Clerodane diterpene 3-deoxycaryoptinol (Clerodin) selectively induces apoptosis in human monocytic leukemia (THP-1) cells and upregulates apoptotic protein caspase-3.

3-deoxycaryoptinol (Clerodin) is a clerodane diterpene isolated from the leaves of Clerodendrum infortunatum. The present research investigates the anticancer therapeutic efficacy of clerodin in human monocytic leukemic (THP-1) cells for the first time. In vitro assay using THP-1 cells showed the cytotoxic ability of clerodin. Further, Annexin-V(FITC)/PI and intracellular ROS (DCFDA) assays carried out using flow cytometry, and confocal laser scanning microscopy confirmed the apoptotic potential of clerodin. Moreover, the western blot was used to detect mitochondrial apoptosis of THP-1 cells. RT-PCR, ELISA, and western blot analysis clearly indicated that clerodin significantly increased the expression of pro-apoptotic marker caspase-3 in THP-1 cells. clerodin also selectively targeted the G2/M phase of THP-1 cells, a key feature for anticancer molecules. Importantly, the clerodin did not exhibit cytotoxicity against human peripheral blood cells. These properties of clerodin make it a potential chemotherapeutic agent that can selectively induce apoptosis in leukemia-like cancer cells.

Postgraduate Training in Prosthodontics: Residents' and Supervisors' Perspectives on Current Training: A Cross-Sectional Study.

OBJECTIVE: To identify postgraduate (PG) prosthodontic trainees' and supervisors' introspective views regarding prosthodontic education and clinical training. STUDY DESIGN: Cross-sectional survey. Place and Duration of the Study: The Aga Khan University Hospital, Karachi, from May to October 2021. METHODOLOGY: Data regarding the trainee induction process, teaching and learning practices, research interests, frequency of assessment, and trainees' satisfaction levels with the adequacy of didactic and clinical training in their centres were collected from Prosthodontic PG trainees and supervisors in Pakistan using REDCap software and analysed using SPSS 23.0. Frequencies of the observed responses from both populations were reported, and associations among private and public training centres were studied with Chi-square (or Fisher's exact). Differences in satisfaction levels across theoretical and clinical training domains were assessed with Mann- Whitney U test. RESULTS: Out of 17 supervisors and 104 PG trainees, 30% reported having a supervisory board in their institute. Most participants had entry tests conducted in their centres. The interference of influential sources during trainee induction was reported by 88.2% of the supervisors, while 66.7% trainees expressed the need for such influence to acquire training. Simulation-based teaching was available to 43.2% of trainees, 83% expressed interest in publishing research and 85% in publishing cases. Trainees were overall satisfied with their didactic and clinical training, whereas their satisfaction level was found neutral with theory and clinical training in maxillofacial-prosthodontics and clinical training of occlusion and temporomandibular disorders. CONCLUSION: The results emphasise the need for regulation and monitoring of the trainee induction process, quality of training, and frequent assessments in the prosthodontics postgraduate training programmes across Pakistan. KEY WORDS: Prosthodontics, Programme evaluation, Graduate dental education, Postgraduate dental education, Postgraduate medical education.

Exploring feasibility of citric acid infused lignocellulosic waste derived from chestnut and water melon peels for phytofiltration of Eosin yellow dye from water.

The adsorption efficiency of cheap, ecofriendly, and easily available agro-waste, Trapa natans (Chestnut) and Citrullus lanatus (Watermelon) peels, has been investigated in their native forms (TNAT and CLAN) as well as citric acid impregnated forms (C-TNAT and C-CLAN), respectively, for the detoxification of toxic, deleterious, and carcinogenic Eosin yellow dye (EYD) from wastewater streams. Different operational parameters were optimized for the investigation of isothermal, kinetic and the thermodynamic models. R2 for sportive decontamination of Eosin by citric acid treated adsorbents were close to one, supporting the applicability of Langmuir, Temkin, and pseudo-second-order in this investigation. Maximum sorption capabilities were 222 and 667 mg/g for chemically treated bio-waste C-TNAT and C-CLAN, respectively, reflecting their efficient and promising performance, while Gibbs free energy revealed exothermic and spontaneous adsorption behavior. The kinetic statics for qe (cal) are quite close to qe (exp), indicating the viability and fitness of pseudo-second-order mechanisms. The present study suggests that both citric acid fabricated bio-waste C-TNAT and C-CLAN can be substantially employed to decontaminate persistent organic pollutants, like: Eosin yellow dye from wastewater using green approach to resolve socio-economic problems of developing countries.

