Tag: statistics

Lasers Med Sci. 2021 Mar 24. doi: 10.1007/s10103-021-03296-z. Online ahead of print.

ABSTRACT

The objective of this article is to study the clinical efficacy and adverse events of laser-assisted drug delivery in the treatment of hypertrophic and keloid scars. We searched the following databases up to 22 October 2020: the Cochrane Skin Group Specialised Register, the Cochrane Central Register of Controlled Trials (Clinical Trials) in The Cochrane Library, MEDLINE, EMBASE, and reference lists of articles for randomised clinical trials (RCTs) of laser-assisted drug delivery for the treatment of hypertrophic and keloid scars. We also searched online trials registries for ongoing trials and contacted trial authors where appropriate. Our outcomes of interest were objective clinical evaluation of scars, participant satisfaction, and adverse effects of the treatments. Two authors independently extracted data and assessed trial quality using Cochrane Risk of Bias 2. Two authors independently abstracted data. We included 10 RCTs involving a total of 329 participants: six trials utilised parallel-arm RCTs whilst four employed split-scar design. Three trials had high risk of bias with the remaining seven rated as having some concerns. The interventions and outcomes were too varied to be combined statistically. High-quality randomised controlled trials assessing laser-assisted delivery for drugs in the context of hypertrophic and/or keloid scarring are needed. Studies with a larger number of participants, with longer follow-up times, and standardised evaluation of outcome and adverse effects are warranted.

PMID:33763827 | DOI:10.1007/s10103-021-03296-z

Int J Comput Assist Radiol Surg. 2021 Mar 24. doi: 10.1007/s11548-021-02352-x. Online ahead of print.

ABSTRACT

PURPOSE: A simple, patient-specific biomechanical model (PSBM) is proposed in which the main surgical tools and actions can be simulated, which enables clinicians to evaluate different strategies for an optimal surgical planning. A prospective and comparative clinical study was performed to assess early clinical and radiological results.

METHODS: From January 2019 to July 2019, a PSBM was created for every operated acetabular fracture (simulation group). DICOM data were extracted from the pre-operative high-resolution CT scans to build a 3D model of the fracture using segmentation methods. A PSBM was implemented in a custom software allowing a biomechanical simulation of the surgery in terms of reduction sequences. From July 2019 to December 2019, every patient with an operated for acetabular fracture without PSBM was included in the standard group. Surgery duration, blood loss, radiological results and per-operative complications were recorded and compared between the two groups.

RESULTS: Twenty-two patients were included, 10 in the simulation group and 12 in the standard group. The two groups were comparable regarding age, time to surgery, fracture pattern distribution and surgical approaches. The mean operative time was significantly lower in the simulation group: 113 min ± 33 (60-180) versus 184 ± 58 (90-260), p = 0.04. The mean blood loss was significantly lower in the simulation group, p = 0.01. No statistical significant differences were found regarding radiological results (p = 0.16). No per-operative complications were recorded.

CONCLUSION: This study confirms that pre-operative planning in acetabular surgery based on a PSBM results in a shorter operative time and a reduction of blood loss during surgery. This study also confirms the feasibility of PSBM planning in daily clinical routine.

LEVEL OF EVIDENCE: II: prospective study.

PMID:33763792 | DOI:10.1007/s11548-021-02352-x

Int Ophthalmol. 2021 Mar 24. doi: 10.1007/s10792-021-01817-x. Online ahead of print.

ABSTRACT

PURPOSE: To compare corneal densitometry in a consecutive series of 52 endothelial keratoplasties (DMEK/DSAEK) using a Scheimpflug-based device after six months of follow-up.

METHODS: Corneal densitometry (CD) values of 102 eyes were divided into three main groups: 33 DMEKs, 19 DSAEKs, and 50 healthy eyes without previous ocular surgery. The CD values were then analyzed and compared between the groups. We measured three main layers in depth and four different concentric zones at 1, 3, and 6 months postoperatively.

RESULTS: In the DMEK group, total CD significantly decreased from 38.02 ± 10.21 grayscale units (GSU) to 31.13 ± 9.25 GSU (P < 0.01) between the first and the sixth month postoperative. In the DSAEK group, we found significant changes only between the first and three months after surgery (from 42.62 ± 9.31 GSU to 38.71 ± 10.53 GSU (P < 0.01). Regarding the concentric zones, CD in the DMEK group significantly decreased in the central zone from 33.55 ± 12.07 GSU to 30.63 ± 10.15 GSU (P < 0.01) and significantly increased in the periphery from 30.63 ± 10.15 GSU to 36.72 ± 9.37 GSU, (P < 0.01). The DSAEK group showed no changes in the central zone (from 36.91 ± 13.80 GSU to 36.14 ± 11.47 GSU, P = 0.52) and CD significantly increased in the periphery (41.91 ± 9.28 GSU, P < 0.01).

