Effect of chitosan nanoemulsion on enhancing the phytochemical contents, health-promoting components, and shelf life of raspberry (Rubus sanctus Schreber)

Due to high water content and perishability, the raspberry fruit is sensitive to postharvest fungal contamination and postharvest losses. In this study, chitosan was used as an edible coating to increase the storage of raspberries, and nanotechnology was used to increase chitosan efficiency. The fruit was treated with an emulsion containing nanoparticles of chitosan (ECNPC) at 0, 2.5, and 5 g L−1, and stored for 9 d. Decay extension rate, fruit phytochemical contents, including total phenolics, flavonoids, and anthocyanin content, phenylalanine ammonia-lyase (PAL), and guaiacol-peroxidase enzymes and antioxidant activity, and other qualitative properties were evaluated during and at the end of storage. After 9 d of storage, the highest amounts of phenolics compounds, PAL enzyme activity, and antioxidant activity were observed in fruit treated with ECNPC at 5 g L−1. The highest levels of total phenol, PAL enzyme activity, and antioxidant activity were 57.53 g L−1, 118.88 μmol/min trans-cinnamic acid, and 85.16%, respectively. ECNPC can be considered as an effective, safe, and environmentally friendly method for enhancing fruit phytochemical contents, postharvest life, and health-promoting capacity

The global burden of cancer attributable to risk factors, 2010-19 : a systematic analysis for the Global Burden of Disease Study 2019

Background Understanding the magnitude of cancer burden attributable to potentially modifiable risk factors is crucial for development of effective prevention and mitigation strategies. We analysed results from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2019 to inform cancer control planning efforts globally. Methods The GBD 2019 comparative risk assessment framework was used to estimate cancer burden attributable to behavioural, environmental and occupational, and metabolic risk factors. A total of 82 risk-outcome pairs were included on the basis of the World Cancer Research Fund criteria. Estimated cancer deaths and disability-adjusted life-years (DALYs) in 2019 and change in these measures between 2010 and 2019 are presented. Findings Globally, in 2019, the risk factors included in this analysis accounted for 4.45 million (95% uncertainty interval 4.01-4.94) deaths and 105 million (95.0-116) DALYs for both sexes combined, representing 44.4% (41.3-48.4) of all cancer deaths and 42.0% (39.1-45.6) of all DALYs. There were 2.88 million (2.60-3.18) risk-attributable cancer deaths in males (50.6% [47.8-54.1] of all male cancer deaths) and 1.58 million (1.36-1.84) risk-attributable cancer deaths in females (36.3% [32.5-41.3] of all female cancer deaths). The leading risk factors at the most detailed level globally for risk-attributable cancer deaths and DALYs in 2019 for both sexes combined were smoking, followed by alcohol use and high BMI. Risk-attributable cancer burden varied by world region and Socio-demographic Index (SDI), with smoking, unsafe sex, and alcohol use being the three leading risk factors for risk-attributable cancer DALYs in low SDI locations in 2019, whereas DALYs in high SDI locations mirrored the top three global risk factor rankings. From 2010 to 2019, global risk-attributable cancer deaths increased by 20.4% (12.6-28.4) and DALYs by 16.8% (8.8-25.0), with the greatest percentage increase in metabolic risks (34.7% [27.9-42.8] and 33.3% [25.8-42.0]). Interpretation The leading risk factors contributing to global cancer burden in 2019 were behavioural, whereas metabolic risk factors saw the largest increases between 2010 and 2019. Reducing exposure to these modifiable risk factors would decrease cancer mortality and DALY rates worldwide, and policies should be tailored appropriately to local cancer risk factor burden. Copyright (C) 2022 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.Peer reviewe

Effect of explants source and different hormonal combinations on direct regeneration of basil plants (Ocimum basilicum L