Phytochemical profile, nutritional composition of pomegranate peel and peel extract as a potential source of nutraceutical: A comprehensive review.

The current study focuses on Punica granatum L. (pomegranate) peel and peel extract and their use as functional foods, food additives, or physiologically active constituents in nutraceutical formulations. The pomegranate peel extract is a good source of bioactive substances needed for the biological activity of the fruit, including phenolic acids, minerals, flavonoids (anthocyanins), and hydrolyzable tannins (gallic acid). The macromolecules found in pomegranate peel and peel extract have been recommended as substitutes for synthetic nutraceuticals, food additives, and chemo-preventive agents because of their well-known ethno-medical significance and chemical properties. Moreover, considering the promises for both their health-promoting activities and chemical properties, the dietary and nutraceutical significance of pomegranate peel and pomegranate peel extract appears to be underestimated. The present review article details their nutritional composition, phytochemical profile, food applications, nutraceutical action, and health benefits.

Comparative study and effects of urban green scape on the land surface temperature of a large metropolis and green city.

Previous studies have provided valuable insights into the impact of green space (GS) on land surface temperature (LST). However, there is a need for in-depth comparative research on changing landscape patterns in cities and their effects on the urban thermal environment. This study investigates the spatial arrangement of GS and the influence of impervious surfaces on LST in urban areas, examining their cooling and warming effects in the urban landscapes of Beijing and Islamabad. The study aims to assess the impact of the spatial arrangement of GS on LST using a moving window of 1 km2 to analyze the overall effect of landscape patterns on the urban environment. Using Gaofen (GF-2) and Landsat-8 satellite data, we examined the biophysical surface properties of core urban areas. The results indicate a significant difference in the mean LST of 5.44 °C and 3.31 °C between impervious surfaces and GS in Beijing and Islamabad, respectively. The barren land and GS in Islamabad experience a higher LST of 3.39 °C compared to Beijing, which accounts for 1.39 °C. In Beijing, configuration metrics show no significant effect on urban LST, while edge density (ED) exhibits a slightly negative trend. In contrast, in the city of Islamabad, the landscape shape index (LSI), patch density (PD), and number of patches (NP) metrics have a significant influence on LST. The cooling effect of GS patches (0.1-0.5 ha) is more pronounced, while that of GS patches of 15-20 ha shows no significant effect on LST. The temperature difference (TD) of 5.01 °C was observed from the edge of GS in Beijing and 3.3 °C in Islamabad. Considering Islamabad's lush green scape compared to Beijing, this study suggests that Islamabad may experience an increase in LST in the future due to urbanization. This study's findings may assist urban policy-makers in designing sustainable green city layouts that effectively address future planning considerations.

Effective application of citric acid treated Trapa natans and Citrullus lanatus lignocellulosic macromolecules for adsorptive remediation of acid Violet-7 dye.

The peels of Trapa natans (TRA) and Citrullus lanatus (CIT), were modified with a variety of chemicals to boost their surface for the optimization of adsorption performance by providing a greater number of additional active binding sites. Citric acid-processed peels (TRAC and CITC) had shown more favorable adsorption performance to eradicate acid violet 7 dye (AVS). Extra and additional active sites generated after chemical processing, including hydroxyl (OH), carboxyl (COOH), amines NH2, carbonyl, and ester (-O-CO-) groups, as evidenced from FTIR and SEM characterizations, may boost the potential of physicochemical integration of adsorbent surface activity in order to promote and encourage the retention of hazardous and risky AVS molecules from the water. The Langmuir isotherm assessed the qmax for the adsorption of AVS on TRAC, CITC, TRA, and CIT to be 212.8, 294, 24.3, and 60.6 mg/g, respectively, whereas the correlation coefficients assessed for both TRAC and CITC were 0.98 and for TRA and CIT were 0.97, closer to unity reflecting monolayer physio-sorption. According to Temkin, the adsorption of AVS on TRAC, TRA, CITC, and CIT gives "BT" values of 1.275, 0.947, 1.085, and 1.211 mg/g, also suggesting physio-sorption. Therefore, chemically modified peels can be employed for detoxification of AVS.

