Category: Statistics

J Infect Public Health. 2021 Jul 28;14(9):1133-1138. doi: 10.1016/j.jiph.2021.07.014. Online ahead of print.

ABSTRACT

BACKGROUND: COVID-19 is newly emerging infectious disease that spread globally at unpredictable and unique pattern to the extent that the World Health Organization announced COVID-19 as a pandemic in the first couple months of 2020. This study aims to describe clinical and demographic features of COVID-19 patients and the influence of various risk factors on the severity of disease.

METHODS: This research is a retrospective study based on Saudi Arabia’s ministry of health’s Covid-19 data. The analysis relies on data of all COVID-19 patients recorded in Riyadh between 1st, March 2020 and 30th, July 2020. Statistical analyses were performed to investigate the effect of demographic characteristic, clinical presentation, and comorbidities on infection severity.

RESULTS: A total number of 1026 COVID-19 patients were identified based on the demographic data as follows: 709 cases (69% of cases) were males and 559 cases (54% of cases) were Saudi. Most of patients were diagnosed with mild signs and symptoms 697 (68% of cases), while 164 patient (16% of cases) demonstrated moderate signs and symptoms, and 103 cases (10%) were severe and 62 (6%) had critical febrile illness. Fever, cough, sore throat, and shortness of breath were the most common symptoms among patients with COVID-19. Among studied comorbidities in COVID-19 patients, diabetes mellitus and hypertension were the most prevalent. The results from the bivariate logistic regression analysis revealed that older age, diabetes mellitus, asthma, smoking, and fever are associated with severe or critically ill cases.

CONCLUSION: The findings of this study show that old age, fever, and comorbidities involving diabetes mellitus, asthma, and smoking were significantly associated with infection severity.

PMID:34343963 | DOI:10.1016/j.jiph.2021.07.014

Chemotherapy. 2021 Aug 3:1-17. doi: 10.1159/000517680. Online ahead of print.

ABSTRACT

BACKGROUND: The aim of this study was to understand physical characteristics of Embosphere microspheres for the clinical use of microsphere chemotherapy embolization of liver cancer.

METHODS: The morphology of Embosphere microspheres in different states, including static, oscillating, and in a magnetic field was observed with the naked eye. Ninety-five patients diagnosed with primary hepatocellular carcinoma (HCC) were separated into 3 groups based on the types of embolic material as follows: 32 cases of sole microspheres, 34 cases of iodinated oil (17 cases with additional application of gelatin sponge particle), and 29 cases of iodinated oil + Embosphere microspheres.

RESULTS: The diameter of the microspheres ranged from 100 to 300 μm, with a sedimentation rate υ = 0.0375 cm/s in physiological saline. The diameter of microspheres ranged from 300 to 500 μm, with a sedimentation rate υ = 0.1875 cm/s. The swelling rate of microspheres was 90%. Microspheres showed nondirectional movement in a 1.5- or 3.0-T magnetic field during magnetic resonance imaging. A volumetric ratio of 1:1.4-1:1.5 between microspheres and contrast agent resulted in optimal suspension properties. Microspheres appeared circular with a smooth surface upon water adsorption. Microsphere embolism was observable in blood vessels of pathological sections. The surface of microspheres can adsorb 5-fluorouracil and arsenic trioxide. There are statistically significant differences in local-regional tumor control conditions among patients treated with sole microspheres, iodinated oil, and iodinated oil + microspheres during transarterial chemoembolization.

CONCLUSIONS: Embosphere microspheres can be used to embolize patients with rupture and hemorrhage of HCC. Embosphere microsphere embolization is superior to iodinated oil and iodinated oil + microsphere for HCC.

PMID:34344008 | DOI:10.1159/000517680

Int J Med Inform. 2021 Jul 22;153:104537. doi: 10.1016/j.ijmedinf.2021.104537. Online ahead of print.

