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In a moderate seismic activity, relying solely on the static force method to estimate the seismic force assessing the vulnerability and behaviour of RCC buildings under seismic loads is insufficient. The seismic response of the building system is highly influenced by the analysis method adopted. In the past years, static approaches were preferred due to their simplicity, although these methods provided safe design, they may have been overly cautious. Dynamic analysis, on the other hand, is a fundamental aspect of structural mechanics, that describes and predicts the behaviour of structures subjected to dynamic loads. Dynamic load includes wind, imposed load, earthquake, etc. Dynamic analysis techniques, such as modal analysis, time history analysis, and dynamic displacement analysis. RCC structures are mainly designed to resist static load. This process of ignoring dynamic events sometimes becomes the cause of disasters, especially in the case of earthquakes. This research paper provides information on the comparison of various parameters in static and dynamic analysis. Two RCC multistoried residential buildings (g+10 and g+25) acting on seismic zone IV are analyzed using the static analysis method and response spectrum method with ETABS 16. The research investigates the effect on various structural parameters such as story displacement, story drift, and support reactions. The results are compared and evaluated to assess the performance of the response spectrum method over the static analysis method.

Entamoeba moshkovskii, according to recent studies, appears to exert a more significant impact on diarrhoeal infections than previously believed. The efficient identification and genetic characterization of E. moshkovskii isolates from endemic areas worldwide are crucial for understanding the impact of parasite genomes on amoebic infections. In this study, we employed a multilocus sequence typing system to characterize E. moshkovskii isolates, with the aim of assessing the role of genetic variation in the pathogenic potential of E. moshkovskii. We incorporated 3 potential genetic markers: KERP1, a protein rich in lysine and glutamic acid; amoebapore C (apc) and chitinase. Sequencing was attempted for all target loci in 68 positive E. moshkovskii samples, and successfully sequenced a total of 33 samples for all 3 loci. The analysis revealed 17 distinct genotypes, labelled M1–M17, across the tested samples when combining all loci. Notably, genotype M1 demonstrated a statistically significant association with diarrhoeal incidence within E. moshkovskii infection (P = 0.0394). This suggests that M1 may represent a pathogenic strain with the highest potential for causing diarrhoeal symptoms. Additionally, we have identified a few single-nucleotide polymorphisms in the studied loci that can be utilized as genetic markers for recognizing the most potentially pathogenic E. moshkovskii isolates. In our genetic diversity study, the apc locus demonstrated the highest Hd value and π value, indicating its pivotal role in reflecting the evolutionary history and adaptation of the E. moshkovskii population. Furthermore, analyses of linkage disequilibrium and recombination within the E. moshkovskii population suggested that the apc locus could play a crucial role in determining the virulence of E. moshkovskii.

Purpose Epidemiological studies on amoebic infections are complicated due to morphologically identical and clinically important Entamoeba species. Therefore, newer, simpler, and more economical diagnostic techniques are required for differentiating clinically important Entamoeba species. Methods We developed a single-round multiplex PCR assay to identify E. histolytica , E. moshkovskii , E. dispar , E. bangladeshi , and E. coli . Primers were designed based on variations in 18 S rRNA sequences. Sensitivity and specificity were assessed using known positive and negative samples. Furthermore, we screened 472 diarrheal samples using this technique alongside the reference PCR method to evaluate its suitability for epidemiological studies and clinical diagnosis. DNA sequencing and phylogenetic analysis of the isolates were conducted. All statistical analyses of the data were performed using GraphPad Prism. Results The designed primers successfully yielded species-specific PCR products of different sizes as expected. We did not observe any non-specific amplifications of the primer set. The diagnostic performance was also convincing. After screening clinical samples using the method, we observed that 2.33% ( n  = 11) tested positive for E. moshkovskii , 1.06% ( n  = 5) tested positive for E. histolytica , and 0.85% ( n  = 4) tested positive for E. bangladeshi in the studied area. DNA sequencing further confirmed the identified species. The constructed phylogenetic tree also demonstrated clear separation of the detected species lineages. Conclusion The study suggests the multiplex PCR assay could be a reliable diagnostic tool for amoebic infections. This study is particularly significant as it marks the first reported occurrence of E. bangladeshi since its documentation in South Africa and its native Bangladesh.

