Dietary anti-inflammatory and anti-bacterial medicinal plants and its compounds in bovine mastitis associated impact on human life.

Bovine mastitis (BM) is the most common bacterial mediated inflammatory disease in the dairy cattle that causes huge economic loss to the dairy industry due to decreased milk quality and quantity. Milk is the essential food in the human diet, and rich in crucial nutrients that helps in lowering the risk of diseases like hypertension, cardiovascular diseases and type 2 diabetes. The main causative agents of the disease include various gram negative, and positive bacteria, along with other risk factors such as udder shape, age, genetic, and environmental factors also contributes much for the disease. Currently, antibiotics, immunotherapy, probiotics, dry cow, and lactation therapy are commonly recommended for BM. However, these treatments can only decrease the rise of new cases but can't eliminate the causative agents, and they also exhibit several limitations. Hence, there is an urgent need of a potential source that can generate a typical and ideal treatment to overcome the limitations and eliminate the pathogens. Among the various sources, medicinal plants and its derived products always play a significant role in drug discovery against several diseases. In addition, they are also known for its low toxicity and minimum resistance features. Therefore, plants and its compounds that possess anti-inflammatory and anti-bacterial properties can serve better in bovine mastitis. In addition, the plants that are serving as a food source and possessing pharmacological properties can act even better in bovine mastitis. Hence, in this evidence-based study, we particularly review the dietary medicinal plants and derived products that are proven for anti-inflammatory and anti-bacterial effects. Moreover, the role of each dietary plant and its compounds along with possible role in the management of bovine mastitis are delineated. In this way, this article serves as a standalone source for the researchers working in this area to help in the management of BM.

Human movement and environmental barriers shape the emergence of dengue.

Understanding how emerging infectious diseases spread within and between countries is essential to contain future pandemics. Spread to new areas requires connectivity between one or more sources and a suitable local environment, but how these two factors interact at different stages of disease emergence remains largely unknown. Further, no analytical framework exists to examine their roles. Here we develop a dynamic modelling approach for infectious diseases that explicitly models both connectivity via human movement and environmental suitability interactions. We apply it to better understand recently observed (1995-2019) patterns as well as predict past unobserved (1983-2000) and future (2020-2039) spread of dengue in Mexico and Brazil. We find that these models can accurately reconstruct long-term spread pathways, determine historical origins, and identify specific routes of invasion. We find early dengue invasion is more heavily influenced by environmental factors, resulting in patchy non-contiguous spread, while short and long-distance connectivity becomes more important in later stages. Our results have immediate practical applications for forecasting and containing the spread of dengue and emergence of new serotypes. Given current and future trends in human mobility, climate, and zoonotic spillover, understanding the interplay between connectivity and environmental suitability will be increasingly necessary to contain emerging and re-emerging pathogens.

Exploiting nonlinearities through geometric engineering to enhance the auxetic behaviour in re-entrant honeycomb metamaterials.

Classical approaches to enhance auxeticity quite often involve exploring or designing newer architectures. In this work, simple geometrical features at the member level are engineered to exploit non-classical nonlinearities and improve the auxetic behaviour. The structural elements of the auxetic unit cell are here represented by thin strip-like beams, or thin-walled tubular beams. The resulting nonlinear stiffness enhances the auxeticity of the lattices, especially under large deformations. To quantify the influence of the proposed structural features on the resulting Poisson's ratio, we use here variational asymptotic method (VAM) and geometrically exact beam theory. The numerical examples reveal that 2D re-entrant type micro-structures made of thin strips exhibit an improvement in terms of auxetic behaviour under compression. For the auxetic unit cell with thin circular tubes as members, Brazier's effect associated with cross-sectional ovalisation improves the auxetic behaviour under tension; the enhancement is even more significant for the 3D re-entrant geometry. Thin strip-based auxetic unit cells were additively manufactured and tested under compression to verify the numerical observations. The experimentally measured values of the negative Poisson's ratio are in close agreement with the numerical results, revealing a 66% increase due to the nonlinearity. Simulation results showcase these alternative approaches to improve the auxetic behaviour through simple geometric engineering of the lattice ribs.

Reactive oxygen species mediated apoptotic death of colon cancer cells: therapeutic potential of plant derived alkaloids.

