Chronic Renal Disease Prediction using Clinical Data and Different Machine Learning Techniques

Chronic Renal Disease (CRD) or Chronic Kidney Disease (CKD) is defined as the continuous loss of kidney function. It's a long-term condition in which the kidney or renal doesn't work properly, gets damaged and can't filter blood on a regular basis. Diabetes, high blood pressure, swollen feet, ankles or hands and other disorders can cause chronic renal disease. By gradual progression and lack of treatment, it can lead to kidney failure. A prior prognosis of CKD can nourish the quality of life to a higher range in such circumstances and can enhance the attribute of life to a larger province. Now a days, bioscience is playing a significant role in the aspect of diagnosing and detecting numerous health conditions. Machine Learning (ML) as well as Data Mining (DM) methods are playing the leading role in the realm of biosciences. Our objective is to predict and diagnose (CKD) with some machine learning algorithms. In this study, an attempt to diagnose chronic renal disease has been taken with four ML algorithms named XGBoost, Adaboost, Logistic Regression (LR) as well as Random Forest (RF). By using decision tree-based classifiers and analyzing the dataset with comparing their performance, we attempted to diagnose CKD in this study. The results of the model in this study showed prosperous indications of a better prognosis for the diagnosis of kidney diseases. Considering and contemplating the performance analysis, it is accomplished that Random Forest ensemble learning algorithm provides better classification performance than other classification methods.acceptedVersionPeer reviewe

Association between Dietary Nitrate, Nitrite Intake, and Site-Specific Cancer Risk: A Systematic Review and Meta-Analysis

Background: People consume nitrates, nitrites, nitrosamines, and NOCs compounds primarily through processed food. Many studies have yielded inconclusive results regarding the association between cancer and dietary intakes of nitrates and nitrites. This study aimed to quantify these associations across the reported literature thus far. Methods: We performed a systematic review following PRISMA and MOOSE guidelines. A literature search was performed using Web of Science, Embase, PubMed, the Cochrane library, and google scholar up to January 2020. STATA version 12.0 was used to conduct meta-regression and a two-stage meta-analysis. Results: A total of 41 articles with 13 different cancer sites were used for analysis. Of these 13 cancer types/sites, meta-regression analysis showed that bladder and stomach cancer risk was greater, and that pancreatic cancer risk was lower with increasing nitrite intakes. Kidney and bladder cancer risk were both lower with increasing nitrate intakes. When comparing highest to lowest (reference) categories of intake, meta-analysis of studies showed that high nitrate intake was associated with an increased risk of thyroid cancer (OR = 1.40, 95% CI: 1.02, 1.77). When pooling all intake categories and comparing against the lowest (reference) category, higher nitrite intake was associated with an increased risk of glioma (OR = 1.12, 95% CI: 1.03, 1.22). No other associations between cancer risk and dietary intakes of nitrates or nitrites were observed. Conclusion: This study showed varied associations between site-specific cancer risks and dietary intakes of nitrate and nitrite. Glioma, bladder, and stomach cancer risks were higher and pancreatic cancer risk was lower with higher nitrite intakes, and thyroid cancer risk was higher and kidney cancer risk lower with higher nitrate intakes. These data suggest type- and site-specific effects of cancer risk, including protective effects, from dietary intakes of nitrate and nitrite

Antioxidant, antibacterial, cytotoxic and thrombolytic activities of flowers of Mirabilis jalapa L: Possible role of phenolics and flavonoids

Mirabilis jalapa L. belongs to Nyctaginaceae family is an ornamental plant and domestically known as Sandhamaloti in Bangladesh. In this report we evaluated phenol and flavonoid content of cold methanol extractives of flower of M. jalapa and their role in antioxidant, antibacterial, cytotoxic and thrombolytic potentials. This study utilized HPLC to identify qualitative phenolic and flavonoid compounds. Gallic acid and Kaempferol were employed as standards for phenolic and flavonoid identification, respectively. Consequently, evaluating the overall phenolic and flavonoid content in the plant extract was considered a logical step. Flower of M. jalapa is an abundant source of phenols and flavonoids. The antioxidant activities in the phosphomolybdenum assay are differed in distinct extractives and have been found as 334.78 ± 0.62, 142.53 ± 0.51, 398.04 ± 0.81, 470.29 ± 0.55 and 480.90 ± 0.59 mg of ascorbic acid equivalent per gm of crude methanol extract (MSF), petroleum ether fraction (PSF), carbon tetrachloride fraction (CTF), chloroform fraction (CSF) and ethyl acetate fraction (ESF), respectively. The IC50 values for DPPH radical scavenging have been evaluated to be 13.70 ± 0.32, 49.05 ± 0.47, 6.76 ± 0.22, 9.30 ± 0.41 and 6.94 ± 0.31 μg/mL for MSF, PSF, CTF, CSF and ESF, respectively. The cytotoxicity was evaluated against brine shrimp lethality bioassay and LC50 values of MSF, PSF, CTF, CSF and ESF have been found 11.83 ± 0.32, 35.50 ± 0.54, 5.00 ± 0.18, 11.30 ± 0.30 and 4.61 ± 0.15 μg/mL, respectively in comparison to a standard vincristine sulphate 2.50 ± 0.11 μg/mL. Plant polyphenols and flavonoids having antioxidant properties exhibit potential antibacterial activity and our results revealed that the entire tested fractions of vegetation of M. jalapa exhibit potential antibacterial activity. On this observation, the thrombolytic activity, is the first strive in M. jalapa to investigate for more modern thrombolytic and its ethyl acetate fraction was shown to substantial (53.81 ± 0.52%) thrombolysis property. A high correlation was observed between antioxidant, cytotoxicity, thrombolytic and antibacterial activity with polyphenol and flavonoid contents (r2 = 0.903 to 0.996, p < 0.05). These data suggest the role of polyphenols and flavonoids in the bioactivities. Further study of this plant may lead to isolation of bioactive polyphenols and flavonoids which will be effective in the management of many chronic diseases

