Potassium Values Research Articles

Assessment of Soil Fertility Status of the Soils of KVK -Muzaffarnagar, Uttar Pradesh, India

The present study was undertaken to assess the soil fertility status of the soils of KVK Muzaffarnagar. Soil samples were collected from two depths viz., 0-15 cm and 15-30 cm and analysed for various soil properties. Results revealed that soils were sandy loam in texture, neutral to slightly alkaline in reaction, non-saline and low in soil organic carbon content. Mean values of CEC, SOC, available nitrogen, available phosphorus, available potassium and available sulphur were 11.22 and 9.62 C mol (p+) kg-1, 0.17 and 0.13 g kg-1, 260.16 and 208.14 kg ha-1, 30.86 and 27.30 kg ha-1, 100.94 and 107.89 kg ha-1 and 13.51 and 12.84 kg ha-1 recorded at 0-15cm and 15-30 cm depth, respectively. The mean of DTPA extractable zinc, DTPA extractable iron, DTPA extractable manganese and DTPA extractable copper were 0.28 and 0.31 mg kg-1, 0.97 and 1.22 mg kg-1, 2.14 and 1.78 mg kg-1 and 0.23 and 0.19 mg kg-1 recorded surface and sub-surface depths, respectively. Soils are low in available nitrogen and potassium and medium in available phosphorus status. Cationic micronutrients such as iron, copper and zinc contents were found deficient in range, whereas manganese content was found to be sufficient.

Clinical prediction model for primary aldosteronism subtyping and special focus on adrenal volumetric assessment.

Our aim was to develop a prediction model based on a simple score with clinical, laboratory, and imaging findings for the subtype diagnosis of primary aldosteronism (PA). The contribution of adrenal volumetric assessment to PA subtyping was also investigated. Thirty-five patients with adequate cannulation in adrenal venous sampling (AVS) were included. Laboratory data, the saline infusion test (SIT), and the AVS results of patients with PA were retrospectively evaluated. Volumetric assessment was performed using magnetic resonance imaging (MRI) and the ratio of adrenal volumes was calculated after adjusting for gender- and side-specific mean reference values of both adrenal glands. The AVS was consistent with unilateral PA in 49% and bilateral in 51% of the patients. Hypertension as a reason for work-up, the highest aldosterone/lowest potassium value higher than 12, the percentage of plasma aldosterone concentration (PAC) reduction after SIT by equal or less than 43.5%, the use of oral potassium replacement, unilateral disease at pre-AVS imaging, and a ratio of adjusted adrenal volumes equal to or below 1.7 were indicative of unilateral disease in univariate logistic regression analysis concerning the distinction of PA subtyping (p < 0.05). Multivariate logistic regression analysis also revealed that adrenal volumetric assessment has an impact on PA subtyping (p < 0.05). In the prediction model, when each of the six parameters that were significant in the univariate logistic regression analysis was assigned one point, < 4 predicted bilateral PA, whereas ≥ 4 predicted unilateral PA (AUC:0.92, p < 0.001). This prediction model before AVS may serve as a convenient and practical approach, while an adjusted adrenal volumetric assessment can make a positive contribution to PA subtyping.

Nutrient content in olive leaves through sustained irrigation with treated wastewater

Water scarcity is posing a major challenge to sustainable development. This issue will become more pressing as the global population grows, living standards rise, food habits evolve, and the effects of climate change intensify, resulting in rising temperatures globally. Reusing treated wastewater reduces effluent discharge into natural water bodies while also providing a reliable water source for applications that do not require high-quality water. A field trial was conducted on plots where olive trees grow. Two water treatment scenarios were investigated: one with a conventional water source as the control and one with treated wastewater as an alternative.Soil and irrigation water were characterized in the study regions, and chlorophyll, macro and micronutrients in olive leaves were analyzed. Soils treated to long-term wastewater irrigation had considerably higher electrical conductivity, total nitrogen, potassium, sodium, magnesium, zinc, and cation exchange capacity values than control. Notably, control soils had much greater levels of zinc and phosphorus than treated wastewater-irrigated soils. Except for phosphorous content (greater in plants from treated wastewater irrigated plots) and zinc content (lower in plants from treated wastewater irrigated plots), no significant variations in nutrients evaluated occurred for crops. In general, when comparing irrigation with reclaimed wastewater to irrigation with fresh water, no negative influence on tree performance was detected over time. Nevertheless, it's crucial to consider the potential risk of soil salinization associated with the consistent use of this water for irrigation.

