Optimizing Drug Combinations for T-PLL: Restoring DNA Damage and P53-Mediated Apoptotic Responses.

T-prolymphocytic leukemia (T-PLL) is a mature T-cell neoplasm associated with marked chemotherapy resistance and continued poor clinical outcomes. Current treatments, i.e. the CD52-antibody alemtuzumab, offer transient responses, with relapses being almost inevitable without consolidating allogeneic transplantation. Recent more detailed concepts of T-PLL's pathobiology fostered the identification of actionable vulnerabilities: (i) altered epigenetics, (ii) defective DNA damage responses, (iii) aberrant cell-cycle regulation, and (iv) deregulated pro-survival pathways, including TCR and JAK/STAT signaling. To further develop related pre-clinical therapeutic concepts, we studied inhibitors of (H)DACs, BCL2, CDK, MDM2, and clas-sical cytostatics, utilizing (a) single-agent and combinatorial compound testing in 20 well-characterized and molecularly-profiled primary T-PLL (validated by additional 42 cases), and (b) 2 independent murine models (syngeneic transplants and patient-derived xenografts). Overall, the most efficient/selective single-agents and combinations (in vitro and in mice) in-cluded Cladribine, Romidepsin ((H)DAC), Venetoclax (BCL2), and/or Idasanutlin (MDM2). Cladribine sensitivity correlated with expression of its target RRM2. T-PLL cells revealed low overall apoptotic priming with heterogeneous dependencies on BCL2 proteins. In additional 38 T-cell leukemia/lymphoma lines, TP53 mutations were associated with resistance towards MDM2 inhibitors. P53 of T-PLL cells, predominantly in wild-type configuration, was amenable to MDM2 inhibition, which increased its MDM2-unbound fraction. This facilitated P53 activa-tion and down-stream signals (including enhanced accessibility of target-gene chromatin re-gions), in particular synergy with insults by Cladribine. Our data emphasize the therapeutic potential of pharmacologic strategies to reinstate P53-mediated apoptotic responses. The identified efficacies and their synergies provide an informative background on compound and patient selection for trial designs in T-PLL.

Epigenome-wide methylation study identified two novel CpGs associated with T2DM risk and a network of co-methylated CpGs capable of patient's classifications.

Prevention of Type 2 diabetes mellitus (T2DM) pandemic needs markers that can precisely predict the disease risk in an individual. Alterations in DNA methylations due to exposure towards environmental risk factors are widely sought markers for T2DM risk prediction. To identify such individual DNA methylation signatures and their effect on disease risk, we performed an epigenome-wide association study (EWAS) in 844 Indian individuals of Indo-European origin. We identified and validated methylation alterations at two novel CpG sites in MIR1287 (cg01178710) and EDN2-SCMH1 (cg04673737) genes associated with T2DM risk at the epigenome-wide-significance-level (P < 1.2 × 10-7). Further, we also replicated the association of two known CpG sites in TXNIP, and CPT1A in the Indian population. With 535 EWAS significant CpGs (P < 1.2 × 10-7) identified in the discovery phase samples, we created a co-methylation network using weighted correlation network analysis and identified four modules among the CpGs. We observed that methylation of one of the module associates with T2DM risk factors (e.g. BMI, insulin and C-peptide) and can be used as markers to segregate T2DM patients with good glycemic control (e.g. low HbA1c) and dyslipidemia (low HDL and high TG) from the other patients. Additionally, an intronic SNP (rs6503650) in the JUP gene, a member of the same module, associated with methylation at all the 14 hub CpG sites of that module as methQTL. Our network-assisted EWAS is the first to systematically explore DNA methylation variations conferring risks to T2DM in Indians and use the identified risk CpG sites for patient segregation with different clinical outcomes. These findings can be useful for better stratification of patients to improve the clinical management and treatment effects.

Genome-Wide Association Study Identifies 4 Novel Risk Loci for Small Intestinal Neuroendocrine Tumors Including a Missense Mutation in LGR5.

