Low UV radiation influenced DNA methylation, gene regulation, cell proliferation, viability, and biochemical differentiation in the cell suspension cultures of Cannabis indica.

The effect of low artificial Ultraviolet (UV) on the DNA methylation remains controversial. This study addresses how differential photoperiods of UV radiation affect the biochemical and molecular behaviors of Cannabis indica cell suspension cultures. The cell suspensions were illuminated with the compact fluorescent lamps (CFL), emitting a combination of 10% UVB, 30% UVA, and the rest visible wavelengths for 0, 4, 8, and 16 h. The applied photoperiods influenced cell morphological characteristics. The 4 h photoperiod was the most effective treatment for improving biomass, growth index and cell viability percentage while these indices remained non-significant in the 16 h treatment. The methylation-sensitive amplified polymorphism (MASP) assay revealed that the UV radiation was epigenetically accompanied by DNA hypermethylation. The light-treated cells significantly displayed higher relative expression of the cannabidiolic| acid synthase (CBDAS) and delta9-tetrahydrocannabinolic acid synthase (THCAS) genes about 4-fold. The expression of the olivetolic acid cyclase (OAC) and olivetol synthase (OLS) genes exhibited an upward trend in response to the UV radiation. The light treatments also enhanced the proline content and protein concentration. The 4 h illumination was significantly capable of improving the cannabidiol (CBD) and delta-9-tetrahydrocannabinol (THC) concentrations, in contrast with 16 h. By increasing the illumination exposure time, the activity of the phenylalanine ammonia-lyase (PAL) enzyme linearly upregulated. The highest amounts of the phenylpropanoid derivatives were observed in the cells cultured under the radiation for 4 h. Taken collective, artificial UV radiation can induce DNA methylation modifications and impact biochemical and molecular differentiation in the cell suspensions in a photoperiod-dependent manner.

Effect of oil contaminants on antioxidant responses and antioxidant properties of Pleurotus florida (P. Kumm).

This research investigated the antioxidant responses of Pleurotus florida at different concentrations of gas oil [0% (control), 2.5%, 5%, and 10% (v:v)] for 30 days. The activities of superoxide dismutase and catalase enzymes decreased in responses to the gas oil presence by an average of 83% and 49%, respectively. In contrast, the activities of the ascorbate peroxidase and glutathione peroxidase enzymes displayed an upward trend in the groups cultured in oil-contaminated media. The gas oil contaminant increased total phenol and flavonoid accumulation, reflecting the variation in secondary metabolism. According to the 1,2-diphenyl-2-picrylhydrazyl radical scavenging, the 2.5% gas oil treatment resulted in the highest antioxidant activity (48 µg mL-1). The highest scavenging activity of nitric oxide radicals (IC50 = 272 µg mL-1) was observed in the treatment with the highest gas oil concentration (10%). Also, this treatment showed an excellent ability to chelate Fe+2 ions (IC50 = 205 µg mL-1). The IC50 values of methanolic extract for nitric oxide scavenging activity and metal chelating ability were significantly reduced by increasing gas oil concentration in the treatments. With increasing the gas oil concentration, malondialdehyde content as a criterion measure of lipid peroxidation level showed significant reduction. These results show that P. florida is resistant to and a compatible mushroom with oil pollutants. Also, the activity of glutathione peroxidase and the ascorbate-glutathione cycle detoxify nitric oxide radicals and products of reactive oxygen species-induced lipid peroxidation in the gas oil treatments.

Silicon nanoparticles (SiNPs) stimulated secondary metabolism and mitigated toxicity of salinity stress in basil (Ocimum basilicum) by modulating gene expression: a sustainable approach for crop protection.