Ayurveda provided many innovative solutions during the COVID-19 pandemic. It is important to explore the phytochemical constituents in effective Ayurvedic formulations. The main aim of the work is to identify active phytoconstituents from five Ayurvedic formulations employed in treating COVID-19 patients in an Ayurvedic hospital. Pharmacoinformatics technologies were employed in this study. The chemoinformatics, 3D molecular structure building, and molecular docking of 967 compounds on eight different macromolecular viral targets associated with SARS-CoV-2 were carried out using GLIDE software. Molecular dynamics simulations were also performed. SwissADME web server was employed to determine the physicochemical, lipophilicity and absorption, distribution, metabolism, and excretion (ADME) parameters. The molecular docking results indicate that quercetin-3-O-arabinoglucoside, quercetin-3,7-O-diglucoside, glycyrrhizin, calceolarioside B, mucic acid-2-gallate, protodioscin and indioside D are the phytochemicals which effectively bind to eight of the proteins of SARS-CoV-2 virus and these may be treated as new lead compounds for multi-target drug discovery for SARS-CoV-2 inhibition. MD simulations helped in identifying five leads out of seven chosen from docking analysis. Five Ayurvedic formulations were used to treat respiratory illnesses associated with COVID-19. Five phytoconstituents present in these formulations were identified as leads by employing pharmacoinformatics techniques.

Arogyamrita Kwath (AMK) is a polyherbal decoction comprising ten medicinal plants, viz., Albizia lebbeck, Andrographis paniculata, Tinospora cordifolia, Adhatoda vasica, Solanum xanthocarpum, Curcuma longa, Glycyrrhiza glabra, Terminalia bellirica, Withania somnifera and Trachyspermum ammi. The plants of the AMK formulation are traditionally used for the treatment of inflammation and respiratory ailments, but no scientific evidence has been reported so far for this formulation. To evaluate anti-inflammatory activity of AMK formulation in vitro and its fractions and to predict in silico anti-viral activity of identified potential phytoconstituents. The MTT cell cytotoxicity assay, nitric oxide (NO) inhibition assay and cytokines assay were carried out at concentrations 100 and 200 μg/mL. The phytoconstituents were identified by UPLC-PDA and UPLC-HRMS analyses. For pharmacoinformatics study molecular docking and molecular dynamics methods were used. The study revealed that AMK significantly inhibited NO in comparison to dexamethasone (100 μg/mL) and pro-inflammatory cytokines in RAW264.7 cells. The three fractions, n-hexane, EtOAc and n-BuOH prepared from the AMK formulation were non-cytotoxic against RAW264.7 murine macrophage cells during MTT cytotoxicity assay and showed satisfactory results during cytokines assay. Ethyl acetate fraction contains active phytoconstituents in appreciable quantities. 16 phytoconstituents have been identified by UPLC-HRMS analysis in the formulation and four phytocompounds were quantified by UPLC-PDA. Molecular dynamics study helped in identifying two macromolecular targets (viral replicase and the membrane protein), which are relatively more important. In the present study, anti-inflammatory activity of AMK was evaluated and the claimed anti-viral property was re-confirmed by molecular modelling in this work. The results clearly established that AMK showed remarkable anti-inflammatory and anti-viral activities. •AMK formulation is a decoction used for the treatment of respiratory ailments.•Sixteen compounds were identified by UPLC-HRMS analysis in the AMK formulation.•AMK formulation significantly lowered NF-kB and TNF-α levels in concentration-dependent manner.•Quercetin and vasicine, present in AMK showed significant binding affinity against seven viral targets.•MD simulations revealed that viral replicase and membrane protein were found to be the preferred targets for AMK formulation.