BACKGROUND & AIMS: Small intestinal neuroendocrine tumor (SI-NET) is a rare disease, but its incidence has increased over the past 4 decades. Understanding the genetic risk factors underlying SI-NETs can help in disease prevention and may provide clinically beneficial markers for diagnosis. Here the results of the largest genome-wide association study of SI-NETs performed to date with 405 cases and 614,666 controls are reported. METHODS: Samples from 307 patients with SI-NETs and 287,137 controls in the FinnGen study were used for the identification of SI-NET risk-associated genetic variants. The results were also meta-analyzed with summary statistics from the UK Biobank (n = 98 patients with SI-NET and n = 327,529 controls). RESULTS: We identified 6 genome-wide significant (P < 5 × 10-8) loci associated with SI-NET risk, of which 4 (near SEMA6A, LGR5, CDKAL1, and FERMT2) are novel and 2 (near LTA4H-ELK and in KIF16B) have been reported previously. Interestingly, the top hit (rs200138614; P = 1.80 × 10-19) was a missense variant (p.Cys712Phe) in the LGR5 gene, a bona-fide marker of adult intestinal stem cells and a potentiator of canonical WNT signaling. The association was validated in an independent Finnish collection of 70 patients with SI-NETs, as well as in the UK Biobank exome sequence data (n = 92 cases and n = 392,814 controls). Overexpression of LGR5 p.Cys712Phe in intestinal organoids abolished the ability of R-Spondin1 to support organoid growth, indicating that the mutation perturbed R-Spondin-LGR5 signaling. CONCLUSIONS: Our study is the largest genome-wide association study to date on SI-NETs and reported 4 new associated genome-wide association study loci, including a novel missense mutation (rs200138614, p.Cys712Phe) in LGR5, a canonical marker of adult intestinal stem cells.

Uncovering novel regulatory variants in carbohydrate metabolism: a comprehensive multi-omics study of glycemic traits in the Indian population.

Clinical biomarkers such as fasting glucose, HbA1c, and fasting insulin, which gauge glycemic status in the body, are highly influenced by diet. Indians are genetically predisposed to type 2 diabetes and their carbohydrate-centric diet further elevates the disease risk. Despite the combined influence of genetic and environmental risk factors, Indians have been inadequately explored in the studies of glycemic traits. Addressing this gap, we investigate the genetic architecture of glycemic traits at genome-wide level in 4927 Indians (without diabetes). Our analysis revealed numerous variants of sub-genome-wide significance, and their credibility was thoroughly assessed by integrating data from various levels. This identified key effector genes, ZNF470, DPP6, GXYLT2, PITPNM3, BEND7, and LORICRIN-PGLYRP3. While these genes were weakly linked with carbohydrate intake or glycemia earlier in other populations, our findings demonstrated a much stronger association in the Indian population. Associated genetic variants within these genes served as expression quantitative trait loci (eQTLs) in various gut tissues essential for digestion. Additionally, majority of these gut eQTLs functioned as methylation quantitative trait loci (meth-QTLs) observed in peripheral blood samples from 223 Indians, elucidating the underlying mechanism of their regulation of target gene expression. Specific co-localized eQTLs-meth-QTLs altered the binding affinity of transcription factors targeting crucial genes involved in glucose metabolism. Our study identifies previously unreported genetic variants that strongly influence the diet-glycemia relationship. These findings set the stage for future research into personalized lifestyle interventions integrating genetic insights with tailored dietary strategies to mitigate disease risk based on individual genetic profiles.

SynergyFinder 3.0: an interactive analysis and consensus interpretation of multi-drug synergies across multiple samples.

SynergyFinder (https://synergyfinder.fimm.fi) is a free web-application for interactive analysis and visualization of multi-drug combination response data. Since its first release in 2017, SynergyFinder has become a popular tool for multi-dose combination data analytics, partly because the development of its functionality and graphical interface has been driven by a diverse user community, including both chemical biologists and computational scientists. Here, we describe the latest upgrade of this community-effort, SynergyFinder release 3.0, introducing a number of novel features that support interactive multi-sample analysis of combination synergy, a novel consensus synergy score that combines multiple synergy scoring models, and an improved outlier detection functionality that eliminates false positive results, along with many other post-analysis options such as weighting of synergy by drug concentrations and distinguishing between different modes of synergy (potency and efficacy). Based on user requests, several additional improvements were also implemented, including new data visualizations and export options for multi-drug combinations. With these improvements, SynergyFinder 3.0 supports robust identification of consistent combinatorial synergies for multi-drug combinatorial discovery and clinical translation.