Abstract Ocimum basilicum L. a herbaceous species belonging to the lamiaceae family is considered as a valuable plant for its pharmaceutical, aromatic and culinary properties. The major problem with the use of Lamiaceae species for pharmaceutical purposes is the plant to plant variability, mainly due to genetic and biochemical heterogeneity. In vitro shoot regeneration and multiplication is an impressive mean for precipitate propagation of species in which it is necessary to obtain a progeny with a high level of uniformity. In this research, two successive experiments were performed: first, the effects of explants source on MS medium supplemented with four different concentrations of BAP were studied in order to investigate the morphogenic responses; and second, the effects of different levels of two growth regulators (BAP and IAA) either individually or in combination on multiple shoot induction from nodal segments were evaluated. Maximum percent of regeneration (96.67±0.33) and average number of shoot (5.6±1.15) were observed on the medium containing 11 µM BAP + 0 µM IAA. Regenerated shoots were separated and rooted on the same half strength MS medium supplemented with 3.42 µM IAA alone for two weeks. Similarly in the second experiment, increasing BAP concentration led to decreased rooting. Moreover, a positive correlation between increasing the BAP level in culture media and vitrification of regenerated shoots was observed. The lowest and the highest vitrification values were achieved in the media containing 0 and 33 µm BAP, respectively

24-Epibrasinolide modulates the vase life of Lisianthus cut flowers by modulating ACC oxidase enzyme activity and physiological responses

Ethylene is the most important factor playing roles in senescence and deterioration of harvested crops including cut flowers. Brassinosteroids (BRs), as natural phytohormones, have been reported to differently modulate ethylene production and related senescence processes in different crops. This study was carried out to determine the effects of different levels of 24-epibrassinolide (EBL) on ACC oxidase enzyme activity, the final enzyme in ethylene biosynthesis pathway, vase life, and senescence rate in lisianthus cut flowers. Harvested flowers were treated with EBL (at 0, 3, 6, and 9 µmol/L) and kept at 25 °C for 15 days. The ACC oxidase activity, water absorption, malondialdehyde (MDA) production and vase solution absorption rates, chlorophyll and anthocyanin contents, and the vase life of the flowers were evaluated during and at the end of storage. EBL at 3 µmol/L significantly (p ≤ 0.01) enhanced the flower vase life by decreasing the ACC oxidase activity, MDA production and senescence rates, and enhancing chlorophyll and anthocyanin biosynthesis and accumulation, relative water content, and vase solution absorption rates. By increasing the concentration, EBL negatively affected the flower vase life and postharvest quality probably via enhancing the ACC oxidase enzyme activity and subsequent ethylene production. EBL at 6 and 9 µmol/L and in a concentration dependent manner, enhanced the ACC oxidase activity and MDA production rate and decreased chlorophyll and anthocyanin accumulation and water absorption rate. The results indicate that the effects of brassinosteroids on ethylene production and physiology of lisianthus cut flowers is highly dose dependent

Distribution of wild service tree based on some ecological factors in Sangdeh forests, north of Iran

This research was carried out in 40000 ha of Mazandaran central forests (southern coasts of Caspian sea) located in 100 km south of Sari city. The study area was divided into four altitudinal sections and a total of 185 plots with at least one wild service tree were laid out. Geographic and ecological condition of each plot and some characteristics of trees were recorded. The results showed that the number of Sorbus tree in south west and west slopes were more than the north, north east and east exposures. However, the best quality of Sorbus trees has been seen in Fagetum on north exposure. The slops with 25- 50% and 1750- 2000 m.a.s.l were the best habitats for Sorbus torminalis. The biggest individual Sorbus tree showed 32 m height and 103 cm in d.b.h. The height and trunk length of Sorbus trees decreased with increasing altitude and slop

Effect of Aloysia citrodora essential oil on biochemicals, antioxidant characteristics, and shelf life of strawberry fruit during storage

Strawberry fruits are highly susceptible to cold burning, resulting in low storage periods at low temperatures. Plant extracts or essential oils (EOs) can potentially be used as preservatives in fruits throughout the refrigerated period. In the present study, the biochemicals, antioxidant characteristics, and shelf life of treated strawberries with Aloysia citrodora essential oil (ACEOs) were evaluated during keeping time. The treatments were produced as follows: T1, control; T2, 250 ppm ACEOs; T3, 500 ppm ACEOs; and T4, 750 ppm ACEOs. Total soluble solids (TSS), weight loss, titratable acidity (TA), antioxidant activity (DPPH assay), total phenolic (TPC), flavonoid and anthocyanin contents (TFC), and enzymes activity (peroxidase and ascorbate peroxidase) were evaluated during the refrigerated period (5 °C with relative humidity of 85–90% for 20 days). The results revealed that weight loss and TA were reduced in all treatments during storage, being that the rates were lower in samples treated with ACEOs. TPC, TFC, TSS, antioxidant, and enzymes activity were higher in treated fruits than control