Machine learning methods in differential diagnosis of ACTH-dependent hypercortisolism

To develop a noninvasive method of differential diagnosis of ACTH-dependent hypercortisolism, as well as to evaluate the effectiveness of an optimal algorithm for predicting the probability of ectopic ACTH syndrome (EAS) obtained using machine learning methods based on the analysis of clinical data. As part of a single-center, one-stage, cohort study, a retrospective prediction of the probability of EAS among patients with ACTH-dependent hypercortisolism was carried out. Patients were randomly stratified into 2 samples: training (80%) and test (20%). Eleven machine learning algorithms were used to develop predictive models: Linear Discriminant Analysis, Logistic Regression, elastic network (GLMNET), Support Vector machine (SVM Radial), k-nearest neighbors (kNN), Naive Bayes, binary decision tree (CART), C5.0 decision tree algorithms, Bagged CART, Random Forest, Gradient Boosting (Stochastic Gradient Boosting, GBM). The study included 223 patients (163 women, 60 men) with ACTH-dependent hypercortisolism, of which 175 patients with Cushing's disease (CD), 48 - with EAS. As a result of preliminary data processing and selection of the most informative signs, the final variables for the classification and prediction of EAS were selected: ACTH level at 08:00 hours, potassium level (the minimum value of potassium in the active stage of the disease), 24-h urinary free cortisol, late-night serum cortisol, late-night salivary cortisol, the largest size of pituitary adenoma according to MRI of the brain. The best predictive ability in a training sample of all trained machine learning models for all three final metrics (ROC-AUC (0.867), sensitivity (90%), specificity (56.4%)) demonstrated a model of gradient boosting (Generalized Boosted Modeling, GBM). In the test sample, the AUC, sensitivity and specificity of the model in predicting EAS were 0.920; 77.8% and 97.1%, respectively. The prognostic model based on machine learning methods makes it possible to differentiate patients with EAS and CD based on basic clinical results and can be used as a primary screening of patients with ACTH-dependent hypercortisolism.

Nutrient Status of Coastal Soils-24 Parganas (South), West Bengal, India

The study conducted in the year 2023-24 at the Department of Soil Science and Agricultural Chemistry, The Neotia University, 24 Parganas (South), situated in coastal region of West Bengal, aimed to assess the nutrient status of the soils in the instructional farm. Soil samples (105 No) were collected at 0-15 cm depth and analyzed for pH, electrical conductivity (EC), organic carbon (OC), and major nutrients such as available nitrogen, phosphorus, and potassium. The result showed that the soils were neutral in reaction and saline in nature, whereas organic carbon and available nitrogen content were at a medium status, but available phosphorus and potassium status were high. Moreover organic carbon showed negative correlation with soil electrical conductivity (-0.384, p&lt;0.05). The nutrient index values for available nitrogen and available phosphorus were between 1.66-2.33 range, categorizing it as medium fertile. Conversely, the nutrient index value for available potassium exceeded 2.33, classifying it as high fertile.

Evolving dynamics of insect frass fertilizer for sustainable nematode management and potato production.