Application of phytoaccumulation perspective of Monochoria hastate L. on fluoride contaminated water in hydroponic treatment: its statistical design and characterization studies.

The present research work approaches the accumulation of fluoride ions from contaminated water using an aquatic plant Monochoria hastate L. in hydroponic culture. A design of experiment (DOE) has been adopted and an analysis of variance has been conducted to establish the statistical significance of various process parameters. The different experimental factors are root and shoot (Factor A), fluoride concentration (Factor B), and experimental days (Factor C) largely influence the output response. Plants treated with 5 mg/L of fluoride solutions accumulated the highest concentration in root biomass 1.23 mg/gm, and shoot biomass 0.820 mg/gm, dry weight after 21 days' experimentation. The accumulation mechanism and potentiality of treated plants depend on root cells of the plasma membrane and energy-capturing molecules of adenosine triphosphate. Monochoria hastate L. root biomass was characterized to confirm the accumulation of fluoride ions in the experimented plants using scanning electron micrographs-energy dispersive spectrum (SEM-EDS), and Fourier transforms infrared analysis (FTIR) analysis.

The novelty of this study is the high fluoride accumulation efficiency in hydroponic treatment by Monochoria hastate L an excellent choice for phytoremediation technique. The Design of Experiment (DOE) has a good approach for the optimization of fluoride in the accumulation process. The maximum absorption of fluoride ions in root biomass is 1.23 mg/gm, and shoot biomass is 0.820 mg/gm, dry weight after 21 days of treatment. To know the fluoride ions in shoot and root biomass are characterized using scanning electron micrographs-energy dispersive spectrum (SEM-EDS), and Fourier transforms infrared analysis (FTIR).

Implementation of medicine take-back concept at community level in Nepal: a pilot study.

BACKGROUND: Most households may have leftover, unwanted, unused and expired (UUE) medicines. The present research aimed to analyze feasibility of implementation of medicine take-back in select communities in Nepal. METHODS: Exploratory (i.e. feasibility) study was conducted among 400 adults from July 2017 to January 2018. Study sites and participants were selected by simple random sampling and respondents were interviewed about their awareness about medicine disposal, hazards and willingness to support take-back program using semi-structured questionnaire. Multinomial logistic regression analysis was applied to explore relationship of take-back related outcomes with the predictors. The P-value < 0.05 was statistically significant at 95% confidence level. RESULTS: Land pollution and effect on health of children was significantly related with inappropriate disposal of medicines such as site of disposal (P value < 0.01), river (P value, 0.02), garbage (P value, 0.04) and dumping site (P value, 0.01). Analysis of willingness to follow take-back program with the techniques of support showed significant relationship with the establishment of collection center and participation on seminar (P value < 0.01). CONCLUSION: Most participants were interested to support take-back, if implemented in their community but main constraint was the budget. Take-back concept could be initiated and implemented on government funding or other sources.

SynergyFinder 2.0: visual analytics of multi-drug combination synergies.

SynergyFinder (https://synergyfinder.fimm.fi) is a stand-alone web-application for interactive analysis and visualization of drug combination screening data. Since its first release in 2017, SynergyFinder has become a widely used web-tool both for the discovery of novel synergistic drug combinations in pre-clinical model systems (e.g. cell lines or primary patient-derived cells), and for better understanding of mechanisms of combination treatment efficacy or resistance. Here, we describe the latest version of SynergyFinder (release 2.0), which has extensively been upgraded through the addition of novel features supporting especially higher-order combination data analytics and exploratory visualization of multi-drug synergy patterns, along with automated outlier detection procedure, extended curve-fitting functionality and statistical analysis of replicate measurements. A number of additional improvements were also implemented based on the user requests, including new visualization and export options, updated user interface, as well as enhanced stability and performance of the web-tool. With these improvements, SynergyFinder 2.0 is expected to greatly extend its potential applications in various areas of multi-drug combinatorial screening and precision medicine.

