Tag: nevin manimala

Rom J Intern Med. 2022 Sep 25. doi: 10.2478/rjim-2022-0016. Online ahead of print.

ABSTRACT

Introduction: This study aims to compare the predictive capacity of ABCD, DiaRem2, Ad-DiaRem, and DiaBetter scoring systems for type 2 diabetes mellitus (T2DM) remission in Turkish adult morbidly obese patients who underwent SG. Methods: This retrospective cohort study included 80 patients who underwent sleeve gastrectomy (SG) operation, were diagnosed with T2DM preoperatively, and had at least one-year follow-up after surgery. Because bariatric surgery is performed on patients with class III obesity (BMI ≥ 40 kg/m2) or class II obesity (BMI ≥ 35 kg/m2) with obesity releated comorbid conditions in our hospital, our study cohort consisted of these patients. Results: The diagnostic performance of the DiaBetter, DiaRem2, Ad-DiaRem and ABCD for identifying diabetes remission, assessed by the AUC was 0.882 (95% CI, 0.807-0.958, p < 0.001), 0.862 (95% CI, 0.779-0.945, p < 0.001), 0.849 (95% CI, 0.766-0.932, p < 0.001) and 0.726 (95% CI, 0.601-0.851, p = 0.002), respectively. The AUCs of the Ad-Diarem, DiaBetter and DiaRem2 were statistically higher than AUC of the ABCD (all p-value < 0.001). Besides, there was no statistically significant difference in AUCs of the Ad-Diarem, DiaBetter and DiaRem scores (all p-value > 0.05). Conclusion: Ad-Dairem, DiaBetter and DaiRem scoring systems were found to provide a successful prediction for diabetes remission in sleeve gastrectomy patients. It was observed that the predictive power of the ABCD scoring system was lower than the other systems. We think that the use of scoring systems for diabetes remission, which have a simple use, will become widespread.

PMID:36153731 | DOI:10.2478/rjim-2022-0016

Acta Dermatovenerol Croat. 2022 Jul;30(1):18-24.

ABSTRACT

Tests which have proven their efficacy and reliability in the follow-up of pemphigus patients are used only on a limited scale or take time to complete due to a lack of technical facilities in several centers. Therefore, more accessible methods are being considered for monitoring disease activity. We aimed to investigate the relationship between platelet function and disease activity based on the change in proinflammatory cytokine profile in pemphigus pathogenesis. The size of platelets correlates positively with their activity. Platelet sizes can be evaluated by the platelet volume index consisting of mean platelet volume (MPV), platelet-crit (PCT), and platelet distribution width (PDW). These indicators can be easily measured in complete blood count (CBC) with automatic blood counting devices, which do not require additional costs and are readily available. Patients diagnosed with pemphigus between April 2010 and February 2016 (n=18) in our center were retrospectively included in the study. Demographic data, follow-up period, clinical variants of the patients, platelet parameters (MPV, PDW, PCT), and platelet count (PLT) in CBC analysis with concurrent clinical activity, as well as indirect immunofluorescence (IIF) findings (positive highest titer) at the 6th and 12th month were recorded for each patient. MPV changes were consistent with the course of the disease. A statistically significant decrease in PCT levels was observed at the 12th month compared with the baseline levels (P<0.05). According to the baseline measurement, a statistically significant positive correlation (58.9%) was found between the 12-month difference measurements of IIF and PCT. Our data demonstrated that PCT decrease is correlated with IIF values. The significant correlation between PCT and IIF values in our study is important in showing the possible role of platelet index in the measurement of disease activity.

PMID:36153715

Health Rep. 2022 Sep 15;33(9):21-31. doi: 10.25318/82-003-x202200900003-eng.

ABSTRACT

BACKGROUND: In 2018, Canada legalized the use and sale of non-medical cannabis, with most provinces also permitting home cultivation. To advance the knowledge of home cultivation patterns in Canada within the context of legalization, this study examines (1) the demographics and use patterns of cannabis home growers before and after legalization and (2) the relationship between home cultivation and cannabis-related risks, including workplace use and driving after cannabis use(DACU).

DATA AND METHODS: The study is based on seven waves of the National Cannabis Survey, dating from 2018 to 2019. Descriptive statistics were used to analyze home cultivation across several individual and sociodemographic characteristics pre- and post-legalization. Logistic regression was used to examine whether home cultivation is correlated to selected cannabis-related risks.

