and diagnosis approach can be achieved.