Potato production faces major challenges from inadequate soil fertility, and nematode infestation, yet synthetic fertilizers and nematicides are costly and harmful to the environment. This study explored the potential of chitin-fortified black soldier fly-composted organic fertilizer (BSFCOF) as a multipurpose organic fertilizer amendment for enhancing potato yield and suppressing potato cyst nematodes (PCN). The BSFCOF was applied at a rate equivalent to 150 kg N ha-1 and fortified with chitin from black soldier fly pupal exuviae at inclusion rates equivalent to 0.5, 1, 2, 3, 4 and 5% chitin. Data were collected on potato growth characteristics, PCN population densities, and soil chemical properties for two growing cycles. Results showed that chitin fortified BSFCOF significantly improved potato growth parameters, chlorophyll concentration, marketable tuber yield and number of marketable tubers. The marketable tuber yield achieved using chitin-fortified BSFCOF was 70 - 362%, and 69 - 238% higher than the values achieved using unfertilized soil during the first and second growing cycles, respectively. Soil amendment with chitin-fortified BSFCOF significantly reduced the number of cysts per 200 g soil-1, number of eggs and J2 per cyst-1, eggs g-1 soil and reproduction rate by 32 - 87%, 9 - 92%, 31- 98% and 31 - 98%, respectively. The PCN suppression increased with chitin inclusion rates. There were significantly higher values for soil pH, ammonium nitrogen, nitrate nitrogen, available phosphorus, calcium, magnesium, potassium, and cation exchange capacity in soil amended with BSFCOF compared to unamended soil. This study demonstrates that BSFCOF fortified with 5% chitin is an effective soil enhancer with multiple benefits, including improved soil fertility, potato performance, and effective management of potato cyst nematodes.

Assessment of the Electrolyte Heterogeneity of Tissues in Mandibular Bone-Infiltrating Head and Neck Cancer Using Laser-Induced Breakdown Spectroscopy.

Laser-induced breakdown spectroscopy (LIBS) was recently introduced as a rapid bone analysis technique in bone-infiltrating head and neck cancers. Research efforts on laser surgery systems with controlled tissue feedback are currently limited to animal specimens and the use of nontumorous tissues. Accordingly, this study aimed to characterize the electrolyte composition of tissues in human mandibular bone-infiltrating head and neck cancer. Mandible cross-sections from 12 patients with bone-invasive head and neck cancers were natively investigated with LIBS. Representative LIBS spectra (n = 3049) of the inferior alveolar nerve, fibrosis, tumor stroma, and cell-rich tumor areas were acquired and histologically validated. Tissue-specific differences in the LIBS spectra were determined by receiver operating characteristics analysis and visualized by principal component analysis. The electrolyte emission values of calcium (Ca) and potassium (K) significantly (p < 0.0001) differed in fibrosis, nerve tissue, tumor stroma, and cell-rich tumor areas. Based on the intracellular detection of Ca and K, LIBS ensures the discrimination between the inferior alveolar nerve and cell-rich tumor tissue with a sensitivity of ≥95.2% and a specificity of ≥87.2%. The heterogeneity of electrolyte emission values within tumorous and nontumorous tissue areas enables LIBS-based tissue recognition in mandibular bone-infiltrating head and neck cancer.

Crop Recommendation System Using Machine Learning

Abstract: Agricultural Crop Recommendation System relies on a multitude of input factors. This proposes a hybrid approach for suggesting suitable crops in specific areas, taking into account various attributes like soil type, rainfall levels, temperature value, Nitrogen value, Phosphorus value, Potassium value, humidity value and PH value of the soil. Utilizing technology to recommend crops for agriculture empowers farmers to enhance crop yields by suggesting the most suitable crops based on geographic and climatic conditions with respect to soil. The fundamental concept of this paper revolves around implementing a crop selection method to address numerous agricultural and farmer-related challenges. Using a group of different machine learning models to make better predictions is like having a team of experts give you advice. Each expert has their own knowledge and experience, and by listening to all of them, you can make a better decision. Accuracy of different ML algorithms in crop recommendation system are such as that Support Vector Machine algorithm gives 99.54% accurate results, Neural networks algorithm gives 91% accurate results, Decision Tree algorithm gives us 87% of accuracy. While K- Nearest algorithm, Logistic Regression and Naive Bayes algorithm gives us 75%-85% accurate results. We’re gonna use machine learning algorithms to make the project more accurate and efficient enough to provide the real time results. In this crop recommendation system using machine learning Support Vector Classifier and Decision tree algorithms are being used as an expectation of most accurate outcomes of the model. Keywords: Support Vector Machine, Feature selection, ,,Crop suitability indices, Training dataset, Testing dataset, Model evaluation.

