Clinical Pertinence and Determinants of Potential Drug-Drug Interactions in Chronic Kidney Disease Patients: A Cross-sectional Study.

Background: Chronic kidney disease (CKD) is one of the major health issues effecting around 15% of world population, and its further complications has raised the need of polypharmacy for management. But this polypharmacy also upsurges the risk of potential drug-drug interactions (pDDIs) in CKD patients, which may further be responsible for increased morbidity and mortality. Objective: The main objective is therefore to evaluate the distribution, severity, causes, associated drug interactions, and clinical relevance of determination of pDDIs in CKD patients. Methods: Medical files of CKD patients examined at nephrology department, Maharishi Markandeshwar Institute of Medical Sciences and Research (MMIMSR), Mullana, between December 2022 and May 2023 were cross-sectionally assessed for this study. Medscape drug interaction checker was used to study patient profiles for pDDIs, and suggestive measures to minimize those pDDIs were studied using DDInter to ensure better clinical decision-making and patient safety. IBM SPSS (version 24) was utilized for statistical analysis. Results: The data reveal that 74.5% of the 200 medical files being evaluated had 839 pDDIs in total, out of which nearly 78.3% of patients had moderate, 15.6% had minor, and 6.07% had serious interactions. The potential adverse outcomes of pDDIs included an irregular heartbeat, hypokalemia, central nervous system (CNS) adverse effects, hypoglycemia, and a decline in therapeutic efficacy. The prevalence of pDDIs was discovered to be substantially correlated with age ≥60 years, (odds ratio [OR] = 0.65; 95% CI = 0.4-0.9; P = 0.040), length of stay ≥10 days (OR = 4.0; 95% CI = 1.29-6.1; P = 0.016), and number of prescribed drugs ≥10 (OR = 5.5; 95% CI = 2.45-10.69; P = 0.004). Conclusion: Patients with CKD have a high incidence of pDDIs (mainly mild to moderate). Older age, duration of hospital stays, and polypharmacy all raise the risk of pDDIs. Healthcare professionals (physicians and clinical pharmacist) should use drug interaction checker software programs like Medscape and DDInter to acquire knowledge about different pDDIS and their alternative measures so that the associated adverse drug reactions (ADRs) can be controlled and rational drug combination can be prescribed for management of CKD ensuring better patient care.

Renal artery pseudoaneurysm post-COVID-19 infection: An unusual delayed complication.

This case report discusses a rare and severe complication of COVID-19 recovery - renal mucormycosis leading to right renal artery pseudoaneurysm. A 59-year-old patient, previously treated for COVID-19, presented with dry cough, flank pain, and hematuria. He was diagnosed with renal artery pseudoaneurysm with renal mucormycosis. Successful management included urgent angioembolization, systemic liposomal amphotericin B, and subsequent radical nephrectomy post-stabilization. The case underscores the importance of vigilant post-COVID-19 follow-up, particularly in patients treated with steroids, and highlights the need for a multidisciplinary approach for timely diagnosis and effective management of mucormycosis related complications.

The machine learning and geostatistical approach for assessment of arsenic contamination levels using physicochemical properties of water.

Arsenic contamination in groundwater due to natural or anthropogenic sources is responsible for carcinogenic and non-carcinogenic risks to humans and the ecosystem. The physicochemical properties of groundwater in the study area were determined in the laboratory using the samples collected across the Varanasi region of Uttar Pradesh, India. This paper analyses the physicochemical properties of water using machine learning, descriptive statistics, geostatistical and spatial analysis. Pearson correlation was used for feature selection and highly correlated features were selected for model creation. Hydrochemical facies of the study area were analyzed and the hyperparameters of machine learning models, i.e., multilayer perceptron, random forest (RF), naïve Bayes, and decision tree were optimized before training and testing the groundwater samples as high (1) or low (0) arsenic contamination levels based on the WHO 10 µg/L guideline value. The overall performance of the models was compared based on accuracy, sensitivity, and specificity value. Among all models, the RF algorithm outclasses other classifiers, as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. The accuracy result was compared to prior research, and the machine learning model may be used to continually monitor the amount of arsenic pollution in groundwater.

Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover.

