Tag: nevin manimala

Sci Rep. 2023 Jun 10;13(1):9463. doi: 10.1038/s41598-023-36469-7.

ABSTRACT

The memory-type control charts, such as cumulative sum (CUSUM) and exponentially weighted moving average control chart, are more desirable for detecting a small or moderate shift in the production process of a location parameter. In this article, a novel Bayesian adaptive EWMA (AEWMA) control chat utilizing ranked set sampling (RSS) designs is proposed under two different loss functions, i.e., square error loss function (SELF) and linex loss function (LLF), and with informative prior distribution to monitor the mean shift of the normally distributed process. The extensive Monte Carlo simulation method is used to check the performance of the suggested Bayesian-AEWMA control chart using RSS schemes. The effectiveness of the proposed AEWMA control chart is evaluated through the average run length (ARL) and standard deviation of run length (SDRL). The results indicate that the proposed Bayesian control chart applying RSS schemes is more sensitive in detecting mean shifts than the existing Bayesian AEWAM control chart based on simple random sampling (SRS). Finally, to demonstrate the effectiveness of the proposed Bayesian-AEWMA control chart under different RSS schemes, we present a numerical example involving the hard-bake process in semiconductor fabrication. Our results show that the Bayesian-AEWMA control chart using RSS schemes outperforms the EWMA and AEWMA control charts utilizing the Bayesian approach under simple random sampling in detecting out-of-control signals.

PMID:37301897 | DOI:10.1038/s41598-023-36469-7

Sci Rep. 2023 Jun 10;13(1):9480. doi: 10.1038/s41598-023-36362-3.

ABSTRACT

Machine learning (ML) could have advantages over traditional statistical models in identifying risk factors. Using ML algorithms, our objective was to identify the most important variables associated with mortality after dementia diagnosis in the Swedish Registry for Cognitive/Dementia Disorders (SveDem). From SveDem, a longitudinal cohort of 28,023 dementia-diagnosed patients was selected for this study. Sixty variables were considered as potential predictors of mortality risk, such as age at dementia diagnosis, dementia type, sex, body mass index (BMI), mini-mental state examination (MMSE) score, time from referral to initiation of work-up, time from initiation of work-up to diagnosis, dementia medications, comorbidities, and some specific medications for chronic comorbidities (e.g., cardiovascular disease). We applied sparsity-inducing penalties for three ML algorithms and identified twenty important variables for the binary classification task in mortality risk prediction and fifteen variables to predict time to death. Area-under-ROC curve (AUC) measure was used to evaluate the classification algorithms. Then, an unsupervised clustering algorithm was applied on the set of twenty-selected variables to find two main clusters which accurately matched surviving and dead patient clusters. A support-vector-machines with an appropriate sparsity penalty provided the classification of mortality risk with accuracy = 0.7077, AUROC = 0.7375, sensitivity = 0.6436, and specificity = 0.740. Across three ML algorithms, the majority of the identified twenty variables were compatible with literature and with our previous studies on SveDem. We also found new variables which were not previously reported in literature as associated with mortality in dementia. Performance of basic dementia diagnostic work-up, time from referral to initiation of work-up, and time from initiation of work-up to diagnosis were found to be elements of the diagnostic process identified by the ML algorithms. The median follow-up time was 1053 (IQR = 516-1771) days in surviving and 1125 (IQR = 605-1770) days in dead patients. For prediction of time to death, the CoxBoost model identified 15 variables and classified them in order of importance. These highly important variables were age at diagnosis, MMSE score, sex, BMI, and Charlson Comorbidity Index with selection scores of 23%, 15%, 14%, 12% and 10%, respectively. This study demonstrates the potential of sparsity-inducing ML algorithms in improving our understanding of mortality risk factors in dementia patients and their application in clinical settings. Moreover, ML methods can be used as a complement to traditional statistical methods.

PMID:37301891 | DOI:10.1038/s41598-023-36362-3

Sci Rep. 2023 Jun 10;13(1):9461. doi: 10.1038/s41598-023-36454-0.

