In vitro-in silico pharmacology and chemistry of Stercularin, isolated from Sterculia diversifolia.

Stercularin is a coumarin, isolated from the ethyl acetate fraction of stem bark and leaves of S. diversifolia. Pharmacologically it is active against cancer, diabetes, and inflammation etc. The molecule is further screened for in vitro pharmacological activities. In addition, a detailed description on its drug likeness and pharmacokinetic profile has been established to further explore its fate as a drug candidate. Stercularin exhibited antiglycation, immunomodulatory, and leishmanicidal activity in three different in vitro models. The IC50 values obtained in these three assays were 80.22 ± 0.46 mg/ml, 12.8 ± 1.6 µg/ml, and 8.32 ± 0.42 µg/ml, respectively. In case of drug likeness evaluation, Stercularin has acceptable physicochemical properties and compliant with major drug likeness descriptors i.e., Lipinski rule, Pfizer rule, GSK rule, and "golden triangle". Accepting Lipinski rule implies the oral drug development of Stercularin. Pharmacokinetically, Stercularin is permeable to Caco-2 and MDCK cell lines. 'Boiled-egg' plot suggest intestinal route of absorption, blood brain barrier nonpermeating, and not affected by p-glycoprotein. Stercularin has high plasma protein binding with low free fraction circulating in the plasma. Stercularin proved to be the substrate and/or inhibitor of CYP 450 system with a moderate half-life and clearance rate to allow flexible dosing regimen. Finally, slight risk of toxicity exists for Stercularin, but not being limiting factors of drug knock out. A nature isolated Stercularin possess pharmacological activities and is predicted to have acceptable pharmacokinetic profile. Further drug development and in vivo studies are desirable for optimization.

Overprescription of antibiotics for treating hospitalized COVID-19 patients: A systematic review & meta-analysis.

Background: Empirical use of antibiotics was reported throughout the coronavirus disease of 2019 (COVID-19) pandemic; however, evidence of bacterial coinfection or secondary bacterial infection among COVID-19 patients was sparse. Antibiotic overprescription for COVID-19 patients without confirmed bacterial coinfection can increase antimicrobial resistance (AMR). The objective of this study is to assess the appropriateness of antibiotic use during COVID-19 by summarizing the frequency of antibiotic use among hospitalized COVID-19 and the frequency of antibiotic use in patients with COVID-19. Methods: A systematic search was conducted of the Embase, Medline, Web of Science, and Cochrane Library databases by generating search terms using the concepts of "COVID-19," "Bacterial Coinfection," "Secondary bacterial infection," and "Antimicrobial resistance" to identify studies reporting antibiotic prescription for hospitalized COVID-19 patients with or without bacterial coinfection. We excluded studies on outpatients, studies informed infection due to mechanical ventilation, and randomized controlled trials. The pooled estimate of the percentage of the total and confirmed appropriate antibiotic prescriptions provided to hospitalized COVID-19 patients was generated using a random effect meta-analysis with inverse variance weighting. The study protocol registration DOI is osf.io/d3fpm. Results: Of 157,623 participants from 29 studies (11 countries, 45 % women) included in our review, antibiotics were prescribed to 67 % of participants (CI 64 %-71 %, P < 0·001), of which 80 % (CI 76 %-83 %, P < 0·001) of prescriptions were for COVID-19 patients without confirmed bacterial coinfections. Antibiotic overprescription varied during different periods of the pandemic and between High-Income and Upper and Lower Middle-Income Countries. We found heterogeneity among the studies (I2 = 100 %). The risk of bias analysis showed that 100 % of the included studies had the proper sample framing, and we are at low risk of bias due to sampling. Discussion: We find greater than expected use of antibiotics to treat hospitalized COVID-19 patients without bacterial coinfections, which may contribute to AMR globally. Concrete guidelines for using antibiotics to treat COVID-19 patients, strict monitoring, and administering Antimicrobial Stewardship are needed to prevent overprescription.

Molecular Insights into the In Vivo Analgesic and Anti-Inflammatory Activity of Indomethacin Analogues.