The underlying mechanisms through which silicon oxide nanoparticles (SiNPs) can confer salinity resistance to plants are poorly understood. This study explored the efficacy of supplementing nutrient solution with SiNPs (20-30 nm; 10 mg kg-1 soil) to stimulate metabolism and alleviate the risks associated with salinity (0.73 g kg-1 soil) in basil seedlings. For this purpose, variations in photosynthetic indices, proline osmoprotectant, antioxidant markers, phenylpropanoid metabolism, and transcriptional behaviors of genes were investigated. SiNPs increased shoot fresh weight (38%) and mitigated the risk associated with the salinity stress by 14%. SiNPs alleviated the inhibitory effects of salinity on the total chlorophyll concentration by 15%. The highest increase (twofold) in proline content was recorded in the SiNP-treated seedlings grown under salinity. The nano-supplement enhanced the activity of enzymatic antioxidants, including peroxidase (2.5-fold) and catalase (4.7-fold). SiNPs induced the expression of gamma-cadinene synthase (CDS) and caffeic acid O-methyltransferase (COMT) genes by 6.5- and 18.3-fold, respectively. SiNPs upregulated the eugenol synthase (EGS1) and fenchol synthase (FES) genes by six- and nine-fold, respectively. Salinity transcriptionally downregulated the geraniol synthase (GES) gene, while this gene displayed an upward trend in response to SiNPs by eight-fold. The nano-supplement transcriptionally stimulated the R-linalool synthase (LIS) gene by 3.3-fold. The terpinolene synthase (TES) gene displayed a similar trend to that of the GES gene. The highest expression (25-fold) of the phenylalanine ammonia-lyase (PAL) gene was recorded in seedlings supplemented with SiNPs. The physiological and molecular assessments demonstrated that employing SiNPs is a sustainable strategy for improving plant primary/secondary metabolism and crop protection.

Effect of Pre-dialysis Serum Sodium Measurement on Reduction of Hemodialysis Complications.

INTRODUCTION: Despite great advances in hemodialysis, complications during dialysis remain in force. Accurate assessment of dry weight is a determining factor in the prevention of hemodialysis complications. This study is designed to evaluate the effect of adjustment of ultrafiltration rate, on hemodialysis complications, based on dry weight calculation, by measuring the pre-dialysis serum sodium. METHODS: In this single-blind clinical trial 50 patients were included. The patients were randomly divided into case and control groups. First, in the intervention group, the blood sodium level was measured before dialysis. Then, the dry weight of the patients was determined, ultrafiltration was adjusted according to the dry weight, and the patients' dialysis program was performed. In the control group, dry weight was determined routinely. Blood pressure, muscle cramps, nausea, and vomiting were recorded in both groups for 3 months. RESULTS: The results showed a significant difference between the two groups in the rate of postoperative nausea and vomiting (P < .05) and muscle cramps during dialysis (P < .05). There were no significant differences between the two groups in blood pressure drop during dialysis and fatigue after hemodialysis in the first, second, and third months (P > .05). CONCLUSION: Accurate assessment of dry weight by the pre-dialysis blood sodium formula, reduces muscle cramps, nausea, and, vomiting.  DOI: 10.52547/ijkd.7170.

Applications of different machine learning approaches in prediction of breast cancer diagnosis delay.

Background: The increasing rate of breast cancer (BC) incidence and mortality in Iran has turned this disease into a challenge. A delay in diagnosis leads to more advanced stages of BC and a lower chance of survival, which makes this cancer even more fatal. Objectives: The present study was aimed at identifying the predicting factors for delayed BC diagnosis in women in Iran. Methods: In this study, four machine learning methods, including extreme gradient boosting (XGBoost), random forest (RF), neural networks (NNs), and logistic regression (LR), were applied to analyze the data of 630 women with confirmed BC. Also, different statistical methods, including chi-square, p-value, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUC), were utilized in different steps of the survey. Results: Thirty percent of patients had a delayed BC diagnosis. Of all the patients with delayed diagnoses, 88.5% were married, 72.1% had an urban residency, and 84.8% had health insurance. The top three important factors in the RF model were urban residency (12.04), breast disease history (11.58), and other comorbidities (10.72). In the XGBoost, urban residency (17.54), having other comorbidities (17.14), and age at first childbirth (>30) (13.13) were the top factors; in the LR model, having other comorbidities (49.41), older age at first childbirth (82.57), and being nulliparous (44.19) were the top factors. Finally, in the NN, it was found that being married (50.05), having a marriage age above 30 (18.03), and having other breast disease history (15.83) were the main predicting factors for a delayed BC diagnosis. Conclusion: Machine learning techniques suggest that women with an urban residency who got married or had their first child at an age older than 30 and those without children are at a higher risk of diagnosis delay. It is necessary to educate them about BC risk factors, symptoms, and self-breast examination to shorten the delay in diagnosis.