Influence of Zeolite Application on Soil Infiltrated with Sewage Effluent

A laboratory-based soil column study was undertaken to assess the influence of zeolite application on soil treated with sewage effluent. Seven treatments, varying in zeolite proportions (T1=0.25%, T2=0.50%, T3=0.75%, T4=1.00%, T5=1.25%, T6=1.50%, and T7=control without zeolite), were replicated three times for the experiment. Analysis of leachate and residual soil after the experiment included parameters such as pH, electrical conductivity (EC), organic carbon (OC), available nitrogen, phosphorus, and potassium. Results indicated a significant increase in soil pH, EC, organic carbon, available nitrogen, phosphorus, and potassium with zeolite application. Leachate from the control column exhibited the highest values for pH (7.74), EC (190.33 µScm-1), BOD (34.33 mg L-1), COD (72.67 mg L-1), nitrate-N (40.23 mg L-1), phosphorus (5.96 mg L-1), and potassium (12.31 mg L-1), differing significantly from leachate collected from treatment columns.

Nutrient patterns in relation to insulin resistance and endothelial dysfunction in Iranian women.

Prior studies have mainly focused on the association of one specific nutrient with insulin resistance (IR) and endothelial dysfunction and limited studies have assessed the association with different nutrient patterns (NPs). We examined the association between various NPs and IR and endothelial dysfunction among Iranian women. This cross-sectional study was carried out on a sample of 368 female nurses. A 106-items food frequency questionnaire (FFQ) was applied for dietary assessments. Using factor analysis, the relationships between NPs and markers of insulin resistance (HOMA-IR, HOMA-β, and QUICKY), and endothelial dysfunction (E-selectin, sICAM-1, and sVCAM-1) were assessed. Mean age and body mass index of participants were respectively 35.21years and 24.04kg/m2. Three major NPs were identified. NP1, named as "dairy, fruits, and vegetables" had high values of potassium, folate, vitamins A and C, magnesium, and beta carotene. No significant association was observed between this NP and insulin resistance or endothelial dysfunction indices. The second NP was full of chromium, selenium, copper, vitamin B6, monounsaturated fatty acid (MUFA), thiamin, vitamin D, and iron. Adherence to NP2 (named "legumes, nuts, and protein foods") was associated with lower values of insulin (6.8 ± 1.1 versus 8.4 ± 1.1, P = 0.01), homeostasis model assessment-Insulin resistance (HOMA-IR) (1.3 ± 0.2 versus 1.7 ± 0.2, P = 0.02), and vascular adhesion molecule 1 (VCAM-1) (444.2 ± 27.9 versus 475.8 ± 28.4, P = 0.03). However, adherence to the third NP, rich in saturated fatty acid (SFA), cholesterol, sodium, zinc, vitamin E, and B12, described as "animal fat and meat + vitamin E", was associated with higher amounts of homeostasis model assessment-β (HOMA-β) (531.3 ± 176.2 versus 48.7 ± 179.8, P = 0.03). In conclusion, following the NP2, correlated with higher intakes of chromium, selenium, copper, vitamin B6, MUFA and thiamin was associated with lower values of insulin, HOMA-IR, and sVCAM-1. Adherence to NP3, rich in SFA, cholesterol, vitamin E, vitamin B12, and zinc was associated with higher levels of HOMA-β.

A nomogram to predict the risk of surgery-related pressure ulcers in patients undergoing cardiovascular surgery.

In this study, we aimed to establish a new nomogram score to predict the occurrence of surgery-related pressure ulcers (SRPU) in patients undergoing cardiovascular surgery. We conducted a retrospective study among patients who underwent cardiovascular surgery between February 2016 and November 2020. We established a prediction model based on a logistic regression model and tested the calibration and discrimination. We included 1163 patients who had undergone cardiovascular surgery. We formulated the logistic regression model, with Logit(P) = -11.745 + 0.024 preoperative hemoglobin value + 0.118 serum sodium value - 0.014 prealbumin value - 0.213 intraoperative mean temperature - 0.058 minimum mean arterial pressure + 0.646 preoperative blood potassium value + 0.264 smoking frequency + 0.760 hypertension history + 0.536 age ≥70 years. In this model ,"+" indicates that the factor is positively related to the occurrence risk of SRPU and "-" indicates that the factor is negatively associated with SRPU risk. The predictive model and nomogram had good accuracy in estimating the risk of SRPU, with a C-index of 0.755 (95% confidence interval: 0.719-0.792). The present model can be used to effectively screen patients with a high risk of SRPU to devise targeted nursing intervention strategies and ultimately reduce the incidence rate of SRPU.