Groundwater is an essential resource; around 2.5 billion people depend on it for drinking and irrigation. Groundwater arsenic contamination is due to natural and anthropogenic sources. The World Health Organization (WHO) has proposed a guideline value for arsenic concentration in groundwater samples of 10[Formula: see text]g/L. Continuous consumption of arsenic-contaminated water causes various carcinogenic and non-carcinogenic health risks. In this paper, we introduce a geospatial-based machine learning method for classifying arsenic concentration levels as high (1) or low (0) using physicochemical properties of water, soil type, land use land cover, digital elevation, subsoil sand, silt, clay, and organic content of the region. The groundwater samples were collected from multiple sites along the river Ganga's banks of Varanasi district in Uttar Pradesh, India. The dataset was subjected to descriptive statistics and spatial analysis for all parameters. This study assesses the various contributing parameters responsible for the occurrence of arsenic in the study area based on the Pearson correlation feature selection method. The performance of machine learning models, i.e., Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), Decision Tree, Random Forest, Naïve Bayes, and Deep Neural Network (DNN), were compared to validate the parameters responsible for the dissolution of arsenic in groundwater aquifers. Among all the models, the DNN algorithm outclasses other classifiers as it has a high accuracy of 92.30%, a sensitivity of 100%, and a specificity of 75%. Policymakers can utilize the accuracy of the DNN model to approximate individuals prone to arsenic poisoning and formulate mitigation strategies based on spatial maps.

Exploring temperature dynamics in Madhya Pradesh: a spatial-temporal analysis.

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen's slope (SS) estimator were used to detect the trends, and Pettitt's test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt's test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951-2004 to 25.78 °C in 2005-2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951-2010 to 46.24 °C in 2011-2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951-1999 to 3.19 °C in 2000-2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation.

Probiotic mediated NF-κB regulation for prospective management of type 2 diabetes.

Diabetes and other lifestyle disorders have been recognized as the leading cause of morbidity and mortality globally. Nuclear factor kappa B (NF-κB) is a major factor involved in the early pathobiology of diabetes and studies reveal that hyperglycemic conditions in body leads to NF-κB mediated activation of several cytokines, chemokines and inflammatory molecules. NF-κB family comprises of certain DNA-binding protein factors that elicit the transcription of pro-inflammatory molecules. Various studies have identified NF-κB as a promising target for diabetic management. Probiotics have been proposed as bio-therapeutic agents for treatment of inflammatory disorders and many other chronic clinical stages. The precise mechanisms by which probiotics acts is yet to be fully understood, however research findings have indicated their role in NF-κB modulation. The current review highlights NF-κB as a bio-therapeutic target for probable management of type 2 diabetes through probiotic intervention.

Electronic Structure of a Graphene-like Artificial Crystal of NdNiO3.

Artificial complex-oxide heterostructures containing ultrathin buried layers grown along the pseudocubic [111] direction have been predicted to host a plethora of exotic quantum states arising from the graphene-like lattice geometry and the interplay between strong electronic correlations and band topology. To date, however, electronic-structural investigations of such atomic layers remain an immense challenge due to the shortcomings of conventional surface-sensitive probes with typical information depths of a few angstroms. Here, we use a combination of bulk-sensitive soft X-ray angle-resolved photoelectron spectroscopy (SX-ARPES), hard X-ray photoelectron spectroscopy (HAXPES), and state-of-the-art first-principles calculations to demonstrate a direct and robust method for extracting momentum-resolved and angle-integrated valence-band electronic structure of an ultrathin buckled graphene-like layer of NdNiO3 confined between two 4-unit cell-thick layers of insulating LaAlO3. The momentum-resolved dispersion of the buried Ni d states near the Fermi level obtained via SX-ARPES is in excellent agreement with the first-principles calculations and establishes the realization of an antiferro-orbital order in this artificial lattice. The HAXPES measurements reveal the presence of a valence-band bandgap of 265 meV. Our findings open a promising avenue for designing and investigating quantum states of matter with exotic order and topology in a few buried layers.

Limitations of GCTA as a solution to the missing heritability problem.