Acute insults from viruses, infections, or alcohol are established causes of decompensation leading to acute-on-chronic liver failure (ACLF). Information regarding drugs as triggers of ACLF is lacking. We examined data regarding drugs producing ACLF and analyzed clinical features, laboratory characteristics, outcome, and predictors of mortality in patients with drug-induced ACLF. We identified drugs as precipitants of ACLF among prospective cohort of patients with ACLF from the Asian Pacific Association of Study of Liver (APASL) ACLF Research Consortium (AARC) database. Drugs were considered precipitants after exclusion of known causes together with a temporal association between exposure and decompensation. Outcome was defined as death from decompensation. Of the 3,132 patients with ACLF, drugs were implicated as a cause in 329 (10.5%, mean age 47 years, 65% men) and other nondrug causes in 2,803 (89.5%) (group B). Complementary and alternative medications (71.7%) were the commonest insult, followed by combination antituberculosis therapy drugs (27.3%). Alcoholic liver disease (28.6%), cryptogenic liver disease (25.5%), and non-alcoholic steatohepatitis (NASH) (16.7%) were common causes of underlying liver diseases. Patients with drug-induced ACLF had jaundice (100%), ascites (88%), encephalopathy (46.5%), high Model for End-Stage Liver Disease (MELD) (30.2), and Child-Turcotte-Pugh score (12.1). The overall 90-day mortality was higher in drug-induced (46.5%) than in non-drug-induced ACLF (38.8%) (P = 0.007). The Cox regression model identified arterial lactate (P < 0.001) and total bilirubin (P = 0.008) as predictors of mortality. Drugs are important identifiable causes of ACLF in Asia-Pacific countries, predominantly from complementary and alternative medications, followed by antituberculosis drugs. Encephalopathy, bilirubin, blood urea, lactate, and international normalized ratio (INR) predict mortality in drug-induced ACLF.

The relationship between ammonia and liver-related complications (LRCs) in acute-on-chronic liver failure (ACLF) patients is not clearly established. This study aimed to evaluate the association between ammonia levels and LRCs in patients with ACLF. The study also evaluated the ability of ammonia in predicting mortality and progression of LRCs. The study prospectively recruited ACLF patients based on the APASL definition from the ACLF Research Consortium (AARC) from 2009 to 2019. LRCs were a composite endpoint of bacterial infection, overt hepatic encephalopathy (HE), and ascites. A total of 3871 cases were screened. Of these, 701 ACLF patients were enrolled. Patients with LRCs had significantly higher ammonia levels than those without. Ammonia was significantly higher in patients with overt HE and ascites, but not in those with bacterial infection. Multivariate analysis found that ammonia was associated with LRCs. Additionally, baseline arterial ammonia was an independent predictor of 30-day mortality, but it was not associated with the development of new LRCs within 30 days. In summary, baseline arterial ammonia levels are associated with 30-day mortality and LRCs, mainly overt HE and ascites in ACLF patients.