Differential privacy preserved federated learning for prognostic modeling in COVID‐19 patients using large multi‐institutional chest CT dataset

Background Notwithstanding the encouraging results of previous studies reporting on the efficiency of deep learning (DL) in COVID‐19 prognostication, clinical adoption of the developed methodology still needs to be improved. To overcome this limitation, we set out to predict the prognosis of a large multi‐institutional cohort of patients with COVID‐19 using a DL‐based model. Purpose This study aimed to evaluate the performance of deep privacy‐preserving federated learning (DPFL) in predicting COVID‐19 outcomes using chest CT images. Methods After applying inclusion and exclusion criteria, 3055 patients from 19 centers, including 1599 alive and 1456 deceased, were enrolled in this study. Data from all centers were split (randomly with stratification respective to each center and class) into a training/validation set (70%/10%) and a hold‐out test set (20%). For the DL model, feature extraction was performed on 2D slices, and averaging was performed at the final layer to construct a 3D model for each scan. The DensNet model was used for feature extraction. The model was developed using centralized and FL approaches. For FL, we employed DPFL approaches. Membership inference attack was also evaluated in the FL strategy. For model evaluation, different metrics were reported in the hold‐out test sets. In addition, models trained in two scenarios, centralized and FL, were compared using the DeLong test for statistical differences. Results The centralized model achieved an accuracy of 0.76, while the DPFL model had an accuracy of 0.75. Both the centralized and DPFL models achieved a specificity of 0.77. The centralized model achieved a sensitivity of 0.74, while the DPFL model had a sensitivity of 0.73. A mean AUC of 0.82 and 0.81 with 95% confidence intervals of (95% CI: 0.79–0.85) and (95% CI: 0.77–0.84) were achieved by the centralized model and the DPFL model, respectively. The DeLong test did not prove statistically significant differences between the two models ( p ‐value = 0.98). The AUC values for the inference attacks fluctuate between 0.49 and 0.51, with an average of 0.50 ± 0.003 and 95% CI for the mean AUC of 0.500 to 0.501. Conclusion The performance of the proposed model was comparable to centralized models while operating on large and heterogeneous multi‐institutional datasets. In addition, the model was resistant to inference attacks, ensuring the privacy of shared data during the training process.</p

Differentiation of COVID‐19 pneumonia from other lung diseases using CT radiomic features and machine learning : A large multicentric cohort study

To derive and validate an effective machine learning and radiomics‐based model to differentiate COVID‐19 pneumonia from other lung diseases using a large multi‐centric dataset. In this retrospective study, we collected 19 private and five public datasets of chest CT images, accumulating to 26 307 images (15 148 COVID‐19; 9657 other lung diseases including non‐COVID‐19 pneumonia, lung cancer, pulmonary embolism; 1502 normal cases). We tested 96 machine learning‐based models by cross‐combining four feature selectors (FSs) and eight dimensionality reduction techniques with eight classifiers. We trained and evaluated our models using three different strategies: #1, the whole dataset (15 148 COVID‐19 and 11 159 other); #2, a new dataset after excluding healthy individuals and COVID‐19 patients who did not have RT‐PCR results (12 419 COVID‐19 and 8278 other); and #3 only non‐COVID‐19 pneumonia patients and a random sample of COVID‐19 patients (3000 COVID‐19 and 2582 others) to provide balanced classes. The best models were chosen by one‐standard‐deviation rule in 10‐fold cross‐validation and evaluated on the hold out test sets for reporting. In strategy#1, Relief FS combined with random forest (RF) classifier resulted in the highest performance (accuracy = 0.96, AUC = 0.99, sensitivity = 0.98, specificity = 0.94, PPV = 0.96, and NPV = 0.96). In strategy#2, Recursive Feature Elimination (RFE) FS and RF classifier combination resulted in the highest performance (accuracy = 0.97, AUC = 0.99, sensitivity = 0.98, specificity = 0.95, PPV = 0.96, NPV = 0.98). Finally, in strategy #3, the ANOVA FS and RF classifier combination resulted in the highest performance (accuracy = 0.94, AUC =0.98, sensitivity = 0.96, specificity = 0.93, PPV = 0.93, NPV = 0.96). Lung radiomic features combined with machine learning algorithms can enable the effective diagnosis of COVID‐19 pneumonia in CT images without the use of additional tests