Effect of fire and post-fire management on soil microbial communities in a lower subtropical forest ecosystem after a mountain fire.

Wildfires and post-fire management exert profound effects on soil properties and microbial communities in forest ecosystems. Understanding microbial community recovery from fire and what the best post-fire management should be is very important but needs to be sufficiently studied. In light of these gaps in our understanding, this study aimed to assess the short-term effects of wildfire and post-fire management on both bacteria and fungi community composition, diversity, structure, and co-occurrence networks, and to identify the principal determinants of soil processes influencing the restoration of these communities. Specifically, we investigated soil bacterial and fungal community composition, diversity, structure, and co-occurrence networks in lower subtropical forests during a short-term (<3 years) post-fire recovery period at four main sites in Guangdong Province, southern China. Our results revealed significant effects of wildfires on fungal community composition, diversity, and co-occurrence patterns. Network analysis indicated reduced bacterial network complexity and connectivity post-fire, while the same features were enhanced in fungal networks. However, post-fire management effects on microbial communities were negligible. Bacterial diversity correlated positively with soil microbial biomass nitrogen, soil organic carbon, and soil total nitrogen. Conversely, based on the best random forest model, fungal community dynamics were negatively linked to nitrate-nitrogen and soil water content. Spearman's correlation analysis suggested positive associations between bacterial networks and soil factors, whereas fungal networks exhibited predominantly negative associations. This study elucidates the pivotal role of post-fire management in shaping ecological outcomes. Additionally, it accentuates the discernible distinctions between bacterial and fungal responses to fire throughout a short-term recovery period. These findings contribute novel insights that bear significance in evaluating the efficacy of environmental management strategies.

FabNet: A Features Agglomeration-Based Convolutional Neural Network for Multiscale Breast Cancer Histopathology Images Classification.

The definitive diagnosis of histology specimen images is largely based on the radiologist's comprehensive experience; however, due to the fine to the coarse visual appearance of such images, experts often disagree with their assessments. Sophisticated deep learning approaches can help to automate the diagnosis process of the images and reduce the analysis duration. More efficient and accurate automated systems can also increase the diagnostic impartiality by reducing the difference between the operators. We propose a FabNet model that can learn the fine-to-coarse structural and textural features of multi-scale histopathological images by using accretive network architecture that agglomerate hierarchical feature maps to acquire significant classification accuracy. We expand on a contemporary design by incorporating deep and close integration to finely combine features across layers. Our deep layer accretive model structure combines the feature hierarchy in an iterative and hierarchically manner that infers higher accuracy and fewer parameters. The FabNet can identify malignant tumors from images and patches from histopathology images. We assessed the efficiency of our suggested model standard cancer datasets, which included breast cancer as well as colon cancer histopathology images. Our proposed avant garde model significantly outperforms existing state-of-the-art models in respect of the accuracy, F1 score, precision, and sensitivity, with fewer parameters.

Comparative zinc tolerance and phytoremediation potential of four biofuel plant species.

Soil pollution has become a serious environmental problem worldwide due to rapid industrialization and urbanization. Zinc (Zn) contamination has raised concerns about potential effects on plants and human health. This study was conducted to assess the capability of four biofuel plants: Abelmoschus esculentus, Avena sativa, Guizotia abyssinica, and Glycine max to remediate and restore Zn contaminated soil. Selected plants were grown in soil exposed to different Zn treatments (50, 100, 200, 300, 400, 600, 800 and 1000 mg Zn kg-1) for 12 weeks. Soil without spike taken as control. Zn induced toxicity significantly (p < 0.05) reduced seed germination and inhibited plant growth and leaf chlorophyll content. The investigated plants can tolerate a soil content of 800 mg Zn kg-1 with the exception of A. sativa, which was most tolerant to high Zn concentrations (1000 mg Zn kg-1) for all growth criteria. Moreover, increasing Zn content in soil resulted in a significant (p < 0.05) increase in Zn accumulation in various tissues of the four biofuel plants. According to phytoremediation efficiency, the four biofuel plants studied were arranged as follows: A. sativa (5.05%) > A. esculentus (4.15%) > G. max (2.31%) > G. abyssinica (1.17%). This study concluded that all tested biofuel plants species, especially A. sativa exhibited high Zn concentrations in roots and shoots, high Zn uptake capability, high tolerance, and high biomass at 50-800 mg Zn kg-1 treatments. Consequently, these biofuel plants are excellent candidates for phytoremediation in Zn contaminated soils.

