bluuebunny/crossref_metadata_2025_split huggingface dataset:
Surgical Outcome Measures in a Cohort of Patients at High Risk of Breast Cancer Treated by Bilateral Risk-Reducing Mastectomy and Breast Reconstruction
Authors: Gandhi Ashu , Duxbury Paula , Clancy Tara , Lalloo Fiona , Wisely Julie A. , Kirwan Cliona C. , Foden Philip , Stocking Katie , Howell Anthony , Evans D. Gareth 
Published: June 2022

Background:
Women with breast cancer–related genetic pathogenic variants (e.g., BRCA1, BRCA2) or with a strong family history carry lifetime risks of developing breast cancer of up to 80 to 90 percent. A significant proportion of these women proceed to bilateral risk-reducing mastectomy. The authors aimed to document the surgical morbidity of risk-reducing mastectomy and establish whether a diagnosis of breast cancer at the time of surgery impacted outcomes.

Methods:
Clinical details of 445 women identified as having a greater than 25 percent lifetime risk of developing breast cancer who underwent risk-reducing mastectomy and breast reconstruction were interrogated for surgical outcomes such as planned, unplanned, and emergency procedures; complication rates; length of stay; and longevity of breast reconstruction. These outcome measures were recorded in women diagnosed with breast cancer perioperatively (cancer group) and those without malignancy (benign group).

Results:
Median follow-up was similar in both groups (benign group, 70 months; cancer group, 73 months). Patients were older in the cancer group than in the benign group (43 years versus 39 years; p < 0.001). Women in the cancer group required more planned procedures to complete reconstruction than those in the benign group (four versus two; p = 0.002). Emergency procedures, unplanned surgical interventions (e.g., capsulectomy), and postreconstruction complication rates were similar between groups. One in five women overall required revision surgery. Patients with autologous reconstructions had a revision rate of 1.24 per 1000 person-years compared with 2.52 per 1000 person-years in the implant reconstruction group.

Conclusions:
Women contemplating risk-reducing mastectomy can be reassured that this is a safe and effective procedure but will likely take multiple interventions. This knowledge should be integral to obtaining informed consent.

CLINICAL QUESTION/LEVEL OF EVIDENCE:
Risk, II.
31P-MRS of healthy human brain: revealing the hidden PME signals under phosphoethanolamine and phosphocholine resonances at 7T
Authors: Ren Jimin 
Published: June 2022

AbstractPurposeFor decades, it has been common practice to quantify brain phosphomonoester (PME) 31P signals by a two-component model composed of phosphoethanoamine (PE) and phosphcholine (PC). This study presented the evidence of hidden PME (h-PME) signals and their selective detections toward accurate quantification of PE and PC.MethodsGaussian lineshape analysis was modeled to reveal h-PME. Inversion-recovery (IR) sequence was employed to null the PE and PC resonances for selective detection of h-PME. The fully-relaxed 31P spectra after h-PME correction were used to quantify PE, PC and other brain metabolites in a group of 16 healthy subjects.ResultsSpectral lineshape analysis and IR modulation revealed previously overlooked h-PME signals underlying sharp PE and PC resonances. The h-PME signals appeared as a broad “bump” (LW1/2: 105 ± 25 Hz, N = 16), leading to poor spectral resolution between PE and PC. Fast relaxing h-PME signals, tentatively assigned to blood 2,3-DPG, were selectively detectable using IR sequence at an optimal inversion delay of 5.8 s. In fully relaxed 31P spectra, h-PME measured 44 ± 9 % of the total PME signal, equivalent to 1.36 ± 0.39 mM in single phosphoryl unit, compared to 1.41 ± 0.23 mM for PE and 0.31± 0.10 mM for PC.ConclusionHidden PME signals are a significant constitute of PME signals in human brain 31P spectra, and need to be taken into account for quantifying PE and PC as biomarkers of altered phospholipid metabolism in brain pathologies.
Demystifying the role of prognostic biomarkers in breast cancer through integrated transcriptome and pathway enrichment analyses
Authors: Mishra Divya , Mishra Ashish , Singh M.P. 
Published: June 2022