ABSTRACT

Acetabular labral tears have shown to be difficult to diagnose and manage in an active and competitive athletic population. The goal of this study was to compare NCAA Division 1 collegiate athletes undergoing operative and non-operative management of their labral injuries by assessing ability to return to competition and secondarily evaluate days lost from sport. A retrospective cohort analysis was conducted on Division 1 collegiate athletes from 2005 to 2020, incorporating all varsity university sports. Records showing MRI confirmed diagnosis were included in the cohort, as well as all pertinent clinical data. Data revealed 10/18 (55%) of individuals managed conservatively versus 23/29 (79%) surgically (p-value = 0.0834) were able to return to sport following treatment. Of those athletes, 22 surgical patients experienced a mean of 324 days ± 223 days lost from sport and nine conservatively managed patients experienced a mean of 27 days ± 70 lost days (p-value < 0.001) Seven of nine conservatively managed patients were able to continue competition while undergoing treatment. Findings suggest no statistical significance regarding operative vs non-operative management of acetabular labral tears. The majority of athletes returning to sport and treated conservatively were able to resume competition during treatment. Therefore, treatment of these injuries should be individualized based on athlete’s symptoms.

PMID:37301848 | DOI:10.1038/s41598-023-36454-0

Cell Death Dis. 2023 Jun 10;14(6):357. doi: 10.1038/s41419-023-05870-5.

ABSTRACT

Pediatric Acute Myeloid Leukemia (AML) is a rare and heterogeneous disease characterized by a high prevalence of gene fusions as driver mutations. Despite the improvement of survival in the last years, about 50% of patients still experience a relapse. It is not possible to improve prognosis only with further intensification of chemotherapy, as come with a severe cost to the health of patients, often resulting in treatment-related death or long-term sequels. To design more effective and less toxic therapies we need a better understanding of pediatric AML biology. The NUP98-KDM5A chimeric protein is exclusively found in a particular subgroup of young pediatric AML patients with complex karyotypes and poor prognosis. In this study, we investigated the impact of NUP98-KDM5A expression on cellular processes in human Pluripotent Stem Cell models and a patient-derived cell line. We found that NUP98-KDM5A generates genomic instability through two complementary mechanisms that involve accumulation of DNA damage and direct interference of RAE1 activity during mitosis. Overall, our data support that NUP98-KDM5A promotes genomic instability and likely contributes to malignant transformation.

PMID:37301844 | DOI:10.1038/s41419-023-05870-5

BMC Cancer. 2023 Jun 10;23(1):532. doi: 10.1186/s12885-023-10919-x.

ABSTRACT

BACKGROUND: Cancer patients with newly created ostomies face complications that reduce quality of life (QOL) and increase morbidity and mortality. This proof-of-concept study examined the feasibility, usability, acceptability, and initial efficacy of an eHealth program titled the “Patient Reported Outcomes-Informed Symptom Management System” (PRISMS) during post-ostomy creation care transition.

METHODS: We conducted a 2-arm pilot randomized controlled trial among 23 patients who received surgical treatment with curative intent for bladder and colorectal cancer and their caregivers. After assessing QOL, general symptoms, and caregiver burden at baseline, participants were randomly assigned to PRISMS (n = 16 dyads) or usual care (UC) (n = 7 dyads). After a 60-day intervention period, participants completed a follow-up survey and post-exit interview. We used descriptive statistics and t-tests to analyze the data.

RESULTS: We achieved an 86.21% recruitment rate and a 73.91% retention rate. Among the PRISMS participants who used the system and biometric devices (n = 14, 87.50%), 46.43% used the devices for ≥ 50 days during the study period. Participants reported PRISMS as useful and acceptable. Compared to their UC counterparts, PRISMS patient social well-being scores decreased over time and had an increased trend of physical and emotional well-being; PRISMS caregivers experienced a greater decrease in caregiver burden.

CONCLUSIONS: PRISMS recruitment and retention rates were comparable to existing family-based intervention studies. PRISMS is a useful and acceptable multilevel intervention with the potential to improve the health outcomes of cancer patients needing ostomy care and their caregivers during post-surgery care transition. A sufficiently powered RCT is needed to test its effects.

TRIAL REGISTRATION: ClinicalTrial.gov ID: NCT04492007. Registration date: 30/07/2020.

PMID:37301841 | DOI:10.1186/s12885-023-10919-x

BMC Genomics. 2023 Jun 10;24(1):312. doi: 10.1186/s12864-023-09420-3.