Heavy metal contamination of soil poses a serious threat to the environment. Physical and chemical remediation methods are commonly used to remediate metal contaminated sites. It is not commercially viable, except that it is harmful and causes soil degradation. Alternatively, biological remediation techniques are cheap and environmentally friendly. Different plant species have been found to differ in their ability to accumulate metals under contaminated soil. Therefore, the present study provides a unique opportunity to study the metal accumulation potential of four biofuel plants (Abelmoschus esculentus, Avena sativa, Guizotia abyssinica and Glycine max) under zinc metal spiked soils. These plants are able to grow rapidly by developing a strong root system, high biomass production, and high tolerance to metal toxicity that helps them survive in contaminated soil environments. The investigated biofuel plant can be used to decontaminate contaminated sites and serves as a source of commercially valuable products that extract metals from biomass through combustion.

Entanglement-assisted quantum communication with simple measurements.

Dense coding is the seminal example of how entanglement can boost qubit communication, from sending one bit to sending two bits. This is made possible by projecting separate particles onto a maximally entangled basis. We investigate more general communication tasks, in both theory and experiment, and show that simpler measurements enable strong and sometimes even optimal entanglement-assisted qubit communication protocols. Using only partial Bell state analysers for two qubits, we demonstrate quantum correlations that cannot be simulated with two bits of classical communication. Then, we show that there exists an established and operationally meaningful task for which product measurements are sufficient for the strongest possible quantum predictions based on a maximally entangled two-qubit state. Our results reveal that there are scenarios in which the power of entanglement in enhancing quantum communication can be harvested in simple and scalable optical experiments.

DAD-Net: Classification of Alzheimer's Disease Using ADASYN Oversampling Technique and Optimized Neural Network.

Alzheimer's Disease (AD) is a neurological brain disorder that causes dementia and neurological dysfunction, affecting memory, behavior, and cognition. Deep Learning (DL), a kind of Artificial Intelligence (AI), has paved the way for new AD detection and automation methods. The DL model's prediction accuracy depends on the dataset's size. The DL models lose their accuracy when the dataset has an imbalanced class problem. This study aims to use the deep Convolutional Neural Network (CNN) to develop a reliable and efficient method for identifying Alzheimer's disease using MRI. In this study, we offer a new CNN architecture for diagnosing Alzheimer's disease with a modest number of parameters, making it perfect for training a smaller dataset. This proposed model correctly separates the early stages of Alzheimer's disease and displays class activation patterns on the brain as a heat map. The proposed Detection of Alzheimer's Disease Network (DAD-Net) is developed from scratch to correctly classify the phases of Alzheimer's disease while reducing parameters and computation costs. The Kaggle MRI image dataset has a severe problem with class imbalance. Therefore, we used a synthetic oversampling technique to distribute the image throughout the classes and avoid the problem. Precision, recall, F1-score, Area Under the Curve (AUC), and loss are all used to compare the proposed DAD-Net against DEMENET and CNN Model. For accuracy, AUC, F1-score, precision, and recall, the DAD-Net achieved the following values for evaluation metrics: 99.22%, 99.91%, 99.19%, 99.30%, and 99.14%, respectively. The presented DAD-Net outperforms other state-of-the-art models in all evaluation metrics, according to the simulation results.

Transformational leadership and project success: The mediating role of trust and job satisfaction.

