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"Nabi, Ghulam"
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Green Synthesis of TiO2 Nanoparticle Using Cinnamon Powder Extract and the Study of Optical Properties
The pure TiO
2
nanoparticles have been synthesized by a simplistic eco-friendly green method using extract of cinnamon powder for the first time. The cinnamic acid present in the cinnamon works as the capping agent during the reaction. TiO
2
nanoparticles were characterized by using X-ray diffraction (XRD) which conformed the anatase phase TiO
2
with average crystallite size 70.1 nm. Scanning electron microscopy (SEM) micrographs suggests that the particles exhibit spherical shapes and uniformly distributed over the surface with size range 70–150 nm. The energy dispersive X-ray spectroscopy (EDX) shows the presence of oxygen and titanium peaks which confirmed the formation of TiO
2
pure nanoparticles. From the UV–Vis spectroscopic studies the band gap comes out to be 3.2 eV which confirmed the formation of TiO
2
nanoparticles. The optical properties have also been studied by PL that indicates the formation of oxygen vacancies and self-trapped excitons in the material. The samples showed the enhanced photocatalytic property.
Journal Article
Fast and Accurate Detection of COVID-19 Along With 14 Other Chest Pathologies Using a Multi-Level Classification: Algorithm Development and Validation Study
COVID-19 has spread very rapidly, and it is important to build a system that can detect it in order to help an overwhelmed health care system. Many research studies on chest diseases rely on the strengths of deep learning techniques. Although some of these studies used state-of-the-art techniques and were able to deliver promising results, these techniques are not very useful if they can detect only one type of disease without detecting the others.
The main objective of this study was to achieve a fast and more accurate diagnosis of COVID-19. This study proposes a diagnostic technique that classifies COVID-19 x-ray images from normal x-ray images and those specific to 14 other chest diseases.
In this paper, we propose a novel, multilevel pipeline, based on deep learning models, to detect COVID-19 along with other chest diseases based on x-ray images. This pipeline reduces the burden of a single network to classify a large number of classes. The deep learning models used in this study were pretrained on the ImageNet dataset, and transfer learning was used for fast training. The lungs and heart were segmented from the whole x-ray images and passed onto the first classifier that checks whether the x-ray is normal, COVID-19 affected, or characteristic of another chest disease. If it is neither a COVID-19 x-ray image nor a normal one, then the second classifier comes into action and classifies the image as one of the other 14 diseases.
We show how our model uses state-of-the-art deep neural networks to achieve classification accuracy for COVID-19 along with 14 other chest diseases and normal cases based on x-ray images, which is competitive with currently used state-of-the-art models. Due to the lack of data in some classes such as COVID-19, we applied 10-fold cross-validation through the ResNet50 model. Our classification technique thus achieved an average training accuracy of 96.04% and test accuracy of 92.52% for the first level of classification (ie, 3 classes). For the second level of classification (ie, 14 classes), our technique achieved a maximum training accuracy of 88.52% and test accuracy of 66.634% by using ResNet50. We also found that when all the 16 classes were classified at once, the overall accuracy for COVID-19 detection decreased, which in the case of ResNet50 was 88.92% for training data and 71.905% for test data.
Our proposed pipeline can detect COVID-19 with a higher accuracy along with detecting 14 other chest diseases based on x-ray images. This is achieved by dividing the classification task into multiple steps rather than classifying them collectively.
Journal Article
Complex renal cysts (Bosniak ≥IIF): interobserver agreement, progression and malignancy rates
Objective
The objective was to assess the interobserver agreement rate, progression rates and malignancy rates in the assessment of complex renal cysts (≥ Bosniak IIF) using a population-based database.
Methods
A regional database identified 452 complex renal cysts in 415 patients between 2009 and 2019. Each patient was tracked and followed up using a unique identifier and deterministic linkage methodology. The interobserver agreement rate between radiologists was calculated using a weighted kappa statistic. Progression and malignancy rates of cysts (Bosniak ≥IIF) over the 11-year period were calculated.
Results
The linear-weighted kappa value was 0.69 for all complex cysts. The rate of progression and regression of Bosniak IIF cysts was 4.6% (7/151) and 3.3% (5/151), respectively. All malignant IIF cysts progressed within 16 months of diagnosis. The malignancy rate of surgically resected Bosniak III and IV cysts was 79.3% (23/29) and 84.5% (39/46), respectively. Of all malignant tumours, 73.8% and 93.7% were of low ISUP grade and low stage, respectively.
Conclusions
This study further confirms that there is a good degree of agreement between radiologists in classifying complex renal masses using the Bosniak classification. The progression rate of Bosniak IIF cysts is low, but the malignancy rates of surgically resected Bosniak IIF, III and IV cysts are high. Benign cysts are frequently resected, and a very high proportion of histopathologically confirmed cancers in complex renal cysts are of low grade and stage.
