Tag: pubmed

PLoS Negl Trop Dis. 2022 Jun 15;16(6):e0010390. doi: 10.1371/journal.pntd.0010390. Online ahead of print.

ABSTRACT

BACKGROUND: Leishmaniases are neglected tropical diseases that inflict great burden to poor areas of the globe. Intense research has aimed to identify parasite genetic signatures predictive of infection outcomes. Consistency of diagnostic tools based on these markers would greatly benefit from accurate understanding of Leishmania spp. population genetics. We explored two chromosomal loci to characterize a population of L. braziliensis causing human disease in Northeast Brazil.

METHODOLOGY/PRINCIPAL FINDINGS: Two temporally distinct samples of L. braziliensis were obtained from patients attending the leishmaniasis clinic at the village of Corte de Pedra: (2008-2011) primary sample, N = 120; (1999-2001) validation sample, N = 35. Parasites were genotyped by Sanger’s sequencing of two 600 base pairs loci starting at nucleotide positions 3,074 and 425,451 of chromosomes 24 and 28, respectively. Genotypes based on haplotypes of biallelic positions in each locus were tested for several population genetic parameters as well as for geographic clustering within the region. Ample geographic overlap of genotypes at the two loci was observed as indicated by non-significant Cusick and Edward’s comparisons. No linkage disequilibrium was detected among combinations of haplotypes for both parasite samples. Homozygous and heterozygous genotypes displayed Hardy-Weinberg equilibrium (HWE) at both loci in the two samples when straight observed and expected counts were compared by Chi-square (p>0.5). However, Bayesian statistics using one million Monte-Carlo randomizations disclosed a less robust HWE for chromosome 24 genotypes, particularly in the primary sample (p = 0.04). Fixation indices (Fst) were consistently lower than 0.05 among individuals of the two samples at both tested loci, and no intra-populational structuralization could be detected using STRUCTURE software.

CONCLUSIONS/SIGNIFICANCE: These findings suggest that L. braziliensis can maintain stable populations in foci of human leishmaniasis and are capable of robust genetic recombination possibly due to events of sexual reproduction during the parasite’s lifecycle.

PMID:35704664 | DOI:10.1371/journal.pntd.0010390

J Osteopath Med. 2022 Jun 15. doi: 10.1515/jom-2022-0027. Online ahead of print.

ABSTRACT

CONTEXT: Evidence-based medicine (EBM) is the application of scientific evidence while treating a patient. To date, however, there is very little evidence describing how residents in emergency medicine understand and incorporate EBM into practice.

OBJECTIVES: The aim of this study was to determine EBM theoretical and quantitative knowledge in emergency medicine residents in community hospital-based training programs.

METHODS: A sample of emergency medicine residents from nine hospitals was enrolled to complete a cross-sectional assessment of EBM skills from April 2021 through June 2021. Performance on the Fresno Test of Evidence-Based Medicine (FTEBM) was assessed utilizing descriptive statistics, t tests, and one-way analysis of variance.

RESULTS: A total of 50.8% (124/244) of current emergency medicine residents completed the FTEBM during the study period. No significant difference on FTEBM scores was noted between the different types of medical degrees (DO vs. MD) (p=0.511), holding an advanced research degree (p=0.117), or between each postgraduate year of training (p=0.356). The mean score of those residents who rated their knowledge of EBM as average or higher was 36.0% (32.8-39.1%). The mean score of those residents who rated their programs as having an “average” or higher institutional focus on EBM was 34.9% (32.2-37.6%).

CONCLUSIONS: Participating emergency medicine residents show an incomplete understanding of EBM both in theory and applied computations despite rating themselves as having an average understanding. Emergency medicine residencies would be well suited to implement a standardized EBM curriculum that focuses on longitudinal reinforcement of key concepts needed for the practicing physician.

PMID:35704661 | DOI:10.1515/jom-2022-0027

PLoS One. 2022 Jun 15;17(6):e0269359. doi: 10.1371/journal.pone.0269359. eCollection 2022.