Machine learning algorithms to uncover risk factors of breast cancer: insights from a large case-control study.

Introduction: This large case-control study explored the application of machine learning models to identify risk factors for primary invasive incident breast cancer (BC) in the Iranian population. This study serves as a bridge toward improved BC prevention, early detection, and management through the identification of modifiable and unmodifiable risk factors. Methods: The dataset includes 1,009 cases and 1,009 controls, with comprehensive data on lifestyle, health-behavior, reproductive and sociodemographic factors. Different machine learning models, namely Random Forest (RF), Neural Networks (NN), Bootstrap Aggregating Classification and Regression Trees (Bagged CART), and Extreme Gradient Boosting Tree (XGBoost), were employed to analyze the data. Results: The findings highlight the significance of a chest X-ray history, deliberate weight loss, abortion history, and post-menopausal status as predictors. Factors such as second-hand smoking, lower education, menarche age (>14), occupation (employed), first delivery age (18-23), and breastfeeding duration (>42 months) were also identified as important predictors in multiple models. The RF model exhibited the highest Area Under the Curve (AUC) value of 0.9, as indicated by the Receiver Operating Characteristic (ROC) curve. Following closely was the Bagged CART model with an AUC of 0.89, while the XGBoost model achieved a slightly lower AUC of 0.78. In contrast, the NN model demonstrated the lowest AUC of 0.74. On the other hand, the RF model achieved an accuracy of 83.9% and a Kappa coefficient of 67.8% and the XGBoost, achieved a lower accuracy of 82.5% and a lower Kappa coefficient of 0.6. Conclusion: This study could be beneficial for targeted preventive measures according to the main risk factors for BC among high-risk women.

Using machine learning in prediction of ICU admission, mortality, and length of stay in the early stage of admission of COVID-19 patients.

The recent COVID-19 pandemic has affected health systems across the world. Especially, Intensive Care Units (ICUs) have played a pivotal role in the treatment of critically-ill patients. At the same time however, the increasing number of admissions due to the vast prevalence of the virus have caused several problems for ICU wards such as overburdening of staff and shortages of medical resources. These issues might have affected the quality of healthcare services provided directly impacting a patient's survival. The objective of this research is to leverage Machine Learning (ML) on hospital data in order to support hospital managers and practitioners with the treatment of COVID-19 patients. This is accomplished by providing more detailed inference about a patient's likelihood of ICU admission, mortality and in case of hospitalization the length of stay (LOS). In this pursuit, the outcome variables are in three separate models predicted by five different ML algorithms: eXtreme Gradient Boosting (XGB), K-Nearest Neighbor (KNN), Random Forest (RF), bagged-CART (b-CART), and LogitBoost (LB). With the exception of KNN, the studied models show good predictive capabilities when evaluating relevant accuracy scores, such as area under the curve. By implementing an ensemble stacking approach (either a Neural Net or a General Linear Model) on top of the aforementioned ML algorithms the performance is further boosted. Ultimately, for the prediction of admission to the ICU, the ensemble stacking via a Neural Net achieved the best result with an accuracy of over 95%. For mortality at the ICU, the vanilla XGB performed slightly better (1% difference with the meta-model). To predict large length of stays both ensemble stacking approaches yield comparable results. Besides it direct implications for managing COVID-19 patients, the approach presented serves as an example how data can be employed in future pandemics or crises.

Predicting the necessity of oxygen therapy in the early stage of COVID-19 using machine learning.

Medical oxygen is a critical element in the treatment process of COVID-19 patients which its shortage impacts the treatment process adversely. This study aims to apply machine learning (ML) to predict the requirement for oxygen-based treatment for hospitalized COVID-19 patients. In the first phase, demographic information, symptoms, and patient's background were extracted from the databases of two local hospitals in Iran, and preprocessing actions were applied. In the second step, the related features were selected. Lastly, five ML models including logistic regression (LR), random forest (RF), XGBoost, C5.0, and neural networks (NNs) were implemented and compared based on their accuracy and capability. Among the variables related to the patient's background, consuming opium due to the high rate of opium users in Iran was considered in the models. Of the 398 patients included in the study, 112 (28.14%) received oxygen-based treatment. Shortness of breath (71.42%), fever (62.5%), and cough (59.82%) had the highest frequency in patients with oxygen requirements. The most important variables for prediction were shortness of breath, cough, age, and fever. For opioid-addicted patients, in addition to the high mortality rate (23.07%), the rate of oxygen-based treatment was twice as high as non-addicted patients. XGBoost and LR obtained the highest area under the curve with values of 88.7% and 88.3%, respectively. For accuracy, LR and NNs achieved the best and same accuracy (86.42%). This approach provides a tool that accurately predicts the need for oxygen in the treatment process of COVID-19 patients and helps hospital resource management.

