Category: Nevin Manimala

Asia Pac J Clin Oncol. 2022 Jan 30. doi: 10.1111/ajco.13748. Online ahead of print.

ABSTRACT

As an important transcription factor that is widely expressed in most tissues of the human body, Myc-associated zinc finger protein (MAZ) has been reported highly expressed in many malignant tumors and thought to be a promising therapeutic target for cancer treatment. In this review, we aim to offer a comprehensive understanding of MAZ regulation in malignant tumors. The carboxy terminal of MAZ protein contains six C2H2 zinc fingers, and its regulation of transcription is based on the interaction between the GC-rich DNA binding sites of target genes and its carboxy-terminal zinc finger motifs. MAZ protein has been found to activate or inhibit the transcriptional initiation process of many target genes, as well as play an important role in the transcriptional termination process of some target genes, so MAZ poses dual regulatory functions in the initiation and termination process of gene transcription. Through the transcriptional regulation of c-myc and Ras gene family, MAZ poses an important role in the occurrence and development of breast cancer, pancreatic cancer, prostate cancer, glioblastoma, neuroblastoma, and other malignant tumors. Our review shows a vital role of MAZ in many malignant tumors and provides novel insight for cancer diagnosis and treatment.

PMID:35098656 | DOI:10.1111/ajco.13748

Phytochem Anal. 2022 Jan 30. doi: 10.1002/pca.3107. Online ahead of print.

ABSTRACT

INTRODUCTION: In this era of ‘omics’ technology in natural products studies, the complementary aspects of mass spectrometry (MS)- and nuclear magnetic resonance (NMR)-based techniques must be taken into consideration. The advantages of using both analytical platforms are reflected in a higher confidence of results especially when using replicated samples where correlation approaches can be used to statistically link results from MS to NMR.

OBJECTIVES: Demonstrate the use of Statistical Total Correlation (STOCSY) for linking results from MS and NMR data to reach higher confidence in compound identification.

METHODOLOGY: Essential oil samples of Melaleuca alternifolia and M. rhaphiophylla (Myrtaceae) were used as test objects. Aliquots of 10 samples were collected for GC-MS and NMR data acquisition [proton (¹ H)-NMR, and carbon-13 (¹³ C)-NMR as well as two-dimensional (2D) heteronuclear single quantum correlation (HSQC), heteronuclear multiple-bond correlation (HMBC), and HSQC-total correlated spectroscopy (TOCSY) NMR]. The processed data was imported to Matlab where STOCSY was applied.

RESULTS: STOCSY calculations led to the confirmation of the four main constituents of the sample-set. The identification of each was accomplished using; MS spectra, retention time comparison, ¹³ C-NMR data, and scalar correlations of the 2D NMR spectra.

CONCLUSION: This study provides a pipeline for high confidence in compound identification using a set of essential oils samples as test objects for demonstration.

PMID:35098600 | DOI:10.1002/pca.3107

Stat Med. 2022 Jan 31. doi: 10.1002/sim.9327. Online ahead of print.

ABSTRACT

Adaptive enrichment designs in clinical trials have been developed to enhance drug developments. They permit, at interim analyses during the trial, to select the sub-populations that benefits the most from the treatment. Because of this selection, the naive maximum likelihood estimation of the treatment effect, commonly used in classical randomized controlled trials, is biased. In the literature, several methods have been proposed to obtain a better estimation of the treatments’ effects in such contexts. To date, most of the works have focused on normally distributed endpoints, and some estimators have been proposed for time-to-event endpoints but they have not all been compared side-by-side. In this work, we conduct an extensive simulation study, inspired by a real case-study in heart failure, to compare the maximum-likelihood estimator (MLE) with an unbiased estimator, shrinkage estimators, and bias-adjusted estimators for the estimation of the treatment effect with time-to-event data. The performances of the estimators are evaluated in terms of bias, variance, and mean squared error. Based on the results, along with the MLE, we recommend to provide the unbiased estimator and the single-iteration bias-adjusted estimator: the former completely eradicates the selection bias, but is highly variable with respect to a naive estimator; the latter is less biased than the MLE estimator and only slightly more variable.

PMID:35098579 | DOI:10.1002/sim.9327

Stat Med. 2022 Jan 31. doi: 10.1002/sim.9337. Online ahead of print.

ABSTRACT

In the era of immunotherapies and targeted therapies, the focus of early phase clinical trials has shifted from finding the maximum tolerated dose to identifying the optimal biological dose (OBD), which maximizes the toxicity-efficacy trade-off. One major impediment to using adaptive designs to find OBD is that efficacy or/and toxicity are often late-onset, hampering the designs’ real-time decision rules for treating new patients. To address this issue, we propose the model-assisted TITE-BOIN12 design to find OBD with late-onset toxicity and efficacy. As an extension of the BOIN12 design, the TITE-BOIN12 design also uses utility to quantify the toxicity-efficacy trade-off. We consider two approaches, Bayesian data augmentation and an approximated likelihood method, to enable real-time decision making when some patients’ toxicity and efficacy outcomes are pending. Extensive simulations show that, compared to some existing designs, TITE-BOIN12 significantly shortens the trial duration while having comparable or higher accuracy to identify OBD and a lower risk of overdosing patients. To facilitate the use of the TITE-BOIN12 design, we develop a user-friendly software freely available at http://www.trialdesign.org.

