Nevin Manimala

Nephrology (Carlton). 2022 Aug 31. doi: 10.1111/nep.14108. Online ahead of print.

ABSTRACT

INTRODUCTION: Predicting outcome after transplantation of marginal kidneys is a challenging task. Donor creatinine or estimated glomerular filtration rate (eGFR) are integral components of the respective risk scores. However, there is uncertainty on which of their values obtained successively during procurement is the most suitable.

MATERIAL AND METHODS: This is a retrospective study of 221 adult brain death donors with marginal kidneys, transplanted in 223 recipients. We applied logistic regression analysis to investigate the association between initial (at hospital admission), nadir (lowest), zenith (highest) and terminal (at recovery) donor eGFR with primary non-function (PNF), delayed graft function (DGF), 3- and 12-months graft function and 1- and 3-years patient- and death censored graft survival.

RESULTS: In the multivariate analysis, admission, terminal, and lowest donor eGFR could most accurately predict DGF. The respective ORs [95% CI] were: 0.875 [0.771-0.993], 0.818 [95% CI: 0.726-0.922] and 0.793 [0.689-0.900]. Although not being significant for DGF (OR 0.931 [95% CI: 0.817-1.106]), the highest eGFR was the best predictor of 3-month graft function (adjusted b coefficient 1.161 [95% CI: 0.355-1.968]). Analysis of primary nonfunction showed that determination of initial and highest eGFR proved to be the best predictors. The respective ORs [95% CI] were: 0.804 [0.667-0.968] and 0.750 [0.611-0.919]. There were no differences in the risk associations of each of the four eGFR recordings with patient- and graft survival.

CONCLUSION: The various eGFR recordings determined during the procurement process of marginal donors can predict PNF, DGF and 3- and 12-months graft function. Regarding short term patient- and graft survival, there appears to be impacted by recipient factors rather than donor kidney function. This article is protected by copyright. All rights reserved.

PMID:36043436 | DOI:10.1111/nep.14108

Database (Oxford). 2022 Aug 31;2022:baac069. doi: 10.1093/database/baac069.

ABSTRACT

The coronavirus disease 2019 (COVID-19) pandemic has been severely impacting global society since December 2019. The related findings such as vaccine and drug development have been reported in biomedical literature-at a rate of about 10 000 articles on COVID-19 per month. Such rapid growth significantly challenges manual curation and interpretation. For instance, LitCovid is a literature database of COVID-19-related articles in PubMed, which has accumulated more than 200 000 articles with millions of accesses each month by users worldwide. One primary curation task is to assign up to eight topics (e.g. Diagnosis and Treatment) to the articles in LitCovid. The annotated topics have been widely used for navigating the COVID literature, rapidly locating articles of interest and other downstream studies. However, annotating the topics has been the bottleneck of manual curation. Despite the continuing advances in biomedical text-mining methods, few have been dedicated to topic annotations in COVID-19 literature. To close the gap, we organized the BioCreative LitCovid track to call for a community effort to tackle automated topic annotation for COVID-19 literature. The BioCreative LitCovid dataset-consisting of over 30 000 articles with manually reviewed topics-was created for training and testing. It is one of the largest multi-label classification datasets in biomedical scientific literature. Nineteen teams worldwide participated and made 80 submissions in total. Most teams used hybrid systems based on transformers. The highest performing submissions achieved 0.8875, 0.9181 and 0.9394 for macro-F1-score, micro-F1-score and instance-based F1-score, respectively. Notably, these scores are substantially higher (e.g. 12%, higher for macro F1-score) than the corresponding scores of the state-of-art multi-label classification method. The level of participation and results demonstrate a successful track and help close the gap between dataset curation and method development. The dataset is publicly available via https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/ for benchmarking and further development. Database URL https://ftp.ncbi.nlm.nih.gov/pub/lu/LitCovid/biocreative/.

PMID:36043400 | DOI:10.1093/database/baac069

Am J Epidemiol. 2022 Aug 31:kwac155. doi: 10.1093/aje/kwac155. Online ahead of print.

ABSTRACT

The reporting and analysis of population-based cancer statistics in the United States has traditionally been done for counties. However, counties are not ideal for analysis of cancer rates due to wide variation in population size, with larger counties having considerable socio-demographic variation within their borders and sparsely populated counties having less reliable estimates of cancer rates that are often suppressed due to confidentiality concerns. There is a need and an opportunity to utilize zone design procedures in the context of cancer surveillance to generate coherent, statistically stable geographic units that are more optimal for cancer reporting and analysis than counties. To achieve this goal, we sought to create areas within each U.S. state that are: 1) similar in population size and large enough to minimize rate suppression; 2) socio-demographically homogeneous; 3) compact; and 4) custom crafted to represent areas that are meaningful to cancer registries and stakeholders. The resulting geographic units reveal the heterogeneity of rates that are hidden when reported at the county-level while substantially reducing the need to suppress data. This effort will facilitate more meaningful comparative analysis of cancer rates for small geographic areas and will advance the understanding of cancer burden in the United States.