Prediction of the stage of cancer plays an important role in planning the course of treatment and has been largely reliant on imaging tools which do not capture molecular events that cause cancer progression. Gene-expression data–based analyses are able to identify these events, allowing RNA-sequence and microarray cancer data to be used for cancer analyses. Breast cancer is the most common cancer worldwide, and is classified into four stages — stages 1, 2, 3, and 4 [2]. While machine learning models have previously been explored to perform stage classification with limited success, multi-class stage classification has not had significant progress. There is a need for improved multi-class classification models, such as by investigating deep learning models. Gene-expression-based cancer data is characterised by the small size of available datasets, class imbalance, and high dimensionality. Class balancing methods must be applied to the dataset. Since all the genes are not necessary for stage prediction, retaining only the necessary genes can improve classification accuracy. The breast cancer samples are to be classified into 4 classes of stages 1 to 4. Invasive ductal carcinoma breast cancer samples are obtained from The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) datasets and combined. Two class balancing techniques are explored, synthetic minority oversampling technique (SMOTE) and SMOTE followed by random undersampling. A hybrid feature selection pipeline is proposed, with three pipelines explored involving combinations of filter and embedded feature selection methods: Pipeline 1 — minimum-redundancy maximum-relevancy (mRMR) and correlation feature selection (CFS), Pipeline 2 — mRMR, mutual information (MI) and CFS, and Pipeline 3 — mRMR and support vector machine–recursive feature elimination (SVM-RFE). The classification is done using deep learning models, namely deep neural network, convolutional neural network, recurrent neural network, a modified deep neural network, and an AutoKeras generated model. Classification performance post class-balancing and various feature selection techniques show marked improvement over classification prior to feature selection. The best multiclass classification was found to be by a deep neural network post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with a Cohen-Kappa score of 0.303 and a classification accuracy of 53.1%. For binary classification into early and late-stage cancer, the best performance is obtained by a modified deep neural network (DNN) post SMOTE and random undersampling, and feature selection using mRMR and recursive feature elimination, with an accuracy of 81.0% and a Cohen-Kappa score (CKS) of 0.280. This pipeline also showed improved multiclass classification performance on neuroblastoma cancer data, with a best area under the receiver operating characteristic (auROC) curve score of 0.872, as compared to 0.71 obtained in previous work, an improvement of 22.81%. The results and analysis reveal that feature selection techniques play a vital role in gene-expression data-based classification, and the proposed hybrid feature selection pipeline improves classification performance. Multi-class classification is possible using deep learning models, though further improvement particularly in late-stage classification is necessary and should be explored further.

Purpose This paper will consider the hypothesis about the promotion of retention of Generation Y and Generation Z nurses in India as a means of Flexible Work Arrangements (FWA), flexitime, self-rostering, and compressed schedules. The study is the Delhi-National Capital Region (Delhi, Gurugram, Noida/Gautam Buddha Nagar) a densely concentrated healthcare area with non-homogeneous types of hospitals, multi-shift times, shortages of early-career nurses, and high rates of turnover that increase the cost of staffing and break continuity of care. Design/methodology/approach Our design is qualitative and phenomenological, and, therefore, we held semi-structured interviews with 25 frontline nurses in the public, private, and semi-public hospitals in urban and semi-urban areas of Delhi-NCR. The interviews were conducted in English or Hindi, as was preferred. Reflexive thematic analysis of data in NVivo informed by Self-Determination Theory, Psychological Empowerment and Organisational Flexibility scholarship was applied. Findings It came up with four themes, namely: (1) empowerment with scheduling autonomy; (2) work-life integration; (3) managerial and cultural barriers; and (4) psychological outcomes influencing retention intentions. Being able to access FWAs, in particular, schedule control and predictable rotations were linked to greater job satisfaction, reduced burnout, and enhanced organisational commitment, which supported turnover intentions. Control was limited through understaffing, scepticism amongst managers and instability of institutions, especially in high-throughput wards. Relevant gains comprise reduced cost in terms of decreased recruitment/induction processes and patient care in terms of staffing and operational stability. Research limitations/implications Results indicate a small, cross-sectional, qualitative sample and self-reports. Next-generation studies should use a mixed-method or longitudinal study design as objective retention and patient-care outcomes to ascertain causal paths. Practical implications It is recommended to develop a gradual implementation strategy: explicit FWA policy guidelines, management development of capabilities, AI-based rostering/demand prediction, and motivated pilots holding performance-based incentives and preset KPIs. Social implications FWAs have the potential to enhance the sustainability of the workforce, employment that is gender-inclusive, and patient experience, particularly in resource-deprived public and semi-urban environments.