Colorectal cancer (CRC) is one of the most deaths causing diseases worldwide. Several risk factors including hormones like insulin and insulin like growth factors (e.g., IGF-1) have been considered responsible for growth and progression of colon cancer. Though there is a huge advancement in the available screening as well as treatment techniques for CRC. There is no significant decrease in the mortality of cancer patients. Moreover, the current treatment approaches for CRC are associated with serious challenges like drug resistance and cancer re-growth. Given the severity of the disease, there is an urgent need for novel therapeutic agents with ideal characteristics. Several pieces of evidence suggested that natural products, specifically medicinal plants, and derived phytochemicals may serve as potential sources for novel drug discovery for various diseases including cancer. On the other hand, cancer cells like colon cancer require a high basal level of reactive oxygen species (ROS) to maintain its own cellular functions. However, excess production of intracellular ROS leads to cancer cell death via disturbing cellular redox homeostasis. Therefore, medicinal plants and derived phytocompounds that can enhance the intracellular ROS and induce apoptotic cell death in cancer cells via modulating various molecular targets including IGF-1 could be potential therapeutic agents. Alkaloids form a major class of such phytoconstituents that can play a key role in cancer prevention. Moreover, several preclinical and clinical studies have also evidenced that these compounds show potent anti-colon cancer effects and exhibit negligible toxicity towards the normal cells. Hence, the present evidence-based study aimed to provide an update on various alkaloids that have been reported to induce ROS-mediated apoptosis in colon cancer cells via targeting various cellular components including hormones and growth factors, which play a role in metastasis, angiogenesis, proliferation, and invasion. This study also provides an individual account on each such alkaloid that underwent clinical trials either alone or in combination with other clinical drugs. In addition, various classes of phytochemicals that induce ROS-mediated cell death in different kinds of cancers including colon cancer are discussed.

Geographic clustering of travel-acquired infections in Ontario, Canada, 2008-2020.

As the frequency of international travel increases, more individuals are at risk of travel-acquired infections (TAIs). In this ecological study of over 170,000 unique tests from Public Health Ontario's laboratory, we reviewed all laboratory-reported cases of malaria, dengue, chikungunya, and enteric fever in Ontario, Canada between 2008-2020 to identify high-resolution geographical clusters for potential targeted pre-travel prevention. Smoothed standardized incidence ratios (SIRs) and 95% posterior credible intervals (CIs) were estimated using a spatial Bayesian hierarchical model. High- and low-incidence areas were described using data from the 2016 Census based on the home forward sortation area of patients testing positive. A second model was used to estimate the association between drivetime to the nearest travel clinic and incidence of TAI within high-incidence areas. There were 6,114 microbiologically confirmed TAIs across Ontario over the study period. There was spatial clustering of TAIs (Moran's I = 0.59, p<0.0001). Compared to low-incidence areas, high-incidence areas had higher proportions of immigrants (p<0.0001), were lower income (p = 0.0027), had higher levels of university education (p<0.0001), and less knowledge of English/French languages (p<0.0001). In the high-incidence Greater Toronto Area (GTA), each minute increase in drive time to the closest travel clinic was associated with a 3% reduction in TAI incidence (95% CI 1-6%). While urban neighbourhoods in the GTA had the highest burden of TAIs, geographic proximity to a travel clinic in the GTA was not associated with an area-level incidence reduction in TAI. This suggests other barriers to seeking and adhering to pre-travel advice.

Ignorance Isn't Bliss: We Must Close the Machine Learning Knowledge Gap in Pediatric Critical Care.

Pediatric intensivists are bombarded with more patient data than ever before. Integration and interpretation of data from patient monitors and the electronic health record (EHR) can be cognitively expensive in a manner that results in delayed or suboptimal medical decision making and patient harm. Machine learning (ML) can be used to facilitate insights from healthcare data and has been successfully applied to pediatric critical care data with that intent. However, many pediatric critical care medicine (PCCM) trainees and clinicians lack an understanding of foundational ML principles. This presents a major problem for the field. We outline the reasons why in this perspective and provide a roadmap for competency-based ML education for PCCM trainees and other stakeholders.

Machine Learning Applied to Routinely Collected Health Administrative Data.

There has been considerable growth in the development of machine learning algorithms for clinical applications. The authors survey recent machine learning models developed with the use of large health administrative databases at ICES and highlight three areas of ongoing development that are particularly important for health system applications.

Study of Significance of Fasting & Stimulated Serum C Peptide Levels in Newly Detected Young Diabetes Mellitus Among the Age Group of 35-40.