PMID:36043397 | DOI:10.1093/aje/kwac155

Phytother Res. 2022 Aug 31. doi: 10.1002/ptr.7596. Online ahead of print.

ABSTRACT

The purpose of this meta-analysis is to explore whether the supplement of sea buckthorn affects the factors related to metabolic syndrome. The related RCTs from five databases were systematically searched and comprehensively random effects model was used to calculate SMD and 95% CI. The Cochrane deviation risk tool was used to evaluate the deviation risk. Fifteen studies were involved in the meta-analysis. First, sea buckthorn supplementation reduced triglycerides [-0.722 (-1.129, -0.316); p < .001], total cholesterol [-0.345 (-0.639, -0.051); p = .021], low density lipoprotein cholesterol [-0.396 (-0.755, -0.037); p = .031], and increased high density lipoprotein cholesterol [0.370 (0.056, 0.684); p = .021] in overall subjects. Second, subgroup analysis showed that sea buckthorn supplementation reduced lipids only in people with abnormal lipid metabolism. Third, sea buckthorn had no effect on blood sugar, blood pressure, and BMI of the overall subjects. Sea buckthorn may affect the lipid metabolism in circulation, but it cannot affect blood glucose, blood pressure, and BMI. These indicators are closely associated with metabolic syndrome. This study may contribute to the development and utilization of sea buckthorn, and may provide a new and safer way for the prevention and treatment of metabolic syndrome. The limitation of this study is high heterogeneity, even if subgroup analysis is used. However, more clinical studies are needed to determine the real effect of sea buckthorn on metabolic syndrome.

PMID:36043374 | DOI:10.1002/ptr.7596

Nurse Res. 2022 Aug 31. doi: 10.7748/nr.2022.e1848. Online ahead of print.

ABSTRACT

BACKGROUND: Using a structured process to develop a self-administered questionnaire provides a robust tool for collecting data that enhances the credibility of the results. Describing this process mitigates any complexity and confusion for the nurse researcher which can be generated by many sources of information that either lack detail or have complex statistical approaches.

AIM: To discuss the development of a self-administered questionnaire with a focus on face, content, construct validity and reliability testing.

DISCUSSION: Adopting a well-established, sequential, five-step approach ensures that important concepts of questionnaire development are addressed: assessing existing tools and qualitative data, if available; drafting of the questionnaire with consideration for question styles, comprehension, acquiescent bias and face validity; expert panel review to establish content validity and inter-rater reliability; pilot testing to assess construct validity; and exploratory factor analysis to establish reliability testing. This approach results in a robust and credible tool for collecting data.

CONCLUSION: This article provides nurse researchers with a structured process for developing self-administered questionnaires.

IMPLICATIONS FOR PRACTICE: Investing time and effort to assess a newly developed questionnaire for validity and reliability and consider question styles, comprehension and acquiescent bias results in an improved and strengthened tool for collecting data. This in turn enhances the quality and credibility of a study’s findings.

PMID:36043328 | DOI:10.7748/nr.2022.e1848

Singapore Med J. 2022 May;63(5):256-262. doi: 10.11622/smedj.2020140. Epub 2020 Sep 21.

ABSTRACT

INTRODUCTION: Septic arthritis (SA) is a devastating infection with a high rate of sequelae. The aim of this retrospective study was to determine the epidemiology, clinically significant sequelae and risk factors for developing these sequelae in children admitted to our hospital with SA.

METHODS: Patients with bacteriologically and/or radiologically confirmed SA from January 1999 to December 2014 were identified from discharge and laboratory records. Data was collected through a retrospective review of the case notes.

RESULTS: A total of 75 patients (62.7% male) met the inclusion criteria. The median age at presentation was six years (range two weeks to 15 years), and six patients were neonates. Microbiologic aetiology was determined in 40 (53.3%) patients, with Staphylococcus aureus being the most common organism. 68.0% of the patients underwent arthrotomy, and the average hospital stay was 15.3 days. Sequelae of SA were observed in nine patients on follow-up. Univariate and multivariate statistical analyses showed that young age, pyogenic bacterial isolation and concomitant osteomyelitis were significant risk factors for developing sequelae.

CONCLUSION: Our study demonstrated that young age, pyogenic bacterial isolation and concomitant osteomyelitis are associated with a high risk of sequelae. Timely microbiologic diagnosis by novel polymerase chain reaction methods and the use of magnetic resonance imaging in high-risk children to identify adjacent infection could possibly prevent lifelong disabling sequelae in SA.

PMID:36043293 | DOI:10.11622/smedj.2020140

Singapore Med J. 2022 May;63(5):251-255. doi: 10.11622/smedj.2020164. Epub 2020 Dec 2.

ABSTRACT

INTRODUCTION: The clinical outcomes and factors associated with treatment failure of post-traumatic osteomyelitis have been investigated in many studies. However, limb functionality and quality of life following treatment for this condition have not been thoroughly studied.

