Category: Nevin Manimala

Sci Rep. 2021 Aug 5;11(1):15907. doi: 10.1038/s41598-021-95372-1.

ABSTRACT

Programmed cell death ligend-1 (PD-L1) expression by immunohistochemistry (IHC) assays is a predictive marker of anti-PD-1/PD-L1 therapy response. With the popularity of anti-PD-1/PD-L1 inhibitor drugs, quantitative assessment of PD-L1 expression becomes a new labor for pathologists. Manually counting the PD-L1 positive stained tumor cells is an obviously subjective and time-consuming process. In this paper, we developed a new computer aided Automated Tumor Proportion Scoring System (ATPSS) to determine the comparability of image analysis with pathologist scores. A three-stage process was performed using both image processing and deep learning techniques to mimic the actual diagnostic flow of the pathologists. We conducted a multi-reader multi-case study to evaluate the agreement between pathologists and ATPSS. Fifty-one surgically resected lung squamous cell carcinoma were prepared and stained using the Dako PD-L1 (22C3) assay, and six pathologists with different experience levels were involved in this study. The TPS predicted by the proposed model had high and statistically significant correlation with sub-specialty pathologists’ scores with Mean Absolute Error (MAE) of 8.65 (95% confidence interval (CI): 6.42-10.90) and Pearson Correlation Coefficient (PCC) of 0.9436 ([Formula: see text]), and the performance on PD-L1 positive cases achieved by our method surpassed that of non-subspecialty and trainee pathologists. Those experimental results indicate that the proposed automated system can be a powerful tool to improve the PD-L1 TPS assessment of pathologists.

PMID:34354151 | DOI:10.1038/s41598-021-95372-1

Sci Rep. 2021 Aug 5;11(1):15950. doi: 10.1038/s41598-021-95321-y.

ABSTRACT

Discovering genes involved in complex human genetic disorders is a major challenge. Many have suggested that machine learning (ML) algorithms using gene networks can be used to supplement traditional genetic association-based approaches to predict or prioritize disease genes. However, questions have been raised about the utility of ML methods for this type of task due to biases within the data, and poor real-world performance. Using autism spectrum disorder (ASD) as a test case, we sought to investigate the question: can machine learning aid in the discovery of disease genes? We collected 13 published ASD gene prioritization studies and evaluated their performance using known and novel high-confidence ASD genes. We also investigated their biases towards generic gene annotations, like number of association publications. We found that ML methods which do not incorporate genetics information have limited utility for prioritization of ASD risk genes. These studies perform at a comparable level to generic measures of likelihood for the involvement of genes in any condition, and do not out-perform genetic association studies. Future efforts to discover disease genes should be focused on developing and validating statistical models for genetic association, specifically for association between rare variants and disease, rather than developing complex machine learning methods using complex heterogeneous biological data with unknown reliability.

PMID:34354131 | DOI:10.1038/s41598-021-95321-y

Sci Rep. 2021 Aug 5;11(1):15882. doi: 10.1038/s41598-021-95314-x.

ABSTRACT

Bacterial bloodstream infections are of great concern globally. Of late, the emergence of drug resistant bacteria worsen the related morbidity and mortality. This study was aimed to determine the bacterial profile, antimicrobial susceptibility patterns, and associated factors among the blood stream infection (BSI) suspected patients attending the Arba Minch General Hospital (AMGH), southern Ethiopia, from 01 June through 31st August, 2020. A cross-sectional study was conducted among 225 BSI suspected patients. Systematic random sampling method was used to select patients. Blood culture was done to isolate bacterial pathogens. Antimicrobial susceptibility test was performed by employing the Kirby-Bauer disc diffusion method. Descriptive statistics and multivariable logistic regression analysis were done by Statistical Package for Social Service (SPSS) version 22. The rate of prevalence of bacteriologically confirmed cases was 22/225 (9.8%). Majority of BSI were caused by Gram-positive cocci, 13/22 (59.1%), particularly the isolates of S. aureus, 7/22 (31.8%) followed by Enterococci species, 4/22 (18.2%) and coagulase-negative Staphylococci (CoNS), 2/22 (9.1%). Among the Gram-negative bacteria 9/22 (41.1%), Klebsiella species 4/22 (18.2%) was the prominent one followed by Escherichia coli 2/22 (9.1%), Pseudomonas aeruginosa 2/22 (9.1%), and Enterobacter species 1/22 (4.5%). All the isolates of Gram-negative bacteria were susceptible to meropenem whereas 69.2% of the isolates of Gram-positive counterparts were susceptible to erythromycin. Slightly above two third (68.2%) of the total isolates were multidrug resistant. Insertion of a peripheral intravenous line was significantly associated with BSI [p = 0.03; Adjusted Odds Ratio = 4.82; (Confidence Interval: 1.08-21.46)]. Overall results revealed that eventhough the prevalence of BSI in Arba Minch is comparatively lower (9.8%), multidrug resistance is alarmingly on the rise, which is to be addressed through effective surveillance and control strategies.