CONCLUSION: When comparing CD values in DMEK versus DSAEK, we found no differences by layers or at central-paracentral concentric zones, although CD differences in the peripheral zones were statistically significant. This finding may be attributed to the thicker graft at periphery with a delayed clearance and less anatomical interphase in DSAEK.

PMID:33763796 | DOI:10.1007/s10792-021-01817-x

Sci Rep. 2021 Mar 24;11(1):6806. doi: 10.1038/s41598-021-85544-4.

ABSTRACT

Constantly decreasing costs of high-throughput profiling on many molecular levels generate vast amounts of multi-omics data. Studying one biomedical question on two or more omic levels provides deeper insights into underlying molecular processes or disease pathophysiology. For the majority of multi-omics data projects, the data analysis is performed level-wise, followed by a combined interpretation of results. Hence the full potential of integrated data analysis is not leveraged yet, presumably due to the complexity of the data and the lacking toolsets. We propose a versatile approach, to perform a multi-level fully integrated analysis: The Knowledge guIded Multi-Omics Network inference approach, KiMONo ( https://github.com/cellmapslab/kimono ). KiMONo performs network inference by using statistical models for combining omics measurements coupled to a powerful knowledge-guided strategy exploiting prior information from existing biological sources. Within the resulting multimodal network, nodes represent features of all input types e.g. variants and genes while edges refer to knowledge-supported and statistically derived associations. In a comprehensive evaluation, we show that our method is robust to noise and exemplify the general applicability to the full spectrum of multi-omics data, demonstrating that KiMONo is a powerful approach towards leveraging the full potential of data sets for detecting biomarker candidates.

PMID:33762588 | DOI:10.1038/s41598-021-85544-4

Sci Rep. 2021 Mar 24;11(1):6713. doi: 10.1038/s41598-021-85987-9.

ABSTRACT

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R² and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R² (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

PMID:33762599 | DOI:10.1038/s41598-021-85987-9

Sci Rep. 2021 Mar 24;11(1):6734. doi: 10.1038/s41598-021-85991-z.

ABSTRACT

Novel methods to characterize the plasma proteome has made it possible to examine a wide range of proteins in large longitudinal cohort studies, but the complexity of the human proteome makes it difficult to identify robust protein-disease associations. Nevertheless, identification of individuals at high risk of early mortality is a central issue in clinical decision making and novel biomarkers may be useful to improve risk stratification. With adjustment for established risk factors, we examined the associations between 138 plasma proteins measured using two proximity extension assays and long-term risk of all-cause mortality in 3,918 participants of the population-based Malmö Diet and Cancer Study. To examine the reproducibility of protein-mortality associations we used a two-step random-split approach to simulate a discovery and replication cohort and conducted analyses using four different methods: Cox regression, stepwise Cox regression, Lasso-Cox regression, and random survival forest (RSF). In the total study population, we identified eight proteins that associated with all-cause mortality after adjustment for established risk factors and with Bonferroni correction for multiple testing. In the two-step analyses, the number of proteins selected for model inclusion in both random samples ranged from 6 to 21 depending on the method used. However, only three proteins were consistently included in both samples across all four methods (growth/differentiation factor-15 (GDF-15), N-terminal pro-B-type natriuretic peptide, and epididymal secretory protein E4). Using the total study population, the C-statistic for a model including established risk factors was 0.7222 and increased to 0.7284 with inclusion of the most predictive protein (GDF-15; P < 0.0001). All multiple protein models showed additional improvement in the C-statistic compared to the single protein model (all P < 0.0001). We identified several plasma proteins associated with increased risk of all-cause mortality independently of established risk factors. Further investigation into the putatively causal role of these proteins for longevity is needed. In addition, the examined methods for identifying multiple proteins showed tendencies for overfitting by including several putatively false positive findings. Thus, the reproducibility of findings using such approaches may be limited.

PMID:33762603 | DOI:10.1038/s41598-021-85991-z

Eur Endod J. 2021 Mar 8. doi: 10.14744/eej.2020.42204. Online ahead of print.

ABSTRACT

OBJECTIVE: This study aimed to assess the antibacterial effect of Easy Clean®, passive ultrasonic irrigation and sonic irrigation against Enterococcus faecalis biofilm in oval-shaped canals.

METHODS: Fifty-five extracted human teeth with similar dimensions were selected. Access cavities were prepared and the root canals were instrumented using Wave One® Primary files (25.08). The root canals were then contaminated with an E. faecalis suspension following incubation for thirty days. The contaminated roots were divided into three experimental groups (n=15) according to the irrigant agitation protocol (Easy Clean, passive ultrassonic irrigation and sonic irrigation), in addition to a positive control group (n=5) and a negative control group (n=5). Microbiological samples were taken from the root canals before instrumentation (S1), after instrumentation (S2) and after the final irrigation protocol (S3). The samples were assayed and incubated for 48 hours in order to obtain the residual titer of E. faecalis cells. Viable cells were quantified by colony-forming units (CFU) measurement. The collected data was submitted to statistical analysis by using Shapiro-Wilk`s test, Wilcoxon paired test, Kruskal-Wallis test and Dunn’s post-hoc test. The level of significance was set at 5%.