METHODS: This cross-sectional study included 47 patients with post-traumatic osteomyelitis of the lower limb. Functional outcome was assessed using the Lower Extremity Functional Score (LEFS), and quality of life was assessed using the validated Malay version of the Short Form-36 questionnaire version 2.

RESULTS: The mean follow-up period was 4.6 (range 2.3-9.5) years, and the median age of the patients was 44 years. Osteomyelitis was located in the tibia for 26 patients and in the femur for 21 patients. Osteomyelitis was consequent to internal infection in 38 patients and due to infected open fractures in nine patients. 42 (89.4%) patients had fracture union and control of infection. Bone defect was found to be a significant contributing factor for treatment failure (p = 0.008). The median LEFS for the success group was 65, compared to 49 for the failure group. Although the success group showed better scores with regard to quality of life, the difference between the two groups was not statistically significant.

CONCLUSION: Treatment of post-traumatic osteomyelitis of the lower limb had a high success rate. The presence of a bone defect was associated with treatment failure. Successfully treated patients had significantly better functional outcomes than in those in whom treatment failed.

PMID:36043271 | DOI:10.11622/smedj.2020164

J Korean Assoc Oral Maxillofac Surg. 2022 Aug 31;48(4):219-224. doi: 10.5125/jkaoms.2022.48.4.219.

ABSTRACT

OBJECTIVES: There have been few studies to date on the residual effect of bisphosphonate. This study investigated the radiographic changes of mandibular cortical thickness upon bisphosphonate drug holiday.

MATERIALS AND METHODS: This retrospective study includes 36 patients diagnosed with MRONJ (medication-related osteonecrosis of the jaw) at Ajou University Dental Hospital in 2010-2021. All patients stopped taking bisphosphonate under consultation with the prescribing physicians. Panoramic radiographs were taken at the start of discontinuation (T₀), 12 months after (T₁), and 18 months after (T₂) discontinuation of bisphosphonate, respectively. Mental index and panoramic mandibular index were calculated using Ledgerton’s method. Paired t-tests were used to analyze differences over time.

RESULTS: The difference in indices (mental index and panoramic mandibular index) between T₀ and T₁ was not statistically significant (paired t-test, P>0.05). However, the difference in these indices between T₁ and T₂ was statistically significant (paired t-test, P<0.05).

CONCLUSION: The cortical thickness of the mandible decreased in the late stage (after 18 months) as observed by panoramic radiograph.

PMID:36043252 | DOI:10.5125/jkaoms.2022.48.4.219

Air Qual Atmos Health. 2022 Aug 26:1-12. doi: 10.1007/s11869-022-01238-4. Online ahead of print.

ABSTRACT

A number of previous studies have shown that statistical model with a combination of satellite-derived aerosol optical depth (AOD) and PM_2.5 measured by the monitoring stations could be applied to predict spatial ground-level PM_2.5 concentration, but few studies have been conducted in Thailand. This study aimed to estimate ground-level PM_2.5 over the Bangkok Metropolitan Region in 2020 using linear regression model that incorporates the Moderate Resolution Imaging Spectroradiometer (MODIS) AOD measurements and other air pollutants, as well as various meteorological factors and greenness indicators into the model. The 12-fold cross-validation technique was used to examine the accuracy of model performance. The annual mean (standard deviation) concentration of observed PM_2.5 was 22.37 (± 12.55) µg/m³ and the mean (standard deviation) of PM_2.5 during summer, winter, and rainy season was 18.36 (± 7.14) µg/m³, 33.60 (± 14.48) µg/m³, and 15.30 (± 4.78) µg/m³, respectively. The cross-validation yielded R ² of 0.48, 0.55, 0.21, and 0.52 with the average of predicted PM_2.5 concentration of 22.25 (± 9.97) µg/m³, 21.68 (± 9.14) µg/m³, 29.43 (± 9.45) µg/m³, and 15.74 (± 5.68) µg/m³ for the year round, summer, winter, and rainy season, respectively. We also observed that integrating NO₂ and O₃ into the regression model improved the prediction accuracy significantly for a year round, summer, winter, and rainy season over the Bangkok Metropolitan Region. In conclusion, estimating ground-level PM_2.5 concentration from the MODIS AOD measurement using linear regression model provided the satisfactory model performance when incorporating many possible predictor variables that would affect the association between MODIS AOD and PM_2.5 concentration.

SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s11869-022-01238-4.

PMID:36043224 | PMC:PMC9411850 | DOI:10.1007/s11869-022-01238-4

Adv Data Anal Classif. 2022;16(3):691-723. doi: 10.1007/s11634-021-00454-7. Epub 2021 Jun 28.

ABSTRACT

A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world problems. This model is an extension of the latent class analysis model that introduces two clustering structures for hyperedges and captures variation in the size of hyperedges. An expectation maximization algorithm with minorization maximization steps is developed to perform parameter estimation. Model selection using Bayesian Information Criterion is proposed. The model is applied to simulated data and two real-world data sets where interesting results are obtained.

PMID:36043219 | PMC:PMC9418112 | DOI:10.1007/s11634-021-00454-7