PMID:34354138 | DOI:10.1038/s41598-021-95314-x

Sci Rep. 2021 Aug 5;11(1):15904. doi: 10.1038/s41598-021-94962-3.

ABSTRACT

Blastocystis hominis and Cystoisospora belli are considered to be common opportunistic intestinal protozoa in HIV/AIDS patients. In order to investigate the prevalence and genetic characteristics of B. hominis and C. belli in HIV/AIDS patients, a total of 285 faecal samples were individually collected from HIV/AIDS patients in Guangxi, China. B. hominis and C. belli were investigated by amplifying the barcode region of the SSU rRNA gene and the internal transcribed spacer 1 (ITS-1) region of the rRNA gene, respectively. Chi-square test or Fisher’s exact test were conducted to assess the risk factors related to B. hominis and C. belli infection. The prevalence of B. hominis and C. belli was 6.0% (17/285) and 1.1% (3/285) respectively. Four genotypes of B. hominis were detected, with ST3 (n = 8) and ST1 (n = 6) being predominant, followed by ST6 (n = 2) and ST7 (n = 1). Females had a statistically higher prevalence of B. hominis (11.6%) than males (4.2%). The statistical analysis also showed that the prevalence of B. hominis was significantly associated with age group and educational level. Our study provides convincing evidence for the genetic diversity of B. hominis, which indicates its potential zoonotic transmission and is the first report on the molecular characteristics of C. belli in HIV/AIDS patients in China.

PMID:34354101 | DOI:10.1038/s41598-021-94962-3

Sci Rep. 2021 Aug 5;11(1):15910. doi: 10.1038/s41598-021-95037-z.

ABSTRACT

The burst coding hypothesis posits that the occurrence of sudden high-frequency patterns of action potentials constitutes a salient syllable of the neural code. Many neurons, however, do not produce clearly demarcated bursts, an observation invoked to rule out the pervasiveness of this coding scheme across brain areas and cell types. Here we ask how detrimental ambiguous spike patterns, those that are neither clearly bursts nor isolated spikes, are for neuronal information transfer. We addressed this question using information theory and computational simulations. By quantifying how information transmission depends on firing statistics, we found that the information transmitted is not strongly influenced by the presence of clearly demarcated modes in the interspike interval distribution, a feature often used to identify the presence of burst coding. Instead, we found that neurons having unimodal interval distributions were still able to ascribe different meanings to bursts and isolated spikes. In this regime, information transmission depends on dynamical properties of the synapses as well as the length and relative frequency of bursts. Furthermore, we found that common metrics used to quantify burstiness were unable to predict the degree with which bursts could be used to carry information. Our results provide guiding principles for the implementation of coding strategies based on spike-timing patterns, and show that even unimodal firing statistics can be consistent with a bivariate neural code.

PMID:34354118 | DOI:10.1038/s41598-021-95037-z

Sci Data. 2021 Aug 5;8(1):209. doi: 10.1038/s41597-021-00990-z.

ABSTRACT

Testicular cancer is a rare tumor with a worldwide incidence that has increased over the last few decades. The majority of these tumors are testicular non-germ (TNGCTs) and germ cell tumors (TGCTs); the latter divided into two broad classes – seminomatous (SGCTs) and non-seminomatous germ cell tumors (NSGCTs). Although ultrasonography (US) maintains a primary role in the diagnostic workup of scrotal pathology, magnetic resonance imaging (MRI) has emerged as the imaging modality recommended for challenging cases, providing additional information to clarify inconclusive/equivocal US. In this work we describe and publicly share a collection of 44 images of annotated T2-weighted MRI lesions from 42 patients. Given that testicular cancer is a rare tumor, we are confident that this collection can be used to validate statistical models and to further investigate TNGCT and TGCT peculiarities using medical imaging features.

PMID:34354085 | DOI:10.1038/s41597-021-00990-z

Sci Rep. 2021 Aug 5;11(1):15923. doi: 10.1038/s41598-021-94959-y.

ABSTRACT

Complex genetic diseases may be modulated by a large number of epistatic interactions affecting a polygenic phenotype. Identifying these interactions is difficult due to computational complexity, especially in the case of higher-order interactions where more than two genomic variants are involved. In this paper, we present BitEpi, a fast and accurate method to test all possible combinations of up to four bi-allelic variants (i.e. Single Nucleotide Variant or SNV for short). BitEpi introduces a novel bitwise algorithm that is 1.7 and 56 times faster for 3-SNV and 4-SNV search, than established software. The novel entropy statistic used in BitEpi is 44% more accurate to identify interactive SNVs, incorporating a p-value-based significance testing. We demonstrate BitEpi on real world data of 4900 samples and 87,000 SNPs. We also present EpiExplorer to visualize the potentially large number of individual and interacting SNVs in an interactive Cytoscape graph. EpiExplorer uses various visual elements to facilitate the discovery of true biological events in a complex polygenic environment.