ABSTRACT

BACKGROUND: The emergence and rapid spread of new severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) variants have challenged the control of the COVID-19 pandemic globally. Burundi was not spared by that pandemic, but the genetic diversity, evolution, and epidemiology of those variants in the country remained poorly understood. The present study sought to investigate the role of different SARS-COV-2 variants in the successive COVID-19 waves experienced in Burundi and the impact of their evolution on the course of that pandemic. We conducted a cross-sectional descriptive study using positive SARS-COV-2 samples for genomic sequencing. Subsequently, we performed statistical and bioinformatics analyses of the genome sequences in light of available metadata.

RESULTS: In total, we documented 27 PANGO lineages of which BA.1, B.1.617.2, AY.46, AY.122, and BA.1.1, all VOCs, accounted for 83.15% of all the genomes isolated in Burundi from May 2021 to January 2022. Delta (B.1.617.2) and its descendants predominated the peak observed in July-October 2021. It replaced the previously predominant B.1.351 lineage. It was itself subsequently replaced by Omicron (B.1.1.529, BA.1, and BA.1.1). Furthermore, we identified amino acid mutations including E484K, D614G, and L452R known to increase infectivity and immune escape in the spike proteins of Delta and Omicron variants isolated in Burundi. The SARS-COV-2 genomes from imported and community-detected cases were genetically closely related.

CONCLUSION: The global emergence of SARS-COV-2 VOCs and their subsequent introductions in Burundi was accompanied by new peaks (waves) of COVID-19. The relaxation of travel restrictions and the mutations occurring in the virus genome played an important role in the introduction and the spread of new SARS-COV-2 variants in the country. It is of utmost importance to strengthen the genomic surveillance of SARS-COV-2, enhance the protection by increasing the SARS-COV-2 vaccine coverage, and adjust the public health and social measures ahead of the emergence or introduction of new SARS-COV-2 VOCs in the country.

PMID:37301830 | DOI:10.1186/s12864-023-09420-3

Lipids Health Dis. 2023 Jun 10;22(1):71. doi: 10.1186/s12944-023-01838-8.

ABSTRACT

OBJECTIVES: Eicosapentaenoic acid in its ethyl ester form is the single active component of icosapent ethyl (IPE). This study was a phase III, multi-center trial assessing the safety and efficiency of IPE for treating very high triglyceride (TG) in a Chinese cohort.

METHODS: Patients having TG levels (5.6-22.6 mmol/L) were enrolled and randomly assigned to receive a treatment of oral intake of 4 g or 2 g/day of IPE, or placebo. Before and after 12 weeks of treatment, TG levels were assessed and the median was calculated to determine the change between the baseline and week 12. In addition to examining TG levels, the impact of such treatments on other lipid changes was also investigated. The official Drug Clinical Trial Information Management Platform has registered this study (CTR20170362).

RESULTS: Random assignments were performed on 373 patients (mean age 48.9 years; 75.1% male). IPE (4 g/day) lowered TG levels by an average of 28.4% from baseline and by an average of 19.9% after correction for placebo (95% CI: 29.8%-10.0%, P < 0.001). In addition, plasma concentration of non-high-density lipoprotein cholesterol (non-HDL-C), very low-density lipoprotein (VLDL) cholesterol, and VLDL-TG remarkedly reduced after IPE (4 g/day) treatment by a median of 14.6%, 27.9%, and 25.2%, respectively compared with participants in placebo group. Compared to the placebo, neither 4 nor 2 g of IPE daily elevated LDL-C levels with statistical significance. IPE was well tolerated by all the treatment groups.

CONCLUSIONS: IPE at 4 g/day dramatically lowered other atherogenic lipids without a noticeable increase in LDL-C, thereby decreasing TG levels in an exceptionally high-TG Chinese population.

PMID:37301827 | DOI:10.1186/s12944-023-01838-8

BioData Min. 2023 Jun 10;16(1):17. doi: 10.1186/s13040-023-00333-1.

ABSTRACT

Single-cell RNA-sequencing (scRNA-seq) data can serve as a good indicator of cell-to-cell heterogeneity and can aid in the study of cell growth by identifying cell types. Recently, advances in Variational Autoencoder (VAE) have demonstrated their ability to learn robust feature representations for scRNA-seq. However, it has been observed that VAEs tend to ignore the latent variables when combined with a decoding distribution that is too flexible. In this paper, we introduce ScInfoVAE, a dimensional reduction method based on the mutual information variational autoencoder (InfoVAE), which can more effectively identify various cell types in scRNA-seq data of complex tissues. A joint InfoVAE deep model and zero-inflated negative binomial distributed model design based on ScInfoVAE reconstructs the objective function to noise scRNA-seq data and learn an efficient low-dimensional representation of it. We use ScInfoVAE to analyze the clustering performance of 15 real scRNA-seq datasets and demonstrate that our method provides high clustering performance. In addition, we use simulated data to investigate the interpretability of feature extraction, and visualization results show that the low-dimensional representation learned by ScInfoVAE retains local and global neighborhood structure data well. In addition, our model can significantly improve the quality of the variational posterior.