RESULTS: The rate and demographics of home cultivation remained relatively unchanged post-legalization. Those most likely to cultivate cannabis post-legalization were male; 35 years and older; not single; married, common law, divorced, separated or widowed; lived in the Atlantic provinces; consumed cannabis medically or medically and non-medically on a daily or almost daily basis; had more than a high school diploma; and reported “smoking” as their primary consumption method. Home cultivation was correlated to workplace use but not to DACU.

INTERPRETATION: The research provides early insights into home cultivation within a legalized framework. It also shows a relationship between home cultivation and certain cannabis-related risks (e.g., workplace use), suggesting a need for future research to determine whether tailored education and policy interventions are needed to target cannabis home growers.

PMID:36153711 | DOI:10.25318/82-003-x202200900003-eng

Health Rep. 2022 Sep 15;33(9):11-20. doi: 10.25318/82-003-x202200900002-eng.

ABSTRACT

The Canadian Community Health Survey (CCHS) – Nutrition 2004 (n=35,107; interview dates from January 2004 to January 2005) linked to the Canadian Vital Statistics – Death Database (CVSD) (2011) represents a novel linkage of a population-based, nationally representative nutrition survey with routinely collected mortality records (including date and cause of death). The linkage was done through individual tax data in Canada, and contains longitudinal records for 29,897 Canadians aged 0 years and older-1,753 of whom died-in the 10 provinces of Canada. The median follow-up time was 7.49 years, with 102,953 person-years among males and 114,876 person-years among females (unweighted), and included a special sampling survey weight (for linked data) to account for those who did not agree to share and link their information. The CCHS – Nutrition 2004 linked to CVSD has been used to evaluate associations between lifestyle and sociodemographic characteristics and mortality. Using these data, statistical methods have been developed and tested to control random and systematic measurement errors when evaluating the relationship between different dietary exposures (evaluated using repeated 24-hour dietary recalls) and health outcomes. The linked data are available through Statistics Canada’s Research Data Centres.

PMID:36153710 | DOI:10.25318/82-003-x202200900002-eng

Health Rep. 2022 Sep 15;33(9):3-10. doi: 10.25318/82-003-x202200900001-eng.

ABSTRACT

BACKGROUND: Throughout the COVID-19 pandemic, Canadian public health officials have mandated and recommended precautions to slow the spread of COVID-19. This study examined which population groups were less compliant with precautions, such as mask-wearing and self-isolating, and where they were located in Canada.

DATA AND METHODS: Results are from the Canadian COVID-19 Antibody and Health Survey, a national survey aimed at estimating how many Canadians who were older than one year and living in private households had antibodies in their blood against the SARS-CoV-2 virus. Questionnaire data were collected in the 10 provinces and 3 territorial capitals, from November 2020 to April 2021. Respondents were asked about compliance with precautions related to COVID-19. Weighted prevalences and logistic regression models were used to identify which population groups were less compliant with precautions to prevent the spread of COVID-19, and where they were located in Canada.

RESULTS: Significant differences in compliance with precautions were found by sex, region, urban versus rural location, age, income, presence of chronic conditions, household size and work status. With covariate adjustment, Canadians who were less compliant with precautions were males, those living in the territorial capitals, those in rural areas, and people aged 34 and younger (compared with people aged 65 and older). Additional differences were found when analyzing compliance with consistently recommended precautions compared with those usually recommended.

INTERPRETATION: As Canada continues to navigate the waves of the pandemic, and with the emergence of new variants, precautions are still being mandated or recommended in many jurisdictions and locations. Continuing to understand which population groups were less compliant in earlier waves and where they were located in Canada can be beneficial to ongoing and future public health efforts to slow the transmission of COVID-19.

PMID:36153709 | DOI:10.25318/82-003-x202200900001-eng

J Clin Nurs. 2022 Sep 25. doi: 10.1111/jocn.16545. Online ahead of print.

ABSTRACT

AIMS AND OBJECTIVES: The study was conducted to describe long-term perceived health among patients after a percutaneous coronary intervention as well as clarify the associations between perceived health and various factors.

BACKGROUND: Perceived health is an important outcome for coronary heart disease patients who have undergone percutaneous coronary intervention. Poor perceived health predicts low adherence to treatment, morbidity and mortality.

DESIGN: An explanatory and descriptive survey with a six-year follow-up (STROBE Statement: File S1).

METHODS: Baseline data (n = 416) were collected in 2013, with follow-up data collected from the same study group in 2019 (n = 154) at two university hospitals and three central hospitals in Finland. The employed self-reported questionnaire was based on the EuroQoL visual analogue scale and EuroQol five-dimensional scale. Data were analysed using descriptive statistics and multivariate methods.