The primary objective of this research was to identify and explore the most potent and efficacious cyclooxygenase inhibitors, utilizing indole acetic acid drugs as a lead molecule. To achieve this objective, various derivatives (2a-2c and 2e-2g) of the selected lead molecule, indomethacin, were synthesized using a reflux condensation process, targeting the hydroxyl group. The synthesized analogues were subjected to different spectroscopic procedures to determine their structure and confirm their analogues. These derivatives were further screened for acute toxicity and anti-nociceptive and anti-inflammatory activity using established protocols. Docking analysis was performed to evaluate the possible protein-ligand interaction. The test compounds were found to be safe at doses of 50, 75, 100, and 200 mg/kg, i.p. The pharmacological screening revealed that test compounds 2a-2f had a superior peripheral analgesic effect at a dose of 10 mg/kg, in comparison to the parent drug indomethacin, while compound 2g exhibited slightly lower activity at the same dose. The hot plate results showed lower central analgesic activity of the test compounds compared to the standard Tramal, but it was still significant. Anti-inflammatory results were significant, comparable to Diclofenac sodium and indomethacin, except for compounds 2b, 2c, and 2e at a dose of 10 mg/kg body weight. Molecular docking analysis demonstrated that the derived compounds had augmented negative binding energies (-149.39, -146.72, -160.85, -159.34, -140.03, and -150.91 KJ/mol) compared to the parent drugs (-141.07), which supported the research's theme of producing stronger derivatives of standard drugs with significant anti-nociceptive and anti-inflammatory potential. The derived compounds exhibited significant analgesic and anti-inflammatory activities and, therefore, have the potential to be studied further as new drug candidates for pain and inflammation.

Genome-wide analysis of the GDSL esterase/lipase family genes in Physcomitrium patens and the involvement of GELP31 in spore germination.

In plants, the ability to produce hydrophobic substances that would provide protection from dehydration was required for the transition to land. This genome-wide investigation outlines the evolution of GDSL-type esterase/lipase (GELP) proteins in the moss Physcomitrium patens and suggests possible functions of some genes. GELP proteins play roles in the formation of hydrophobic polymers such as cutin and suberin that protect against dehydration and pathogen attack. GELP proteins are also implicated in processes such as pollen development and seed metabolism and germination. The P. patens GELP gene family comprises 48 genes and 14 pseudogenes. Phylogenetic analysis of all P. patens GELP sequences along with vascular plant GELP proteins with reported functions revealed that the P. patens genes clustered within previously identified A, B and C clades. A duplication model predicting the expansion of the GELP gene family within the P. patens lineage was constructed. Expression analysis combined with phylogenetic analysis suggested candidate genes for functions such as defence against pathogens, cutin metabolism, spore development and spore germination. The presence of relatively fewer GELP genes in P. patens may reduce the occurrence of functional redundancy that complicates the characterization of vascular plant GELP genes. Knockout lines of GELP31, which is highly expressed in sporophytes, were constructed. Gelp31 spores contained amorphous oil bodies and germinated late, suggesting (a) role(s) of GELP31 in lipid metabolism in spore development or germination. Future knockout studies of other candidate GELP genes will further elucidate the relationship between expansion of the family and the ability to withstand the harsh land environment.

Assessing the Self-reported After Events Following Immunization of COVID-19 Vaccines in Turkey and Bangladesh.

Though mass vaccination programs helped to reduce the severity of the ongoing pandemic, various unwanted effects were reported in Turkey and Bangladesh after taking vaccines. The purpose of this study was to evaluate and compare the adverse effects of several vaccines in Turkey and Bangladesh and how the population of both countries prioritizes the continuation of vaccination compared to the side effects. An online survey with a pretest was conducted to gather data over the research period from July 10, 2021 to December 10, 2021. Finally, the questionnaire was shared with the mass population of Turkey and Bangladesh who have received at least one or two doses of the COVID-19 vaccines. The quality of the questionnaire was evaluated with Cronbach's alpha test. The study consisted of 1508 respondents from Bangladesh and 602 respondents from Turkey. Among the total 2110 respondents, 50.0% were male 66.8% were from the 18-30 years age range, and 77.5% reported living in the city area. Among all the respondents, 64.99% of those vaccinated in Bangladesh and 67.28% of those vaccinated in Turkey reported side effects after vaccinations. Participants receiving mRNA vaccines (Pfizer and Moderna) experienced the most side effects, with many reporting pain at the injection site in both nations. Following that, fever, body pain, and headache were common in Bangladesh, whereas body pain, fatigue, and arm numbness were common in Turkey. The study found no significant adverse events reported in Turkey and Bangladesh following the first and second doses of COVID-19 vaccination. These COVID-19 vaccines showed similar patterns of efficacy and safety during the short period of analysis. Vaccines from different manufacturers showed a non-significant level of adverse events during this binational AEFI approach to COVID-19 vaccines. More studies are recommended on the efficacy and safety of several vaccines to discover unexpected effects.