Diabetes mellitus refers to a group of common metabolic disorders that share the phenotype of hyperglycemia. The worldwide prevalence of DM has risen dramatically over the past two decades.Diabetes mellitus is a major cause of mortality and morbidity worldwide. Assay for C-peptide can be used to provide an index of endogenous insulin production and pancreatic beta cell function. MATERIAL: This is a hospital based cross section study involving 50 newly detected diabetic subjects of age group 35-40 years. The subjects were evaluated with their fasting and stimulated c-peptide levels assay, fasting and postprandial blood sugars, HbA1c. OBSERVATION: Eight subjects out of 50 subjects had a fasting serum C-peptide value less than normal value. Thirteen subjects out of 50 subjects had a low stimulated serum C-peptide. CONCLUSION: This study suggests measurement of C-peptide levels in newly detected diabetic subjects especially of younger age group is of value in differentiating type of diabetes and appropriate next line of management.

Predicting hospitalisations related to ambulatory care sensitive conditions with machine learning for population health planning: derivation and validation cohort study.

OBJECTIVE: To predict older adults' risk of avoidable hospitalisation related to ambulatory care sensitive conditions (ACSC) using machine learning applied to administrative health data of Ontario, Canada. DESIGN, SETTING AND PARTICIPANTS: A retrospective cohort study was conducted on a large cohort of all residents covered under a single-payer system in Ontario, Canada over the period of 10 years (2008-2017). The study included 1.85 million Ontario residents between 65 and 74 years old at any time throughout the study period. DATA SOURCES: Administrative health data from Ontario, Canada obtained from the (ICES formely known as the Institute for Clinical Evaluative Sciences Data Repository. MAIN OUTCOME MEASURES: Risk of hospitalisations due to ACSCs 1 year after the observation period. RESULTS: The study used a total of 1 854 116 patients, split into train, validation and test sets. The ACSC incidence rates among the data points were 1.1% for all sets. The final XGBoost model achieved an area under the receiver operating curve of 80.5% and an area under precision-recall curve of 0.093 on the test set, and the predictions were well calibrated, including in key subgroups. When ranking the model predictions, those at the top 5% of risk as predicted by the model captured 37.4% of those presented with an ACSC-related hospitalisation. A variety of features such as the previous number of ambulatory care visits, presence of ACSC-related hospitalisations during the observation window, age, rural residence and prescription of certain medications were contributors to the prediction. Our model was also able to capture the geospatial heterogeneity of ACSC risk in Ontario, and especially the elevated risk in rural and marginalised regions. CONCLUSIONS: This study aimed to predict the 1-year risk of hospitalisation from ambulatory-care sensitive conditions in seniors aged 65-74 years old with a single, large-scale machine learning model. The model shows the potential to inform population health planning and interventions to reduce the burden of ACSC-related hospitalisations.

Social and obstetric risk factors of antenatal depression: A cross-sectional study from South-India.

PURPOSE: Antenatal depression is as prevalent as postpartum depression and studies on it are very few. It has been relatively neglected leading to adverse effects on the growing child as well as the mother. Hence screening of depression in high risk individuals, planning and adopting important strategies for prevention needs to be undertaken. Our study aimed to assess the modifiable social and obstetric risk factors of antenatal depression. METHODS: Third trimester pregnant women of 18-40 years attending obstetric out-patient department and admitted in tertiary hospitals who had no past psychiatric illness were screened using Edinburgh postnatal depression scale after obtaining written consent, socio-demographic and obstetric details. Statistical analysis was calculated using IBM version SPSS 23. RESULTS: Among 222 women recruited, 25.6% had antenatal depression. Significant associations were found between lower level of education (p = 0.02,O.R=1.87), urban population (p = 0.04,O.R=5.139), intimate partner violence (p = 0.01,O.R=15.769), daily alcohol use by husband (p < 0.00,O.R=15.281), poor relationship with in-laws (p < 0.000,O.R=21.733) and parents (p < 0.000,O.R=15.281), number of previous pregnancies (p = 0.026,O.R=5.545), parity (p = 0.04,O.R=4.187), previous abortions (p = 0.007,O.R=2.834), fear of labour (p < 0.000,O.R=5.77) and complications during pregnancy (p < 0.000,O.R=3.017) with antenatal depression. Living in urban area (p = 0.023, A.O.R=3.132), fear of labour (p < 0.000, A.O.R=7.398), intimate partner violence (p = 0.026, A.O.R=36.655), poor relationship with in-laws (p = 0.001, A.O.R=36.855) and parents (p = 0.042, A.O.R=8.377) were found to be predictors of antenatal depression. CONCLUSION: Antenatal depression is multifactorial in origin and requires a multifactorial approach in prevention and treatment. Routine antenatal screening for depression must be conducted with efforts to build strong family, peer and social support at community level.

