Category: Nevin Manimala

Clin Transl Oncol. 2023 May 18. doi: 10.1007/s12094-023-03208-3. Online ahead of print.

ABSTRACT

PURPOSE: HOX transcribed antisense RNA (HOTAIR) is a long noncoding RNA (LncRNA) that promotes tumor progression. Exosomes are critically involved in cancer progression. The presence of HOTAIR in the circulating exosomes and the roles of exosomal HOTAIR in gastric cancer (GC) remains unknown. This study aimed to investigate the role of HOTAIR in exosomes in promoting the growth and metastasis of GC.

METHODS: Serum exosomes from GC patients were captured by CD63 immunoliposome magnetic spheres (CD63-IMS), and the biological characteristics of the exosomes were identified. The expression levels of HOTAIR in GC cells, tissues, serum and serum exosomes were detected by fluorescence quantitative PCR (qRT-PCR), and the clinicopathological correlation was statistically analyzed. The growth and metastasis abilities of GC cells with HOTAIR knockdown in vitro were evaluated by cell experiment. The effects of HOTAIR highly-expressed NCI-N87 cell-derived exosomes were used to treat HOTAIR lowly-expressed MKN45 cells on GC growth and metastasis were also evaluated.

RESULTS: The exosomes isolated by CD63-IMS had a particle size of 89.78 ± 4.8 nm and were oval membranous particles. The expression of HOTAIR in tumor tissues and serum of GC patients was increased (P < 0.05), and the expression of HOTAIR in serum exosomes was significantly increased (P < 0.01). The in NCI-N87 and MKN45 cell experiment demonstrated that HOTAIR knockdown by RNA interference suppressed cell growth and metastasis in NCI-N87 cells. Coculture of exosomes secreted by NCI-N87 cells with MKN45 cells significantly increased the expression of HOTAIR, and enhanced cell growth and metastasis.

CONCLUSION: LncRNA HOTAIR can be used as a potential biomarker which provides a new way for the diagnosis and treatment of GC.

PMID:37199906 | DOI:10.1007/s12094-023-03208-3

Cancer. 2023 May 18. doi: 10.1002/cncr.34837. Online ahead of print.

ABSTRACT

BACKGROUND: Primary brain tumors (BTs) are rare, but cause morbidity and mortality disproportionately to their incidence. Prevalence estimates population-level cancer burdens at a specified time. This study estimates the prevalence of malignant and non-malignant BTs in comparison to other cancers.

METHODS: Incidence data were obtained from the Central Brain Tumor Registry of the United States (2000-2019, varying), a combined data set including the Center for Disease Control and Prevention’s National Program of Cancer Registries and National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) Program. Incidence of non-BT cancers were obtained from the United States Cancer Statistics (2001-2019). Incidence and survival estimates for all cancers were obtained from SEER (1975-2018). Complete prevalence as of December 31, 2019, was estimated using prevEst. Estimates were generated overall for non-BT cancers, by BT histopathology, age groups at prevalence (0-14, 15-39, 40-64, 65+ years), and sex.

RESULTS: We estimated 1,323,121 individuals with a diagnosis of BTs at the date of prevalence. The majority of BT cases had non-malignant tumors (85.3%). Among all cancers, BTs were the most prevalent cancer type among those ages 15 to 39 years, second among those ages 0 to 14 years, and in the top five among those ages 40 to 64 years. The plurality of prevalent cases (43.5%) occurred among those ages 65+ years. Overall, females had a higher prevalence of BTs than males, with an overall female:male prevalence ratio of 1.68.

CONCLUSIONS: BTs contribute significantly to the cancer burden in the United States, particularly among those younger than age 65 years. Understanding complete prevalence is crucial for monitoring cancer burden to inform clinical research and public policy. We include a comparison of prevalence estimates for all primary brain tumors to other common cancers by age group in the United States. We also provide a description of the complete prevalence of primary malignant and non-malignant brain and other central nervous system tumors in the United States for 2019, an update to a previous study.

PMID:37199898 | DOI:10.1002/cncr.34837

Health Care Manag Sci. 2023 May 18. doi: 10.1007/s10729-023-09639-2. Online ahead of print.