ABSTRACT

AIM BACKGROUND: Many health care services are implementing or planning to undergo digital transformation to keep pace with increasing Electronic Medical Record (EMR) functionality. The aim of this study was to objectively measure nursing care delivery before and following introduction of an EMR.

DESIGN AND METHODS: An extensive program of work to expand an EMR across our health service using a ‘big bang’ methodology was undertaken. The program incorporated digital care delivery workflows including physiological observations, clinical notes and closed loop medication management. The validated Work Observation Method by Activity Timing (WOMBAT) method was applied to undertake a direct observational time and motion study of nurses’ work in a major Australian hospital immediately prior to and six months following the introduction of a full clinical EMR.

RESULTS: Time and motion results were from observing approximately one week of nursing time pre (paper) to six months post (EMR) implementation. A non-significant 6.4% increase in the proportion of time spent on direct care was observed when using the EMR with a statistically significant increase in mean time per direct care task (2.5 min vs 3.9 min, p = 0.001). The proportion of time spent on medication-related activities did not significantly change although the average time per task rose from 2.0 to 2.9 min (p = 0.008). A significant reduction in proportion of time spent in transit and indirect care tasks when using the electronic workflows was reported. No statistically significant changes to the proportions of time spent on professional communication, direct care or documentation were observed.

CONCLUSIONS: Successful EMR implementation is possible without adversely affecting allocation of nursing time. Our findings from deploying a large scale EMR across all healthcare craft groups and workflows have described for nurses that an EMR enables them to spend longer with patients per direct care episode and use their time on other activities more effectively.

PMID:34343955 | DOI:10.1016/j.ijmedinf.2021.104537

Int J Med Inform. 2021 Jul 24;153:104541. doi: 10.1016/j.ijmedinf.2021.104541. Online ahead of print.

ABSTRACT

BACKGROUND: An integrated kidney disease company uses machine learning (ML) models that predict the 12-month risk of an outpatient hemodialysis (HD) patient having multiple hospitalizations to assist with directing personalized interdisciplinary interventions in a Dialysis Hospitalization Reduction Program (DHRP). We investigated the impact of risk directed interventions in the DHRP on clinic-wide hospitalization rates.

METHODS: We compared the hospital admission and day rates per-patient-year (ppy) from all hemodialysis patients in 54 DHRP and 54 control clinics identified by propensity score matching at baseline in 2015 and at the end of the pilot in 2018. We also used paired T test to compare the between group difference of annual hospitalization rate and hospitalization days rates at baseline and end of the pilot.

RESULTS: The between group difference in annual hospital admission and day rates was similar at baseline (2015) with a mean difference between DHRP versus control clinics of -0.008 ± 0.09 ppy and -0.05 ± 0.96 ppy respectively. The between group difference in hospital admission and day rates became more distinct at the end of follow up (2018) favoring DHRP clinics with the mean difference being -0.155 ± 0.38 ppy and -0.97 ± 2.78 ppy respectively. A paired t-test showed the change in the between group difference in hospital admission and day rates from baseline to the end of the follow up was statistically significant (t-value = 2.73, p-value < 0.01) and (t-value = 2.29, p-value = 0.02) respectively.

CONCLUSIONS: These findings suggest ML model-based risk-directed interdisciplinary team interventions associate with lower hospitalization rates and hospital day rate in HD patients, compared to controls.

PMID:34343957 | DOI:10.1016/j.ijmedinf.2021.104541

Forensic Sci Int. 2021 Jul 29;326:110938. doi: 10.1016/j.forsciint.2021.110938. Online ahead of print.