ABSTRACT

Oral health is a critical component of human health but is sometimes forgotten, particularly during humanitarian crises. This research aimed to ascertain the state of oral health among Rohingya refugees living in one of the largest refugee camps and evaluate their knowledge and practice of oral health. A multicenter cross-sectional survey was conducted among 477 participants from July to September 2021 using a structured questionnaire. There were 34 Rohingya camps and out of those 14 camps were accessible for data collection. The study participants were between 18-82 years residing in the refugee camps under Cox’s Bazar. The majority of participants (53.88%) were female and between the ages of 25 and 45. Around 46.12% of respondents did not have basic oral health knowledge, while 53.67% were in need of dental care. Nearly half of the participants demonstrated poor oral health practices. Participants’ age and educational level were positively associated with oral health knowledge (p = 0.02 and p<0.001). Furthermore, the knowledge level was positively associated with oral health practice (p = .025). Participants with a history of teeth pain and discomfort in the last 12 months were ten times more likely to seek treatment (OR = 9.93, CI: 5.591-17.64). The study indicated a growing demand for dental care among Rohingya refugees staying in Bangladesh. To reduce the severity of oral health issues, use of minimally invasive restorative procedures can be suggested in camps. New oral health promotion campaigns should be emphasized and proper education, ideally in their original language, can be beneficial.

PMID:35704660 | DOI:10.1371/journal.pone.0269359

PLoS Comput Biol. 2022 Jun 15;18(6):e1009486. doi: 10.1371/journal.pcbi.1009486. Online ahead of print.

ABSTRACT

The tumor microenvironment (TME), which characterizes the tumor and its surroundings, plays a critical role in understanding cancer development and progression. Recent advances in imaging techniques enable researchers to study spatial structure of the TME at a single-cell level. Investigating spatial patterns and interactions of cell subtypes within the TME provides useful insights into how cells with different biological purposes behave, which may consequentially impact a subject’s clinical outcomes. We utilize a class of well-known spatial summary statistics, the K-function and its variants, to explore inter-cell dependence as a function of distances between cells. Using techniques from functional data analysis, we introduce an approach to model the association between these summary spatial functions and subject-level outcomes, while controlling for other clinical scalar predictors such as age and disease stage. In particular, we leverage the additive functional Cox regression model (AFCM) to study the nonlinear impact of spatial interaction between tumor and stromal cells on overall survival in patients with non-small cell lung cancer, using multiplex immunohistochemistry (mIHC) data. The applicability of our approach is further validated using a publicly available multiplexed ion beam imaging (MIBI) triple-negative breast cancer dataset.

PMID:35704658 | DOI:10.1371/journal.pcbi.1009486

PLoS One. 2022 Jun 15;17(6):e0268441. doi: 10.1371/journal.pone.0268441. eCollection 2022.

ABSTRACT

BACKGROUND: Trachoma is the leading infectious disease that leads to blindness worldwide, especially in developing countries. Though Ethiopia had targeted a trachoma elimination program by 2020, the problem worsens, particularly in the Amhara Region. Even though sustained intervention measures are undertaken across the region, it is unclear why trachoma is still a significant public health problem. So, this study assessed the prevalence of active trachoma and associated factors among 1-9 years of age children from model and non-model kebeles in Dangila district Amhara Region, Northwest Ethiopia.

METHODS: A community-based comparative cross-sectional study was conducted from 20th September 2019 to 29th October 2019. A multistage stratified random sampling technique was used to reach 704 children from model and non-model kebeles. Samples were allocated proportionally to model and non-model kebeles. A structured and pretested data collection tool and observational checklist was used to manage the necessary data. Data were coded and entered in Epidata version 4.6, and further analysis was done using SPSS version 20 software. Bivariable and multivariable logistic regression analysis was employed to identify factors associated with active trachoma. Adjusted Odds Ratios (AOR), p-value, and respected Confidence Interval (CI) were used to report the findings.