AbstractBreast cancer (BC) is the most commonly diagnosed cancer and the leading cause of death in women. There has been discovered an increasing number of molecular targets for BC prognosis and therapy. However, it is still urgent to identify new biomarkers. Therefore, we evaluated biomarkers that may contribute to the diagnosis and treatment of BC. We searched TCGA datasets and identified differentially expressed genes (DEGs) by comparing tumor (100 samples) and non-tumor (100 samples) tissues using the Deseq2 package. Pathway and functional enrichment analysis of the DEGs were done using DAVID (The Database for Annotation, Visualization, and Integrated Discovery) database. The protein-protein interaction (PPI) network was identified using the STRING database and visualized through Cytoscape software. Hub gene analysis of the PPI network was done using Cytohubba plugins. The associations between the identified genes and overall survival (OS) were analyzed using Kaplan–Meier plot. Finally, we have identified hub genes at the transcriptome level. A total of 824 DEGs were identified, which were mostly enriched in cell proliferation, signal transduction, and cell division. The PPI network comprised 822 nodes and 12145 edges. Elevated expression of the 5 hub genes AURKA, BUB1B, CCNA2, CCNB2, and PBK are related to poor OS in breast cancer patients. A promoter methylation study showed these genes to be hypomethylated. Validation through genetic alteration and missense mutations resulted in chromosomal instability leading to improper chromosome segregation causing aneuploidy. The enriched functions and pathways included the cell cycle, oocyte meiosis, and the p53 signaling pathway. The identified five hub genes in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.
Nose-only Exposure to Cherry and Tobacco Flavored E-cigarettes Induced Lung Inflammation in Mice in a Sex-dependent Manner
Authors: Lamb Thomas , Muthumalage Thivanka , Meehan-Atrash Jiries , Rahman Irfan 
Published: June 2022

AbstractFlavoring chemicals utilized in electronic nicotine delivery systems (ENDS) have been shown to result in an increase in cellular inflammation, meanwhile, the effects of fruit and tobacco flavors on lung inflammation by nose-only exposures to mice are relatively unknown. We hypothesized that C57BL/6J mice exposed to flavored e-cigarettes would result in an increase in lung inflammation. C57BL/6J mice were exposed to air, propylene glycol/vegetable glycerin (PG/VG), and e-liquids “Apple”, “Cherry”, “Strawberry”, “Wintergreen”, and “Smooth & Mild Tobacco”, for one hour per day for a three day exposure. Quantification of flavoring chemicals was measured by proton nuclear magnetic resonance spectroscopy (1H NMR), differential cell counts by flow cytometry, pro-inflammatory cytokines/chemokines by ELISA, and matrix metalloproteinase levels by western blot. Exposure to PG/VG, Apple, and Smooth & Mild Tobacco resulted in an increase in neutrophil cell count in lung bronchoalveolar lavage fluid (BALF). Strawberry exposure increased KC levels in BALF while in lung homogenate KC levels were increased in PG/VG, Cherry, and Smooth & Mild Tobacco exposure. Exposure to PG/VG and Cherry increased IL-6 levels and in all exposed mice there was a male-specific decrease in MCP-1 levels in lung homogenate. Mice exposed to PG/VG, Apple, Cherry, and Wintergreen resulted in an increase in MMP2 levels. Our results indicate that female mice exposed to cherry flavored e-liquids and male mice exposed to tobacco flavored e-liquids resulted in an increase in inflammation, while exposure to mint flavored e-liquids resulted in a decrease in inflammatory cytokine and an increase in tissue repair proteins. This study revealed that flavored-based e-cigarette exposure elicited sex-specific alterations in lung inflammation, with cherry flavors/benzaldehyde eliciting female-specific increases in inflammation. This highlights the toxicity of flavored chemicals and the further need for regulation of flavoring chemicals.
A metagenomic view on fungal diversity in freshwater lakes
Authors: Sanyal Anushree , Kluge Mariana , Redondo Miguel Angel , Buck Moritz , Mehrshad Maliheh , Garcia Sarahi L , Bertilsson Stefan , Peura Sari 
Published: June 2022

