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Objective: Pelvic floor dysfunctions (pfds) such as urinary incontinence, pelvic organ prolapse, and vaginismus are very common and are affecting the quality of life of the female suffering through it. The pelvic floor muscles can be in spasm, thus resulting in the inability to relax or the pelvic floor muscles can be extremely weak or loose, resulting in the inability to contract strongly and completely. The spasmodic pelvic floor can result in vaginismus, dyspareunia, chronic pelvic pain, constipation, etc., Moreover, a weak pelvic floor can lead to issues such as urinary incontinence, pelvic organ prolapse, and sexual dysfunction. There is a scarcity of a valid and reliable scale that assesses both the components of pfd. Subjects and Methods: The present study was conducted on 1349 participants which were divided into three groups on the basis of their symptoms. Group A – No symptoms (n = 439), Group B – Hypotonus pfd (n = 658), and Group C – Hypertonus pfd (n = 252). Examination: Vaginal examination to assess the pelvic floor muscles relaxation and contraction was done by Het’s Manual Muscle Testing (MMT). Inter rater reliability was assessed by comparing the data of two independent examiners on the same day. The intra rater reliability was assessed by comparing the data of test and retest at an interval of 1 week. Results: The intra rater reliability at 95% confidence interval (CI) was 0.92 – Normal pelvic floor function, 0.96 – Hypotonus pfd, and 0.97 – Hypertonus pfd. Moreover, inter rater reliability at 95% CI was 0.93 – normal pelvic floor function, 0.95 for hypotonus pfd, and 0.98 – hypertonus pfd. Conclusions: This study concludes Het’s MMT is a highly reliable manual muscle testing tool to assess the pelvic floor muscles.

Abstract The climate crisis seriously threatens Central European forests and their ecosystem functions. There are indications that old-growth forests are relatively resilient and efficient in micro-climatic regulation during extreme climatic conditions. This study evaluates five well-protected old beech forests in Germany, part of a UNESCO World Heritage Site. We examined temperature dynamics and vitality in core, buffer, and border zones during hot days from 2017 to 2023, using Landsat 8 and 9 imageries to assess Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI), alongside on-site Air Temperature (AT) measurements. Our findings reveal that all five forests were impacted by recent extreme heat events, with core zones remaining cooler and more vital, followed by buffer zones. Temperature-regulating patterns varied with landscape characteristics and the surrounding matrixes. We observed a site-dependent cooling effect of the forest interior that increased with higher LST. Our study highlights the value of old-growth forests and recommends increasing effective protection around mature forests, establishing corridors between isolated patches, and creating mosaics in managed landscapes that include unmanaged areas capable of developing into old-growth ecosystems.

According to the World health Organization there are a group of 20 diverse infectious Neglected Tropical Disease (NTD) conditions that primarily affect populations in low-income and developing regions. Despite the limited attention and funding compared to other health concerns, significant efforts to develop drugs for treating and controlling NTDs have been made. However, there is room for developing NTD drugs with improved safety, efficacy and ecotoxicological profiles. In order to facilitate this, we have adapted our existing validated data-driven workflows for understanding disease comorbidity to systematically evaluate the approved drugs that target the major World Health Organization defined NTDs. The foundation for this work comprised assembling the physicochemical, biological and clinical properties of each NTD drug and identifying patterns that reveal the underlying cause of their efficacy and side-effect profiles. Subsequently, computational methods were employed to identify analogs with potentially improved profiles and validated in a case study focusing on the teratogenic antileishmanial drug miltefosine. The wider impact of NTD drugs with regards to a One Health cross-disciplinary perspective at the human-animal-environment interface are also discussed.

Abstract In the pharmaceutical industry, the patent protection of drugs and medicines is accorded importance because of the high costs involved in the development of novel drugs. Over the years, researchers have analyzed patent documents to identify freedom-to-operate spaces for novel drug candidates. To assist this, several well-established public patent document data repositories have enabled automated methodologies for extracting information on therapeutic agents. In this study, we delve into one such publicly available patent database, SureChEMBL, which catalogues patent documents related to life sciences. Our exploration begins by identifying patent compounds across public chemical data resources, followed by pinpointing sections in patent documents where the chemical annotations were found. Next, we exhibit the potential of compounds to serve as drug candidates by evaluating their conformity to drug-likeness criteria. Lastly, we examine the drug development stage reported for these compounds to understand their clinical success. In summary, our investigation aims at providing a comprehensive overview of the patent compounds catalogued in SureChEMBL, assessing their relevance to pharmaceutical drug discovery.

Objective The European Health Data Space (EHDS) shapes the digital transformation of healthcare in Europe. The EHDS regulation will also accelerate the use of health data for research, innovation, policy-making, and regulatory activities for secondary use of data (known as EHDS2). The Integration of heterogeneous Data and Evidence towards Regulatory and HTA Acceptance (IDERHA) project builds one of the first pan-European health data spaces in alignment with the EHDS2 requirements, addressing lung cancer as a pilot. Methods In this study, we conducted a comprehensive review of the EHDS regulation, technical requirements for EHDS2, and related projects. We also explored the results of the Joint Action Towards the European Health Data Space (TEHDAS) to identify the framework of IDERHA’s alignment with EHDS2. We also conducted an internal webinar and an external workshop with EHDS experts to share expertise on the EHDS requirements and challenges. Results We identified the lessons learned from the existing projects and the minimum-set of requirements for aligning IDERHA infrastructure with EHDS2, including user journey, concepts, terminologies, and standards. The IDERHA framework (i.e., platform architecture, standardization approaches, documentation, etc.) is being developed accordingly. Discussion The IDERHA's alignment plan with EHDS2 necessitates the implementation of three categories of standardization for: data discoverability: Data Catalog Vocabulary (DCAT-AP), enabling semantics interoperability: Observational Medical Outcomes Partnership (OMOP), and health data exchange (DICOM and FHIR). The main challenge is that some standards are still being refined, e.g., the extension of the DCAT-AP (HealthDCAT-AP). Additionally, extensions to the Observational Health Data Sciences and Informatics (OHDSI) OMOP Common Data Model (CDM) to represent the patient-generated health data are still needed. Finally, proper mapping between standards (FHIR/OMOP) is a prerequisite for proper data exchange. Conclusions The IDERHA's plan and our collaboration with other EHDS initiatives/projects are critical in advancing the implementation of EHDS2.