Key Points
•
There is a good degree of agreement between radiologists in classifying complex renal masses using the Bosniak classification.
•
The rate of progression of Bosniak IIF cysts is low, and malignant cysts progress early during surveillance. Although the malignancy rates of resected Bosniak IIF, III and IV cysts are high, the rate of benign cyst resection is significant.
Journal Article
Prediction of prostate cancer Gleason score upgrading from biopsy to radical prostatectomy using pre-biopsy multiparametric MRI PIRADS scoring system
An increase or ‘upgrade’ in Gleason Score (GS) in prostate cancer following Transrectal Ultrasound (TRUS) guided biopsies remains a significant challenge to overcome. to evaluate whether MRI has the potential to narrow the discrepancy of histopathological grades between biopsy and radical prostatectomy, three hundred and thirty men treated consecutively by laparoscopic radical prostatectomy (LRP) between July 2014 and January 2019 with localized prostate cancer were included in this study. Independent radiologists and pathologists assessed the MRI and histopathology of the biopsies and prostatectomy specimens respectively. A multivariate model was constructed using logistic regression analysis to assess the ability of MRI to predict upgrading in biopsy GS in a nomogram. A decision-analysis curve was constructed assessing impact of nomogram using different thresholds for probabilities of upgrading. PIRADS scores were obtained from MRI scans in all the included cases. In a multivariate analysis, the PIRADS v2.0 score significantly improved prediction ability of MRI scans for upgrading of biopsy GS (p = 0.001, 95% CI [0.06–0.034]), which improved the C-index of predictive nomogram significantly (0.90 vs. 0.64, p < 0.05). PIRADS v2.0 score was an independent predictor of postoperative GS upgrading and this should be taken into consideration while offering treatment options to men with localized prostate cancer.
Journal Article
Advancements and Prospects of Genome-Wide Association Studies (GWAS) in Maize
Genome-wide association studies (GWAS) have emerged as a powerful tool for unraveling intricate genotype–phenotype association across various species. Maize (Zea mays L.), renowned for its extensive genetic diversity and rapid linkage disequilibrium (LD), stands as an exemplary candidate for GWAS. In maize, GWAS has made significant advancements by pinpointing numerous genetic loci and potential genes associated with complex traits, including responses to both abiotic and biotic stress. These discoveries hold the promise of enhancing adaptability and yield through effective breeding strategies. Nevertheless, the impact of environmental stress on crop growth and yield is evident in various agronomic traits. Therefore, understanding the complex genetic basis of these traits becomes paramount. This review delves into current and future prospectives aimed at yield, quality, and environmental stress resilience in maize and also addresses the challenges encountered during genomic selection and molecular breeding, all facilitated by the utilization of GWAS. Furthermore, the integration of omics, including genomics, transcriptomics, proteomics, metabolomics, epigenomics, and phenomics has enriched our understanding of intricate traits in maize, thereby enhancing environmental stress tolerance and boosting maize production. Collectively, these insights not only advance our understanding of the genetic mechanism regulating complex traits but also propel the utilization of marker-assisted selection in maize molecular breeding programs, where GWAS plays a pivotal role. Therefore, GWAS provides robust support for delving into the genetic mechanism underlying complex traits in maize and enhancing breeding strategies.
Journal Article
Separation of the Impact of Landuse/Landcover Change and Climate Change on Runoff in the Upstream Area of the Yangtze River, China
Landuse/landcover change (LULCC) and climate change (CC) impacts on streamflow in high elevated catchments are very important for sustainable management of water resources and ecological developments. In this research, a statistical technique was used in combination with the Soil and Water Assessment Tool (SWAT) to the Upstream Area of the Yangtze River (UAYR). Different performance criteria (e.g., R2, NSE, and PBIAS) were used to evaluate the acceptability of the model simulation results. The model provided satisfactory results for monthly simulations in the calibration (R2; 0.80, NSE; 0.78 and PBIAS; 22.3%) and the validation period (R2; 0.89, NSE; 0.75 and PBIAS; 19.1%). Major landuse/landcover transformations from 1990 to 2005 have occurred from low grassland to medium grassland (2%) and wetlands (0.9%), bare land to medium grassland (0.2%), glaciers to wetland (16.8%), and high grassland to medium grassland (5.8%). The results show that there is an increase in average annual runoff at the Zhimenda station in UAYR by 15 mm of, which approximately 98% is caused by climate change and only 2% by landuse/landcover change. The changes evapotranspiration are larger due to climate change as compared to landuse/landcover change, particularly from August to October. Precipitation and temperature have increased during these months. On the contrary, there has been a decrease in evapotranspiration and runoff from October to March which depicts the intra-annual variations in the vegetation in the study area.