ABSTRACT

The common method of preparing teeth prior to DNA extraction involves cleaning, decontamination, drying and pulverisation. Moisture in post-mortem teeth can promote bacterial growth and hydrolytic damage that could contribute to DNA degradation, whilst also possibly reducing the efficiency of sample pulverisation and DNA release. Here we compared DNA extraction from pig teeth, with- and without freeze-drying, to examine the impact of removing moisture on DNA yield. Quantitative real-time polymerase chain reaction (qPCR) was used to quantify an 83 bp mitochondrial DNA fragment and two nuclear DNA fragments of 82 bp and 150 bp. The comparative results showed that sample preparation with freeze-drying resulted in a higher DNA yield without compromising the DNA quality. This study highlights the advantage of incorporating a freeze-drying to improve the DNA yield and minimising the loss of DNA during sample preparation of teeth.

PMID:34343942 | DOI:10.1016/j.forsciint.2021.110938

Food Chem. 2021 Jul 24;366:130689. doi: 10.1016/j.foodchem.2021.130689. Online ahead of print.

ABSTRACT

This research established the optimal conditions for infusion extraction (IE) and ultrasound-assisted extraction (UAE) of bioactive components from laver (Porphyra dentata) using response surface methodology (RSM) and artificial neural network coupled with genetic algorithm (RSM-ANN-GA). The variables, temperatures (60, 80, and 100 ℃) and times (10, 15, and 20 min) were designed to optimise total phenolic, total flavonoid, total amino acid, a* value, and R-phycoerythrin content of laver extract. The optimised condition for IE and UAE was achieved at 60 ℃ for 18.08 min and 80.66℃ for 14.76 min in RSM while showing 60 ℃ for 19 min and 80℃ for 15 min in the RSM-ANN-GA mode, respectively. Results revealed that RSM-ANN-GA provided better predictability and greater accuracy than the RSM model and laver extract from UAE gave the higher values of responses compared to those from IE. These findings highlight the high-efficient extraction method along with better statistical approach.

PMID:34343950 | DOI:10.1016/j.foodchem.2021.130689

Micron. 2021 Jul 21;150:103123. doi: 10.1016/j.micron.2021.103123. Online ahead of print.

ABSTRACT

Electron channeling contrast imaging (ECCI) is a powerful technique to characterize the structural defects present in a sample and to obtain relevant statistics about their density. Using ECCI, such defects can only be properly visualized, if the information depth is larger than the depth at which defects reside. Furthermore, a systematic correlation of the features observed by ECCI with the defect nature, confirmed by a complementary technique, is required for defect analysis. Therefore, we present in this paper a site-specific ECCI-scanning transmission electron microscopy (STEM) inspection. Its value is illustrated by the application to a partially relaxed epitaxial Si_0.7Ge_0.3 on a Si substrate. All experiments including the acquisition of ECCI micrographs, the carbon marking and STEM specimen preparation by focused ion beam, and the in-situ-subsequent-STEM-in-scanning electron microscopy (SEM) characterization were executed in one SEM/FIB-based system, thus significantly improving the analysis efficiency. The ECCI information depth in Si_0.7Ge_0.3 has been determined through measuring stacking fault widths using different beam energies. ECCI is further utilized to localize the defects for STEM sample preparation and in-situ-subsequent-STEM-in-SEM investigation. This method provides a correlative 2.5D defect analysis from both the surface and cross-section. Using these techniques, the nature of different line-featured defects in epilayers can be classified, as illustrated by our study on Si_0.7Ge_0.3, which helps to better understand the formation of those detrimental defects.

PMID:34343885 | DOI:10.1016/j.micron.2021.103123

Comput Biol Med. 2021 Jul 21;136:104670. doi: 10.1016/j.compbiomed.2021.104670. Online ahead of print.

ABSTRACT

The first case of COVID-19 in USA was reported on January 20, 2020. The number of COVID-19 confirmed cases and death has increased since the first reported case and the outbreak has appeared in all states. This paper analyzes disease outbreak using Topological Weighted Centroid (TWC), which is a data driven intelligent geographical dynamical system that models disease spread in space and time. In this analysis the COVID-19 cases in USA on March 26, 2020 as provided by Johns Hopkins University is used. The COVID-19 outbreak is mapped by the TWC method. We were able to predict and capture some features of the pandemic spread using the early data. Although we have used the geographical distance from the latitude and longitude coordinates, our results indicate that one of the main paths of diseases spread are arguably airline routes. In this analysis, we used a large set of data. A modified version of TWC, is named TWC-Windowing to elaborate the effect of data from all places.