Artificial intelligence (AI) is a technological breakthrough that is rapidly progressing all over the world. AI has a variety of uses in medicine and dentistry, ranging from data analysis to using neural networks for diagnosis. AI has received enormous attention and has gone through a transition stage from being a pure statistical tool to being one of the main drivers of modern medicine. We are at the dawn of a new era, and AI is undoubtedly the future of dental practice management. This aim article is to review the various applications of AI in all the specialties of dentistry.

Abstract Plants are one of the primary sources of natural products for drug development. However, despite centuries of research, only a limited region of the phytochemical space has been studied. To understand the scope of what is explored versus unexplored in the phytochemical space, we begin by reconstructing the known chemical space of the plant kingdom, mapping the distribution of secondary metabolites, chemical classes, and plants traditionally used for medicinal purposes (i.e., medicinal plants) across various levels of the taxonomy. We identify hotspot taxonomic clades occupied by a large proportion of medicinal plants and characterized secondary metabolites, as well as clades requiring further characterization with regard to their chemical composition. In a complementary analysis, we build a chemotaxonomy which has a high level of concordance with the taxonomy at the genus level, highlighting the close relationship between chemical profiles and evolutionary relationships within the plant kingdom. Next, we delve into regions of the phytochemical space with known bioactivity that have been used in modern drug discovery. While we find that the vast majority of approved drugs from phytochemicals are derived from known medicinal plants, we also show that medicinal and non-medicinal plants do not occupy distinct regions of the known phytochemical landscape and their phytochemicals exhibit properties similar to bioactive compounds. Moreover, we also reveal that only a few thousand phytochemicals have been screened for bioactivity and that there are hundreds of known bioactive compounds present in both medicinal and non-medicinal plants, suggesting that non-medicinal plants also have potential therapeutic applications. Overall, these results support the hypothesis that there are many plants with medicinal properties awaiting discovery.

The quality of Food depends on the nature of the ingredients used to prepare it, if the ingredient remains in best eminence without any adulteration then only we can get the pure quality food. This study aimed to detect the quality and adulterants in commonly used food ingredients and spices from local markets of Kolhapur city of India, which are usually used to prepare local ethnic food. Some tests from (the Food Safety and Standards Authority of India) are performed to check adulterants like insoluble impurities, chalk powder, washing soda, red lead salts, brick powder, and artificial colors in food ingredients. 5 sugar samples, 8 chili powder samples, 3 Jaggery samples, 6 turmeric powder samples, 6 common salt samples, 2 black pepper samples, 2 ghee or butter samples, 3 dal samples, 3 wheat flour samples, 2 honey samples, 2 cinnamon samples, and 3 coffee powder samples were analyzed. 2 chili, 1 black pepper, 1 cinnamon, 1 Jaggery 1 Honey, and 1 coffee powder samples are found with adulterants whereas most were without adulterants. Most of the local food ingredients are of good quality.

Introduction Fatigue is prevalent across a wide range of medical conditions and can be debilitating and distressing. It is likely that fatigue is experienced differently according to the underlying aetiology, but this is poorly understood. Digital health technologies present a promising approach to give new insights into fatigue.The aim of this study is to use digital health technologies, real-time self-reports and qualitative interview data to investigate how fatigue is experienced over time in participants with myeloma, long COVID, heart failure and in controls without problematic fatigue. Objectives are to understand which sensed parameters add value to the characterisation of fatigue and to determine whether study processes are feasible, acceptable and scalable.Methods and analysis An ecological momentary assessment study will be carried out over 2 or 4 weeks (participant defined). Individuals with fatigue relating to myeloma (n=10), heart failure (n=10), long COVID (n=10) and controls without problematic fatigue or a study condition (n=10) will be recruited. ECG patches will measure heart rate variability, respiratory rate, body temperature, activity and posture. A wearable bracelet accompanied by environment beacons will measure physical activity, sleep and room location within the home. Self-reports of mental and physical fatigue will be collected via smartphone app four times daily and on-demand. Validated fatigue and affect questionnaires will be completed at baseline and at 2 weeks. End-of-study interviews will investigate experiences of fatigue and study participation. A feedback session will be offered to participants to discuss their data.Data will be analysed using multilevel modelling and machine learning. Interviews and feedback sessions will be analysed using content or thematic analyses.Ethics and dissemination This study was approved by the East of England—Cambridge East Research Ethics Committee (22/EE/0261). The results will be disseminated in peer-reviewed journals and at international conferences.Trial registration number NCT05622669.