Transformational leadership (TFL) impacts on project and organizational success are well established. However, many underlying factors that make TFL effective are still missing. Therefore, we formulated hypotheses and tested the mediating role of trust (TS) and job satisfaction (JS) in linking TFL to project success (PS). A time-lagged methodology was used to collect quantitative data using a structured questionnaire from 326 project manager-team member dyads working in Pakistan's public sector. Our results showed that TS, JS, and TFL significantly impacted project success. Moreover, we found that TS and JS mediate the relationship between TFL and PS. These findings highlight the importance of trust and job satisfaction as mechanisms that translate TFL into the success of projects for organizations.

A Robust Deep Model for Classification of Peptic Ulcer and Other Digestive Tract Disorders Using Endoscopic Images.

Accurate patient disease classification and detection through deep-learning (DL) models are increasingly contributing to the area of biomedical imaging. The most frequent gastrointestinal (GI) tract ailments are peptic ulcers and stomach cancer. Conventional endoscopy is a painful and hectic procedure for the patient while Wireless Capsule Endoscopy (WCE) is a useful technology for diagnosing GI problems and doing painless gut imaging. However, there is still a challenge to investigate thousands of images captured during the WCE procedure accurately and efficiently because existing deep models are not scored with significant accuracy on WCE image analysis. So, to prevent emergency conditions among patients, we need an efficient and accurate DL model for real-time analysis. In this study, we propose a reliable and efficient approach for classifying GI tract abnormalities using WCE images by applying a deep Convolutional Neural Network (CNN). For this purpose, we propose a custom CNN architecture named GI Disease-Detection Network (GIDD-Net) that is designed from scratch with relatively few parameters to detect GI tract disorders more accurately and efficiently at a low computational cost. Moreover, our model successfully distinguishes GI disorders by visualizing class activation patterns in the stomach bowls as a heat map. The Kvasir-Capsule image dataset has a significant class imbalance problem, we exploited a synthetic oversampling technique BORDERLINE SMOTE (BL-SMOTE) to evenly distribute the image among the classes to prevent the problem of class imbalance. The proposed model is evaluated against various metrics and achieved the following values for evaluation metrics: 98.9%, 99.8%, 98.9%, 98.9%, 98.8%, and 0.0474 for accuracy, AUC, F1-score, precision, recall, and loss, respectively. From the simulation results, it is noted that the proposed model outperforms other state-of-the-art models in all the evaluation metrics.

Investigation of Klotho G395A and C1818T Polymorphisms and Their Association with Serum Glucose Level and Risk of Type 2 Diabetes Mellitus.

Objective: The objective was to study the association of Klotho gene G395A and C1818T single nucleotide polymorphisms with glycemia, serum, glycosylated hemoglobin (HbA1c) level and the risk of type 2 diabetes mellitus (T2DM) in the Pashtun population of Pakistan. Methods: In this study, 195 normal individuals and 217 T2DM patients were enrolled. All subjects were divided into three groups, namely overall subjects (control + T2DM patients), control individuals and T2DM patients, and their fasting glucose, HbA1c level, lipid profile and C1818T and G395A polymorphisms were determined. Results: The allele frequencies of G395A in overall subjects were 0.568 for A and 0.432 for G. Similarly, allele frequencies for G395A in overall subjects were 0.597 and 0.403 for C and T alleles, respectively. The AA genotype of G395A was observed to be a risk factor for T2DM. In normal individuals, no significant (p > 0.05) association was observed between klotho C1818T and G395A polymorphisms and hyperglycemia. In overall subjects, the C1818T polymorphism was associated (p < 0.05) with high fasting glucose and HbA1c levels in female subjects only. In T2DM patients, both C1818T and G395A polymorphisms were found to be significantly (p < 0.05) associated with high fasting glucose and HbA1c levels both in males and females. Conclusion: The G395A polymorphism was observed to increase the risk of T2DM. Both C1818T and G395 were associated with high fasting glucose and HbA1c levels in T2DM patients.

A unified graph model based on molecular data binning for disease subtyping.