Towards adaptive technology in routine mental health care.

This paper summarizes the information technology-related research findings after 5 years with the INTROducing Mental health through Adaptive Technology project. The aim was to improve mental healthcare by introducing new technologies for adaptive interventions in mental healthcare through interdisciplinary research and development. We focus on the challenges related to internet-delivered psychological treatments, emphasising artificial intelligence, human-computer interaction, and software engineering. We present the main research findings, the developed artefacts, and lessons learned from the project before outlining directions for future research. The main findings from this project are encapsulated in a reference architecture that is used for establishing an infrastructure for adaptive internet-delivered psychological treatment systems in clinical contexts. The infrastructure is developed by introducing an interdisciplinary design and development process inspired by domain-driven design, user-centred design, and the person based approach for intervention design. The process aligns the software development with the intervention design and illustrates their mutual dependencies. Finally, we present software artefacts produced within the project and discuss how they are related to the proposed reference architecture. Our results indicate that the proposed development process, the reference architecture and the produced software can be practical means of designing adaptive mental health care treatments in correspondence with the patients' needs and preferences. In summary, we have created the initial version of an information technology infrastructure to support the development and deployment of Internet-delivered mental health interventions with inherent support for data sharing, data analysis, reusability of treatment content, and adaptation of intervention based on user needs and preferences.

Assessment of phytotoxic, genotoxic and enzyme inhibition potential of.

OBJECTIVE: To evaluate Sterculia diversifolia stem bark and leaves for phytotoxic, genotoxic and enzymes inhibition potential. METHODS: Phytotoxic activity of both stem bark and leaves were screened using Lemna minor. The genotoxic activity of Sterculia diversifolia stem bark and leaves extracts were tested using comet assay protocol while enzyme inhibition activity of crude extract and various fractions of both stem bark and leaves were evaluated using acetyl cholinesterase, lipoxygenase, ß-glu-curonidase, urease, xanthine oxidase and carbonic anhydrase. RESULTS: Phytotoxic activity showed significant results in dose dependant manner in both stem bark (ethyl acetate and n-butanol) and leaves (ethyl acetate, n-butanol and n-hexane) fractions. In genotoxic activity, dichloromethane fraction showed significant activity followed by ethyl acetate fraction. Acetyl cholinesterease inhibitory activity showed significant results in both stem bark and leaves fractions, while significant lipoxygenase inhibition was shown by ethyl acetate, dichloromethane, crude extract and n-hexane fractions of both stem bark and leaves. ß-glucuronidase, urease and carbonic anhydrase inhibitory activity showed highly significant results in ethyl acetate fraction of both stem bark and leaves, while xanthine oxidase inhibition was shown by dichloromethane fraction of stem bark and leaves extracts. CONCLUSIONS: This study emphasizes the important phytotoxic, genotoxic and enzyme inhibition effects of Sterculia diversifolia stem bark and leaves. Hence, it is clear that Sterculia diversifolia stem bark and leaves possess phytotoxic, genotoxic and enzyme inhibitory agents.

A Reference Architecture for Data-Driven and Adaptive Internet-Delivered Psychological Treatment Systems: Software Architecture Development and Validation Study.