RESULTS: Perceived health did not significantly differ four months or six years after percutaneous coronary intervention. The respondents most commonly reported pain and discomfort (62.1%), problems in mobility (50.3%), issues with usual activities (27.5%), and anxiety and depression (24.0%). Managing self-care (8.5%) was least likely to be an issue for the respondents. A majority of the reported problems were of a mild nature. The consumption of an adequate amount of vegetables, lower systolic blood pressure, regular follow-up treatment, lack of prior invasive procedures, and younger age predicted better scores for both perceived health and its separate dimensions.

CONCLUSION: Regular follow-up is important to ensure after percutaneous coronary intervention to identify patients with pain and discomfort, mobility problems, depression and anxiety. Healthcare professionals should pay particular attention to elderly patients, who have undergone severe invasive procedures.

RELEVANCE TO CLINICAL PRACTICE: This study confirms the importance of regular follow-ups for post-percutaneous coronary intervention patients.

PATIENT OR PUBLIC CONTRIBUTION: Patients have completed a self-reported questionnaire based on informed consent.

PMID:36153702 | DOI:10.1111/jocn.16545

BMC Biol. 2022 Sep 24;20(1):208. doi: 10.1186/s12915-022-01398-w.

ABSTRACT

BACKGROUND: Transcriptomic analysis is crucial for understanding the functional elements of the genome, with the classic method consisting of screening transcriptomics datasets for differentially expressed genes (DEGs). Additionally, since 2005, weighted gene co-expression network analysis (WGCNA) has emerged as a powerful method to explore relationships between genes. However, an approach combining both methods, i.e., filtering the transcriptome dataset by DEGs or other criteria, followed by WGCNA (DEGs + WGCNA), has become common. This is of concern because such approach can affect the resulting underlying architecture of the network under analysis and lead to wrong conclusions. Here, we explore a plot twist to transcriptome data analysis: applying WGCNA to exploit entire datasets without affecting the topology of the network, followed with the strength and relative simplicity of DEG analysis (WGCNA + DEGs). We tested WGCNA + DEGs against DEGs + WGCNA to publicly available transcriptomics data in one of the most transcriptomically complex tissues and delicate processes: vertebrate gonads undergoing sex differentiation. We further validate the general applicability of our approach through analysis of datasets from three distinct model systems: European sea bass, mouse, and human.

RESULTS: In all cases, WGCNA + DEGs clearly outperformed DEGs + WGCNA. First, the network model fit and node connectivity measures and other network statistics improved. The gene lists filtered by each method were different, the number of modules associated with the trait of interest and key genes retained increased, and GO terms of biological processes provided a more nuanced representation of the biological question under consideration. Lastly, WGCNA + DEGs facilitated biomarker discovery.

CONCLUSIONS: We propose that building a co-expression network from an entire dataset, and only thereafter filtering by DEGs, should be the method to use in transcriptomic studies, regardless of biological system, species, or question being considered.

PMID:36153614 | DOI:10.1186/s12915-022-01398-w

Parasit Vectors. 2022 Sep 24;15(1):336. doi: 10.1186/s13071-022-05471-9.

ABSTRACT

BACKGROUND: Mathematical modelling of host-parasite systems has seen tremendous developments and broad applications in theoretical and applied ecology. The current study focuses on the infection dynamics of a gyrodactylid-fish system. Previous experimental studies have explored the infrapopulation dynamics of co-infecting ectoparasites, Gyrodactylus turnbulli and G. bullatarudis, on their fish host, Poecilia reticulata, but questions remain about parasite microhabitat preferences, host survival and parasite virulence over time. Here, we use more advanced statistics and a sophisticated mathematical model to investigate these questions based on empirical data to add to our understanding of this gyrodactylid-fish system.

METHODS: A rank-based multivariate Kruskal-Wallis test coupled with its post-hoc tests and graphical summaries were used to investigate the spatial and temporal parasite distribution of different gyrodactylid strains across different host populations. By adapting a multi-state Markov model that extends the standard survival models, we improved previous estimates of survival probabilities. Finally, we quantified parasite virulence of three different strains as a function of host mortality and recovery across different fish stocks and sexes.

RESULTS: We confirmed that the captive-bred G. turnbulli and wild G. bullatarudis strains preferred the caudal and rostral regions respectively across different fish stocks; however, the wild G. turnbulli strain changed microhabitat preference over time, indicating microhabitat preference of gyrodactylids is host and time dependent. The average time of host infection before recovery or death was between 6 and 14 days. For this gyrodactylid-fish system, a longer period of host infection led to a higher chance of host recovery. Parasite-related mortalities are host, sex and time dependent, whereas fish size is confirmed to be the key determinant of host recovery.