PMID:37301826 | DOI:10.1186/s13040-023-00333-1

Environ Sci Pollut Res Int. 2023 Jun 10. doi: 10.1007/s11356-023-27883-5. Online ahead of print.

ABSTRACT

Taxis pose a higher threat to global climate change and human health through air emissions. However, the evidence on this topic is scarce, especially, in developing countries. Therefore, this study conducted estimation of fuel consumption (FC) and emission inventories on Tabriz taxi fleet (TTF), Iran. A structured questionnaire to obtain operational data of TTF, municipality organizations, and literature review were used as data sources. Then modeling was used to estimate fuel consumption ratio (FCR), emission factors (EFs), annual FC, and emissions of TTF using uncertainty analysis. Also, the impact of COVID-19 pandemic period was considered on the studied parameters. The results showed that TTF have high FCRs of 18.68 L/100 km (95% CI=17.67-19.69 L/100 km), which are not affected by age or mileage of taxis, significantly. The estimated EFs for TTF are higher than Euro standards, but the differences are not significant. However, it is critical as can be an indication of inefficiency of periodic regulatory technical inspection tests for TTF. COVID-19 pandemic caused significant decrease in annual total FC and emissions (9.03-15.6%), but significant increase in EFs of per-passenger-kilometer traveled (47.9-57.3%). Annual vehicle-kilometer-traveled by TTF and the estimated EFs for gasoline-compressed natural gas bi-fueled TTF are the main influential parameters in the variability of annual FC and emission levels. More studies on sustainable FC and emissions mitigation strategies are needed for TTF.

PMID:37301810 | DOI:10.1007/s11356-023-27883-5

Clin Transl Oncol. 2023 Jun 10. doi: 10.1007/s12094-023-03233-2. Online ahead of print.

ABSTRACT

PURPOSE: The CoVID-TE model was developed with the aim of predicting venous thrombotic events (VTE) in cancer patients with Sars-Cov-2 infection. Moreover, it was capable of predicting hemorrhage and mortality 30 days following infection diagnosis. The model is pending validation.

METHODS/PATIENTS: Multicenter retrospective study (10 centers). Adult patients with active oncologic disease/ antineoplastic therapy with Sars-Cov-2 infection hospitalized between March 1, 2020 and March 1. 2022 were recruited. The primary endpoint was to study the association between the risk categories of the CoVID-TE model and the occurrence of thrombosis using the Chi-Square test. Secondary endpoints were to demonstrate the association between these categories and the occurrence of post-diagnostic Sars-Cov-2 bleeding/ death events. The Kaplan-Meier method was also used to compare mortality by stratification.

RESULTS: 263 patients were enrolled. 59.3% were men with a median age of 67 years. 73.8% had stage IV disease and lung cancer was the most prevalent tumor (24%). A total of 86.7% had an ECOG 0-2 and 77.9% were receiving active antineoplastic therapy. After a median follow-up of 6.83 months, the incidence of VTE, bleeding, and death 90 days after Sars-Cov-2 diagnosis in the low-risk group was 3.9% (95% CI 1.9-7.9), 4.5% (95% CI 2.3-8.6), and 52.5% (95% CI 45.2-59.7), respectively. For the high-risk group it was 6% (95% CI 2.6-13.2), 9.6% (95% CI 5.0-17.9), and 58.0% (95% CI 45.3-66.1). The Chi-square test for trends detected no statistically significant association between these variables (p > 0.05). Median survival in the low-risk group was 10.15 months (95% CI 3.84-16.46), while in the high-risk group it was 3.68 months (95% CI 0.0-7.79). The differences detected were not statistically significant (p = 0.375).

CONCLUSIONS: The data from our series does not validate of the CoVID-TE as a model to predict thrombosis, hemorrhage, or mortality in cancer patients with Sars-Cov-2 infection.

PMID:37301805 | DOI:10.1007/s12094-023-03233-2