BACKGROUND: Internet-delivered psychological treatment (IDPT) systems are software applications that offer psychological treatments via the internet. Such IDPT systems have become one of the most commonly practiced and widely researched forms of psychotherapy. Evidence shows that psychological treatments delivered by IDPT systems can be an effective way of treating mental health morbidities. However, current IDPT systems have high dropout rates and low user adherence. The primary reason is that the current IDPT systems are not flexible, adaptable, and personalized as they follow a fixed tunnel-based treatment architecture. A fixed tunnel-based architecture follows predefined, sequential treatment content for every patient, irrespective of their context, preferences, and needs. Moreover, current IDPT systems have poor interoperability, making it difficult to reuse and share treatment materials. There is a lack of development and documentation standards, conceptual frameworks, and established (clinical) guidelines for such IDPT systems. As a result, several ad hoc forms of IDPT models exist. Consequently, developers and researchers have tended to reinvent new versions of IDPT systems, making them more complex and less interoperable. OBJECTIVE: This study aimed to design, develop, and evaluate a reference architecture (RA) for adaptive systems that can facilitate the design and development of adaptive, interoperable, and reusable IDPT systems. METHODS: This study was conducted in collaboration with a large interdisciplinary project entitled INTROMAT (Introducing Mental Health through Adaptive Technology), which brings together information and communications technology researchers, information and communications technology industries, health researchers, patients, clinicians, and patients' next of kin to reach its vision. First, we investigated previous studies and state-of-the-art works based on the project's problem domain and goals. On the basis of the findings from these investigations, we identified 2 primary gaps in current IDPT systems: lack of adaptiveness and limited interoperability. Second, we used model-driven engineering and Domain-Driven Design techniques to design, develop, and validate the RA for building adaptive, interoperable, and reusable IDPT systems to address these gaps. Third, based on the proposed RA, we implemented a prototype as the open-source software. Finally, we evaluated the RA and open-source implementation using empirical (case study) and nonempirical approaches (software architecture analysis method, expert evaluation, and software quality attributes). RESULTS: This paper outlines an RA that supports flexible user modeling and the adaptive delivery of treatments. To evaluate the proposed RA, we developed an open-source software based on the proposed RA. The open-source framework aims to improve development productivity, facilitate interoperability, increase reusability, and expedite communication with domain experts. CONCLUSIONS: Our results showed that the proposed RA is flexible and capable of adapting interventions based on patients' needs, preferences, and context. Furthermore, developers and researchers can extend the proposed RA to various health care interventions.

Forecasting the incidence of dengue in Bangladesh-Application of time series model.

Background: Dengue is an alarming public health concern in terms of its preventive and curative measures among people in Bangladesh; moreover, its sudden outbreak created a lot of suffering among people in 2018. Considering the greater burden of disease in larger epidemic years and the difficulty in understanding current and future needs, it is highly needed to address early warning systems to control epidemics from the earliest. Objective: The study objective was to select the most appropriate model for dengue incidence and using the selected model, the authors forecast the future dengue outbreak in Bangladesh. Methods and Materials: This study considered a secondary data set of monthly dengue occurrences over the period of January 2008 to January 2020. Initially, the authors found the suitable model from Autoregressive Integrated Moving Average (ARIMA), Error, Trend, Seasonal (ETS) and Trigonometric seasonality, Box-Cox transformation, ARMA errors, Trend and Seasonal (TBATS) models with the help of selected model selection criteria and finally employing the selected model make forecasting of dengue incidences in Bangladesh. Results: Among ARIMA, ETS, and TBATS models, the ARIMA model performs better than others. The Box-Jenkin's procedure is applicable here and it is found that the best-selected model to forecast the dengue outbreak in the context of Bangladesh is ARIMA (2,1,2). Conclusion: Before establishing a comprehensive plan for future combating strategies, it is vital to understand the future scenario of dengue occurrence. With this in mind, the authors aimed to select an appropriate model that might predict dengue fever outbreaks in Bangladesh. The findings revealed that dengue fever is expected to become more frequent in the future. The authors believe that the study findings will be helpful to take early initiatives to combat future dengue outbreaks.

Bioactive Compounds and Diabetes Mellitus: Prospects and Future Challenges.