Among the plethora of E3 Ligases, only a few have been utilized for the novel PROTAC technology. However, extensive knowledge of the preparation of E3 ligands and their utilization for PROTACs is already present in several databases. Here we provide, together with an analysis of functionalized E3 ligands, a comprehensive list of trained ML models to predict the probability to be an E3 ligase binder. We compared the different algorithms based on the different description schemes used and identified that the pharmacophore-based ML approach was the best. Due to the peculiar pharmacophores present in E3 ligase binders and the presence of an explainable model, we were able to show the capability of our ErG model to filter compound libraries for fast virtual screening or focused library design. A particular focus was also given to target E3 ligase prediction and to find a subset of candidate E3 ligase binders within known public and commercial compound collections.

Patents play a crucial role in the drug discovery process by providing legal protection for discoveries and incentivising investments in research and development. By identifying patterns within patent data resources, researchers can gain insight into the market trends and priorities of the pharmaceutical and biotechnology industries, as well as provide additional perspectives on more fundamental aspects such as the emergence of potential new drug targets. In this paper, we used the patent enrichment tool, PEMT, to extract, integrate, and analyse patent literature for rare diseases (RD) and Alzheimer's disease (AD). This is followed by a systematic review of the underlying patent landscape to decipher trends and applications in patents for these diseases. To do so, we discuss prominent organisations involved in drug discovery research in AD and RD. This allows us to gain an understanding of the importance of AD and RD from specific organisational (pharmaceutical or university) perspectives. Next, we analyse the historical focus of patents in relation to individual therapeutic targets and correlate them with market scenarios allowing the identification of prominent targets for a disease. Lastly, we identified drug repurposing activities within the two diseases with the help of patents. This resulted in identifying existing repurposed drugs and novel potential therapeutic approaches applicable to the indication areas. The study demonstrates the expanded applicability of patent documents from legal to drug discovery, design, and research, thus, providing a valuable resource for future drug discovery efforts. Moreover, this study is an attempt towards understanding the importance of data underlying patent documents and raising the need for preparing the data for machine learning-based applications.

Abstract The COVID-19 pandemic has highlighted the need for FAIR (Findable, Accessible, Interoperable, and Reusable) data more than any other scientific challenge to date. We developed a flexible, multi-level, domain-agnostic FAIRification framework, providing practical guidance to improve the FAIRness for both existing and future clinical and molecular datasets. We validated the framework in collaboration with several major public-private partnership projects, demonstrating and delivering improvements across all aspects of FAIR and across a variety of datasets and their contexts. We therefore managed to establish the reproducibility and far-reaching applicability of our approach to FAIRification tasks.

Abstract The notion that data should be Findable, Accessible, Interoperable and Reusable, according to the FAIR Principles, has become a global norm for good data stewardship and a prerequisite for reproducibility. Nowadays, FAIR guides data policy actions and professional practices in the public and private sectors. Despite such global endorsements, however, the FAIR Principles are aspirational, remaining elusive at best, and intimidating at worst. To address the lack of practical guidance, and help with capability gaps, we developed the FAIR Cookbook, an open, online resource of hands-on recipes for “FAIR doers” in the Life Sciences. Created by researchers and data managers professionals in academia, (bio)pharmaceutical companies and information service industries, the FAIR Cookbook covers the key steps in a FAIRification journey, the levels and indicators of FAIRness, the maturity model, the technologies, the tools and the standards available, as well as the skills required, and the challenges to achieve and improve data FAIRness. Part of the ELIXIR ecosystem, and recommended by funders, the FAIR Cookbook is open to contributions of new recipes.

Summary: For millennia, numerous cultures and civilizations have relied on traditional remedies derived from plants to treat a wide range of conditions and ailments. Here, we systematically analyzed ethnobotanical patterns across taxonomically related plants, demonstrating that congeneric medicinal plants are more likely to be used for treating similar indications. Next, we reconstructed the phytochemical space covered by medicinal plants to reveal that (i) taxonomically related medicinal plants cover a similar phytochemical space, and (ii) chemical similarity correlates with similar therapeutic usage. Lastly, we present several case scenarios illustrating how mining this information can be used for drug discovery applications, including: (i) investigating taxonomic hotspots around particular indications, (ii) exploring shared patterns of congeneric plants located in different geographic areas, but which have been used to treat the same indications, and (iii) showing the concordance between ethnobotanical patterns among non-taxonomically related plants and the presence of shared bioactive phytochemicals.

The drug discovery community faces high costs in bringing safe and effective medicines to market, in part due to the rising volume and complexity of data which must be generated during the research and development process. Fully utilising these expensively created experimental and computational data resources has become a key aim of scientists due to the clear imperative to leverage the power of artificial intelligence (AI) and machine learning-based analyses to solve the complex problems inherent in drug discovery. In turn, AI methods heavily rely on the quantity, quality, consistency, and scope of underlying training data. While pre-existing preclinical and clinical data cannot fully replace the need for de novo data generation in a project, having access to relevant historical data represents a valuable asset, as its reuse can reduce the need to perform similar experiments, therefore avoiding a “reinventing the wheel” scenario. Unfortunately, most suitable data resources are often archived within institutes, companies, or individual research groups and hence unavailable to the wider community. Hence, enabling the data to be Findable, Accessible, Interoperable, and Reusable (FAIR) is crucial for the wider community of drug discovery and development scientists to learn from the work performed and utilise the findings to enhance comprehension of their own research outcomes. In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process.

Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that the COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications, and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize, and store studies, datasets, and metadata related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.