RESULTS: All experimental groups presented significant reduction on bacterial load after instrumentation (P<0.05) with similar reduction among the groups. After the agitation protocols, significant reduction in bacterial load was demonstrated for all groups (P<0.05). However, no differences were shown among Easy Clean®, PUI and SI (P>0.05).

CONCLUSION: The results showed that all tested groups exhibited similar disinfection effectiveness. However, none of them was able to render all root canals free from microorganisms.

PMID:33762531 | DOI:10.14744/eej.2020.42204

J Public Health Manag Pract. 2021 May-Jun 01;27(3):233-239. doi: 10.1097/PHH.0000000000001344.

ABSTRACT

OBJECTIVE: To more comprehensively estimate COVID-19-related mortality in Los Angeles County by determining excess all-cause mortality and pneumonia, influenza, or COVID (PIC) mortality.

DESIGN: We reviewed vital statistics data to identify deaths registered in Los Angeles County between March 15, 2020, and August 15, 2020. Deaths with an ICD-10 (International Classification of Diseases, Tenth Revision) code for pneumonia, influenza, or COVID-19 listed as an immediate or underlying cause of death were classified as PIC deaths. Expected deaths were calculated using negative binomial regression. Excess mortality was determined by subtracting the expected from the observed number of weekly deaths. The Department of Public Health conducts surveillance for COVID-19-associated deaths: persons who died of nontraumatic/nonaccidental causes within 60 days of a positive COVID-19 test result were classified as confirmed COVID-19 deaths. Deaths without a reported positive SARS-Cov-2 polymerase chain reaction result were classified as probable COVID-19 deaths if COVID-19 was listed on their death certificate or the death occurred 60 to 90 days of a positive test. We compared excess PIC deaths with the number of confirmed and probable COVID-19 deaths ascertained by surveillance.

SETTING: Los Angeles County.

PARTICIPANTS: Residents of Los Angeles County who died.

MAIN OUTCOME MEASURE: Excess mortality.

RESULTS: There were 7208 excess all-cause and 5128 excess PIC deaths during the study period. The Department of Public Health also reported 5160 confirmed and 323 probable COVID-19-associated deaths.

CONCLUSIONS: The number of excess PIC deaths estimated by our model was approximately equal to the number of confirmed and probable COVID-19 deaths identified by surveillance. This suggests our surveillance definition for confirmed and probable COVID-19 deaths might be sufficiently sensitive for capturing the true burden of deaths caused directly or indirectly by COVID-19.

PMID:33762539 | DOI:10.1097/PHH.0000000000001344

J Public Health Manag Pract. 2021 May-Jun 01;27(3):258-267. doi: 10.1097/PHH.0000000000001351.

ABSTRACT

OBJECTIVE: The primary aim of this study was to investigate whether students in minority race categories are more likely to experience race-related bias and hatred in their lifetime and since the onset of COVID-19, after controlling the effect of demographic and other variables.

METHODS: This quantitative study used primary data from the survey of 1249 college students at one of the universities in Georgia during April and May 2020. We performed multinomial logistic regression, computing 2 models for the 2 ordinal dependent variables concerning students’ experience of race-related bias and hatred-(a) during their lifetime and (b) since the onset of COVID-19 in March 2020-both measured as “never,” “rarely,” “sometimes,” and “fairly often or very often.”

RESULTS: During their lifetime, 47.5% of students had experienced some level of bias or hatred, ranging from “rarely” to “very often.” Since the onset of COVID-19 on March 2 in Georgia, in a short period of 1 to 2 months, 17.6% of students reported experiencing race-related bias or hatred. Univariate statistics revealed substantial differences in race-related bias and hatred by race, experienced during students’ lifetime as well as since the onset of COVID-19. Results of multinomial logistic regression showed that the odds of having experienced bias or hatred during their lifetime were significantly higher (P < .05) for the Black students than for White students (adjusted odds ratio [AOR] = 75.8, for very often or often vs never; AOR = 42 for sometimes vs never). Compared with White students, the odds of hatred and bias were also significantly higher for students who were Asian, multiple races, or another non-White race. The odds of having experienced race-related bias and hatred since the onset of COVID-19 were also higher for Black Asian, multiple races, and other non-White students.

CONCLUSIONS: This study adds critical scientific evidence about variation in the perception of bias and hatred that should draw policy attention to race-related issues experienced by college students in the United States.

PMID:33762541 | DOI:10.1097/PHH.0000000000001351

Nat Commun. 2021 Mar 24;12(1):1966. doi: 10.1038/s41467-021-22384-w.

NO ABSTRACT

PMID:33762573 | DOI:10.1038/s41467-021-22384-w