PMID:35098585 | DOI:10.1002/sim.9337

Aliment Pharmacol Ther. 2022 Jan 30. doi: 10.1111/apt.16778. Online ahead of print.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) has recently been applied to endoscopy and questionnaires for the evaluation of oesophageal diseases (ODs).

AIM: We performed a systematic review with meta-analysis to evaluate the performance of AI in the diagnosis of malignant and benign OD.

METHODS: We searched MEDLINE, EMBASE, EMBASE Classic and the Cochrane Library. A bivariate random-effect model was used to calculate pooled diagnostic efficacy of AI models and endoscopists. The reference tests were histology for neoplasms and the clinical and instrumental diagnosis for gastro-oesophageal reflux disease (GERD). The pooled area under the summary receiver operating characteristic (AUROC), sensitivity, specificity, positive and negative likelihood ratio (PLR and NLR) and diagnostic odds ratio (DOR) were estimated.

RESULTS: For the diagnosis of Barrett’s neoplasia, AI had AUROC of 0.90, sensitivity 0.89, specificity 0.86, PLR 6.50, NLR 0.13 and DOR 50.53. AI models’ performance was comparable with that of endoscopists (P = 0.35). For the diagnosis of oesophageal squamous cell carcinoma, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.97, 0.95, 0.92, 12.65, 0.05 and DOR 258.36, respectively. In this task, AI performed better than endoscopists although without statistically significant differences. In the detection of abnormal intrapapillary capillary loops, the performance of AI was: AUROC 0.98, sensitivity 0.94, specificity 0.94, PLR 14.75, NLR 0.07 and DOR 225.83. For the diagnosis of GERD based on questionnaires, the AUROC, sensitivity, specificity, PLR, NLR and DOR were 0.99, 0.97, 0.97, 38.26, 0.03 and 1159.6, respectively.

CONCLUSIONS: AI demonstrated high performance in the clinical and endoscopic diagnosis of OD.

PMID:35098562 | DOI:10.1111/apt.16778

J Clin Lab Anal. 2022 Jan 31:e24263. doi: 10.1002/jcla.24263. Online ahead of print.

ABSTRACT

BACKGROUND: Breast cancer (BC) is one of the leading causes of death among women around the world. Circular RNAs (circRNAs) are a newly discovered group of non-coding RNAs that their roles are being investigated in BC and other cancer types. In this study, we evaluated the association of hsa_circ_0005986 and hsa_circ_000839 in tumor and adjacent normal tissues of BC patients with their clinicopathological characteristics.

MATERIALS AND METHODS: Total RNA was extracted from tumors and adjacent non-tumor tissues by the Trizol isolation reagent, and cDNA was synthesized using First Strand cDNA Synthesis Kit (Thermo Scientific). The expression level of hsa_circ_0005986 and hsa_circ_000839 was quantified using RT-qPCR. Online in silico tools were used for identifying potentially important competing endogenous RNA (ceRNA) networks of these two circRNAs.

RESULTS: The expression level of hsa_circ_0005986 and hsa_circ_000839 was lower in the tumor as compared to adjacent tissues. The expression level of hsa_circ_0005986 in the patients who had used hair dye in the last 5 years was significantly lower. Moreover, a statistically significant negative correlation between body mass index (BMI) and hsa_circ_000839 expression was observed. In silico analysis of the ceRNA network of these circRNAs revealed mRNAs and miRNAs with crucial roles in BC.

CONCLUSION: Downregulation of hsa_circ_000839 and hsa_circ_0005986 in BC tumors suggests a tumor-suppressive role for these circRNAs in BC, meriting the need for more experimentations to delineate the exact mechanism of their involvement in BC pathogenesis.

PMID:35098570 | DOI:10.1002/jcla.24263

Hepatology. 2022 Jan 31. doi: 10.1002/hep.32372. Online ahead of print.

NO ABSTRACT

PMID:35098558 | DOI:10.1002/hep.32372

J Clin Psychol. 2022 Jan 30. doi: 10.1002/jclp.23318. Online ahead of print.

ABSTRACT

OBJECTIVES: Much of the research on hoarding is focused on cognition and behavior, with less focus on emotion and its regulation.

METHOD: A comprehensive search yielded nine studies (out of 5581) from which to draw data for the current study. Across the eight studies (nine independent effect sizes) which provided data for 1595 total participants (Mean_age = 34.46, SD = 8.78; 64.26% females).