Context: Allergic rhinosinusitis (AR) is common disabling disorder which is prevalent more in western India. Most of these patients have chronic rhinosinusitis and nasal polyposes. The present study assessed disability and its impact on quality of life (QoL) in AR that would help to include psychosocial measures along with the drugs treatment. Aims: To study to the impact of Disability, QoL and mechanism of Coping in AR. Settings and Design: The study was conducted in the ENT out-patient department of a tertiary care hospital over a 3 month period. Materials and Methods: Patients with age more than 18 years and without any comorbidity were included in the study. Gujarati versions of Rhinosinusitis Disability Index (for disability), SF-36 survey (QoL) and Coping Strategies Inventory-Short-Form were used. Statistical Analysis: SPSS (IBM Corp. Released 2013.IBM SPSS Statistics for Windows, Version 1.0. Armonk, NY: IBM Corp.) was used for all analysis. Descriptive statistics (mean, standard deviation) and correlation were calculated. Results: A significant impact of disability on QoL (general health and health change) was noted among these patients (P = 0.505, P = 0.370), more in urban than rural patients with greater distress and significant relationship between engagement and disengagement based coping style (r = 0.701). Conversely, social functioning and engagement based coping style had a negative co-relationship (−0.210). Sample was predominantly male using emotional coping than practical one. Conclusions: Faulty coping can lead to increased morbidity which may be altered with disease specific psychoeducation and counseling.

In the past one and half year elapsed since the identification of the SARS-CoV-2 virus and its genome, an extraordinary effort by the scientific community has led to the development of many vaccine projects. More than ten vaccine candidates throughout the world have been granted approval for emergency use. Existing data suggest that these vaccines have the potential to protect individuals and curb the spread of COVID-19 pandemic. However, long term side-effects and certain unresolved issues associated with vaccine use need to be assessed as the time passes. This study reviews the most recent data of 12 vaccines which have been approved for use and presents information on their doses, composition, mechanism of action, side effects, etc.

We report an interesting case of a 20-year-old man who was admitted in intensive cardiac care unit with congestive cardiac failure along with thromboembolic manifestations and a raised erythrocyte sedimentation rate. On the basis of two-dimensional echocardiography, he was diagnosed as left ventricular noncompaction syndrome. However, cardiac magnetic resonance imaging (MRI) ruled out noncompaction. After extensive workup, the patient was diagnosed with Takayasu aortoarteritis. Post therapy, the patient has normal cardiac function and cardiac MRI without thromboembolic event in 8 years' follow-up.

Abstract The SARS-CoV-2 pandemic has challenged researchers at a global scale. The scientific community’s massive response has resulted in a flood of experiments, analyses, hypotheses, and publications, especially in the field of drug repurposing. However, many of the proposed therapeutic compounds obtained from SARS-CoV-2 specific assays are not in agreement and thus demonstrate the need for a singular source of COVID-19 related information from which a rational selection of drug repurposing candidates can be made. In this paper, we present the COVID-19 PHARMACOME, a comprehensive drug-target-mechanism graph generated from a compilation of 10 separate disease maps and sources of experimental data focused on SARS-CoV-2/COVID-19 pathophysiology. By applying our systematic approach, we were able to predict the synergistic effect of specific drug pairs, such as Remdesivir and Thioguanosine or Nelfinavir and Raloxifene, on SARS-CoV-2 infection. Experimental validation of our results demonstrate that our graph can be used to not only explore the involved mechanistic pathways, but also to identify novel combinations of drug repurposing candidates.

Abstract Background Cytokine storm triggered by Severe Coronavirus Disease 2019 (COVID-19) is associated with high mortality. With high Interleukin -6 (IL-6) levels reported in COVID-19 related deaths in China, IL-6 is considered to be the key player in COVID-19 cytokine storm. Tocilizumab, a monoclonal antibody against IL-6 receptor, is used on compassionate grounds for treatment of COVID-19 cytokine storm. The aim of this study was to assess effect of tocilizumab on mortality due to COVID-19 cytokine storm. Method This retrospective, observational study included patients of severe COVID-19 pneumonia with persistent hypoxia (defined as saturation 94% or less on supplemental Oxygen of 15 L per minute through non-rebreathing mask or PaO2/FiO2 ratio of less than 200) who were admitted to a tertiary care center in Mumbai, India, between 31st March to 5th July 2020. In addition to standard care, single Inj. Tocilizumab 400 mg was given intravenously to 151 consecutive COVID-19 patients with persistent hypoxia, from 13th May to 5th July 2020. These 151 patients were retrospectively analysed and compared with historic controls, ie consecutive COVID-19 patients with persistent hypoxia, defined as stated above (N = 118, from our first COVID-19 admission on 31st March to 12th May 2020 i.e., till tocilizumab was available in hospital). Univariate and multivariate Cox regression analysis was performed for identifying predictors of survival. Statistical analysis was performed using IBM SPSS version 26. Results Out of 269 (151 in tocilizumab group and 118 historic controls) patients studied from 31st March to 5th July 2020, median survival in the tocilizumab group was significantly longer than in the control group; 18 days (95% CI, 11.3 to 24.7) versus 9 days (95% CI, 5.7 to 12.3); log rank p 0.007. On multivariate Cox regression analysis, independent predictors of survival were use of tocilizumab (HR 0.621, 95% CI 0.427–0.903, P 0.013) and higher oxygen saturation. Conclusion Tocilizumab may improve survival in severe COVID-19 pneumonia with persistent hypoxia. Randomised controlled trials on use of tocilizumab as rescue therapy in patients of severe COVID-19 pneumonia with hypoxia (PaO2/FiO2 less than 200) due to hyperinflammatory state, are warranted.

