Correction: Dhaliwal et al. Developments and Prospects in Imperative Underexploited Vegetable Legumes Breeding: A Review. Int. J. Mol. Sci. 2020, 21, 9615.

In the publication [...].

Long-term Outcome of Coronavirus Disease-associated Mucormycosis: 1-year Follow-up Study from India.

IMPORTANCE: Invasive fungal infections have recently become a public health problem, particularly in India following the second wave of coronavirus disease 2019 (COVID-19). India harbors the world's largest population of patients suffering from diabetes. What prompted the sudden spike of mucormycosis infections in the COVID pandemic needs investigation. OBJECTIVE: To determine if COVID-19 infection prompted the spike in invasive fungal infections in diabetic population. To determine the long-term outcome of COVID-associated mucormycosis. To determine if COVID-19 infection causes diabetes mellitus transiently. DESIGN: The study was a prospective cohort study comprising patients suffering from mucormycosis. The study was planned from 20 May 2021, until 30 November 2022, to investigate the long-term follow-up (1 year) of mucormycosis patients. SETTING: The study setting was a referral hospital. PARTICIPANTS: All the consecutive patients admitted to this hospital for treatment of mucormycosis were included in the study who consented to it. Intervention(s) (for clinical trials) or exposure(s) (for observational studies): All patients suffering with mucormycosis underwent treatment at this hospital with surgery and injectable systemic antifungal drugs alongside diabetes management. MAIN OUTCOME(S) AND MEASURE(S): Primary outcome measurement was in the form of survival with cure of mucormycosis. Hypothesis being tested was formulated during data collection. RESULTS: The data of 98 participants was collected, but analysis was done after excluding the case of cutaneous mucormycosis (infant patient). Mean age for patients was 55.5 years, varying from 28 to 88 years. In our study, 63.3% of patients with mucormycosis were males and 37.8% were females, of which 55.7% (34) and 58.3% (21) were known diabetics, respectively. Previous history of diabetes mellitus was identified as an underlying comorbid condition in 56.7% of patients, while the rest were diagnosed with new-onset diabetes mellitus. Sugar levels ranged (on admission) from 112 to 494 mg/dL (median 212 mg/dL) for known diabetics and from 132 to 356 mg/dL (median 204 mg/dL) for newly diagnosed diabetics. Other comorbidities included hypertension (19.5%), ischemic heart disease (8.2%), chronic renal illness (3.09%), and one case (1.03%) of postoperative renal cell carcinoma (disease-free). The majority of cases (91.8%) were not vaccinated for COVID-19, while only two patients reported a history of vaccination with two doses, and six others had received only a single dose. At the 1-year follow-up, 57.7% of cases were disease-free, 30.9% had expired, and 11.3% were lost to follow-up. The mean glycated hemoglobin (HbA1c) at the time of admission was found to be statistically significant when compared between known diabetics and newly diagnosed ones [confidence interval (CI)-95%, p ≤ 0.01]. A total of seven patients from the newly diagnosed diabetic group no longer required medicines for diabetes at the end of 1 year (CI-95%, p ≤ 0.01). CONCLUSIONS AND RELEVANCE: Diabetes mellitus, particularly with poor glycemic control, was the single most important factor associated with and predictor of outcome. Contrary to the popular hypothesis, industrial oxygen and oxygen masks were not the reasons for the mucormycosis pandemic. Additionally, immunization against COVID provided protection not only from severe COVID but also from COVID-associated mucormycosis. It is recommended that patients with mucormycosis be followed for longer periods as a few patients could be suffering from transient diabetes, particularly against the backdrop of a pandemic.

Sample Preparation from Plant Tissue for Gas Chromatography-Mass Spectrometry (GC-MS)we.

Metabolites are intermediate products formed during metabolism. Metabolites play different roles, including providing energy, supporting structure, transmitting signals, catalyzing reactions, enhancing defense, and interacting with other species. Plant metabolomics research aims to detect precisely all metabolites found within tissues of plants through GC-MS. This chapter primarily focuses on extracting metabolites using chemicals such as methanol, chloroform, ribitol, MSTFA, and TMCS. The metabolic analysis method is frequently used according to the specific kind of sample or matrix being investigated and the analysis objective. Chromatography (LC, GC, and CE) with mass spectrometry and NMR spectroscopy is used in modern metabolomics to analyze metabolites from plant samples. The most frequently used method for metabolites analysis is the GC-MS. It is a powerful technique that combines gas chromatography's separation capabilities with mass spectrometry, offering detailed information, including structural identification of each metabolite. This chapter contains an easy-to-follow guide to extract plant-based metabolites. The current protocol provides all the information needed for extracting metabolites from a plant, precautions, and troubleshooting.

Carotenoid Extraction from Plant Tissues.

Carotenoids are the natural pigments available in nature and exhibit different colors such as yellow, red, and orange. These are a class of phytonutrients that have anti-cancer, anti-inflammatory, anti-oxidant, immune-modulatory, and anti-aging properties. These were used in food, pharmaceutical, nutraceutical, and cosmetic industries. They are divided into two classes: carotenes and xanthophylls. The carotenes are non-oxygenated derivatives and xanthophylls are oxygenated derivatives. The major source of carotenoids are vegetables, fruits, and tissues. Carotenoids also perform the roles of photoprotection and photosynthesis. In addition to the roles mentioned above, they are also involved and act as precursor molecules for the biosynthesis of phytohormones such as strigolactone and abscisic acid. This chapter briefly introduces carotenoids and their extraction method from plant tissue. Proposed protocol describes the extraction of carotenoid using solvents chloroform and dichloromethane. Reverse-phase HPLC can be performed with C30 columns using gradient elution. The column C30 is preferred to the C18 column because the C30 column has salient features, which include selective nature in the separation of structural isomers and hydrophobic, long-chain compounds, and shows the best compatibility with highly aqueous mobile phases. A complete pipeline for the extraction of carotenoids from plant tissue is given in the present protocol.

Fully Automated Agatston Score Calculation From Electrocardiography-Gated Cardiac Computed Tomography Using Deep Learning and Multi-Organ Segmentation: A Validation Study.

To evaluate deep learning-based calcium segmentation and quantification on ECG-gated cardiac CT scans compared with manual evaluation. Automated calcium quantification was performed using a neural network based on mask regions with convolutional neural networks (R-CNNs) for multi-organ segmentation. Manual evaluation of calcium was carried out using proprietary software. This is a retrospective study of archived data. This study used 40 patients to train the segmentation model and 110 patients were used for the validation of the algorithm. The Pearson correlation coefficient between the reference actual and the computed predictive scores shows high level of correlation (0.84; P < .001) and high limits of agreement (±1.96 SD; -2000, 2000) in Bland-Altman plot analysis. The proposed method correctly classifies the risk group in 75.2% and classifies the subjects in the same group. In total, 81% of the predictive scores lie in the same categories and only seven patients out of 110 were more than one category off. For the presence/absence of coronary artery calcifications, the deep learning model achieved a sensitivity of 90% and a specificity of 94%. Fully automated model shows good correlation compared with reference standards. Automating process reduces evaluation time and optimizes clinical calcium scoring without additional resources.