Analysis of non-pharmaceutical interventions impacts on COVID-19 pandemic in Iran.

The COVID-19 pandemic shows to have a huge impact on people's health and countries' infrastructures around the globe. Iran was one of the first countries that experienced the vast prevalence of the coronavirus outbreak. The Iranian authorities applied various non-pharmaceutical interventions to eradicate the epidemic in different periods. This study aims to investigate the effectiveness of non-pharmaceutical interventions in managing the current Coronavirus pandemic and to predict the next wave of infection in Iran. To achieve the research objective, the number of cases and deaths before and after the interventions was studied and the effective reproduction number of the infection was analyzed under various scenarios. The SEIR generic model was applied to capture the dynamic of the pandemic in Iran. To capture the effects of different interventions, the corresponding reproduction number was considered. Depending on how people are responsive to interventions, the effectiveness of each intervention has been investigated. Results show that the maximum number of the total of infected individuals will occur around the end of May and the start of June 2021. It is concluded that the outbreak could be smoothed if full lockdown and strict quarantine continue. The proposed modeling could be used as an assessment tool to evaluate the effects of different interventions in new outbreaks.

Cold plasma can alleviate cadmium stress by optimizing growth and yield of wheat (Triticum aestivum L.) through changes in physio-biochemical properties and fatty acid profile.

Cold plasma (CP) application has increasing interest due to its environmental-friendly, high efficient, and low cost aspects to mitigate deletion effects of heavy metals on plants. A pot experiment was carried out to evaluate the CP application on yield, physiological, and fatty acid profile of wheat (Triticum aestivum L.) in a completely randomized design (CRD) with five replicates. Cadmium (Cd) was applied at four levels (0, 50, 100, and 150 µM), and CP were used on germinated seeds at three levels (0, 60, and 120 s) in a hydroponic system. The results showed CP alleviated the Cd accumulation in roots, shoots, and grains. The significant reduction of grain yield (GY) and thousand grain yield (TGY) was observed in plants exposed to 100 and 150 µM compared with the control plants; however, CP improved GY and TGY particularly at severe Cd stress. The minimum chlorophyll (Chl) and relative water content (RWC) were observed in plants exposed in 100 µM Cd and non-CP treatments. Proline increased by Cd stress but decreased with CP in most treatments. Unlike proline, methionine showed significant reduction under Cd stress. The fatty acid profile of wheat represented that severe Cd stress decreased monounsaturated fatty acid (MUFA) but increased polyunsaturated fatty acid (PUFA). Heat map (HM) showed that GY and methionine were the most sensitive traits under treatments of Cd and CP. Totally, we suggest the use of 120 s of CP to mitigate Cd stress on wheat plants.

Mycoremediation of oil contaminant by Pleurotus florida (P.Kumm) in liquid culture.

This study investigated the potential functions of Pleurotus florida (an edible mushroom) in the biodegradation of gas oil at concentrations of 0 (control), 2.5, 5, and 10% (V: V) for 30 days. The gas oil increased dry weight and protein concentration in all treatments (by an average of 19.5 and 108%, respectively). Moreover, the pH, surface tension (ST), and interfacial tension (IFT) were reduced by the mushroom supplementation. The lowest surface tension (31.9 mN m-1) and the highest biosurfactant production belonged to the 10% gas oil treatment (0.845 ± 0.03 mg mL-1). The results demonstrated that the adsorption isotherm agreed well with the Langmuir isotherm. The maximum Langmuir adsorption capacity was calculated at 0.743 mg g-1 wet biomass of P. florida. The fungal supplementation efficiently remedied the total petroleum hydrocarbons (TPHs) by an average of 55% after 30 days. Gas chromatography (GC) analysis revealed that P. florida effectively detoxified C13-C28 hydrocarbons, Pristane, and Phytane, implying its high mycoremediation function. The toxicity test showed that mycoremediation increased the germination by an average of 35.82% ± 8.89 after 30 days. Laccase activity increased significantly with increasing gas oil concentration in the treatments. The maximum laccase activity was obtained in the 10% gas oil treatment (142.25 ± 0.72 U L-1). The presence of pollutants was also associated with induction in the tyrosinase activity when compared to the control. These results underline the high mycoremediation capacity of P. florida through the involvement of biosurfactants, laccase, and tyrosinase.