Introduction: In recent years, social media use has been increased by Healthcare Professionals (HCPs) in their healthcare practice and it has emerged as a new platform for disseminationof knowledge, information exchange, and interpersonal communication. Aim: This study was conducted with an aim to assess the use of social media in dental practice among dental practitioners in India. Materials and Methods: This cross-sectional survey was conducted from October 2019 to March 2020 at Bharati Vidyapeeth (Deemed to be University) Dental College and Hospital, Pune, Maharashtra, India by implementing a 15-item questionnaire to 36218 dental practitioners in India registered with Indian Dental Association (IDA) through an Electronic Survey Application. The questionnaire consisted of a section on demographic details of the participants which included their age, gender, type and number of years of practice. Second section consisted of items about the use of social media in dental practice and one open ended question on the topic. Data was analysed using descriptive statistics and comparisons were made using a Chi-square test. The Statistical Package for Social Sciences (SPSS) version 20.0 was used for data analysis. Level of significance was kept at 5%. Results: A total of 3180 dentists responded and returned the filled questionnaire. Social media was used by 1980 (62.3%) of dentists in their practice and the most commonly used platform was WhatsApp 1650 (83.3% of social media users). Most common reason for using social media in dental practice was for learning new things related to dentistry and staying updated 1470 (74.2%) followed by participation into discussion related to dentistry 1170 (59.1%). Dentists younger than 45 years (p-value=0.002), dentists with practice of less than 10 years (p-value

Introduction Epistaxis is the commonest otorhinolaryngological emergency affecting upto 60% of population. The aetiology of epistaxis can be local or systemic. Often it may be difficult to categorize the epistaxis and no clear cause is found; then it is labelled as idiopathic. Materials and Methods It was a prospective observational study. All the patients of both gender, age ≥18 years with epistaxis without obvious cause coming to the ENT OPD/ Emergency were included in the study. The patient's vitals were recorded and hemodynamical stability was established first. All patients of epistaxis underwent thorough clinical examination and proper history was taken. Measures to stop bleeding included nasal packing, electro/chemical cautery of local area or ligation of sphenopalatine vessels etc. Efforts were made to establish a primal relationship of adult epistaxis with various factors. Results There were 97 patients in the study. Male to female ratio was 5.7:1. Majority were above 40 years. The commonest factor associated with epistaxis was Hypertension(38%) followed by idiopathic(21%). Anterior nasal bleeding was present in 71% patients whereas posterior nasal bleeding was there in 29% patients. Non-surgical measures like nasal packing and cauterization were the main intervention methods. Duration of hospital stay was more than 3 days in 73% patients. Conclusion Hypertension, blood thinners and trauma were the most common risk factors among the patients in whom aetiology was found although in 21% of the patients, aetiology could not be found. Most cases were successfully managed with conservative (non-surgical) treatment.

Network-based approaches are becoming increasingly popular for drug discovery as they provide a systems-level overview of the mechanisms underlying disease pathophysiology. They have demonstrated significant early promise over other methods of biological data representation, such as in target discovery, side effect prediction and drug repurposing. In parallel, an explosion of -omics data for the deep characterization of biological systems routinely uncovers molecular signatures of disease for similar applications. Here, we present RPath, a novel algorithm that prioritizes drugs for a given disease by reasoning over causal paths in a knowledge graph (KG), guided by both drug-perturbed as well as disease-specific transcriptomic signatures. First, our approach identifies the causal paths that connect a drug to a particular disease. Next, it reasons over these paths to identify those that correlate with the transcriptional signatures observed in a drug-perturbation experiment, and anti-correlate to signatures observed in the disease of interest. The paths which match this signature profile are then proposed to represent the mechanism of action of the drug. We demonstrate how RPath consistently prioritizes clinically investigated drug-disease pairs on multiple datasets and KGs, achieving better performance over other similar methodologies. Furthermore, we present two case studies showing how one can deconvolute the predictions made by RPath as well as predict novel targets.

En este ensayo analizamos cuatro poemas de Clementina Suárez, poeta hondureña. Su voz poética es un sujeto en proceso según los planteamientos de Julia Kristeva; representa la subversión y lda disidencia de la mujer a través del antiesencialismo y proyecta una voz andrógina en su poesía. En esa constante alternancia entre tono femenino y tono masculino, la poeta propone denunciar las injusticias sociales y a su género. Esa ambigüedad de género que vemos en algunos de los poemas de Clementina es su manera de mostrar la marginalidad que ha vivido la mujer dentro del contexto social e intelectual de su tiempo.

Introduction: It is considered that the variability of respiratory symptoms in chronic obstructive pulmonary disease (COPD) is low or nonexistent; however, some authors state that there may be fluctuations. Objectives: To describe the variability of symptoms in patients with COPD throughout the day and night during four weeks using a patient diary, and to validate a questionnaire created for such purpose (the Colombian instrument for symptoms variability in COPD - COPD-CoVaSy). Materials and methods: Cohort study conducted in 96 patients with COPD treated between June and December, 2016, at the Centro de Atención Pulmonar - CAP, in Barranquilla, Colombia, who completed the patient diary for four weeks and, after this period, the self-administered COPD-CoVaSy instrument. The independence and comparison of frequencies of categorical and continuous variables were established using the chi-square and the Fisher's exact tests and the Pearson's correlation coefficient, respectively. A MANOVA was performed, using linear regression models, to determine the correlations between the results of the diary and those of the instrument. Results: Participants’ mean age was 73.3±8.3 years and 71.87% were male. According to the analysis of the diaries, the average scores (visual analog scale) for all symptoms and the performance of daily activities ranged between 0.5 and 2.5, being higher in the morning (with average scores between 1.5 and 2.5) than in the afternoon and evening (with average scores between 0.5 and 1.5); however, symptom variability was minimal. These results were similar to those obtained in the COPD-CoVaSy instrument, evidencing a high correlation between both instruments, which allowed confirming that COPD-CoVaSy is useful tool to measure such variability. Conclusions. Based on the findings of the present study, it can be concluded that there is a slight variability in COPD symptoms throughout the day, which should be considered when establishing treatment regimens for this disease. Likewise, it was determined that the COPD-CoVaSy is valid to measure such variability in Colombian patients with COPD.