RESULTS: Seven hundred four children were included in this study, with a response rate of 97.8%. The overall prevalence of active trachoma was 6% (95% CI: 4.5, 8.1). The prevalence of active trachoma among non-model and model Kebele was not significantly different. Still, the prevalence of active trachoma among children from model Kebele were [4.5%, (95% CI: 2.4%, 7.1%)] relatively lower compared with non-model kebeles, [7.6%, 95% CI: (4.9%, 10.9%)]. Moreover, not using latrine (AOR = 4.29, 95% CI: 1.96, 9.34), fly-eye contact (AOR = 2.59, 95% CI: 1.11, 6.03), presence of sleep in eyes (AOR = 2.46, 95% CI: 1.10, 5.47), presence of ocular discharge (AOR = 2.79, 95% CI: 1.30, 6.00), presence of nasal discharges (AOR = 2.67, 95% CI: 1.21, 5.90) and washing faces with soap (AOR = 0.22, 95% CI: 0.07, 0.69) were found significantly associated with the prevalence of active trachoma among children 1-9 years old.

CONCLUSIONS: The prevalence of active trachoma in the model and non-model kebeles was high and did not show a statistical difference. Attention to be given to latrine utilization, washing face with soap, and other personal hygiene activities.

PMID:35704657 | DOI:10.1371/journal.pone.0268441

PLoS One. 2022 Jun 15;17(6):e0269919. doi: 10.1371/journal.pone.0269919. eCollection 2022.

ABSTRACT

BACKGROUND: Non-adherence to insulin therapy is a major global public health issue that has a causal relationship with increased diabetic complications that leads to further increase in the health care cost. However, adherence to insulin therapy and associated factors among diabetic mellitus (DM) patients are still not studied adequately in Ethiopia.

OBJECTIVE: To assess the adherence to insulin therapy and associated factors among type 1 and type 2 diabetic patients on follow-up at Madda Walabu University-Goba Referral Hospital, South East Ethiopia.

METHOD: An institution-based, cross-sectional study was employed among 311 both type 1 and type 2 diabetic patients, Madda Walabu University-Goba Referral Hospital from March 4 to April 30, 2020. Study participants were recruited with simple random sampling method. Adherence to insulin therapy was measured by 8-item Morisky medication adherence scale. Therefore from these 8-items, those who score 6 or more are considered as adherent to insulin therapy. The data were collected through interviewer administered questionnaires by trained graduating class nurse students. The data were entered to Epidata version 3.1, and analyzed with SPSS version 25. Bivariate and multivariable logistic regression analyses were used to identify factors associated with adherence to insulin therapy. Statistical significance were declared at p <0.05.

RESULT: A total of 311 patients participate in the study with response rate of 100%. Among these only 38.9% of them were adherent to insulin therapy with a CI of [33.5, 44.3]. Having glucometer (AOR = 3.88; 95% CI [1.46, 10.35]), regular hospital follow-up (AOR = 3.13; 95% CI [1.12, 8.70]), being knowledgeable (AOR = 3.36; 95% CI [1.53, 7.37]), and favorable attitudes (AOR = 4.55; 95%CI [1.68, 12.34]) were the factor associated with adherence to insulin therapy.

CONCLUSION: This study concluded that adherence to insulin therapy was low in the study area. Having glucometer, regular hospital follow-up, being knowledgeable, and favorable attitudes were the factor associated with adherence to insulin therapy. Attention should be paid to help diabetic patients on acquiring knowledge regarding the need of consistent adherence to insulin therapy and its complications.

PMID:35704654 | DOI:10.1371/journal.pone.0269919

PLoS One. 2022 Jun 15;17(6):e0266945. doi: 10.1371/journal.pone.0266945. eCollection 2022.

ABSTRACT

BACKGROUND: The COVID-19 pandemic in Russia has already resulted in 500,000 excess deaths, with more than 5.6 million cases registered officially by July 2021. Surveillance based on case reporting has become the core pandemic monitoring method in the country and globally. However, population-based seroprevalence studies may provide an unbiased estimate of the actual disease spread and, in combination with multiple surveillance tools, help to define the pandemic course. This study summarises results from four consecutive serological surveys conducted between May 2020 and April 2021 at St. Petersburg, Russia and combines them with other SARS-CoV-2 surveillance data.