Asian Journal of Medical and Biological Research Segregation pattern and inbreeding depression in F 2 generation of some hybrid okra varieties

Abstract: An experiment was conducted in randomized complete block design (RCBD) with three replications in the experimental field of Regional Horticulture Research Station (RHRS), Bangladesh Agriculture Research Institute (BARI), Lebukhali, Patuakhali during April, 2014 to October, 2014 for assessing the inbreeding depression, genetic parameters, gene action and segregation pattern of Okra [Abelmoschusesculentus (L.) Moench]. The experiment was comprised of five commercial hybrid Okra genotypes such as Tara sonali, Bimala, Juboraj, Suvo 1and Noor, their respective F 2 progenies along with a check variety named as BARI Dherosh 1. Results of the experiment indicated that there were considerable variability among the F 1 and their F 2 . The yield were in-between 14.81 to 7.92 Kg plot -1 in case of F 1 generation, which deteriorate to 10.32 to 5.32 Kg plot -1 in F 2 generation. Broad sense heritability computed through variance component method showed that all the quantitative traits were moderate to highly heritable. The trait yield per plot exhibited 68.83% broad sense heritability coupled with 50.96% genetic advance suggesting the existence of sufficient amount of genetic variability for improvement of this trait and also indicates that the trait is more amenable to selection and could be improved easily. In case of segregation pattern, plant height and pod pubescence content exhibit as polygenic trait. Leaf shape, fruit base shape and branching pattern showed complete dominance and fruit color displayed incomplete dominance. The present investigation thus provide information about the nature and magnitude of genetic variation, segregation pattern and inbreeding depression for yield and its components in okra so as to formulate suitable breeding strategy and isolate potential parents and promising crosses for further breeding program

Efficient Prediction of Water Quality Index (WQI) Using Machine Learning Algorithms

Abstract The quality of water has a direct influence on both human health and the environment. Water is utilized for a variety of purposes, including drinking, agriculture, and industrial use. The water quality index (WQI) is a critical indication for proper water management. The purpose of this work was to use machine learning techniques such as RF, NN, MLR, SVM, and BTM to categorize a dataset of water quality in various places across India. Water quality is dictated by features such as dissolved oxygen (DO), total coliform (TC), biological oxygen demand (BOD), Nitrate, pH, and electric conductivity (EC). These features are handled in five steps: data pre-processing using min-max normalization and missing data management using RF, feature correlation, applied machine learning classification, and model’s feature importance. The highest accuracy Kappa, Accuracy Lower, and Accuracy Upper findings in this research are 99.83, 99.17, 99.07, and 99.99, respectively. The finding showed that Nitrate, PH, conductivity, DO, TC, and BOD are the key qualities that contribute to the orderly classification of water quality, with Variable Importance values of 74.78, 36.805, 81.494, 105.770, 105.166, and 130.173, respectively

First report on population dynamics and stock status of Badis badis in a wetland ecosystem (NW Bangladesh): Insights from new recorded maximum length

Badis badis (Hamilton, 1822) is a popular ornamental fish species in the world. This study provides valuable insights into some biological indices of B. badis using a sample of 293 individuals. These individuals were captured from June 2021 to May 2022 using several traditional fish harvesting gears and traps in the Babu Mondoler beel, a wetland ecosystem in NW Bangladesh. Biometric data were collected for each individual, contributing to a comprehensive understanding of this species. The recorded results revealed a wide range of total length (TL), varying from 2.30 to 11.33 cm. Notably, we observed a maximum length of 11.33 cm TL and a maximum body weight (BW) of 18.18 g, ranging from 0.20 to 18.18 g, setting a new record and showcasing the diversity in size within the population. The estimated allometric coefficient (b) showed that combined sexes had negative allometric growth (b = 2.67). Growth parameters were assessed as L∞ = 11.93 cm, K = 0.95 year−1 and Ø′ = 2.13. The tmax was 3.16 years. The Lm was measured at 7.02 cm TL and tm = 0.89 year. In this study, KF (1.4240 ± 0.3194) was best for the wellbeing of B. badis in the study area. The a3.0 was estimated at 0.0079 and the relative weight WR (100.90 ± 16.994). Physiological status showed that maximum fatty fish were observed at 10.00–12.00 cm TL; lowest at 4.0–6.00 cm TL. Moreover, the Z, Mw, F and E were estimated to be 3.29 year−1, 1.45 year−1, 1.84 year−1and 0.56, respectively. Additionally, the Lopt for this species was found to be 7.91 cm TL. The findings from this study hold great potential for enhancing the assessment and management of the specimen in the study area and its ecological community. These valuable insights into the population parameters, growth patterns, and exploitation rates of B. badis can inform future management strategies, ensuring the sustainable utilization of this fishery resource in Bangladesh and others neighboring countries