Objective Large data on the clinical characteristics and outcome of COVID-19 in the Indian population are scarce. We analysed the factors associated with mortality in a cohort of moderately and severely ill patients with COVID-19 enrolled in a randomised trial on convalescent plasma.Design Secondary analysis of data from a Phase II, Open Label, Randomized Controlled Trial to Assess the Safety and Efficacy of Convalescent Plasma to Limit COVID-19 Associated Complications in Moderate Disease.Setting 39 public and private hospitals across India during the study period from 22 April to 14 July 2020.Participants Of the 464 patients recruited, two were lost to follow-up, nine withdrew consent and two patients did not receive the intervention after randomisation. The cohort of 451 participants with known outcome at 28 days was analysed.Primary outcome measure Factors associated with all-cause mortality at 28 days after enrolment.Results The mean (SD) age was 51±12.4 years; 76.7% were males. Admission Sequential Organ Failure Assessment score was 2.4±1.1. Non-invasive ventilation, invasive ventilation and vasopressor therapy were required in 98.9%, 8.4% and 4.0%, respectively. The 28-day mortality was 14.4%. Median time from symptom onset to hospital admission was similar in survivors (4 days; IQR 3–7) and non-survivors (4 days; IQR 3–6). Patients with two or more comorbidities had 2.25 (95% CI 1.18 to 4.29, p=0.014) times risk of death. When compared with survivors, admission interleukin-6 levels were higher (p10 (9.97, 3.65–27.13, p1.0 mg/L (2.50, 1.14–5.48, p=0.022), ferritin ≥500 ng/mL (2.67, 1.44–4.96, p=0.002) and lactate dehydrogenase ≥450 IU/L (2.96, 1.60–5.45, p=0.001) were significantly associated with death.Conclusion In this cohort of moderately and severely ill patients with COVID-19, severity of illness, underlying comorbidities and elevated levels of inflammatory markers were significantly associated with death.Trial registration number CTRI/2020/04/024775.

Background: The success rate of dental implants depends on the amount of crestal bone around the implants. Crestal module is the transosteal region of implant that is designed to receive the prosthetic component. The design of crestal module influences the crestal bone loss around implants. Aim: The purpose of this study was to evaluate and compare the crestal bone loss around implants with smooth collar and implants with micro threaded rough collar design. Materials and Methods: A total of 150 individuals received 100 dental implants. Each individual received one implant with smooth collar design (Group-A) and one implant with microthreaded rough collar design (Group-B) in the posterior edentulous region. All the 100 implants were prosthetically loaded after a healing period of 3 months. Crestal bone loss was measured on mesial, distal, buccal, and lingual side of each implant using periapical radiographs before loading (baseline), immediately after loading, 6, and 12 months after loading. Results: The average crestal bone loss 12 months after loading the implants in Group A and Group B was 3.75 mm and 3.23 mm, respectively, the value being statistically significant (P < 0.05). In both Group A and Group B, the average crestal bone loss was maximum on the lingual side followed by buccal, distal, and mesial sides. Conclusion: Crestal bone loss around rough-surfaced microthreaded neck implants was significantly lower than smooth-surfaced neck implants.

Vogt–Koyanagi–Harada syndrome (VKH syndrome) is a rare granulomatous inflammatory disease that affects the melanin pigment producing melanocytes and mainly affects the pigmented structures such as eyes, ear, skin, meninges, and hair. VKT is an autoimmune disorder, which is mainly a T CD4+ Th1 lymphocyte–mediated aggression to melanocytes, in individuals with a genetic predisposition, in particular, the presence of HLA-DRB1 * 0405 allele. Melanin usually gives color to skin, hair, and eyes. Melanin is also found in the retina, where it plays a role in normal vision. This disease mainly leads to vision and hearing disturbances, followed by dermal problems. The most common symptoms include vitiligo, headaches, hair loss (alopecia), and hearing loss. This article describes the various signs and symptoms of VKH disease and its pathogenesis.

A significant volume of wood was lost due to wood defects; however, few studies were done to quantify wood loss by wood defects. This study was focused on quantifying wood loss by heart rot, especially hollowness in Shorea robusta. The study was conducted in Tileswornath community forest of Rautahat district. The data were collected from the felling site of the regeneration felling block of Tileswornath community forest. 44 trees were selected randomly, and tree diameter, total height, and volume were measured. The destructive method was followed as heart rot cannot be visible from the surface. Felled trees were sanctioned into 285 logs and separated based on the hollowness. Hollow diameters at both thin end and mid and thick end, as well as length, were measured on the hollow log, and Smalian’s formula was used to calculate the volume of hollowed portion, and volume calculation formula for the cylinder was used to calculate total volume. For the solid logs, mid diameter and length of the log were measured and volume calculation formula for the cylinder was used to calculate total volume. Logistic regression was performed to identify the relation of total height and diameter with the probability of hollowness presence. The study showed that 59% of sampled trees and 34.39% of logs were found to be hollowed due to heart rot. 41.79% volume was occupied by hollow on the hollowed log. Logistic regression discards the relation of height to the hollowness but signified the relation of diameter to the probability of hollowness presence. Before implementation of scientific forest management modality, the timber retained in stump per tree was found as 0.18 cubic feet.