Genome-wide association studies (GWASs) seek to understand the relationship between complex phenotype(s) (e.g., height) and up to millions of single-nucleotide polymorphisms (SNPs). Early analyses of GWASs are commonly believed to have "missed" much of the additive genetic variance estimated from correlations between relatives. A more recent method, genome-wide complex trait analysis (GCTA), obtains much higher estimates of heritability using a model of random SNP effects correlated between genotypically similar individuals. GCTA has now been applied to many phenotypes from schizophrenia to scholastic achievement. However, recent studies question GCTA's estimates of heritability. Here, we show that GCTA applied to current SNP data cannot produce reliable or stable estimates of heritability. We show first that GCTA depends sensitively on all singular values of a high-dimensional genetic relatedness matrix (GRM). When the assumptions in GCTA are satisfied exactly, we show that the heritability estimates produced by GCTA will be biased and the standard errors will likely be inaccurate. When the population is stratified, we find that GRMs typically have highly skewed singular values, and we prove that the many small singular values cannot be estimated reliably. Hence, GWAS data are necessarily overfit by GCTA which, as a result, produces high estimates of heritability. We also show that GCTA's heritability estimates are sensitive to the chosen sample and to measurement errors in the phenotype. We illustrate our results using the Framingham dataset. Our analysis suggests that results obtained using GCTA, and the results' qualitative interpretations, should be interpreted with great caution.

Expression of fibronectin-binding protein of L. acidophilus NCFM and in vitro refolding to adhesion capable native-like protein from inclusion bodies.

The ability of Lactobacilli to adhere to host epithelial surface and intestinal tracts is important for colonization and persistence of bacteria in the host gut. Extracellular matrix components like fibronectin, mucin, collagen and other adhesion molecules serve as substratum for attachment of bacteria. However, the precise structure, function and mechanism of binding of microbial surface adhesion proteins such as Fibronectin-binding protein (FBP) with host molecules remains unclear. This is primarily due to limitations in high expression of these proteins in biologically active form. To study adhesion of its FBP (64 kDa), the fbp gene of L. acidophilus NCFM was cloned and expressed in E. coli. However, the fibronectin-binding protein expressed in soluble form could not be purified by Ni-NTA affinity chromatography possibly because of partially buried Histidine tag in the recombinant fusion protein. Therefore, the protein was expressed as inclusion bodies (IBs) at 37 °C and solubilized in urea followed by purification in denatured form by Ni-NTA affinity chromatography. The purified denatured protein was refolded in vitro to structurally stable and biologically active form. The conformational properties of the refolded protein were studied by circular dichroism, which showed prominence of α+ ß structural element. The refolded FBP also showed significant binding to human intestinal tissue sections. Our optimized refolding protocol from IBs of this recombinant probiotic FBP led into high amounts of biologically active protein. Our results help in increasing understanding of structure-function relation of surface adhesion proteins and host-microbial interactions.

Expression of recombinant truncated domains of mucus-binding (Mub) protein of Lactobacillus plantarum in soluble and biologically active form.

Mucins amount to 70% of total proteins present in mammalian mucus and serve as important substrata for bacterial adhesion. In probiotic bacteria such as Lactobacillus plantarum, surface adhesion proteins mediate its adhesion to mucus and adhesion is pivotal in bi-directional host-microbe interactions. Mucus binding (Mub) proteins are a group of bacterial surface adhesion proteins that bind to mucin proteins. The structural framework and functional role of these proteins needs immediate attention but is poorly understood because of their large size, low yield and lack of highly purified protein. The lp_1643 gene of L. plantarum encodes a large Mub protein of 240 kDa and has six mucus binding (Mub) domains in tandem. In this study, the fragment of lp_1643 containing the last two domains with their preceding spacers herein referred to as Mubs5s6 was cloned and expressed in E. coli for probing its functional role in the adhesion of L. plantarum. The protein was expressed with a solubility enhancing maltose binding protein (MBP) fusion tag, yet the MBP-Mubs5s6 protein expressed majorly (>90%) as biologically insoluble inclusion bodies. Thus, extensive optimization of culture conditions was carried out to achieve high level soluble expression (∼70%) of Mubs5s6 protein from its initial low level of solubility. The recombinant protein was purified up to homogeneity by affinity chromatography. Recombinant MBP-Mubs5s6 protein showed strong adhesion potential by binding with human intestinal tissue sections. Our results show a step-by-step hierarchical approach to improve the solubility of difficult-to-express extracellular surface proteins while retaining high functional viability.

Structural polymorphism of amyloid fibrils in ATTR amyloidosis revealed by cryo-electron microscopy.

ATTR amyloidosis is caused by the deposition of transthyretin in the form of amyloid fibrils in virtually every organ of the body, including the heart. This systemic deposition leads to a phenotypic variability that has not been molecularly explained yet. In brain amyloid conditions, previous studies suggest an association between clinical phenotype and the molecular structures of their amyloid fibrils. Here we investigate whether there is such an association in ATTRv amyloidosis patients carrying the mutation I84S. Using cryo-electron microscopy, we determined the structures of cardiac fibrils extracted from three ATTR amyloidosis patients carrying the ATTRv-I84S mutation, associated with a consistent clinical phenotype. We found that in each ATTRv-I84S patient, the cardiac fibrils exhibited different local conformations, and these variations can co-exist within the same fibril. Our finding suggests that one amyloid disease may associate with multiple fibril structures in systemic amyloidoses, calling for further studies.