METHODS: We conducted four serological surveys of two random samples (May-June, July-August, October-December 2020, and February-April 2021) from adults residing in St. Petersburg recruited with the random digit dialing (RDD), accompanied by a telephone interview to collect information on both individuals who accepted and declined the invitation for testing and account for non-response. We have used enzyme-linked immunosorbent assay CoronaPass total antibodies test (Genetico, Moscow, Russia) to report seroprevalence. We corrected the estimates for non-response using the bivariate probit model and also accounted the test performance characteristics, obtained from independent assay evaluation. In addition, we have summarised the official registered cases statistics, the number of hospitalised patients, the number of COVID-19 deaths, excess deaths, tests performed, data from the ongoing SARS-CoV-2 variants of concern (VOC) surveillance, the vaccination uptake, and St. Petersburg search and mobility trends. The infection fatality ratios (IFR) have been calculated using the Bayesian evidence synthesis model.

FINDINGS: After calling 113,017 random mobile phones we have reached 14,118 individuals who responded to computer-assisted telephone interviewing (CATI) and 2,413 provided blood samples at least once through the seroprevalence study. The adjusted seroprevalence in May-June, 2020 was 9.7% (95%: 7.7-11.7), 13.3% (95% 9.9-16.6) in July-August, 2020, 22.9% (95%: 20.3-25.5) in October-December, 2021 and 43.9% (95%: 39.7-48.0) in February-April, 2021. History of any symptoms, history of COVID-19 tests, and non-smoking status were significant predictors for higher seroprevalence. Most individuals remained seropositive with a maximum 10 months follow-up. 92.7% (95% CI 87.9-95.7) of participants who have reported at least one vaccine dose were seropositive. Hospitalisation and COVID-19 death statistics and search terms trends reflected the pandemic course better than the official case count, especially during the spring 2020. SARS-CoV-2 circulation showed rather low genetic SARS-CoV-2 lineages diversity that increased in the spring 2021. Local VOC (AT.1) was spreading till April 2021, but B.1.617.2 substituted all other lineages by June 2021. The IFR based on the excess deaths was equal to 1.04 (95% CI 0.80-1.31) for the adult population and 0.86% (95% CI 0.66-1.08) for the entire population.

CONCLUSION: Approximately one year after the COVID-19 pandemic about 45% of St. Petersburg, Russia residents contracted the SARS-CoV-2 infection. Combined with vaccination uptake of about 10% it was enough to slow the pandemic at the present level of all mitigation measures until the Delta VOC started to spread. Combination of several surveillance tools provides a comprehensive pandemic picture.

PMID:35704649 | DOI:10.1371/journal.pone.0266945

PLoS One. 2022 Jun 15;17(6):e0269251. doi: 10.1371/journal.pone.0269251. eCollection 2022.

ABSTRACT

Information and communications technology (ICT) has been widely embraced in many developing economies in recent times. Extant research reveals that ICT increases economic growth. Beyond economic growth, improved access to information, markets and economic opportunities via information and communications technology have the potential to influence other dimensions of public welfare. This study quantitatively examines the effects of ICT on selected health and gender dimensions of Pacific Island developing countries’ populations. The results show a statistically significant and positive impact of ICT on health and gender outcomes. Our results are robust with an alternative modeling approach, different control variables, and different measures of health and gender outcomes. We further establish that the health outcome of technology has a valid pass-through of income. The study suggests policy implications for the Pacific and other developing countries striving to enhance the health and gender outcomes of SGDs.

PMID:35704646 | DOI:10.1371/journal.pone.0269251

JMIR Med Inform. 2022 Jun 15;10(6):e34678. doi: 10.2196/34678.

ABSTRACT

BACKGROUND: New artificial intelligence (AI) tools are being developed at a high speed. However, strategies and practical experiences surrounding the adoption and implementation of AI in health care are lacking. This is likely because of the high implementation complexity of AI, legacy IT infrastructure, and unclear business cases, thus complicating AI adoption. Research has recently started to identify the factors influencing AI readiness of organizations.