PMID:34343889 | DOI:10.1016/j.compbiomed.2021.104670

Colloids Surf B Biointerfaces. 2021 Jul 26;207:112002. doi: 10.1016/j.colsurfb.2021.112002. Online ahead of print.

ABSTRACT

Various ligand-functionalized liposomes have been developed for targeted therapies. Typically, the binding properties of the ligands and targeted proteins are measured with surface plasmon resonance (SPR), where the proteins are immobilized on a rigid surface. However, the difference of protein-ligand binding kinetics between liposome-conjugated protein and rigid surface-conjugated protein is not fully understood. In this work, the binding kinetics of P-selectin glycoprotein ligand-1 (PSGL-1) and E-selectin conjugated on liposome and on rigid surfaces are investigated with Atomic Force Microscopy (AFM). The results suggest that protein orientation and diffusion on liposomal membrane can alter the binding kinetics of the protein-ligand interaction. Specifically, the association and dissociation rate constant of AFM probe-conjugated E-selectin and glass-conjugated PSGL-1 are measured as 9.32 × 10⁴ M^-1s^-1 and 1.54 s^-1, respectively. While for the liposome-conjugated E-selectin and glass-conjugated PSGL-1, the kinetic constants are measured as 5.00 × 10⁷ M^-1s^-1 and 2.76 s^-1, respectively. Thus, there is an order’s magnitude increase of binding affinity (from k_d = 16.51 μM to k_d = 0.06 μM) when protein is attached to liposome compared to attached to a rigid surface. The results might provide better understanding and pave the way for the future design of the ligand-targeted liposomes.

PMID:34343911 | DOI:10.1016/j.colsurfb.2021.112002

Water Res. 2021 Jul 16;202:117441. doi: 10.1016/j.watres.2021.117441. Online ahead of print.

ABSTRACT

The suspended sludge and carrier-attached biofilms of three different hybrid moving bed biofilm reactor (MBBR) systems were investigated with respect to their transformation potential for a broad range of micropollutants (MPs) as well as their microbial community composition. For this purpose, laboratory-scale batch experiments were conducted with the separated suspended sludge and the carrier-attached biofilm of every system in triplicate. For all batches the removal of 31 MPs as well as the composition of the microbial community were analyzed. The carrier-attached biofilms from two hybrid MBBR systems showed a significant higher overall transformation potential in comparison to the respective suspended sludge. Especially for the MPs trimethoprim, diclofenac, mecoprop, climbazole and the human metabolite 10,11-dihydro-10-hydroxycarbamazepine consistently higher pseudo-first-order transformation rates could be observed in all three systems. The analysis of the taxonomic composition revealed taxa showing higher relative abundances in the carrier-attached biofilms (e. g. Nitrospirae and Chloroflexi) and in the suspended biomasses (e. g. Bacteroidetes and Betaproteobacteria). Correlations of the biodiversity indices and the MP biotransformation rates resulted in significant positive associations for 11 compounds in suspended sludge, but mostly negative associations for the carrier-attached biofilms. The distinct differences in MP removal between suspended sludge and carrier-attached biofilm of the three different MBBR systems were also reflected by a statistically significant link between the occurrence of specific bacterial taxa (Acidibacter, Nitrospira and Rhizomicrobium) and MP transformation rates of certain MPs. Even though the identified correlations might not necessarily be of causal nature, some of the identified taxa might serve as suitable indicators for the transformation potential of suspended sludge or carrier-attached biofilms.

PMID:34343873 | DOI:10.1016/j.watres.2021.117441