Evaluation of therapeutic and toxic levels of serum digoxin concentration: a cross-sectional study from a tertiary hospital.

Digoxin is a cardiac glycoside for treating heart failure and atrial fibrillation. Despite its limited therapeutic range and complex pharmacokinetic properties, this medication continues to be frequently prescribed. This study aimed to evaluate the serum digoxin concentration (SDC) at therapeutic, subtherapeutic, and toxic levels and explore the factors affecting these levels in patients receiving digoxin therapy for heart failure. In this descriptive and cross-sectional study, the data were obtained from the electronic system of patients who presented to Afyonkarahisar Health Sciences University. For the SDC, the reference range was accepted as 0.5-0.9 ng/mL, and the upper limit was 2.0 ng/mL. The patient's demographic characteristics, comorbidities, and laboratory findings were evaluated. The Mann-Whitney U test, Chi-square test, and logistic regression analysis were used. p<0.05 was considered statistically significant. The data of 419 patients (mean age: 65.9±16.1 years, 68.5% women) were evaluated. The mean SDC was 1.11±1.01 ng/mL, and it was below 0.5 ng/mL in 24.3% of the patients, 0.5-0.9 ng/mL in 23.4%, 0.9-2 ng/mL in 41.3%, and over 2 ng/mL in 11.1%. Age, male gender, the presence of diabetes mellitus, and high HbA1c values were found to be associated with greater SDC levels, but this was not statistically significant. The presence of renal failure, elevated creatinine and magnesium levels, and potassium, sodium, and calcium levels outside the normal limits significantly increased the SDC. High creatinine and low/high potassium values significantly affected the detection of SDC at the toxic level. The measurement of SDC levels holds significance not only in the monitoring of toxicity but also in ensuring adherence to the recommended therapeutic range during therapy. It is recommended to exercise caution in terms of risk factors such as age, kidney function test results, and blood electrolyte levels.

Changes in biochemical profiles of children with severe acute malnutrition on admission and after nutritional rehabilitation

Purpose: The given study was done to find out the changes in biochemical profiles before and after nutritional rehabilitation of the children admitted at the Department of Paediatrics, Assam Medical College with severe acute malnutrition (SAM). Materials and Methods: A hospital based observational study was conducted from May 2020 to April 2021 with 55 consecutive cases of SAM. Vitros System 5600 was used for analysis of 13 biochemical parameters. Results: The mean values of serum sodium, potassium, and chloride at admission were 134.59±19.37 mEq/L, 4.22±0.80 mEq/L, and 101.12±16.36 mEq/L and that after rehabilitation were 136.66±19.95 mEq/L, 4.42±0.68 mEq/L, and 103.97±4.83 mEq/L. While that for magnesium, calcium, and phosphorous on admission were 2.11±0.38 mg/dL, 8.70±1.00 mg/dL, and 4.33±1.23 mg/dL and that after rehabilitation were 2.10±0.29 mg/dL, 9.45±0.65 mg/dL, and 4.76±0.84 mg/dL, respectively. The mean values of serum total protein, albumin, globulin, and A: G on admission were 6.53±1.44 g/dL, 3.30±0.88 g/dL, 3.23±0.87 g/dL, and 1.06±0.33, respectively, and that after rehabilitation were 6.84±0.98 g/dL, 3.86±0.57 g/dL, 2.98±0.69 g/dL, and 1.34±0.28, respectively. The mean of urea and creatinine on admission were found to be 26.25±19.30 mg/dL and 0.34±0.24 mg/dL, respectively, and that after rehabilitation were 23.91±11.76 mg/dL and 0.31±0.15 mg/dL respectively. Random blood sugar (RBS) on admission was 85.20±17.88 mg/dL and that after rehabilitation was 94.18±14.82 mg/dL. After rehabilitation, the mean value of calcium, phosphorous, albumin, A: G, and RBS were significantly raised. The minimum duration required for rehabilitation was 12 days with prevalence being maximum among the Tea-tribes (80.00%). Conclusion: The biochemical derangements in a patient with SAM are associated with significant morbidity and get improved after nutritional rehabilitation.