SynToxProfiler: An interactive analysis of drug combination synergy, toxicity and efficacy.

Drug combinations are becoming a standard treatment of many complex diseases due to their capability to overcome resistance to monotherapy. In the current preclinical drug combination screening, the top combinations for further study are often selected based on synergy alone, without considering the combination efficacy and toxicity effects, even though these are critical determinants for the clinical success of a therapy. To promote the prioritization of drug combinations based on integrated analysis of synergy, efficacy and toxicity profiles, we implemented a web-based open-source tool, SynToxProfiler (Synergy-Toxicity-Profiler). When applied to 20 anti-cancer drug combinations tested both in healthy control and T-cell prolymphocytic leukemia (T-PLL) patient cells, as well as to 77 anti-viral drug pairs tested in Huh7 liver cell line with and without Ebola virus infection, SynToxProfiler prioritized as top hits those synergistic drug pairs that showed higher selective efficacy (difference between efficacy and toxicity), which offers an improved likelihood for clinical success.

Directional dominance on stature and cognition in diverse human populations.

Homozygosity has long been associated with rare, often devastating, Mendelian disorders, and Darwin was one of the first to recognize that inbreeding reduces evolutionary fitness. However, the effect of the more distant parental relatedness that is common in modern human populations is less well understood. Genomic data now allow us to investigate the effects of homozygosity on traits of public health importance by observing contiguous homozygous segments (runs of homozygosity), which are inferred to be homozygous along their complete length. Given the low levels of genome-wide homozygosity prevalent in most human populations, information is required on very large numbers of people to provide sufficient power. Here we use runs of homozygosity to study 16 health-related quantitative traits in 354,224 individuals from 102 cohorts, and find statistically significant associations between summed runs of homozygosity and four complex traits: height, forced expiratory lung volume in one second, general cognitive ability and educational attainment (P < 1 × 10(-300), 2.1 × 10(-6), 2.5 × 10(-10) and 1.8 × 10(-10), respectively). In each case, increased homozygosity was associated with decreased trait value, equivalent to the offspring of first cousins being 1.2 cm shorter and having 10 months' less education. Similar effect sizes were found across four continental groups and populations with different degrees of genome-wide homozygosity, providing evidence that homozygosity, rather than confounding, directly contributes to phenotypic variance. Contrary to earlier reports in substantially smaller samples, no evidence was seen of an influence of genome-wide homozygosity on blood pressure and low density lipoprotein cholesterol, or ten other cardio-metabolic traits. Since directional dominance is predicted for traits under directional evolutionary selection, this study provides evidence that increased stature and cognitive function have been positively selected in human evolution, whereas many important risk factors for late-onset complex diseases may not have been.

Removal of Cr (VI) from aqueous solution by activated charcoal derived from Sapindus trifoliate L fruit biomass using continuous fixed bed column studies.

In this study, the removal of hexavalent chromium from aqueous solution were examined using activated charcoal derived from Sapindus trifoliate L fruit biomass in continuous fixed-bed column studies. The activated S. trifoliate L fruit charcoal was prepared by treating the fruit powder using concentrated nitric acid solution. Experiments were performed to investigate the effect of bed height and initial concentration on the breakthrough and saturation times. The breakthrough and saturation time increases with increase in bed height and initial concentration of chromium solutions. The maximum adsorption capacity of S. trifoliate L charcoal for hexavalent chromium was found to be 1.719 mg/g in the bed height 15 cm and initial concentration 10 mg/L, respectively. Column data required at various conditions were explained using Bohart-Adams and Thomas model. Two models were found to be suitable to describe the definite part of the dynamic behaviour of the column with regard to bed-height and initial concentration of hexavalent chromium. On comparison of Adjusted R2 and estimated standard error, the Thomas model was found to best-fitted model and can be used to predict the adsorption of the hexavalent chromium in fixed-bed column studies. Activated S. trifoliate L fruit charcoal was characterised by SEM-EDX and FTIR analysis.

Multifaceted genome-wide study identifies novel regulatory loci in SLC22A11 and ZNF45 for body mass index in Indians.