Molecular disease subtype discovery from omics data is an important research problem in precision medicine. The biggest challenges are the skewed distribution and data variability in the measurements of omics data. These challenges complicate the efficient identification of molecular disease subtypes defined by clinical differences, such as survival. Existing approaches adopt kernels to construct patient similarity graphs from each view through pairwise matching. However, the distance functions used in kernels are unable to utilize the potentially critical information of extreme values and data variability which leads to the lack of robustness. In this paper, a novel robust distance metric (ROMDEX) is proposed to construct similarity graphs for molecular disease subtypes from omics data, which is able to address the data variability and extreme values challenges. The proposed approach is validated on multiple TCGA cancer datasets, and the results are compared with multiple baseline disease subtyping methods. The evaluation of results is based on Kaplan-Meier survival time analysis, which is validated using statistical tests e.g, Cox-proportional hazard (Cox p-value). We reject the null hypothesis that the cohorts have the same hazard, for the P-values less than 0.05. The proposed approach achieved best P-values of 0.00181, 0.00171, and 0.00758 for Gene Expression, DNA Methylation, and MicroRNA data respectively, which shows significant difference in survival between the cohorts. In the results, the proposed approach outperformed the existing state-of-the-art (MRGC, PINS, SNF, Consensus Clustering and Icluster+) disease subtyping approaches on various individual disease views of multiple TCGA datasets.

Modelling of Land Use/Cover and LST Variations by Using GIS and Remote Sensing: A Case Study of the Northern Pakhtunkhwa Mountainous Region, Pakistan.

Alteration in Land Use/Cover (LULC) considered a major challenge over the recent decades, as it plays an important role in diminishing biodiversity, altering the macro and microclimate. Therefore, the current study was designed to examine the past 30 years (1987−2017) changes in LULC and Land Surface Temperature (LST) and also simulated for next 30 years (2047). The LULC maps were developed based on maximum probability classification while the LST was retrieved from Landsat thermal bands and Radiative Transfer Equation (RTE) method for the respective years. Different approaches were used, such as Weighted Evidence (WE), Cellular Automata (CA) and regression prediction model for the year 2047. Resultantly, the LULC classification showed increasing trend in built-up and bare soil classes (13 km2 and 89 km2), and the decreasing trend in vegetation class (−144 km2) in the study area. In the next 30 years, the built-up and bare soil classes would further rise with same speed (25 km2 and 36.53 km2), and the vegetation class would further decline (−147 km2) until 2047. Similarly for LST, the temperature range for higher classes (27 -< 30 °C) increased by about 140 km2 during 1987−2017, which would further enlarge (409 km2) until 2047. The lower LST range (15 °C to <21 °C) showed a decreasing trend (−54.94 km2) and would further decline to (−20 km2) until 2047 if it remained at the same speed. Prospective findings will be helpful for land use planners, climatologists and other scientists in reducing the increasing LST associated with LULC changes.

Analyses of Public Attention and Sentiments towards Different COVID-19 Vaccines Using Data Mining Techniques.

COVID-19 is a widely spread disease, and in order to overcome its spread, vaccination is necessary. Different vaccines are available in the market and people have different sentiments about different vaccines. This study aims to identify variations and explore temporal trends in the sentiments of tweets related to different COVID-19 vaccines (Covaxin, Moderna, Pfizer, and Sinopharm). We used the Valence Aware Dictionary and Sentiment Reasoner (VADER) tool to analyze the public sentiments related to each vaccine separately and identify whether the sentiments are positive (compound ≥ 0.05), negative (compound ≤ −0.05), or neutral (−0.05 < compound < 0.05). Then, we analyzed tweets related to each vaccine further to find the time trends and geographical distribution of sentiments in different regions. According to our data, overall sentiments about each vaccine are neutral. Covaxin is associated with 28% positive sentiments and Moderna with 37% positive sentiments. In the temporal analysis, we found that tweets related to each vaccine increased in different time frames. Pfizer- and Sinopharm-related tweets increased in August 2021, whereas tweets related to Covaxin increased in July 2021. Geographically, the highest sentiment score (0.9682) is for Covaxin from India, while Moderna has the highest sentiment score (0.9638) from the USA. Overall, this study shows that public sentiments about COVID-19 vaccines have changed over time and geographically. The sentiment analysis can give insights into time trends that can help policymakers to develop their policies according to the requirements and enhance vaccination programs.

Correlation of predisposing factors and Esophageal Malignancy in high risk population of Baluchistan.