Management of tibial shaft fractures with closed intra medullary interlocking nail among Indians.

It is of interest to document data on the well Reamed Intramedullary Nailing in Isolated Tibial Diaphyseal Fractures without Fibular Osteotomy among Indians. 120 patients with isolated tibial diaphyseal fractures were treated with IMIL nail (84 closed fractures, 16 type I open fractures, and 20 type II open fractures) were involved in this study. Research was carried out over a five-and-a-half-year period, from July 2013 to December 2018.According to Johner and Wruh's criteria, good functional findings were achieved in 70% of patients, better operational results in 15%, reasonable functional results in 5%, and poor functional results in 10% of cases after surgery. The percentage of union in the present analysis was 90%. The average time for union was 5 months, with 84 fractures healing before 5 months. Intramedullary Interlocking Nailing reduces length of stay in hospital, lowers the financial load, and promotes early return to work without the need for further surgical treatment such as partial fibular osteotomy.

Management of tendinopathies among south Indians using collagen II peptide, glucosamine and vitamin C.

Tendinopathy is a multi-factorial, broad spectrum of tendon disorder, characterized by activity-related chronic tendon pain and local tenderness. The point of this study was to assess the adequacy of a nutritional supplement containing Glucosamine, type II collagen and vitamin C on the clinical and auxiliary advancement of tendinopathies. The prospective study was Hospital based randomized control trail comparing the efficacy of collage 2 peptide, glucosamine and vitamin c with placebo in various tendinopathies. All diagnosed patients willing for the treatment attending Konaseema Institute of Medical Sciences during period of 2017-2019 were selected with regular follow up of 2nd week, 2nd month & 6 month. The statistics and visualizations of various observations made in the entire study which include a total of 80 patients with various tendinopathies. 60 of them were given collagen 2, glucosamine and vitamin c (cases) and 20 were given placebo (controls). At the end of 6 months almost 90% patients relieved completely of pain. The duration of maximum benefit to reach is almost around 24 weeks. These are seen more commonly to affect non-athletes rather than athletes.

Effects of turmeric (Curcuma longa) on oral health.

The turmeric plant was used in ancient medicine to cure a wide range of diseases, including cough, diabetes, and liver disease. Data shows that the principal chemical component of turmeric, curcumin, has a variety of beneficial effects on the body. Therefore, it is of interest to document data on the therapeutic activities of turmeric, including its extracts and possible medical uses, as well as its oral and dental uses and a safety assessment of those uses. Curcumin, the most pure form of turmeric, has shown promise in dentistry.

Governing partnerships with technology companies as part of the COVID-19 response in Canada: A qualitative case study.

Cross-sector partnerships are vital for maintaining resilient health systems; however, few studies have sought to empirically assess the barriers and enablers of effective and responsible partnerships during public health emergencies. Through a qualitative, multiple case study, we analyzed 210 documents and conducted 26 interviews with stakeholders in three real-world partnerships between Canadian health organizations and private technology startups during the COVID-19 pandemic. The three partnerships involved: 1) deploying a virtual care platform to care for COVID-19 patients at one hospital, 2) deploying a secure messaging platform for physicians at another hospital, and 3) using data science to support a public health organization. Our results demonstrate that a public health emergency created time and resource pressures throughout a partnership. Given these constraints, early and sustained alignment on the core problem was critical for success. Moreover, governance processes designed for normal operations, such as procurement, were triaged and streamlined. Social learning, or the process of learning from observing others, offset some time and resource pressures. Social learning took many forms ranging from informal conversations between individuals at peer organisations (e.g., hospital chief information officers) to standing meetings at the local university's city-wide COVID-19 response table. We also found that startups' flexibility and understanding of the local context enabled them to play a highly valuable role in emergency response. However, pandemic fueled "hypergrowth" created risks for startups, such as introducing opportunities for deviation away from their core value proposition. Finally, we found each partnership navigated intense workloads, burnout, and personnel turnover through the pandemic. Strong partnerships required healthy, motivated teams. Visibility into and engagement in partnership governance, belief in partnership impact, and strong emotional intelligence in managers promoted team well-being. Taken together, these findings can help to bridge the theory-to-practice gap and guide effective cross-sector partnerships during public health emergencies.

A sub-national real-time epidemiological and vaccination database for the COVID-19 pandemic in Canada.