CONCLUSION: From existing empirical data, we provided new insights into the gyrodactylid-fish system. This study could inform the modelling of other host-parasite interactions where the entire infection history of the host is of interest by adapting multi-state Markov models. Such models are under-utilised in parasitological studies and could be expanded to estimate relevant epidemiological traits concerning parasite virulence and host survival.

PMID:36153606 | DOI:10.1186/s13071-022-05471-9

Orphanet J Rare Dis. 2022 Sep 24;17(1):365. doi: 10.1186/s13023-022-02513-4.

ABSTRACT

BACKGROUND: Glut1 deficiency syndrome (Glut1-DS) is a rare metabolic encephalopathy. Familial forms are poorly investigated, and no previous studies have explored aspects of Glut1-DS over the course of life: clinical pictures, intelligence, life achievements, and quality of life in adulthood. Clinical, biochemical and genetic data in a cohort of familial Glut1-DS cases were collected from medical records. Intelligence was assessed using Raven’s Standard Progressive Matrices and Raven’s Colored Progressive Matrices in adults and children, respectively. An ad hoc interview focusing on life achievements and the World Health Organization Quality of Life Questionnaire were administered to adult subjects.

RESULTS: The clinical picture in adults was characterized by paroxysmal exercise-induced dyskinesia (PED) (80%), fatigue (60%), low intelligence (60%), epilepsy (50%), and migraine (50%). However, 20% of the adults had higher-than-average intelligence. Quality of Life (QoL) seemed unrelated to the presence of PED or fatigue in adulthood. An association of potential clinical relevance, albeit not statistically significant, was found between intelligence and QoL. The phenotype of familial Glut1-DS in children was characterized by epilepsy (83.3%), intellectual disability (50%), and PED (33%).

CONCLUSION: The phenotype of familial Glut1-DS shows age-related differences: epilepsy predominates in childhood; PED and fatigue, followed by epilepsy and migraine, characterize the condition in adulthood. Some adults with familial Glut1-DS may lead regular and fulfilling lives, enjoying the same QoL as unaffected individuals. The disorder tends to worsen from generation to generation, with new and more severe symptoms arising within the same family. Epigenetic studies might be useful to assess the phenotypic variability in Glut1-DS.

PMID:36153584 | DOI:10.1186/s13023-022-02513-4

BioData Min. 2022 Sep 24;15(1):20. doi: 10.1186/s13040-022-00308-8.

ABSTRACT

BACKGROUND: Under-five mortality is a matter of serious concern for child health as well as the social development of any country. The paper aimed to find the accuracy of machine learning models in predicting under-five mortality and identify the most significant factors associated with under-five mortality.

METHOD: The data was taken from the National Family Health Survey (NFHS-IV) of Uttar Pradesh. First, we used multivariate logistic regression due to its capability for predicting the important factors, then we used machine learning techniques such as decision tree, random forest, Naïve Bayes, K- nearest neighbor (KNN), logistic regression, support vector machine (SVM), neural network, and ridge classifier. Each model’s accuracy was checked by a confusion matrix, accuracy, precision, recall, F1 score, Cohen’s Kappa, and area under the receiver operating characteristics curve (AUROC). Information gain rank was used to find the important factors for under-five mortality. Data analysis was performed using, STATA-16.0, Python 3.3, and IBM SPSS Statistics for Windows, Version 27.0 software.

RESULT: By applying the machine learning models, results showed that the neural network model was the best predictive model for under-five mortality when compared with other predictive models, with model accuracy of (95.29% to 95.96%), recall (71.51% to 81.03%), precision (36.64% to 51.83%), F1 score (50.46% to 62.68%), Cohen’s Kappa value (0.48 to 0.60), AUROC range (93.51% to 96.22%) and precision-recall curve range (99.52% to 99.73%). The neural network was the most efficient model, but logistic regression also shows well for predicting under-five mortality with accuracy (94% to 95%)., AUROC range (93.4% to 94.8%), and precision-recall curve (99.5% to 99.6%). The number of living children, survival time, wealth index, child size at birth, birth in the last five years, the total number of children ever born, mother’s education level, and birth order were identified as important factors influencing under-five mortality.

CONCLUSION: The neural network model was a better predictive model compared to other machine learning models in predicting under-five mortality, but logistic regression analysis also shows good results. These models may be helpful for the analysis of high-dimensional data for health research.

PMID:36153553 | DOI:10.1186/s13040-022-00308-8