Obesity, a risk factor for multiple diseases (e.g. diabetes, hypertension, cancers) originates through complex interactions between genes and prevailing environment (food habit and lifestyle) that varies across populations. Indians exhibit a unique obesity phenotype with high abdominal adiposity for a given body weight compared to matched white populations suggesting presence of population-specific genetic and environmental factors influencing obesity. However, Indian population-specific genetic contributors for obesity have not been explored yet. Therefore, to identify potential genetic contributors, we performed a two-staged genome-wide association study (GWAS) for body mass index (BMI), a common measure to evaluate obesity in 5973 Indian adults and the lead findings were further replicated in 1286 Indian adolescents. Our study revealed novel association of variants-rs6913677 in BAI3 gene (p = 1.08 × 10-8) and rs2078267 in SLC22A11 gene (p = 4.62 × 10-8) at GWAS significance, and of rs8100011 in ZNF45 gene (p = 1.04 × 10-7) with near GWAS significance. As genetic loci may dictate the phenotype through modulation of epigenetic processes, we overlapped genetic data of identified signals with their DNA methylation patterns in 236 Indian individuals and performed methylation quantitative trait loci (meth-QTL) analysis. Further, functional roles of discovered variants and underlying genes were speculated using publicly available gene regulatory databases (ENCODE, JASPAR, GeneHancer, GTEx). The identified variants in BAI3 and SLC22A11 genes were found to dictate methylation patterns at unique CpGs harboring critical cis-regulatory elements. Further, BAI3, SLC22A11 and ZNF45 variants were located in repressive chromatin, active enhancer, and active chromatin regions, respectively, in human subcutaneous adipose tissue in ENCODE database. Additionally, these genomic regions represented potential binding sites for key transcription factors implicated in obesity and/or metabolic disorders. Interestingly, GTEx portal identify rs8100011 as a robust cis-expression quantitative trait locus (cis-eQTL) in subcutaneous adipose tissue (p = 1.6 × 10-7), and ZNF45 gene expression in skeletal muscle of Indian subjects showed an inverse correlation with BMI indicating its possible role in obesity. In conclusion, our study discovered 3 novel population-specific functional genetic variants (rs6913677, rs2078267, rs8100011) in 2 novel (SLC22A11 and ZNF45) and 1 earlier reported gene (BAI3) for BMI in Indians. Our study decodes key genomic loci underlying obesity phenotype in Indians that may serve as prospective drug targets in future.

Genome-wide association study of blood lipids in Indians confirms universality of established variants.

Lipids foster energy production and their altered levels have been coupled with metabolic ailments. Indians feature high prevalence of metabolic diseases, yet uncharacterized for genes regulating lipid homeostasis. We performed first GWAS for quantitative lipids (total cholesterol, LDL, HDL, and triglycerides) exclusively in 5271 Indians. Further to corroborate our genetic findings, we investigated DNA methylation marks in peripheral blood in Indians at the identified loci (N = 233) and retrieved gene regulatory features from public domains. Recurrent GWAS loci-CELSR2, CETP, LPL, ZNF259, and BUD13 cropped up as lead signals in Indians, reflecting their universal applicability. Besides established variants, we found certain unreported variants at sub-genome-wide level-QKI, REEP3, TMCC2, FAM129C, FAM241B, and LOC100506207. These variants though failed to attain GWAS significance in Indians, but largely turned out to be active CpG sites in human subcutaneous adipose tissue and showed robust association to two or more lipid traits. Of which, QKI variants showed significant association to all four lipid traits and their designated region was observed to be a key gene regulatory segment denoting active transcription particularly in human subcutaneous adipose tissue. Both established and novel loci were observed to be significantly associated with altered DNA methylation in Indians for specific CpGs that resided in key regulatory elements. Further, gene-based association analysis pinpointed novel GWAS loci-LINC01340 and IQCJ-SCHIP1 for TC; IFT27, IFT88, and LINC02141 for HDL; and TEX26 for TG. Present study ascertains universality of selected known genes and also identifies certain novel loci for lipids in Indians by integrating data from various levels of gene regulation.

DNA methylation profiling reveals the presence of population-specific signatures correlating with phenotypic characteristics.