Chronic inflammatory diseases, such as cancer, diabetes mellitus, stroke, ischemic heart diseases, neurodegenerative conditions, and COVID-19 have had a high number of deaths worldwide in recent years. The accurate detection of the biomarkers for chronic inflammatory diseases can significantly improve diagnosis, as well as therapy and clinical care in patients. Graphene derivative materials (GDMs), such as pristine graphene (G), graphene oxide (GO), and reduced graphene oxide (rGO), have shown tremendous benefits for biosensing and in the development of novel biosensor devices. GDMs exhibit excellent chemical, electrical and mechanical properties, good biocompatibility, and the facility of surface modification for biomolecular recognition, opening new opportunities for simple, accurate, and sensitive detection of biomarkers. This review shows the recent advances, properties, and potentialities of GDMs for developing robust biosensors. We show the main electrochemical and optical-sensing methods based on GDMs, as well as their design and manufacture in order to integrate them into robust, wearable, remote, and smart biosensors devices. We also describe the current application of such methods and technologies for the biosensing of chronic disease biomarkers. We also describe the current application of such methods and technologies for the biosensing of chronic disease biomarkers with improved sensitivity, reaching limits of detection from the nano to atto range concentration.

The morphological analysis of patterns in dried droplets has allowed the generation of efficient techniques for the detection of molecules of medical interest. However, the effectiveness of this method to reveal the coexistence of macromolecules of the same species, but different conformational states, is still unknown. To address this problem, we present an experimental study on pattern formation in dried droplets of bovine serum albumin (BSA), in folded and unfolded conformational states, in saline solution (NaCl). Folded proteins produce a well-defined coffee ring and crystal patterns all over the dry droplet. Depending on the NaCl concentration, the crystals can be small, large, elongated, entangled, or dense. Optical microscopy reveals that the relative concentration of unfolded proteins determines the morphological characteristics of deposits. At a low relative concentration of unfolded proteins (above 2%), small amorphous aggregates emerge in the deposits, while at high concentrations (above 16%), the “eye-like pattern”, a large aggregate surrounded by a uniform coating, is produced. The radial intensity profile, the mean pixel intensity, and the entropy make it possible to characterize the patterns in dried droplets. We prove that it is possible to achieve 100% accuracy in identifying 4% of unfolded BSA contained in a protein solution.

El presente ensayo se centra en la temática de formación socio ética, que supone lograr una cultura tecnológica a la luz de los avances de la Venezuela actual. Se consideran los postulados de formación de quienes precisan al ser ético como reflexivo, participativo y responsable de las acciones que realiza en convivencia con otros .La formación socio ética supone la reflexión individual o colectiva que subyace en los individuos, desde su realidad, se abordaran aspectos de relevancia que permitan definir, argumentar y presentar esta categoría, lo que conlleve a vislumbrar los valores inmersos en la formación de profesores como protagonistas de la educación venezolana desde la ética, encaminados en la comprensión de los entornos y comunidades virtuales desde la cultura tecnológica como elemento determinante de las sociedades postmodern

Agrarian communities in the Peruvian Andes depend on local water resources that are threatened by both a changing climate and changes in the socio-politics of water allocation. A community’s local autonomy over water resources and its capacity to plan for a sustainable and secure water future depends, in part, on integrated local environmental knowledge (ILEK), which leverages and blends traditional and western scientific approaches to knowledge production. Over the course of a two-year collaborative water development project with the agrarian district of Zurite, we designed and implemented an applied model of socio-hydrology focused on the coproduction of knowledge among scientists, local knowledge-holders and students. Our approach leveraged knowledge across academic disciplines and cultures, trained students to be valued producers of knowledge, and, most importantly, integrated the needs and concerns of the community. The result is a community-based ILEK that informs sustainable land and water management and has the potential to increase local autonomy over water resources. Furthermore, the direct link between interdisciplinary water science and community benefits empowered students to pursue careers in water development. The long-term benefits of our approach support the inclusion of knowledge coproduction among scholars, students and, in particular, community members, in applied studies of socio-hydrology.

The quality control of medicines guarantees the effectiveness of treatments for diseases. We explore the use of texture analysis of patterns in dried droplets as a tool to readily detect both impurities and changes in drug concentration. Four types of medicines associated with different routes of administration were analyzed: Methotrexate, Ciprofloxacin, Clonazepam, and Budesonide. We use NaCl and a hot substrate at 63 ∘C to promote aggregate formation and to reduce droplet drying time. Depending on the medicine, optical microscopy reveals different complex aggregates such as circular to oval splatters, fern-like islands, crown shapes, crown needle-like and bump-like patterns as well as dendritic branched and star-like crystals. We use some physical features of the stains (as the stain diameter and superficial area) and gray level co-occurrence matrix (GLCM) to characterize patterns of dried droplets. Finally, we show that structural analysis of stains can achieve 95% accuracy in identifying medicines with 30% water dilution, while it achieves 99% accuracy in detecting drugs with 10% other substances.