OBJECTIVE: This study aimed to investigate the factors influencing AI readiness as well as possible barriers to AI adoption and implementation in German hospitals. We also assessed the status quo regarding the dissemination of AI tools in hospitals. We focused on IT decision makers, a seldom studied but highly relevant group.

METHODS: We created a web-based survey based on recent AI readiness and implementation literature. Participants were identified through a publicly accessible database and contacted via email or invitational leaflets sent by mail, in some cases accompanied by a telephonic prenotification. The survey responses were analyzed using descriptive statistics.

RESULTS: We contacted 609 possible participants, and our database recorded 40 completed surveys. Most participants agreed or rather agreed with the statement that AI would be relevant in the future, both in Germany (37/40, 93%) and in their own hospital (36/40, 90%). Participants were asked whether their hospitals used or planned to use AI technologies. Of the 40 participants, 26 (65%) answered “yes.” Most AI technologies were used or planned for patient care, followed by biomedical research, administration, and logistics and central purchasing. The most important barriers to AI were lack of resources (staff, knowledge, and financial). Relevant possible opportunities for using AI were increase in efficiency owing to time-saving effects, competitive advantages, and increase in quality of care. Most AI tools in use or in planning have been developed with external partners.

CONCLUSIONS: Few tools have been implemented in routine care, and many hospitals do not use or plan to use AI in the future. This can likely be explained by missing or unclear business cases or the need for a modern IT infrastructure to integrate AI tools in a usable manner. These shortcomings complicate decision-making and resource attribution. As most AI technologies already in use were developed in cooperation with external partners, these relationships should be fostered. IT decision makers should assess their hospitals’ readiness for AI individually with a focus on resources. Further research should continue to monitor the dissemination of AI tools and readiness factors to determine whether improvements can be made over time. This monitoring is especially important with regard to government-supported investments in AI technologies that could alleviate financial burdens. Qualitative studies with hospital IT decision makers should be conducted to further explore the reasons for slow AI.

PMID:35704378 | DOI:10.2196/34678

JMIR Med Inform. 2022 Jun 15;10(6):e37689. doi: 10.2196/37689.

ABSTRACT

BACKGROUND: Sepsis is diagnosed in millions of people every year, resulting in a high mortality rate. Although patients with sepsis present multimorbid conditions, including cancer, sepsis predictions have mainly focused on patients with severe injuries.

OBJECTIVE: In this paper, we present a machine learning-based approach to identify the risk of sepsis in patients with cancer using electronic health records (EHRs).

METHODS: We utilized deidentified anonymized EHRs of 8580 patients with cancer from the Samsung Medical Center in Korea in a longitudinal manner between 2014 and 2019. To build a prediction model based on physical status that would differ between sepsis and nonsepsis patients, we analyzed 2462 laboratory test results and 2266 medication prescriptions using graph network and statistical analyses. The medication relationships and lab test results from each analysis were used as additional learning features to train our predictive model.

RESULTS: Patients with sepsis showed differential medication trajectories and physical status. For example, in the network-based analysis, narcotic analgesics were prescribed more often in the sepsis group, along with other drugs. Likewise, 35 types of lab tests, including albumin, globulin, and prothrombin time, showed significantly different distributions between sepsis and nonsepsis patients (P<.001). Our model outperformed the model trained using only common EHRs, showing an improved accuracy, area under the receiver operating characteristic (AUROC), and F1 score by 11.9%, 11.3%, and 13.6%, respectively. For the random forest-based model, the accuracy, AUROC, and F1 score were 0.692, 0.753, and 0.602, respectively.

CONCLUSIONS: We showed that lab tests and medication relationships can be used as efficient features for predicting sepsis in patients with cancer. Consequently, identifying the risk of sepsis in patients with cancer using EHRs and machine learning is feasible.

PMID:35704364 | DOI:10.2196/37689