ABSTRACT

During the COVID-19 pandemic, there has been considerable research on how regional and country-level forecasting can be used to anticipate required hospital resources. We add to and build on this work by focusing on ward-level forecasting and planning tools for hospital staff during the pandemic. We present an assessment, validation, and deployment of a working prototype forecasting tool used within a modified Traffic Control Bundling (TCB) protocol for resource planning during the pandemic. We compare statistical and machine learning forecasting methods and their accuracy at one of the largest hospitals (Vancouver General Hospital) in Canada against a medium-sized hospital (St. Paul’s Hospital) in Vancouver, Canada through the first three waves of the COVID-19 pandemic in the province of British Columbia. Our results confirm that traditional statistical and machine learning (ML) forecasting methods can provide valuable ward-level forecasting to aid in decision-making for pandemic resource planning. Using point forecasts with upper 95% prediction intervals, such forecasting methods would have provided better accuracy in anticipating required beds on COVID-19 hospital units than ward-level capacity decisions made by hospital staff. We have integrated our methodology into a publicly available online tool that operationalizes ward-level forecasting to aid with capacity planning decisions. Importantly, hospital staff can use this tool to translate forecasts into better patient care, less burnout, and improved planning for all hospital resources during pandemics.

PMID:37199873 | DOI:10.1007/s10729-023-09639-2

Environ Sci Pollut Res Int. 2023 May 18. doi: 10.1007/s11356-023-27496-y. Online ahead of print.

ABSTRACT

Cerium oxide nanoparticles have so far been investigated for their role as an antioxidant in pathologies involving inflammation and high oxidative stress. However, its role as a plant and bacterial growth modulator and heavy metal stress reliever has been overlooked to date. Heavy metal contamination poses a major threat to mankind and the life-sustaining ecosystem. This study emphasizes the role of cerium oxide produced by the combustion method in promoting growth in Vigna radiata and Bacillus coagulans in the presence of mercury. The results show that cerium oxide nanoparticles significantly reduce the production of reactive oxygen species, hydrogen peroxide, and product of lipid peroxidation malondialdehyde in plants grown in the presence of 50 ppm mercury, thereby reducing oxidative stress. Nanoceria also increases plant growth with respect to those growing solely in mercury. Nanoceria alone does not significantly affect the growth of Vigna radiata as well as Bacillus coagulans and Escherichia coli, thereby proving its non-hazardous nature. It also significantly increases the growth of Bacillus coagulans at 25 ppm and 50 ppm of mercury. This study throws light upon the biologically non-hazardous nature of this particle by revealing how it promotes the growth of two soil bacteria Bacillus coagulans and E.coli at various dosages. The results of this study pave the way for the use of cerium oxide nanoparticles in plants and various other organisms to combat abiotic stress.

PMID:37199849 | DOI:10.1007/s11356-023-27496-y

Environ Sci Pollut Res Int. 2023 May 18. doi: 10.1007/s11356-023-27477-1. Online ahead of print.

ABSTRACT

This study evaluates hydrology and hydrological extremes under future climate change scenarios. The climate change scenarios were developed from multiple Global Circulation Models (GCMs), Representative Concentration Pathway (RCP) scenarios, and statistical downscaling techniques. To ensure hydrological model robustness, the Soil Water Assessment Tool (SWAT) was calibrated and validated using the Differential Split Sample Test (DSST) approach. The model was also calibrated and validated at the multi-gauges of the watershed. Future climate change scenarios revealed a reduction in precipitation (in the order of -9.1% to 4.9%) and a consistent increase in maximum temperature (0.34°C to 4.10°C) and minimum temperature (-0.15 °C to 3.7°C) in different climate model simulations. The climate change scenarios triggered a reduction of surface runoff and streamflow and a moderate increase in evapotranspiration. Future climate change scenarios projected a decrease in high flow (Q5) and low flow (Q95). A higher reduction of Q5 and annual minimum flow is also simulated in future climate scenarios, whereas an increase in annual maximum flow is simulated in climate change scenarios developed from the RCP8.5 emission scenario. The study suggests optimal water management structures which can reduce the effect of change in high and low flows.

PMID:37199844 | DOI:10.1007/s11356-023-27477-1

J Cancer Res Clin Oncol. 2023 May 18. doi: 10.1007/s00432-023-04846-4. Online ahead of print.

ABSTRACT

PURPOSE: The aim of our study was to evaluate, under real-life conditions, survival of patients with advanced HCC (BCLC-C), either initially presenting in that stage or migrating from BCLC-A to BCLC-C within 2 years after curative LR/RFA, treated either with Atezolizumab-Bevacizumab or TKIs.