AbstractFungi are essential components in a wide range of ecosystems and while major efforts have been spent on disentangling the diversity and functional roles of fungi in terrestrial environments, our knowledge about aquatic fungi is lagging. To address this knowledge gap, we explored metagenomes from 25 lakes from the arctic and boreal zone and one tropical reservoir with the aim of describing the community structure of fungi and fungi-like organisms (Oomycota). A second objective was to identify possible environmental factors influencing the composition of the fungal communities. Our results show that the main fungal phyla and orders are the same across all the lakes despite the differences in geographic location and prevailing climate. Still, there was differential distribution of more highly resolved taxa across the lakes that accordingly featured distinct communities, possibly caused by differential availability of carbon substrates in the lakes. A more detailed classification of sequences related to the pathogenic Oomycota genus Phytophthora clearly demonstrated that while technologies now exist for sequencing entire microbial communities in great detail, we are still severely limited by insufficient coverage of eukaryotic sequences and genomes in public databases.
A knowledge integration strategy for the selection of a robust multi-stress biomarkers panel for Bacillus subtilis
Authors: Huang Yiming , Sinha Nishant , Wipat Anil , Bacardit Jaume 
Published: June 2022

AbstractRecent advances in high-throughput omics technologies have enhanced the identification of molecular biomarkers specific to phenotypes or states in bacteria. Using these biomarkers to monitor the state of bacteria used in biotechnological processes promises to increase process efficiency. However, live-cell monitoring systems applied to recognise bacterial cellular states in real time can only accommodate a small number of gene expression biomarkers. Computational methods are required to identify and prioritise robust biomarkers for experimental characterisation and verification. This study focused on designing a knowledge integration strategy for the selection of an optimal minimised gene expression biomarker panel to sense various stress states in Bacillus subtilis. We developed a computational method that ranks the candidate biomarker panels based on complementary information from machine learning model, gene regulatory network and co-expression network. We identified a recommended biomarker panel showing high stress sensing power for a variety of conditions included in both the dataset used for biomarker identification (mean f1-score achieved at 0.99) and the independent datasets from different sources (mean f1-score achieved at 0.98). We discovered a significant correlation between stress sensing power and evaluation metrics such as the number of associated regulators in a B. subtilis gene regulatory network (GRN) and the number of associated modules in a B. subtilis co-expression network (CEN). GRNs and CENs provide information relevant to the diversity of biological processes encoded by biomarker genes. We demonstrate that quantitatively relating meaningful evaluation metrics with stress sensing power has potential for recognising biomarkers that show better sensitivity and robustness to an extended set of stress conditions. We conclude that this approach is readily applicable to biomarker discovery model selection.
Deep-learning-based automatic detection of pulmonary nodules from chest radiographs
Authors: Ajmera Pranav , Pant Richa , Seth Jitesh , Ghuwalewala Suraj , Kathuria Sahil , Rathi Snehal , Patil Sonali , Edara Manaswani , Saini Mukul , Raj Preeti , Duddalwar Vinay , Kulkarni VM , Patil Parag , Kulkarni Viraj , Kharat Amit 
Published: June 2022

ABSTRACTObjectiveTo assess a deep learning-based artificial intelligence model for the detection of pulmonary nodules on chest radiographs and to compare its performance with board-certified human readers.MethodsFor this retrospective study, 308 chest radiographs were obtained between January 2019 to December 2021 from a tertiary care hospital. All radiographs were analyzed using a deep learning AI model called DxNodule AI Screen. Two expert board-certified radiologists established the ground truth, and 11 test readers independently reviewed all radiographs in two sessions (unaided and AI-aided mode) with a washout period of one month.ResultsThe standalone model had an AUROC of 0.905 [0.87, 0.94] in detecting pulmonary nodules. The mean AUROC across the 11 readers improved from 0.798 [0.74, 0.86] for unaided interpretation to 0.846 [0.82, 0.880] for AI-aided interpretation. With DxNodule AI Screen, readers were able to identify nodules at the correct locations, which they otherwise missed. The mean specificity, accuracy, PPV, and NPV of the readers improved significantly from 0.87 [0.78, 0.96], 0.78 [0.72, 0.84], 0.77 [0.65, 0.88], and 0.86 [0.81, 0.90] in the unaided session to 0.89 [0.82, 0.96], 0.83 [0.80, 0.85], 0.82 [0.73, 0.9], and 0.89 [0.86, 0.92], respectively in the aided session.ConclusionDxNodule AI Screen outperformed human readers in nodule detection performance on chest radiographs, and enhanced human readers’ performances when used as an aid.
Defining reference values for body composition indexes by magnetic resonance imaging in UK Biobank
Authors: Dai Liang , Huang Xiao-yan , Lu Yue-qi , Liu Yu-yang , Song Cong-ying , Zhang Jing-wen , Li Jing , Zhang Yue , Shan Ying , Shi Yu 
Published: June 2022