Phenotypic characteristics are known to vary substantially among different ethnicities around the globe. These variations are mediated by number of stochastic events and cannot be attributed to genetic architecture alone. DNA methylation is a well-established mechanism that sculpts our epigenome influencing phenotypic variation including disease manifestation. Since DNA methylation is an important determinant for health issues of a population, it demands a thorough investigation of the natural differences in genome wide DNA methylation patterns across different ethnic groups. This study is based on comparative analyses of methylome from five different ethnicities with major focus on Indian subjects. The current study uses hierarchical clustering approaches, principal component analysis and locus specific differential methylation analysis on Illumina 450K methylation data to compare methylome of different ethnic subjects. Our data indicates that the variations in DNA methylation patterns of Indians are less among themselves compared to other global population. It empirically correlated with dietary, cultural and demographical divergences across different ethnic groups. Our work further suggests that Indians included in this study, despite their genetic similarity with the Caucasian population, are in close proximity with Japanese in terms of their methylation signatures.

Adsorption studies of chromium (VI) removal from water by lanthanum diethanolamine hybrid material.

In the present research work, lanthanum diethanolamine hybrid material is synthesized by co-precipitation method and used for the removal of Cr(VI) from synthetic dichromate solution and hand pump water sample. The sorption experiments were carried out in batch mode to optimize various influencing parameters such as adsorbent dose, contact time, pH, competitive anions and temperature. The characterization of the material and mechanism of Cr(VI) adsorption on the material was studied by using scanning electron microscope, Fourier transform infrared, X-ray diffraction, Brunauer-Emmett-Teller and thermogravimetric analysis-differential thermal analysis. Adsorption kinetics studies reveal that the adsorption process followed first-order kinetics and intraparticle diffusion model with correlation coefficients (R2) of 0.96 and 0.97, respectively. The adsorption data were best fitted to linearly transformed Langmuir isotherm with correlation coefficient (R2) of 0.997. The maximum removal of Cr(VI) is found to be 99.31% at optimal condition: pH = 5.6 of the solution, adsorbent dose of 8 g L(-1) with initial concentration of 10mgL(-1) of Cr(VI) solution and an equilibrium time of 50 min. The maximum adsorption capacity of the material is 357.1 mg g(-1). Thermodynamic parameters were evaluated to study the effect of temperature on the removal process. The study shows that the adsorption process is feasible and endothermic in nature. The value of E (260.6 kJ mol(-1)) indicates the chemisorption nature of the adsorption process. The material is difficult to be regenerated. The above studies indicate that the hybrid material is capable of removing Cr(VI) from water.

Engineering CRISPR/Cas9 therapeutics for cancer precision medicine.

The discovery of Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) and CRISPR-associated protein 9 (Cas9) technology has revolutionized field of cancer treatment. This review explores usage of CRISPR/Cas9 for editing and investigating genes involved in human carcinogenesis. It provides insights into the development of CRISPR as a genetic tool. Also, it explores recent developments and tools available in designing CRISPR/Cas9 systems for targeting oncogenic genes for cancer treatment. Further, we delve into an overview of cancer biology, highlighting key genetic alterations and signaling pathways whose deletion prevents malignancies. This fundamental knowledge enables a deeper understanding of how CRISPR/Cas9 can be tailored to address specific genetic aberrations and offer personalized therapeutic approaches. In this review, we showcase studies and preclinical trials that show the utility of CRISPR/Cas9 in disrupting oncogenic targets, modulating tumor microenvironment and increasing the efficiency of available anti treatments. It also provides insight into the use of CRISPR high throughput screens for cancer biomarker identifications and CRISPR based screening for drug discovery. In conclusion, this review offers an overview of exciting developments in engineering CRISPR/Cas9 therapeutics for cancer treatment and highlights the transformative potential of CRISPR for innovation and effective cancer treatments.

Amino-functionalized novel biosorbent for effective removal of fluoride from water: process optimization using artificial neural network and mechanistic insights.