METHODS: Sixty-four cirrhotic patients with advanced HCC, who either initially presented as BCLC-C and were treated with Atezo-Bev (group A, N = 23) or TKIs (group B, N = 15) or who migrated from BCLC-A to BCLC-C stage within 2 years after LR/RFA and were either treated with Atezo-Bev (group C, N = 12) or TKIs (group D, N = 14), were retrospectively evaluated.

RESULTS: The four groups were comparable for all baseline parameters (demographics/platelets/liver disease etiology/diabetes/varices/Child-Pugh stage/ALBI grade) except for CPT score and MELD-Na. Using Cox-regression analysis, we observed that survival of group C after systemic treatment onset was significantly higher compared to group A (HR 3.71, 1.20-11.46, p = 0.02) and presented a trend to statistical significance when compared to group D (HR 3.14, 0.95-10.35, p = 0.06), adjusted for liver disease severity scores. When all BCLC-C patients classified as such due to PS only were excluded from the study, a trend for the same survival benefit in group C was shown, even in the most difficult-to-treat population with extrahepatic disease or macrovascular invasion.

CONCLUSION: Cirrhotic patients with advanced HCC initially diagnosed in BCLC-C, exhibit the worst survival irrespective of treatment schedule, whereas patients progressing to BCLC-C following disease recurrence after LR/RFA, seem to mostly benefit from Atezo-Bev, even patients with extrahepatic disease and/or macrovascular invasion. Liver disease severity seems to drive survival of these patients.

PMID:37199835 | DOI:10.1007/s00432-023-04846-4

Brain Behav. 2023 May 18:e3072. doi: 10.1002/brb3.3072. Online ahead of print.

ABSTRACT

INTRODUCTION: There are limited therapeutic options for individuals with fibromyalgia. The aim of this study is to analyze changes in health-related quality of life and incidence of adverse events of those prescribed cannabis-based medicinal products (CBMPs) for fibromyalgia.

METHODS: Patients treated with CBMPs for a minimum of 1 month were identified from the UK Medical Cannabis Registry. Primary outcomes were changes in validated patient-reported outcome measures (PROMs). A p-value of <.050 was deemed statistically significant.

RESULTS: In total, 306 patients with fibromyalgia were included for analysis. There were improvements in global health-related quality of life at 1, 3, 6, and 12 months (p < .0001). The most frequent adverse events were fatigue (n = 75; 24.51%), dry mouth (n = 69; 22.55%), concentration impairment (n = 66; 21.57%), and lethargy (n = 65; 21.24%).

CONCLUSION: CBMP treatment was associated with improvements in fibromyalgia-specific symptoms, in addition to sleep, anxiety, and health-related quality of life. Those who reported prior cannabis use appeared to have a greater response. CBMPs were generally well-tolerated. These results must be interpreted within the limitations of study design.

PMID:37199833 | DOI:10.1002/brb3.3072

Indian J Pediatr. 2023 May 18. doi: 10.1007/s12098-023-04563-3. Online ahead of print.

ABSTRACT

OBJECTIVES: Neonatal hypoxic-ischemic encephalopathy is a major cause of perinatal death and neurodevelopmental impairment (NDI). Hypothermia (HT) is the standard of care; however, additional neuroprotective agents are required to improve prognosis. The authors searched for all drugs in combination with HT and compared their effects using a network meta-analysis.

METHODS: The authors searched PubMed, Embase, and Cochrane Library until September 24, 2022 for articles assessing mortality, NDI, seizures, and abnormal brain imaging findings in neonates with hypoxic-ischemic encephalopathy. Direct pairwise comparisons and a network meta-analysis was performed under random effects.

RESULTS: Thirteen randomized clinical trials enroled 902 newborns treated with six combination therapies: erythropoietin magnesium sulfate, melatonin (MT), topiramate, xenon, and darbepoetin alfa. The results of all comparisons were not statistically significant, except for NDI, HT vs. MT+HT: odds ratio = 6.67, 95% confidence interval = 1.14-38.83; however, the overall evidence quality was low for the small sample size.

CONCLUSIONS: Currently, no combination therapy can reduce mortality, seizures, or abnormal brain imaging findings in neonatal hypoxic-ischemic encephalopathy. According to low quality evidence, HT combined with MT may reduce NDI.

PMID:37199820 | DOI:10.1007/s12098-023-04563-3

J Magn Reson Imaging. 2023 May 18. doi: 10.1002/jmri.28776. Online ahead of print.