Diabetes mellitus is a metabolic condition that influences the endocrine framework. Hyperglycemia and hyperlipidemia are two of the most widely recognized metabolic irregularities in diabetes and two of the most well-known reasons for diabetic intricacies. Diabetes mellitus is a persistent illness brought about by metabolic irregularities in hyperglycemic pancreatic cells. Hyperglycemia can be brought about by an absence of insulin-producing beta cells in the pancreas (Type 1 diabetes mellitus) or inadequate insulin creation that does not work effectively (Type 2 diabetes mellitus). Present diabetes medication directs blood glucose levels in the systemic circulation to the typical levels. Numerous advanced prescription medicines have many negative results that can bring about unexpected severe issues during treatment of the bioactive compound from a different source that is beneficially affected by controlling and adjusting metabolic pathways or cycles. Moreover, a few new bioactive medications disengaged from plants have shown antidiabetic action with more noteworthy adequacy than the oral hypoglycemic agent that specialists have utilized in clinical treatment lately. Since bioactive mixtures are collected from familiar sources, they have a great activity in controlling diabetes mellitus. This study discusses bioactive compounds, their activity in managing diabetes mellitus, and their prospects. Though bioactive compounds have many health-beneficial properties, adequate clinical studies still need to acknowledge that they effectively manage diabetes mellitus.

Deep Learning-Enabled Technologies for Bioimage Analysis.

Deep learning (DL) is a subfield of machine learning (ML), which has recently demonstrated its potency to significantly improve the quantification and classification workflows in biomedical and clinical applications. Among the end applications profoundly benefitting from DL, cellular morphology quantification is one of the pioneers. Here, we first briefly explain fundamental concepts in DL and then we review some of the emerging DL-enabled applications in cell morphology quantification in the fields of embryology, point-of-care ovulation testing, as a predictive tool for fetal heart pregnancy, cancer diagnostics via classification of cancer histology images, autosomal polycystic kidney disease, and chronic kidney diseases.

Diversity-Aware Anonymization for Structured Health Data.

Patients' health data are captured by local hospital facilities, which has the potential for data analysis. However, due to privacy and legal concerns, local hospital facilities are unable to share the data with others which makes it difficult to apply data analysis and machine learning techniques over the health data. Analysis of such data across hospitals can provide valuable information to health professionals. Anonymization methods offer privacy-preserving solutions for sharing data for analysis purposes. In this paper, we propose a novel method for anonymizing and sharing data that addresses the record-linkage and attribute-linkage attack models. Our proposed method achieves anonymity by formulating and solving this problem as a constrained optimization problem which is based on the k-anonymity, l-diversity, and t-closeness privacy models. The proposed method has been evaluated with respect to the utility and privacy of data after anonymization in comparison to the original data.

Monitoring Motor Activity Data for Detecting Patients' Depression Using Data Augmentation and Privacy-Preserving Distributed Learning.

Wearable devices are currently being considered to collect personalized physiological information, which is lately being used to provide healthcare services to individuals. One application is detecting depression by utilization of motor activity signals collected by the ActiGraph wearable wristbands. However, to develop an accurate classification model, we require to use a sufficient volume of data from several subjects, taking the sensitivity of such data into account. Therefore, in this paper, we present an approach to extract classification models for predicting depression based on a new augmentation technique for motor activity data in a privacy-preserving fashion. We evaluate our approach against the state-of-the-art techniques and demonstrate its performance based on the mental health datasets associated with the Norwegian INTROducing Mental health through Adaptive Technology (INTROMAT) Project.

Characterization of ESBL-Producing Escherichia coli and Klebsiella pneumoniae Isolated from Clinical Samples in a Northern Portuguese Hospital: Predominance of CTX-M-15 and High Genetic Diversity.