Faecal carriage of high-level aminoglycoside-resistant and ampicillin-resistant Enterococcus species in healthy Iranian children.

OBJECTIVES: High-level aminoglycoside, ampicillin and vancomycin resistance and virulence genes among enterococcal isolates collected from healthy middle-school children in Ardabil, Iran, during 2016 were investigated. METHODS: Totally, 305 faecal specimens were collected. Isolates underwent antimicrobial susceptibility testing, virulence gene detection and molecular typing. RESULTS: Totally, 409 enterococcal isolates were collected, comprising Enterococcus faecium (235; 57.5%), Enterococcus faecalis (56; 13.7%) and other Enterococcus spp. (118; 28.9%). Overall, 71 (17.4%), 11 (2.7%) and 10 (2.4%) isolates were identified as high-level streptomycin-resistant (HLSR), high-level gentamicin-resistant (HLGR) and ampicillin-resistant (AR), respectively. Among HLSR isolates, 40 (56.3%), 5 (7.0%) and 26 (36.6%) were E. faecium, E. faecalis and other Enterococcus spp., respectively. Among HLGR isolates 4 (36.4%) and 7 (63.6%) and among AR isolates 7 (70.0%) and 3 (30.0%) were E. faecium and other Enterococcus spp., respectively. Accordingly, 21.6%, 3.6% and 3.3% of subjects were colonised with HLSR, HLGR and AR Enterococcus spp. Carriage of HLGR, HLSR and AR isolates was associated with prior antibiotic consumption (P≤0.05). Additionally, male sex and antacid consumption were associated with AR enterococcal carriage. Moreover, 69 (97.2%), 10 (90.9%) and 9 (90.0%) of HLSR, HLGR and AR isolates were multidrug-resistant, respectively. No vancomycin-resistant enterococci were detected. ERIC-PCR revealed high genetic diversity among isolates. gelE and asa1 were major virulence genes both in E. faecalis and E. faecium. Presence of gelE was associated with HLSR and HLGR phenotypes (P≤0.05). CONCLUSION: Community intestinal carriage of HLSR enterococci was high; however, carriage of HLGR and AR enterococci was low.

Potent Antioxidant Properties of rolB-transformed Catharanthus roseus (L.) G. Don.

Free radicals play an important role in pathological processes such as aging and developing cancer; hence, natural products inhibit free radical production, and can play an important role in preventing diseases. We aimed to evaluate the scavenging activity of free radicals, reducing power, inhibition of lipid peroxidation and malondialdehyde (MDA) formation, and the antioxidant composition of rolB-transformed hairy roots and leaf callus of Catharanthus roseus. Hairy roots of the Catharanthus roseus were induced using Agrobacterium rhizogenes strain ATCC 15834 to transfer the rolB gene. Polymerase chain reaction analysis was used to identify the presence of the gene in the transformed hairy roots. Folin-Ciocalteu reagent, aluminum chloride calorimetry and high performance liquid chromatography were used to determine the content of total phenolic, flavonoid and gallic acid, respectively. To this end, we assayed the free radicals scavenging activity by 1-diphenyl-2-picrylhydrazyl (DPPH), reducing power, inhibition of lipid peroxidation and inhibition of malondialdehyde production. The results showed that phenolic, flavonoid, and gallic acid contents in the ethanol extract of the hairy roots were significantly higher (p ≤ 0.01) than those naturally found in the extracts of root, leaf, and leaf callus of C. roseus. The hairy roots extract showed the lowest IC50 for the inhibition of DPPH(•). Furthermore, the ethanol extract showed the best reducing power and had the highest potential to inhibit lipid peroxidation and to form the MDA. The transformed hairy roots can be considered a rich natural source of antioxidants.