RESULTS: Emotion dysregulation had a medium association with hoarding symptoms (r = 0.43). The effect was strong (r = 0.61) in some populations and weaker (r = 0.19) in others. However, it was higher in nonclinical samples than in clinical samples. Also, the strength of the association between hoarding and emotion regulation differed by the type of hoarding measures adopted in the individual studies. Moreover, there were no statistically significant differences between emotion dysregulation facets and hoarding.

CONCLUSION: The findings highlight the importance of studying emotions and emotion regulation in hoarding.

PMID:35098535 | DOI:10.1002/jclp.23318

Prostate. 2022 Jan 31. doi: 10.1002/pros.24302. Online ahead of print.

ABSTRACT

OBJECTIVE: Prostate cancer (PCa) is the most prevalent cancer among males. This study attempted to develop a clinically significant prostate cancer (csPCa) risk nomogram including Prostate Imaging-Reporting and Data System (PI-RADS) score and other clinical indexes for initial prostate biopsy in light of the different prostate regions, and internal validation was further conducted.

PATIENTS AND METHODS: A retrospective study was performed including 688 patients who underwent ultrasound-guided transperineal magnetic resonance imaging fusion prostate biopsy from December 2016 to July 2019. We constructed nomograms combining PI-RADS score and clinical variables (prostate-specific antigen [PSA], prostate volume (PV), age, free/total PSA, and PSA density) through univariate and multivariate logistic regression to identify patients eligible for biopsy. The performance of the predictive model was evaluated by bootstrap resampling. The area under the curve (AUC) of the receiver-operating characteristic (ROC) analysis was appointed to quantify the accuracy of the primary nomogram model for csPCa. Calibration curves were used to assess the agreement between the biopsy specimen and the predicted probability of the new nomogram. The χ² test was also applied to evaluate the heterogeneity between fusion biopsy and systematic biopsy based on different PI-RADS scores and prostate regions.

RESULTS: A total of 320 of 688 included patients were diagnosed with csPCa. csPCa was defined as Gleason score ≥7. The ROC and concordance-index both presented good performance. The nomogram reached an AUC of 0.867 for predicting csPCa at the peripheral zone; meanwhile, AUC for transitional and apex zones were 0.889 and 0.757, respectively. Statistical significance was detected between fusion biopsy and systematic biopsy for PI-RADS score >3 lesions and lesions at the peripheral and transitional zones.

CONCLUSION: We produced a novel nomogram predicting csPCa in patients with suspected imaging according to different locations. Our results indicated that PI-RADS score combined with other clinical parameters showed a robust predictive capacity for csPCa before prostate biopsy. The new nomogram, which incorporates prebiopsy data including PSA, PV, age, and PI-RADS score, can be helpful for clinical decision-making to avoid unnecessary biopsy.

PMID:35098557 | DOI:10.1002/pros.24302

Thromb Haemost. 2022 Jan 28. doi: 10.1055/a-1754-7551. Online ahead of print.

ABSTRACT

Background Patients with end-stage kidney disease (ESKD) on hemodialysis (HD) are at increased risk for bleeding. However, despite relevant clinical implications regarding dialysis modalities or anticoagulation, no bleeding risk assessment strategy has been established in this challenging population. Methods Analyses on bleeding risk assessment models were performed in the population-based Vienna InVestigation of Atrial fibrillation and thromboemboLism in patients on hemoDialysIs (VIVALDI) study including 625 patients. In this cohort study, patients were prospectively followed for a median observation period of 3.5 years for the occurrence of major bleeding. First, performances of existing bleeding risk scores (i.e., HAS-BLED, HEMORR2HAGES, ATRIA, and four others) were evaluated in terms of discrimination and calibration. Second, four machine learning-based prediction models that included clinical, dialysis-specific, and laboratory parameters were developed and tested using Monte-Carlo cross-validation. Results Of 625 patients (median age: 66 years, 38% women), 89 (14.2%) developed major bleeding, with a 1-year, 2-year, and 3-year cumulative incidence of 6.1% (95%CI 4.2-8.0), 10.3% (95%CI 8.0-12.8), and 13.5% (95%CI 10.8-16.2), respectively. C-statistics of seven contemporary bleeding risk scores ranged between 0.54 and 0.59 indicating poor discriminatory performance. The HAS-BLED score showed the highest C-statistics of 0.59 (95% 0.53-0.56). Similarly, all four machine learning-based predictions models performed poorly in internal validation (C-statistics ranging from 0.49-0.55). Conclusions Existing bleeding risk scores and a machine learning approach including common clinical parameters fail to assist in bleeding risk prediction of patients on HD. Therefore, new approaches, including novel biomarkers, to improve bleeding risk prediction in patients on HD are needed.

PMID:35098518 | DOI:10.1055/a-1754-7551