BACKGROUND: Enterobacteriaceae are major players in the spread of resistance to ß-lactam antibiotics through the action of CTX-M ß-lactamases. We aimed to analyze the diversity and genetic characteristics of ESBL-producing Escherichia coli and Klebsiella pneumoniae isolates from patients in a Northern Portuguese hospital. METHODS: A total of 62 cefotaxime/ceftazidime-resistant E. coli (n = 38) and K. pneumoniae (n = 24) clinical isolates were studied. Identification was performed by MALDI-TOF MS. Antimicrobial susceptibility testing against 13 antibiotics was performed. Detection of ESBL-encoding genes and other resistance genes, phylogenetic grouping, and molecular typing (for selected isolates) was carried out by PCR/sequencing. RESULTS: ESBL activity was detected in all 62 E. coli and K. pneumoniae isolates. Most of the ESBL-producing E. coli isolates carried a blaCTX-M gene (37/38 isolates), being blaCTX-M-15 predominant (n = 32), although blaCTX-M-27 (n = 1) and blaCTX-M-1 (n = 1) were also detected. Two E. coli isolates carried the blaKPC2/3 gene. The lineages ST131-B2 and ST410-A were detected among the ESBL-producing blood E. coli isolates. Regarding the 24 ESBL-producing K. pneumoniae isolates, 18 carried a blaCTX-M gene (blaCTX-M-15, 16 isolates; blaCTX-M-55, 2 isolates). All K. pneumoniae isolates carried blaSHV genes, including ESBL-variants (blaSHV-12 and blaSHV-27, 14 isolates) or non-ESBL-variants (blaSHV-11 and blaSHV-28, 10 isolates); ten K. pneumoniae isolates also carried the blaKPC2/3 gene and showed imipenem-resistance. ESBL-positive E. coli isolates were ascribed to the B2 phylogenetic group (82%), mostly associated with ST131 lineage and, at a lower rate, to ST410/A. Regarding K. pneumoniae, the three international lineages ST15, ST147, and ST280 were detected among selected isolates. CONCLUSIONS: Different ESBL variants of CTX-M (especially CTX-M-15) and SHV-type (specially SHV-12) were detected among CTX/CAZRE. coli and K. pneumoniae isolates, in occasions associated with carbapenemase genes (blaKPC2/3 gene).

Antimicrobial Resistance Genes and Diversity of Clones among Faecal ESBL-Producing Escherichia coli Isolated from Healthy and Sick Dogs Living in Portugal.

The purpose of this study was to analyse the prevalence and genetic characteristics of ESBL and acquired-AmpC (qAmpC)-producing Escherichia coli isolates from healthy and sick dogs in Portugal. Three hundred and sixty-one faecal samples from sick and healthy dogs were seeded on MacConkey agar supplemented with cefotaxime (2 µg/mL) for cefotaxime-resistant (CTXR) E. coli recovery. Antimicrobial susceptibility testing for 15 antibiotics was performed and the ESBL-phenotype of the E. coli isolates was screened. Detection of antimicrobial resistance and virulence genes, and molecular typing of the isolates (phylogroups, multilocus-sequence-typing, and specific-ST131) were performed by PCR (and sequencing when required). CTXRE. coli isolates were obtained in 51/361 faecal samples analysed (14.1%), originating from 36/234 sick dogs and 15/127 healthy dogs. Forty-seven ESBL-producing E. coli isolates were recovered from 32 sick (13.7%) and 15 healthy animals (11.8%). Different variants of blaCTX-M genes were detected among 45/47 ESBL-producers: blaCTX-M-15 (n = 26), blaCTX-M-1 (n = 10), blaCTX-M-32 (n = 3), blaCTX-M-55 (n = 3), blaCTX-M-14 (n = 2), and blaCTX-M-variant (n = 1); one ESBL-positive isolate co-produced CTX-M-15 and CMY-2 enzymes. Moreover, two additional CTXR ESBL-negative E. coli isolates were CMY-2-producers (qAmpC). Ten different sequence types were identified (ST/phylogenetic-group/ß-lactamase): ST131/B2/CTX-M-15, ST617/A/CTX-M-55, ST3078/B1/CTX-M-32, ST542/A/CTX-M-14, ST57/D/CTX-M-1, ST12/B2/CTX-M-15, ST6448/B1/CTX-M-15 + CMY-2, ST5766/A/CTX-M-32, ST115/D/CMY-2 and a new-ST/D/CMY-2. Five variants of CTX-M enzymes (CTX-M-15 and CTX-M-1 predominant) and eight different clonal complexes were detected from canine ESBL-producing E. coli isolates. Although at a lower rate, CMY-2 ß-lactamase was also found. Dogs remain frequent carriers of ESBL and/or qAmpC-producing E. coli with a potential zoonotic role.