ABSTRACT

BACKGROUND: Radiomics has been applied for assessing lymphovascular invasion (LVI) in patients with breast cancer. However, associations between features from peritumoral regions and the LVI status were not investigated.

PURPOSE: To investigate the value of intra- and peritumoral radiomics for assessing LVI, and to develop a nomogram to assist in making treatment decisions.

STUDY TYPE: Retrospective.

POPULATION: Three hundred and sixteen patients were enrolled from two centers and divided into training (N = 165), internal validation (N = 83), and external validation (N = 68) cohorts.

FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T/dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI).

ASSESSMENT: Radiomics features were extracted and selected based on intra- and peritumoral breast regions in two magnetic resonance imaging (MRI) sequences to create the multiparametric MRI combined radiomics signature (RS-DCE plus DWI). The clinical model was built with MRI-axillary lymph nodes (MRI ALN), MRI-reported peritumoral edema (MPE), and apparent diffusion coefficient (ADC). The nomogram was constructed with RS-DCE plus DWI, MRI ALN, MPE, and ADC.

STATISTICAL TESTS: Intra- and interclass correlation coefficient analysis, Mann-Whitney U test, and least absolute shrinkage and selection operator regression were used for feature selection. Receiver operating characteristic and decision curve analyses were applied to compare performance of the RS-DCE plus DWI, clinical model, and nomogram.

RESULTS: A total of 10 features were found to be associated with LVI, 3 from intra- and 7 from peritumoral areas. The nomogram showed good performance in the training (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.884 vs. 0.695 vs. 0.870), internal validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.813 vs. 0.695 vs. 0.794), and external validation (AUCs, nomogram vs. clinical model vs. RS-DCE plus DWI, 0.862 vs. 0.601 vs. 0.849) cohorts.

DATA CONCLUSION: The constructed preoperative nomogram might effectively assess LVI.

LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

PMID:37199241 | DOI:10.1002/jmri.28776

J Magn Reson Imaging. 2023 May 18. doi: 10.1002/jmri.28782. Online ahead of print.

ABSTRACT

BACKGROUND: Double expression lymphoma (DEL) is a subtype of primary central nervous system lymphoma (PCNSL) that often has a poor prognosis. Currently, there are limited noninvasive ways to detect protein expression.

PURPOSE: To detect DEL in PCNSL using multiparametric MRI-based machine learning.

STUDY TYPE: Retrospective.

POPULATION: Forty PCNSL patients were enrolled in the study among whom 17 were DEL (9 males and 8 females, 61.29 ± 14.14 years) and 23 were non-DEL (14 males and 9 females, 55.57 ± 14.16 years) with 59 lesions (28 DEL and 31 non-DEL).

FIELD STRENGTH/SEQUENCE: ADC map derived from DWI (b = 0/1000 s/mm² ), fast spin echo T2WI, T2FLAIR, and contrast-enhanced T1 weighted imaging (T1CE) were collected at 3.0 T.

ASSESSMENT: Two raters manually segmented lesions by ITK-SNAP on ADC, T2WI, T2FLAIR and T1CE. A total of 2234 radiomics features from the tumor segmentation area were extracted. The t-test was conducted to filter the features, and elastic net regression algorithm combined with recursive feature elimination was used to calculate the essential features. Finally, 12 groups with combinations of different sequences were fitted to 6 classifiers, and the optimal models were selected.

STATISTICAL TESTS: Continuous variables were assessed by the t-test, while categorical variables were assessed by the non-parametric test. Interclass correlation coefficient tested variables’ consistency. Sensitivity, specificity, accuracy F1-score, and area under the curve (AUC) were used to evaluate model performance.

RESULTS: DEL status could be identified to varying degrees with 72 models based on radiomics, and model performance could be improved by combining different sequences and classifiers. Both SVMlinear and logistic regression (LR) combined with four sequence group had similar largest AUCmean (0.92 ± 0.09 vs. 0.92 ± 0.05), and SVMlinear was considered as the optimal model in this study since the F1-score of SVMlinear (0.88) was higher than that of LR (0.83).

DATA CONCLUSION: Multiparametric MRI-based machine learning is promising in DEL detection.

EVIDENCE LEVEL: 4 TECHNICAL EFFICACY STAGE: 2.

PMID:37199225 | DOI:10.1002/jmri.28782