Intestinal Oxalate Absorption, Enteric Hyperoxaluria, and Risk of Urinary Stone Formation in Patients with Crohn's Disease.

Nephrolithiasis is a common urologic manifestation of Crohn's disease. The purpose of this study was to investigate the clinical characteristics, intestinal oxalate absorption, and risk factors for urinary stone formation in these patients. In total, 27 patients with Crohn's disease and 27 healthy subjects were included in the present study. Anthropometric, clinical, and 24 h urinary parameters were determined, and the [13C2]oxalate absorption test was performed. Among all patients, 18 had undergone ileal resection, 9 of whom had a history of urinary stones. Compared to healthy controls, the urinary excretion values of calcium, magnesium, potassium, sulfate, creatinine, and citrate were significantly lower in patients with Crohn's disease. Intestinal oxalate absorption, the fractional and 24 h urinary oxalate excretion, and the risk of calcium oxalate stone formation were significantly higher in patients with urolithiasis than in patients without urolithiasis or in healthy controls. Regardless of the group, between 83% and 96% of the [13C2]oxalate was detected in the urine within the first 12 h after ingestion. The length of ileum resection correlated significantly with the intestinal absorption and urinary excretion of oxalate. These findings suggest that enteric hyperoxaluria can be attributed to the hyperabsorption of oxalate following extensive ileal resection. Oral supplementation of calcium and magnesium, as well as alkali citrate therapy, should be considered as treatment options for urolithiasis.

Nutritional composition of ultra-processed plant-based foods in the out-of-home environment: a multi-country survey with plant-based burgers.

Ultra-processed plant-based foods, such as plant-based burgers, have gained in popularity. Particularly in the out-of-home (OOH) environment, evidence regarding their nutritional profile and environmental sustainability is still evolving. Plant-based burgers available at selected OOH sites were randomly sampled in Amsterdam, Copenhagen, Lisbon and London. Plant-based burgers (patty, bread and condiment) (n 41) were lab analysed for their energy, macronutrients, amino acids and minerals content per 100 g and serving and were compared with reference values. For the plant-based burgers, the median values per 100 g were 234 kcal, 20·8 g carbohydrates, 3·5 g dietary fibre and 12·0 g fat, including 0·08 g TFS and 2·2 g SFA. Protein content was 8·9 g/100 g, with low protein quality according to amino acid composition. Median Na content was 389 mg/100 g, equivalent to 1 g salt. Compared with references, the median serving provided 31% of energy intake based on a 2000 kcal per day and contributed to carbohydrates (17-28%), dietary fibre (42%), protein (40%), total fat (48%), SFA (26%) and Na (54%). One serving provided 15-23% of the reference values for Ca, K and Mg, while higher contributions were found for Zn, Mn, P and Fe (30-67%). The ultra-processed plant-based burgers provide protein, dietary fibre and essential minerals and contain relatively high levels of energy, Na and total fats. The amino acid composition indicated low protein quality. The multifaceted nutritional profile of plant-based burgers highlights the need for manufacturers to implement improvements to better support healthy dietary habits, including reducing energy, Na and total fats.

A multicentral prospective cohort trial of a pharmacist-led nutritional intervention on serum potassium levels in outpatients with chronic kidney disease: The MieYaku-Chronic Kidney Disease project.

Although dietary potassium restriction is an acceptable approach to hyperkalemia prevention, it may be insufficient for outpatients with chronic kidney disease (CKD). Most outpatients with CKD use community pharmacies owing to the free access scheme in Japan. The MieYaku-CKD project included a community pharmacist-led nutritional intervention for dietary potassium restriction, with the goal of determining its efficacy for patients' awareness of potassium restriction and serum potassium levels in outpatients with CKD. This was a five-community pharmacy multicenter prospective cohort study with an open-label, before-and-after comparison design. Eligible patients (n = 25) with an estimated glomerular filtration rate (eGFR) < 45 mL/min/1.73 m2 received nutritional guidance from community pharmacists. The primary outcome was a change in serum potassium levels at 12 weeks post-intervention. The eligible patients' knowledge, awareness, and implementation of potassium restriction were evaluated using a questionnaire. The median value of serum potassium was significantly reduced from 4.7 mEq/L before to 4.4 mEq/L after the intervention [p < 0.001, 95% confidence interval (CI): 0.156-0.500], with no changes in eGFR (p = 0.563, 95% CI: -2.427-2.555) and blood urine nitrogen/serum creatinine ratio (p = 0.904, 95% CI: -1.793-1.214). The value of serum potassium had a tendency of attenuation from 5.3 to 4.6 mEq/L (p = 0.046, 95% CI: 0.272-1.114) in the eGFR < 30 mL/min/1.73 m2 group. A questionnaire revealed that after the intervention, knowledge and attitudes regarding dietary potassium restriction were much greater than before, suggesting that the decrease in serum potassium levels may be related to this nutritional guidance. Our findings indicate that implementing a dietary potassium restriction guidance program in community pharmacies is feasible and may result in lower serum potassium levels in outpatients with CKD.