Effect of nurse led education on anxiety level among coronary artery bypass grafting pre-operative patients.

OBJECTIVE: The objective of this study was to identify the effect of nurse-led pre-operative education in minimizing the level of anxiety among patients waiting for Coronary Artery Bypass Grafting. METHODS: This experimental study was accomplished at National Institute of Cardiovascular Diseases. Out of 136, there were 80 patients identified with the presence of anxiety using AKUADS 4 to 6 weeks before CABG. Data were collected by the primary researcher from July 2016 to December 2016. Patients with anxiety were divided into experimental and non-experimental groups. After pre-operative education to the experimental group, patients from both groups were re-assessed for anxiety level one week before CABG. RESULTS: Data were assessed through non-parametric Wilcoxon sign ranked and Mann Whitney tests. It was found that anxiety reduced significantly in post-assessment among experimental group participants. No significant difference was found for pre-anxiety assessment between experimental and non-experimental groups. It was noted that post assessment anxiety differed significantly between experimental and non-experimental groups. CONCLUSION: Pre-operative education was found effective to decrease level of anxiety among patients waiting for CABG. There was no significant difference between pre and post assessment among patients from non-experimental group.

Chemical constituent with cytotoxicity from Sterculia diversifolia.

One new coumarin (stercularin), along with eleven known compounds, was isolated for the first time from ethyl acetate fraction of Sterculia diversifolia. The structures of isolated compounds were characterized by spectroscopic techniques such as EIMS, 1H-NMR and 13C-NMR. Compound 1 showed significant cytotoxicity by brine shrimp lethality assay (LD50: 8.00 µg/ml) and PC-3 cell lines protocol (IC50: 3.92 ± 0.20 µg/ml), respectively.

Machine learning-enabled multiplexed microfluidic sensors.

High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.

Reactive oxygen species are required for spore wall formation in Physcomitrella patens.

A robust spore wall was a key requirement of terrestrialization by early plants. Sporopollenin in spore and pollen grain walls is thought to be polymerized and cross-linked to other macromolecular components partly through oxidative processes involving H2O2. Therefore, we investigated effects of scavengers of reactive oxygen species (ROS) on formation of spore walls in the moss, Physcomitrella patens. Exposure of sporophytes, containing spores in the process of forming walls, to ascorbate, dimethylthiourea or 4-hydroxy-TEMPO prevented normal wall development in a dose, chemical and stage-dependent manner. Mature spores, exposed while developing to a ROS scavenger, burst when mounted in water on a flat slide under a coverslip (a phenomenon we named "augmented osmolysis" since they did not burst in phosphate-buffered saline or in water on a depression slide). Additionally, walls of exposed spores were more susceptible to alkaline hydrolysis than those of control spores and some were characterized by discontinuities in the exine, anomalies in perine spine structure, abnormal intine and aperture and occasionally wall shedding. Our data support involvement of oxidative cross-linking in spore wall development, including sporopollenin polymerization or deposition, as well as a role for ROS in intine/aperture development.

A Model Driven Approach to the Design of a Gamified e-Learning System for Clinical Guidelines.

Clinical practice guidelines (CPGs) are indispensable in the practice of evidence-based medicine. However, the cost of effective CPG dissemination strategies is prohibitive and not cost-effective. Therefore, scalable strategies using available technology are needed. We describe a formal model-driven approach to design a gamified e-learning system for clinical guidelines. We employ gamification to increase user motivation and engagement in the training of guideline content. Our approach involves the use of models for different aspects of the system, an entity model for the clinical domain, a workflow model for the clinical processes and a game model to manage the training sessions. A game engine instantiates a training session by coupling the workflow and entity models to automatically generate questions based on the data in the model instances. Our approach is flexible and adaptive as it allows for easy updates of the guidelines, integration with different device interfaces and representation of any guideline.