AbstractBackgroundMagnetic resonance imaging (MRI) is the gold standard for body composition measurements, while such reference range data have not been reported yet.MethodsWe used MRI-measured body composition data from the UK Biobank cohort, including three lean tissue parameters and seven adipose tissue parameters. Participants aged between 45 to 74 years from both genders with at least one parameter data were screened. Age- and sex-specific percentile curves were generated by the lambda–mu–sigma method. Three levels of reference ranges were provided, which were equivalent to mean ± 1 standard deviation (SD), 2 SDs, and 2.5 SDs, respectively.ResultsThe final analysis set for each parameter ranged from 4 842 to 14 148 participants (53.4-56.6 % women) with median ages around 61 years after eligibility confirmation. For lean tissue parameters, from age 45 to age 70, median percentiles of total lean tissue volume and total thigh fat-free muscle volume reduced by 2.83 L and 1.73 L in men, and by 3.02 L and 1.51 L in women, respectively. Within the age range, median percentiles of weight-to-muscle ratio elevated by 0.51 kg/L in men and 0.83 kg/L in women, respectively. Adipose tissue parameters presented inconsistent trajectories. For men, median percentiles of muscle fat infiltration, visceral adipose tissue (VAT) volume, total abdominal adipose tissue index, and abdominal fat ratio elevated by 1.48 %, 0.32 L, 0.08 L/m2 and 0.04, while abdominal subcutaneous adipose tissue (ASAT) volume and total adipose tissue volume reduced by 0.47 L and 0.41 L from age 45 to age 70, respectively. And total trunk fat volume remained stable with aging (approximate 9.52 L). For women, muscle fat infiltration and VAT volume showed similar trends with men, increased by 1.68 % and 0.76 L from age 45 to age 70. Other five parameters firstly increased from age 45 to age 60 (ASAT volume: 0.35 L, total adipose tissue volume: 0.78 L, total trunk fat volume: 1.12 L, total abdominal adipose tissue index: 0.49 L/m2, and abdominal fat ratio: 0.06), then kept stable (total abdominal adipose tissue index and abdominal fat ratio) or presented a slight reduction afterwards (ASAT volume: 0.33 L, total adipose tissue volume: 0.14 L, and total trunk fat volume: 0.20 L).ConclusionsWe have established reference ranges for MRI-measured body composition parameters based on large community-dwelling population. The data would benefit more accurate assessment of abnormal adipose and muscle conditions.
The association of personality traits with childhood obesity: a systematic review and meta-analysis
Authors: Jiang Dong-Xia , Huang Tian-Yu , Chen Jing , Xiao Wu-Cai , Liu Zheng 
Published: June 2022

AbstractA growing body of evidence has shown the association of personality with obesity, but findings of such studies in children remain mixed. This review aimed to systematically summarize the existing literature regarding the association between personality and childhood obesity and explore the underlying mechanisms. A meta-analysis was performed based on the data from 12 included studies and personality traits were classified into five domains (Extraversion, Agreeableness, Conscientiousness, Neuroticism, and Openness) based on the five-factor model. Conscientiousness was significantly associated with BMI across 2 studies and the pooled correlation coefficient was −0.09 (95% CI: −0.17 to 0.00; I2 = 0%); another 3 studies indicated a significant difference in Conscientiousness between children with obesity and those with normal weight, and the pooled SMD was −0.08 (95% CI: −0.13 to −0.03; I2 = 66%). No consistent patterns were found in the associations between other personality traits and childhood obesity. We hypothesized three possible pathways (moderation, mediation, and confounding) underlying the personality-obesity associations. Findings of our review indicate that, within the context of childhood obesity interventions, identification of subgroups of children with specific personality traits is necessary, especially for measures of Conscientiousness.
Acceleration of regional, racial and gender disparities in drug overdoses across the United States driven by COVID-19 and fentanyl use
Authors: D’Orsogna Maria R. , Böttcher Lucas , Chou Tom 
Published: June 2022