The SARS-CoV-2 outbreak was declared a worldwide pandemic in 2020. Infection triggers the respiratory tract disease COVID-19, which is accompanied by serious changes in clinical biomarkers such as hemoglobin and interleukins. The same parameters are altered during hemolysis, which is characterized by an increase in labile heme. We present two computational–experimental approaches aimed at analyzing a potential link between heme-related and COVID-19 pathophysiologies. Herein, we performed a detailed analysis of the common pathways induced by heme and SARS-CoV-2 by superimposition of knowledge graphs covering heme biology and COVID-19 pathophysiology. Focus was laid on inflammatory pathways and distinct biomarkers as the linking elements. In a second approach, four COVID-19-related proteins, the host cell proteins ACE2 and TMPRSS2 as well as the viral proteins 7a and S protein were computationally analyzed as potential heme-binding proteins with an experimental validation. The results contribute to the understanding of the progression of COVID-19 infections in patients with different clinical backgrounds and may allow for a more individual diagnosis and therapy in the future.

The data presented in this article is related to the research article “RNAi of selected candidate genes interrupts growth and development of Helicoverpa armigera” (Chikate et al., 2016) [1]. RNA interference (RNAi) is emerging as a potent insect pest control strategy over current methods and their resistance by pest. In this study we tested 15 different in vitro synthesized dsRNAs for gene silencing in Helicoverpa armigera. These dsRNAs were specific against H. armigera enzymes/proteins such as proteases like trypsins (HaTry2, 3, 4 and 6), chymotrypsin (HaChy4) and cysteine proteases such as cathepsin (HaCATHL); glutathione S-transferases (HaGST1a, 6 and 8); esterases (HaAce4, HaJHE); catalase (HaCAT); super-oxide-dismutase (HaCu/ZnSOD); fatty acid binding protein (HaFabp) and chitin deacetylase (HaCda5b). These dsRNAs were fed to second instar larvae at an optimized dose (60 µg/day) for 3 days separately. Effects of dsRNA feeding were observed in terms of larval mass gain, percentage mortality and phenotypic abnormalities in later developmental stages of H. armigera. These findings might provide potential new candidates for designing sequence-specific dsRNA as pesticide in crop protection. Keywords: RNAi, dsRNA, H. armigera, Gene silencing, Pest control

El presente trabajo informa sobre características bioquímicas, actividad hemolítica, citotóxica y citolítica de tres fracciones del veneno de la anémona de mar Anthothoe chilensis. Los tentáculos de 78 ejemplares de A. chilensis, provenientes de la Isla Cabinza-San Lorenzo, Lima, fueron procesados obteniendo un filtrado, el cual se fraccionó por precipitación con tres puntos de saturación con acetona fría: I (20%), II (50%), III (80%). El filtrado mostró una concentración proteica de 1,8 mg/mL. Las pruebas de detección de carbohidratos totales demostraron la presencia de 1,401 mg de glucosa/ml de solución en el filtrado; mientras que la electroforesis en gel de poliacrilamida en presencia de dodecil sulfato de sodio (PAGE-SDS) evidenció proteínas de 14 a 94 kDa, de las cuales la mayor parte fueron glicoproteínas. Se encontró actividad hemolítica sobre eritrocitos humanos en las fracciones I y II. La fracción III tuvo la actividad fosfolipásica más alta. Las tres fracciones tuvieron una ligera actividad proteolítica sobre caseína siendo la más activa la fracción I. Los efectos citotóxico y citológico fueron evaluados aplicando el Ensayo de Toxicidad en Embriones de Erizo de mar (SET). Las anormalidades morfológicas fueron evaluadas a las 48 horas de desarrollo. Las anormalidades citológicas fueron evaluadas en el estadio de gástrula tardía. Las tres fracciones acetónicas produjeron daños citotóxicos y citológicos importantes en los embriones de erizo de mar. Los efectos sobre los embriones fueron retrasar su desarrollo y producir anomalías morfológicas como lisis de blástulas y exogastrulación. Los daños citológicos observados fueron núcleos heteropicnóticos, núcleos gigantes y espacios celulares anormales. La fracción II fue la más citotóxica produciendo una mortalidad de 75,52 ± 5,5% en los primeros estadíos con 1,0 μg/mL. La fracción I produjo el mayor porcentaje de anomalías en los embriones supervivientes 64,15 ± 4,2 % con 1,3 μg/mL. La fracción III fue la menos tóxica.

Various patterns of calcifications occur in the breast; some benign, some malignant. A knowledge of these patterns on mammography helps in accurate interpretation and management.

El presente estudio se realizó en el Instituto Na- cional de Ciencias Agrícolas (INCA), con el objetivo de determinar el efecto de dos cepas de micorrizas sobre el desarrollo de vitroplantas de cafeto de la variedad Robusta, durante la fase de aclimatización. Se utilizó un diseño completamente aleatorizado con nueve tratamientos, realizándose a los 90 días de cultivo evaluaciones de: número de pares de hojas, altura de la planta (cm), masa seca total (g), así como los indicadores fúngicos, porcentaje de colonización, masa del endófito arbuscular (g/g suelo) y esporas totales, y a los 150 días el porcenaje de sobrevivencia. Los datos se procesaron median- te un análisis de varianza de clasificación simple, obteniéndose como resultado diferencias en la efectividad de las cepas de HMA por clon y alcanzándose de forma general incrementos en los indicadores analizados. Los mayores valores se obtuvieron con la cepa Glomus clarum para los clones M-229 y K-234. Se obtuvieron porcentajes de sobrevivencia que oscila- ron entre 95.42 y 98.15 %. Se comprobó la factibilidad de emplear la inoculación micorrízica para incrementar la calidad morfofisiológica de las vitroplantas.