PMID:34354094 | DOI:10.1038/s41598-021-94959-y

Qual Manag Health Care. 2021 Aug 3. doi: 10.1097/QMH.0000000000000320. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: Hospitals worldwide are faced with the problem of discharging patients on time. Delayed discharge creates domino effects with significant implications for hospitals. The accountable care team (ACT) is a multidisciplinary, unit-based approach to identifying opportunities to improve patient care and address inefficiencies in care delivery and throughput, including assuring timely discharges. In response to concerns about emergency department boarding times and delays in timely discharge, the ACT recommended a set of strategies to improve communication across team members and to reduce wait times for transportation within and outside the hospital. Collectively these strategies were thought to increase the proportion of patients discharged on time. In this article, we describe and assess changes in timely discharge resulting from the implementation of strategies recommended by the ACT.

METHODS: This study uses a retrospective, quasi-experimental design to compare the percentage of discharges by 1 PM of hospital units implementing the ACT intervention to those units not implementing the intervention. Median discharge time was compared pre- and post-implementation using the Wilcoxon rank sum test. Difference-in-differences modeling was employed to assess whether changes in the percentage of discharges by 1 PM differed between units implementing the intervention and units not implementing the intervention.

RESULTS: One month post-implementation, the percentage discharged by 1 PM was statistically significantly higher for units implementing the intervention (53.6%) compared with comparison units (22.5%, t = -4.48, P < .01). Statistically significant differences in the percentage discharged by 1 PM were also seen at 3 and 6 months post-implementation. The median discharge time showed a statistically significant decrease by 77 minutes from the baseline to intervention period (P < .01).

CONCLUSION: The result from the study suggests that ACTs can be used to develop approaches aimed at improving patient care in general, and discharge efficiencies in particular. Health care organizations are encouraged to utilize and then evaluate the specific activities of multidisciplinary teams aimed at developing recommendations for practice improvement.

PMID:34354033 | DOI:10.1097/QMH.0000000000000320

Nat Commun. 2021 Aug 5;12(1):4715. doi: 10.1038/s41467-021-25017-4.

ABSTRACT

Methane (CH₄) emissions from oil and natural gas (O&NG) systems are an important contributor to greenhouse gas emissions. In the United States, recent synthesis studies of field measurements of CH₄ emissions at different spatial scales are ~1.5-2× greater compared to official greenhouse gas inventory (GHGI) estimates, with the production-segment as the dominant contributor to this divergence. Based on an updated synthesis of measurements from component-level field studies, we develop a new inventory-based model for CH₄ emissions, for the production-segment only, that agrees within error with recent syntheses of site-level field studies and allows for isolation of equipment-level contributions. We find that unintentional emissions from liquid storage tanks and other equipment leaks are the largest contributors to divergence with the GHGI. If our proposed method were adopted in the United States and other jurisdictions, inventory estimates could better guide CH₄ mitigation policy priorities.

PMID:34354066 | DOI:10.1038/s41467-021-25017-4

Ann Surg. 2021 Aug 4. doi: 10.1097/SLA.0000000000005133. Online ahead of print.

ABSTRACT

OBJECTIVE: The aim of this study was to build a predictive model of operative difficulty in open liver resections (LRs).

SUMMARY BACKGROUND DATA: Recent attempts at classifying open-LR have been focused on postoperative outcomes and were based on predefined anatomical schemes without taking into account other anatomical/technical factors.

METHODS: Four intraoperative variables were perceived by the authors as to reflect operative difficulty: operation and transection times, blood loss, and number of Pringle maneuvers. A hierarchical ascendant classification (HAC) was used to identify homogeneous groups of operative difficulty, based on these variables. Predefined technical/anatomical factors were then selected to build a multivariable logistic regression model (DIFF-scOR), to predict the probability of pertaining to the highest difficulty group. Its discrimination/calibration was assessed. Missing data were handled using multiple imputation.

RESULTS: HAC identified 2 clusters of operative difficulty. In the “Difficult LR” group (20.8% of the procedures), operation time (401 min vs 243 min), transection time (150 vs.63 minute), blood loss (900 vs 400 mL), and number of Pringle maneuvers (3 vs 1) were higher than in the “Standard LR” group. Determinants of operative difficulty were body weight, number and size of nodules, biliary drainage, anatomical or combined LR, transection planes between segments 2 and 4, 4, and 8 or 7 and 8, nonanatomical resections in segments 2, 7, or 8, caval resection, bilioentric anastomosis and number of specimens. The c-statistic of the DIFF-scOR was 0.822. By contrast, the discrimination of the DIFF-scOR to predict 90-day mortality and severe morbidity was poor (c-statistic: 0.616 and 0.634, respectively).

CONCLUSION: The DIFF-scOR accurately predicts open-LR difficulty and may be used for various purposes in clinical practice and research.

PMID:34353987 | DOI:10.1097/SLA.0000000000005133