AbstractObjectivesTo examine trends in drug overdose deaths by gender, race and geography in the United States during the period 2013-2020.MethodsWe used the final National Vital Statistics System multiple cause-of-death mortality files to extract crude rates by gender and race and to calculate the male-to-female ratios of crude rates between 2013 and 2020. We established 2013-2019 temporal trends for four major drug types: psychostimulants with addiction potential (T43.6, such as methamphetamines); heroin (T40.1); natural and semi-synthetic opioids (T40.2, such as those contained in prescription pain-killers); synthetic opioids (T40.4, such as fentanyl and its derivatives) through a quadratic regression and determined whether changes in the pandemic year 2020 were statistically significant. We also identified states, race and gender categories most impacted by drug overdoses.ResultsNationwide, the year 2020 saw statistically significant increases in overdoses for all drug categories except heroin, surpassing predictions based on 2013-2019 trends. Crude rates for Blacks of both genders surpassed those for Whites for fentanyl and psychostimulants in 2018 creating a gap that widened through 2020. In some regions mortality among Whites decreased while overdose rates for Blacks kept rising. The largest 2020 mortality statistic is for Black males in the District of Columbia, with a record 134 overdoses per 100,000 due to fentanyl, 9.4 times more than the fatality rate among White males. Male overdose crude rates in 2020 remain larger than those of females for all drug categories except in Idaho, Utah and Arkansas where crude rates of overdoses by natural and semisynthetic opioids for females exceeded those of males.Public Health ImplicationsDrug prevention, mitigation and no-harm strategies should include racial, geographical and gender-specific efforts, to better identify and serve at risk groups.
Improved Multiple Sclerosis Lesion Detection using an Intelligent Automation Software
Authors: Federau Christian , Hainc Nicolin , Edjlali Myriam , Zhu Guangming , Mastilovic Milica , Nierobisch Nathalie , Uhlemann Jan-Philipp , Paganucci Silvio , Granziera Cristina , Heinzlef Olivier , Kipp Lucas B. , Wintermark Max 
Published: June 2022

AbstractIntroductionThe assessment of multiple sclerosis (MS) lesions on follow-up magnetic resonance imaging (MRI) is tedious, time-consuming, and error-prone. Automation of low-level tasks could enhance the radiologist in this work. We evaluate the intelligent automation software Jazz in a blinded three centers study, for the assessment of new, slowly expanding, and contrast-enhancing MS lesionsMethodsIn three separate centers, 117 MS follow-up MRIs were blindly analyzed on FLuid Attenuated Inversion Recovery (FLAIR), pre- and post-Gadolinium T1-weighted images using Jazz by 2 neuroradiologists in each center. The reading time was recorded. The ground truth was defined in a second reading by side-by-side comparison of both reports from Jazz and the standard clinical report. The number of described new, slowly expanding, and contrast-enhancing lesions described with Jazz was compared to the lesions described in the standard clinical report.ResultsA total of 96 new lesions from 41 patients and 162 slowly expanding lesions (SELs) from 61 patients were described in the ground truth reading. A significantly larger number of new lesions were described using Jazz compared to the standard clinical report (63 versus 24). No SELs were reported in the standard clinical report, while 95 SELs were reported on average using Jazz. A total of 4 new contrast-enhancing lesions were found in all reports. The reading with Jazz was very time efficient, taking on average 2min33sec ± 1min0sec per case. Overall inter-reader agreement for new lesions between the readers using Jazz was moderate for new lesions (Cohen kappa=0.5) and slight for SELs (0.08).DiscussionThe quality and the productivity of neuroradiological reading of MS follow-up MRI scans can be significantly improved using a dedicated software such as Jazz.