Enhancing Chickpea Yield and Nutrient Profile Through Phosphorous and Zinc Application

The field experiments conducted at the Student’s Instructional Farm, Chandra Shekhar Azad University of Agriculture and Technology, Kanpur, during the rabi season of 2021–22 aimed to investigate the impact of phosphorus and zinc on both yield and quality parameters of chickpea. Employing a factorial randomized block design with 16 treatment combinations and three replications, various combinations of phosphorus and zinc were tested, using the chickpea cultivar RVG-203 and following prescribed agricultural techniques. The most promising results were observed in treatment T15 [P90Zn6 ], which yielded the highest grain yield (18.65 q ha-1) and straw yield (21.86 q ha-1). Conversely, treatment T0 [P0 Zn0 ] exhibited the lowest grain yield (13.24 q ha-1) and straw yield (17.92 qha-1). Furthermore, nutrient content analysis showcased that treatment T15 [P90Zn6 ] boasted maximum values for grain nitrogen (N), phosphorus (P), potassium (K), and zinc (Zn) content at 3.35%, 0.65%, 0.65%, and 0.27 mg kg-1, respectively. In contrast, treatment T0 [P0 Zn0 ] displayed the minimum values in these parameters at 3.13%, 0.46%, 0.53%, and 0.20 mg kg-1, respectively. Similar trends were observed in the nutrient content of straw. Nutrient uptake analysis further emphasized the superiority of the treatment T15 [P90Zn6 ], recording maximum values for grain nitrogen (63.15 kg ha-1), phosphorus (12.25 kg ha-1), potassium (12.25 kg ha-1), and zinc (49.91 g ha-1), as well as in straw nitrogen (33.16 kg ha-1), phosphorus (7.47 kg ha-1), potassium (43.66 kg ha-1), and zinc (52.27 g ha-1). Conversely, treatment T0 [P0 Zn0 ] exhibited the lowest nutrient uptake for both grain and straw. In conclusion, the comprehensive analysis of treatment combinations demonstrated that the judicious application of phosphorus and zinc, as exemplified by treatment T15 [P90Zn6 ], significantly enhances chickpea yield and quality parameters, emphasizing the importance of balanced nutrient management for optimal agricultural productivity.

DKA Prediction in Children Using Artificial Intelligence: Improved Emergency Care

Diabetics Keto Acidosis (DKA) is a serious health problem that requires timely intervention to prevent harmful consequences. This study analyzes various types of patient data, including clinical parameters and historical records such as family history, genetics, age, gender, etc., to identify early indications for developing DKA in children. The proposed AI-based system can support healthcare professionals in the emergency department by providing accurate and timely predictions/decisions. The dataset consisted of various clinical, and laboratory parameters such as &amp;quot;blood sugar level, hco3, pH values, urinary ketones, acidosis, sodium, and potassium values&amp;quot;. Furthermore, along with the above parameters the dataset also contained some additional parameters such as &amp;quot;gender, age, and family history&amp;quot;. The total size of the dataset was 5000 rows with 978 rows as &amp;quot;children diagnosed with DKA&amp;quot; and 4021 rows as &amp;quot;normal children&amp;quot;. For the model selection stage, we selected 08 different machine learning models such as logistic regression, k nearest neighbor, naïve Bayes, decision tree, random forest, gradient boosting, xgboost, and extra trees classifier. Additionally, we also split the above dataset into two sets (i.e., train, and test) with an overall distribution of 70%, and 30% for the train (3500 data points), and test (1500 data points); respectively. Throughout, the study, we strictly followed all the ethical guidelines to ensure patient privacy and data protection rights with relevant regulations. The preliminary results showed that the proposed AI-based prediction model achieved a high accuracy for predicting DKA in children based on the above dataset. Although, the dataset contained more samples of normal children (i.e., 80.5%) than DKA-diagnosed children but still the prediction model performed very well in identifying the DKA in the children. The best-performing model from the models selected in the methods part was found to be the extra trees classifier with overall accuracy, precision, recall, f1-score, and MSE of 81%, 78%, 81%, 73%, and 18%. By integrating, the developed predictive model into the emergency department, the medical officers or healthcare professionals can predict different patients at risk very quickly by providing the input data of the patients on which the proposed AI-based prediction model is trained.