Journal Article
Birds and plastic pollution: recent advances
Plastic waste and debris have caused substantial environmental pollution globally in the past decades, and they have been accumulated in hundreds of terrestrial and aquatic avian species. Birds are susceptible and vulnerable to external environments; therefore, they could be used to estimate the negative effects of environmental pollution. In this review, we summarize the effects of macroplastics, microplastics, and plastic-derived additives and plastic-absorbed chemicals on birds. First, macroplastics and microplastics accumulate in different tissues of various aquatic and terrestrial birds, suggesting that birds could suffer from the macroplastics and microplastics-associated contaminants in the aquatic and terrestrial environments. Second, the detrimental effects of macroplastics and microplastics, and their derived additives and absorbed chemicals on the individual survival, growth and development, reproductive output, and physiology, are summarized in different birds, as well as the known toxicological mechanisms of plastics in laboratory model mammals. Finally, we identify that human commensal birds, long-life-span birds, and model bird species could be utilized to different research objectives to evaluate plastic pollution burden and toxicological effects of chronic plastic exposure.
Journal Article
Probing Combined Experimental and Computational Profiling to Identify N-(benzodthiazol-2-yl) Carboxamide Derivatives: A Path to Potent α-Amylase and α-Glucosidase Inhibitors for Treating Diabetes Mellitus
A novel series of benzothiazole scaffolds were presented to test their in vitro α-amylase and α-glucosidase activities for combating diabetes mellitus, which is one of the most rapidly growing diseases. The tested compounds were elucidated structurally by various spectroscopic techniques like 1H NMR, 13C NMR and HRMS. All compounds exhibited a varied range of inhibitory activities against targeted α-amylase and α-glucosidase enzymes, with IC50 values of 1.58 ± 1.20 to 7.54 ± 3.60 µM (α-amylase) and 2.10 ± 1.10 to 8.90 ± 4.10 (α-glucosidase), respectively. The obtained results were compared with the standard acarbose drug, with IC50 values of 0.91 ± 0.20 µM (α-amylase) and 1.80 ± 1.10 µM (α-glucosidase). Specifically, methyl 2-amino-4-((6-methoxypyridin-3-yl)methoxy)benzo[d]thiazole-6-carboxylate (5c) and methyl 4-((6-methoxypyridin-3-yl)methoxy)-2-(thiazole-2-carboxamido)benzo[d]thiazole-6-carboxylate (6b) emerged as potent inhibitors of α-amylase and α-glucosidase enzymes. These potent compounds were further screened for in silico molecular docking studies to investigate possible binding interactions with active sites of targeted enzymes, and results obtained demonstrated that potent compounds exhibited stronger binding affinities toward anti-diabetic enzymes compared to the positive control acarbose.
Journal Article
The trend in delayed childbearing and its potential consequences on pregnancy outcomes: a single center 9-years retrospective cohort study in Hubei, China
Background
Due to the advancement of modern societies, the proportion of women who delay childbearing until or beyond 30 years has dramatically increased in the last three decades and has been linked with adverse maternal-neonatal outcomes.
Objective
To determine the trend in delayed childbearing and its negative impact on pregnancy outcomes.
Material and methods
A tertiary hospital-based retrospective study was conducted in Wuhan University Renmin Hospital, Hubei Province, China, during the years 2011–2019. The joinpoint regression analysis was used to find a trend in the delayed childbearing and the multiple binary logistic regression model was used to estimate the association between maternal age and pregnancy outcomes.
Results
Between 2011 and 2019, the trend in advanced maternal age (AMA ≥35 years) increased by 75% [AAPC 7.5% (95% CI: − 10.3, 28.9)]. Based on maternal education and occupation, trend in AMA increased by 130% [AAPC 11.8% (95% CI: 1.1, 23.7)] in women of higher education level, and 112.5% [AAPC 10.1% (95% CI: 9.4, 10.9)] in women of professional services. After adjusting for confounding factors, AMA was significantly associated with increased risk of gestational hypertension (aOR 1.5; 95% CI: 1.2, 2.1), preeclampsia (aOR 1.6; 95% CI: 1.4, 1.9), sever preeclampsia (aOR 1.7; 95% CI: 1.1, 2.6), placenta previa (aOR 1.8; 95% CI: 1.5, 2.2), gestational diabetes mellitus (aOR 2.5; 95% CI: 2.3, 2.9), preterm births (aOR 1.6; 95% CI: 1.4, 1.7), perinatal mortality (aOR 1.8; 95% CI: 1.3, 2.3), and low birth weight (aOR 1.3; 95% CI: 1.2, 1.4) compared with women aged < 30 years.
Conclusion
Our findings show a marked increase in delayed childbearing and its negative association with pregnancy outcomes.
Journal Article