COVID-19 vaccine hesitancy among pregnant women attending tertiary care centre: A cross-sectional study.

OBJECTIVE: To evaluate the knowledge and attitude towards coronavirus disease 2019 (COVID-19) vaccination during pregnancy and to discover factors that lead to non-acceptance of vaccine. METHODS: A cross-sectional study was performed in the Department of Obstetrics and Gynecology, Hamdard Institute of Medical Science & Research, New Delhi over a period of 3 months through a web-based questionnaire via Google form. The questionnaire was assessed using Cronbach α for internal consistency, which was 0.795. RESULTS: News (74%) was the major source of knowledge among pregnant women. Around 60% women were not willing to receive the vaccine, mainly because of their fear of a harmful effect on pregnancy. The anticipated vaccine acceptance rate was 41% but actual vaccine acceptance rate in pregnancy was 7.3%. CONCLUSION: Efforts should be made to reduce the gap of knowledge regarding vaccine among pregnant women.

The roles of organic farming, renewable energy, and corruption on biodiversity crisis: a European perspective.

The loss of biodiversity has profound implications for nature's contributions to people and their health. This study intends to examine the factors responsible for biodiversity loss as well as the coping mechanisms to address this crisis in the context of 35 European economies covering the 2009-2018 period. The study utilises both the static and dynamic panel estimation techniques to examine the above issue. Specifically, the study applied Driscoll and Kraay (1998a), Driscoll and Kraay (Rev Econ Stat 80:549-560, 1998b) and Panel Corrected Standard Approach (PCSE) for the static panel models. As for dynamic panel models, the study employs linear dynamic panel model by Arrelano and Bond (Rev Econ Stud 58:277-297, 1991) and Arrelano and Bover (J Econom 68:29-51, 1995)/Blundell and Bond (J Econom 87:115-143, 1998) system generalised methods of moments (GMM). Morandeover for robustness purposes, fixed and random effect models are also applied. The findings indicate that renewable energy use increases biodiversity crisis whereas organic farming is beneficial for biodiversity preservation in Europe. Corruption and gender gap were found to increase the biodiversity crisis. The evidence also suggests a positive and significant effect of forest area, e-governance and social progress on biodiversity. Finally, the study provides insightful implications for stakeholders and practitioners associated with energy and biodiversity conservation in Europe.

Indian sewage microbiome has unique community characteristics and potential for population-level disease predictions.

Sewage wastewater pollutes water and poses a public health issue but it could also prove useful in certain research domains. Sewage is a complex niche relevant for research concerning 'one-health', human health, pollution and antibiotic resistance. Indian gut microbiome is also understudied due to sampling constraints and sewage could be used to explore it. Ostensibly, Indian sewage needs to be studied and here, we performed a cross-sectional pan-India sewage sampling to generate the first comprehensive Indian sewage microbiome. Indian sewage showed predominance of Burkholderiaceae, Rhodocyclaceae, Veillonellaceae, Prevotellaceae, etc. and has high representation of gut microbes. The identified gut microbes have overrepresentation of Veillonellaceae, Rikenellaceae, Streptococcaceae, and Bacillaceae. Imputed metagenomics of sewage microbiome indicated dominance of transport, motility, peptidases, amino acid metabolism, and antibiotic resistance genes. Microbiome-disease associations drawn using simple decision tree and random forest analysis identified specific microbes as potential predictors of diabetes and obesity in a city. Altogether, we generated the first Indian sewage microbiome and our non-invasive, high-throughput workflow could be emulated for future research, wastewater-based epidemiology and designing policies concerning public health.

Host-microbe interaction and pathogen exclusion mediated by an aggregation-prone surface layer protein of Lactobacillus helveticus.