Current status of stream water quality around selected Tea Gardens from Southern Western Ghats, Tamil Nadu, India – A case study

In this study, metal concentration in tea garden-associated stream water samples from the south Western Ghats of Tamil Nadu was evaluated using ICP-MS to know the current status of pollution level, sources of pollution, probable water quality, and impacts of heavy metals. Stream water pollution of hill regions communities using the heavy metal pollution index, principal compound analysis, and Pearson correlation analysis were measured. In addition, for this purpose, water phy-chem parameters were also measured, such as (pH, temperature, conductivity, salinity, and TDS) using a pH meter. PO4 and F- UV-spectrophotometer, Na, K, and calcium (flame photo meter), NO3- NO3-N (Electronic method-horiba), and turbidity meter to evaluate the stream-water quality were used, and heavy metals, particularly Al, As, Cd, Fe, Cr, Cu, Zn, Pb, Ni, and Mn are analyzed as heavy metal pollutions. The phy-chem average values of pH, Temp, EC, Tds, Salinity, alkalinity, cod, bod, do, chloride, calcium, C. hardness, M. hardness, T. hardness, N-Nitrogen, Phosphate, Potassium, Sodium, Turbidity, Fluoride, Sulphate, and Sulphur are found 8.13, 27.57 °C, 218, 111.67, 78.67, 70.39, 2029, 5.70, 3.83, 70.83, 737, 62.30, 166.11, 679, 1132, 149, 11, 28.33, 4.44, 07, 178, 180 respectively. Heavy metal concentrations, including aluminum, arsenic, cadmium, iron, chromium, copper, zinc, lead, nickel, and manganese, are detected at 32, 7.2, 6.24, 96.4, 0.00, 0.00, 40, 0.00, 0.00, 30 μg/L, respectively. Concentrations of studied heavy metals, particularly, Mn, Ni, Cd, and Fe, have measurements that are greater than the permissible standard limits of BIS, WHO.

Efeitos do colírio de cetorolaco de trometamol sobre o hemograma, a bioquímica sérica e a urinálise em cães saudáveis

ABSTRACT This study aimed to evaluate whether the use of trometamol ketorolac for 30 consecutive days may change the blood count, the serum biochemistry profile, and the urinalysis of healthy dogs. Eleven small breed dogs (4.6-10kg), with ages ranging from 1 to 9 years were enrolled in the study. Dogs received 40μL of 0.4% trometamol ketorolac eye drops, every 12 h in both eyes for 30 consecutive days. Blood and urine samples were collected at baseline, and following 15 and 30 days of the beginning of the treatment. Creatinine levels increased significantly at day 15 (1.21±0.1mg/dL) and 30 (1.22±0.1mg/dL) when compared with baseline (0.94±0.1mg/dL) (P&lt;0.01). Significantly increased values of serum potassium were observed only at day 30 (4.66±0.15mEq/L), when compared with day 15 (4.32±0.12mEq/L) and baseline (4.36±0.15mEq/L) (P&lt;0.05). The other hematological and biochemical parameters did not change significantly during the study (P&gt;0.05). From observations of our study, it can be concluded that the instillation of trometamol ketorolac for 30 consecutive days did not cause clinically relevant changes in the blood count, biochemistry profile and in the urinalysis of healthy dogs.