Objectives: To determine frequency of esophageal malignancy in Balochistan and to evaluate its correlation with predisposing and dietary factors. Methods: This cross-sectional study was conducted from Jan 2019 to Dec 2020, at two tertiary care hospital of Quetta which caters to the entire population of province. The total number of 207 cases of esophageal biopsies were received and morphological diagnosis done by H&E staining. Results: Out of 207 (N) esophageal biopsies cases, malignancy were observed in 65%, chronic esophagitis in 19%, benign esophageal lesion in 1% and other esophageal lesions were observed in less than 4% of samples. Association with aggravating factors included tea 80.5%, use of drugs 64%, spicy food 57%, salted food 53%, quid & tobacco taken orally and through nose 44% and cigarette smoking 21.5%. The protective factors include fresh fruit 90%, fish 64% and milk 55% which were never or occasionally taken, meat chicken and beef intake was 63% & 53% and vegetable intake was 52%, but 72.5% of cases never used alcohol and mutton meat was not used by 50%. Conclusion: Esophageal cancer was associated in those groups of people which may regard as having high risk factors. These include increased intake of spicy/salted food, hot beverages, drugs, quid and smoked tobacco, coupled with low intake of fruits and vegetables, lack of awareness and low socioeconomic status.

Role of nitrogen and magnesium for growth, yield and nutritional quality of radish.

Nitrogen (N) affects all levels of plant function from metabolism to resource allocation, growth, and development and Magnesium (Mg) is a macronutrient that is necessary to both plant growth and health. Radish (Raphanus sativus L.) occupies an important position in the production and consumption of vegetables globally, but there are still many problems and challenges in its nutrient management. A pot trial was conducted to investigate the effects of nitrogen and magnesium fertilizers on radish during the year 2018-2019. Nitrogen and magnesium was applied at three rates (0, 0.200, and 0.300 g N kg-1 soil) and (0, 0.050, and 0.100 g Mg kg-1 soil) respectively. The experiment was laid out in a completely randomized design (CRD) and each treatment was replicated three times. Growth, yield and quality indicators of radish (plant height, root length, shoot length, plant weight, total soluble sugar, ascorbic acid, total soluble protein, crude fiber, etc.) were studied. The results indicated that different rates of nitrogen and magnesium fertilizer not only influence the growth dynamics and yields but also enhances radish quality. The results revealed that the growth, yield and nutrient contents of radish were increased at a range of 0.00 g N. kg-1 soil to 0.300 g N. kg-1 soil and 0.00 g Mg. kg-1 soil to 0.050 g Mg. kg-1 soil and then decreased gradually at a level of 0.100 g Mg. kg-1 soil. In contrast, the crude fiber contents in radish decreased significantly with increasing nitrogen and magnesium level but increased significantly at Mg2 level (0.050 g Mg. kg-1 soil). The current study produced helpful results for increasing radish quality, decreasing production costs, and diminishing underground water contamination.

Antibacterial Effects of Cinnamon Extract, Clove Oil and Antibiotics against Helicobacter pylori Isolated from Stomach Biopsies

Abstract Helicobacter pylori is a pathogenic bacterium causing gastric problems such as, peptic ulcers and stomach cancer. H. pylori were isolated from the stomach biopsy specimens (n = 100) of gastric patients by performing polymerase chain reaction (PCR) against cagA (cytotoxin associated gene A) and ureC (Urease subunit alpha) genes. Furthermore, antibiogram studies of the H. pylori isolates were evaluated against the common antibiotics. The overall detection rate of H. pylori was 71% in biopsy specimens of gastric patients. The antimicrobial susceptibility test revealed the resistance rate of H. pylori isolates against metronidazole (50%), clarithromycin (28.33%), tetracycline (21.66%), amoxicillin (18.33%), and ciprofloxacin (11.66%). However, the H. pylori isolates were completely resistant to vancomycin, erythromycin and nalidixic acid antibiotics. Clove oil showed a remarkable antimicrobial effect against H. pylori whereas, mild inhibition (10 mm) was observed in case of curcumin extract. Due to increase incident of resistance and high prevalence of H. pylori in gastric patients, natural antimicrobial like clove oil can be explored as an alternative treatment.