The COVID-19 pandemic has demonstrated the need for real-time, open-access epidemiological information to inform public health decision-making and outbreak control efforts. In Canada, authority for healthcare delivery primarily lies at the provincial and territorial level; however, at the outset of the pandemic no definitive pan-Canadian COVID-19 datasets were available. The COVID-19 Canada Open Data Working Group was created to fill this crucial data gap. As a team of volunteer contributors, we collect daily COVID-19 data from a variety of governmental and non-governmental sources and curate a line-list of cases and mortality for all provinces and territories of Canada, including information on location, age, sex, travel history, and exposure, where available. We also curate time series of COVID-19 recoveries, testing, and vaccine doses administered and distributed. Data are recorded systematically at a fine sub-national scale, which can be used to support robust understanding of COVID-19 hotspots. We continue to maintain this dataset, and an accompanying online dashboard, to provide a reliable pan-Canadian COVID-19 resource to researchers, journalists, and the general public.

On the application of additive manufacturing methods for auxetic structures: a review.

Auxetic structures are a special class of structural components that exhibit a negative Poisson's ratio (NPR) because of their constituent materials, internal microstructure, or structural geometry. To realize such structures, specialized manufacturing processes are required to achieve a dimensional accuracy, reduction of material wastage, and a quicker fabrication. Hence, additive manufacturing (AM) techniques play a pivotal role in this context. AM is a layer-wise manufacturing process and builds the structure as per the designed geometry with appreciable precision and accuracy. Hence, it is extremely beneficial to fabricate auxetic structures using AM, which is otherwise a tedious and expensive task. In this study, a detailed discussion of the various AM techniques used in the fabrication of auxetic structures is presented. The advancements and advantages put forward by the AM domain have offered a plethora of opportunities for the fabrication and development of unconventional structures. Therefore, the authors have attempted to provide a meaningful encapsulation and a detailed discussion of the most recent of such advancements pertaining to auxetic structures. The article opens with a brief history of the growth of auxetic materials and later auxetic structures. Subsequently, discussions centering on the different AM techniques employed for the realization of auxetic structures are conducted. The basic principle, advantages, and disadvantages of these processes are discussed to provide an in-depth understanding of the current level of research. Furthermore, the performance of some of the prominent auxetic structures realized through these methods is discussed to compare their benefits and shortcomings. In addition, the influences of geometric and process parameters on such structures are evaluated through a comprehensive review to assess their feasibility for the later-mentioned applications. Finally, valuable insights into the applications, limitations, and prospects of AM for auxetic structures are provided to enable the readers to gauge the vitality of such manufacturing as a production method.

Development and Validation of a Machine Learning Model Using Administrative Health Data to Predict Onset of Type 2 Diabetes.

Importance: Systems-level barriers to diabetes care could be improved with population health planning tools that accurately discriminate between high- and low-risk groups to guide investments and targeted interventions. Objective: To develop and validate a population-level machine learning model for predicting type 2 diabetes 5 years before diabetes onset using administrative health data. Design, Setting, and Participants: This decision analytical model study used linked administrative health data from the diverse, single-payer health system in Ontario, Canada, between January 1, 2006, and December 31, 2016. A gradient boosting decision tree model was trained on data from 1 657 395 patients, validated on 243 442 patients, and tested on 236 506 patients. Costs associated with each patient were estimated using a validated costing algorithm. Data were analyzed from January 1, 2006, to December 31, 2016. Exposures: A random sample of 2 137 343 residents of Ontario without type 2 diabetes was obtained at study start time. More than 300 features from data sets capturing demographic information, laboratory measurements, drug benefits, health care system interactions, social determinants of health, and ambulatory care and hospitalization records were compiled over 2-year patient medical histories to generate quarterly predictions. Main Outcomes and Measures: Discrimination was assessed using the area under the receiver operating characteristic curve statistic, and calibration was assessed visually using calibration plots. Feature contribution was assessed with Shapley values. Costs were estimated in 2020 US dollars. Results: This study trained a gradient boosting decision tree model on data from 1 657 395 patients (12 900 257 instances; 6 666 662 women [51.7%]). The developed model achieved a test area under the curve of 80.26 (range, 80.21-80.29), demonstrated good calibration, and was robust to sex, immigration status, area-level marginalization with regard to material deprivation and race/ethnicity, and low contact with the health care system. The top 5% of patients predicted as high risk by the model represented 26% of the total annual diabetes cost in Ontario. Conclusions and Relevance: In this decision analytical model study, a machine learning model approach accurately predicted the incidence of diabetes in the population using routinely collected health administrative data. These results suggest that the model could be used to inform decision-making for population health planning and diabetes prevention.