Probiotic surface layer proteins (Slps) have multiple functions and bacterial adhesion to host cells is one of them. The precise role of Slps in cellular adhesion is not well understood due to its low native protein yield and self-aggregative nature. Here, we report the recombinant expression and purification of biologically active Slp of Lactobacillus helveticus NCDC 288 (SlpH) in high yield. SlpH is a highly basic protein (pI = 9.4), having a molecular weight of 45 kDa. Circular Dichroism showed a prevalence of beta-strands in SlpH structure and resistance to low pH. SlpH showed binding to human intestinal tissue, enteric Caco-2 cell line, and porcine gastric mucin, but not with fibronectin, collagen type IV and laminin. SlpH inhibited the binding of the enterotoxigenic E. coli by 70 % and 76 % and that of Salmonella Typhimurium SL1344 by 71 % and 75 % to enteric Caco-2 cell line in the exclusion and competition assays, respectively. The pathogen exclusion and competition activity and tolerance to harsh gastrointestinal conditions show the potential for developing SlpH as a prophylactic or therapeutic agent against enteric pathogens.

Milk-Derived Antimicrobial Peptides: Overview, Applications, and Future Perspectives.

The growing consumer awareness towards healthy and safe food has reformed food processing strategies. Nowadays, food processors are aiming at natural, effective, safe, and low-cost substitutes for enhancing the shelf life of food products. Milk, besides being a rich source of nutrition for infants and adults, serves as a readily available source of precious functional peptides. Due to the existence of high genetic variability in milk proteins, there is a great possibility to get bioactive peptides with varied properties. Among other bioactive agents, milk-originated antimicrobial peptides (AMPs) are gaining interest as attractive and safe additive conferring extended shelf life to minimally processed foods. These peptides display broad-spectrum antagonistic activity against bacteria, fungi, viruses, and protozoans. Microbial proteolytic activity, extracellular peptidases, food-grade enzymes, and recombinant DNA technology application are among few strategies to tailor specific peptides from milk and enhance their production. These bioprotective agents have a promising future in addressing the global concern of food safety along with the possibility to be incorporated into the food matrix without compromising overall consumer acceptance. Additionally, in conformity to the current consumer demands, these AMPs also possess functional properties needed for value addition. This review attempts to present the basic properties, synthesis approaches, action mechanism, current status, and prospects of antimicrobial peptide application in food, dairy, and pharma industry along with their role in ensuring the safety and health of consumers.

Connectivity modelling in conservation science: a comparative evaluation.

Landscape connectivity, the extent to which a landscape facilitates the flow of ecological processes such as organism movement, has grown to become a central focus of applied ecology and conservation science. Several computational algorithms have been developed to understand and map connectivity, and many studies have validated their predictions using empirical data. Yet at present, there is no published comparative analysis which uses a comprehensive simulation framework to measure the accuracy and performance of the dominant methods in connectivity modelling. Given the widespread usage of such models in spatial ecology and conservation science, a thorough evaluation of their predictive abilities using simulation techniques is essential for guiding their appropriate and effective application across different contexts. In this paper, we address this by using the individual-based movement model Pathwalker to simulate different connectivity scenarios generated from a wide range of possible movement behaviours and spatial complexities. With this simulated data, we test the predictive abilities of three major connectivity models: factorial least-cost paths, resistant kernels, and Circuitscape. Our study shows the latter two of these three models to consistently perform most accurately in nearly all cases, with their abilities varying substantially in different contexts. For the majority of conservation applications, we infer resistant kernels to be the most appropriate model, except for when the movement is strongly directed towards a known location. We conclude this paper with a review and interdisciplinary discussion of the current limitations and possible future developments of connectivity modelling.

Quantifying the effect of waste on soil health in European Union: what are the roles of technology, natural capital, and institutional quality?

With a surge in both hazardous and non-hazardous waste in recent decades, European Union countries are losing their soil quality which in turn affects the agricultural production of their economies. Taking this into account, this study presents the effect of hazardous and non-hazardous waste, plastic waste, and electronic waste on soil health for 24 European Union (EU) countries during 2004-2018 period. The impacts of several other variables such as technological innovation, ICT, natural capital, fossil fuel energy consumption, and institutional quality on soil health are also examined. To achieve the above objectives, we employ Driscoll-Kraay technique as the main methodology as well as panel spatial correlation consistent (PSCC) standard errors and quantile estimation at median. The results demonstrate that electronic waste has a negative effect on soil health while the effect of total hazardous and non-hazardous waste and plastic waste on soil health remains insignificant. Technological innovation, ICT, and institutional quality, as well as fossil fuel energy consumption, have positive impacts on soil health. Furthermore, natural capital moderates the effect of plastic and electronic waste on soil health. The study finally provides precise policy recommendations for the EU countries such as proper handling of wastes, promoting strong institutional quality as well as use of technology to enrich the soil nutrient balance.