Aqueous fluoride ( F - ) pollution is a global threat to potable water security. The present research envisions the development of novel adsorbents from indigenous Limonia acidissima L. (fruit pericarp) for effective aqueous defluoridation. The adsorbents were characterized using instrumental analysis, e.g., TGA-DTA, ATR-FTIR, SEM-EDS, and XRD. The batch-mode study was performed to investigate the influence of experimental variables. The artificial neural network (ANN) model was employed to validate the adsorption. The dataset was fed to a backpropagation learning algorithm of the ANN (BPNN) architecture. The four-ten-one neural network model was considered to be functioning correctly with an absolute-relative-percentage error of 0.633 throughout the learning period. The results easily fit the linearly transformed Langmuir isotherm model with a correlation coefficient ( R 2 ) > 0.997. The maximum F - removal efficiency was found to be 80.8 mg/g at the optimum experimental condition of pH 7 and a dosage of 6 g/L at 30 min. The ANN model and experimental data provided a high degree of correlation ( R 2 = 0.9964), signifying the accuracy of the model in validating the adsorption experiments. The effects of interfering ions were studied with real F - water. The pseudo-second-order kinetic model showed a good fit to the equilibrium dataset. The performance of the adsorbent was also found satisfactory with field samples and can be considered a potential adsorbent for aqueous defluoridation.

Application of artificial neural network for prediction of fluoride removal efficiency using neutralized activated red mud from aqueous medium in a continuous fixed bed column.

The present research work approaches the removal of fluoride from aqueous medium using neutralized activated red mud (NARM) in a continuous fixed bed column. Artificial neural network (ANN) technique was applied effectively for optimization of the model for the practicability of the removal process. The consequences of various experimental variables, like bed length, adsorbate concentration, experimental time, and adsorbate solution flow rate are studied to know the breakthrough point and saturation times. The highest removal potentiality of NARM was considered to be 3.815 mg g-1 of F- in the bed height of 15 cm, starting concentration 1 ppm, susceptible time 120 min, adsorbate solution flow rate 0.5 mL min-1, and constant room temperature, respectively. Bohart-Adams and Thomas models were considered to describe the fixed bed column effect to the bed height and adsorbate concentrations. The experimental data were applied to a back propagation (BP) learning algorithm programme with a four-seven-one architecture model. The artificial neural network model was considered to be functioning correctly as absolute relative percentage error throughout the learning period. Differentiation between the predicted outcomes from ANN model and actual results from experimental analysis affords a high degree of correlation (R2 = 0.998) stipulating that the model was able to predict the adsorption efficiency. Experimented adsorbent materials were characterized using different instrumental analysis that is scanning electron microscopy-energy dispersive X-ray spectroscopy (SEM-EDS), Fourier transform infrared spectroscopy (FTIR), and X-ray diffraction (XRD).

A cross-sectional assessment of burnout presence among pharmacy professionals at Kathmandu Valley, Nepal.

Introduction: Burnout is a significant concern among healthcare professionals, including pharmacists, as it can lead to adverse effects on their well-being, job satisfaction, and patient care delivery. However, no previous study was conducted among pharmacy professionals in Nepal to assess their burnout cases. This study aimed to evaluate burnout presence and explore its associated factors among pharmacy professionals in Nepal. Methods: A cross-sectional study was conducted among pharmacy professionals of Kathmandu Valley, Nepal. The validated Burnout Assessment Tool measured burnout across multiple domains. Data on demographic and work-related characteristics were also collected. Descriptive statistics and Chi-square tests were used to analyze the data and identify significant associations among the variables. Results: Most participants were in the age group of 21-30 (64.7%), had a graduate degree (47.3%), and worked in hospital pharmacy settings (49.1%). Exhaustion was the most common (39.7%) burnout experienced, while mental distance and emotional and cognitive impairment were reported in one-fourth of the participants. Alternatively, only one in five participants showed secondary symptoms of burnout. Gender, working hours, exercise frequency, and substance abuse were significantly associated with burnout domains. Conclusion: This study provides valuable insights into the prevalence and factors associated with burnout among pharmacy professionals in Nepal. The findings highlight the significance of addressing burnout in this crucial healthcare sector, with gender, exercise frequency, and substance use emerging as notable contributors. These results underscore the need for targeted interventions and support systems to promote the well-being of pharmacy professionals and ensure the continued delivery of high-quality healthcare services in Nepal.