bluuebunny/crossref_metadata_2025_split huggingface dataset:
Classifying breast cancer subtypes on multi-omics data via sparse canonical correlation analysis and deep learning
Authors: Huang Yiran , Zeng Pingfan , Zhong Cheng 
Published: March 2024

AbstractBackgroundClassifying breast cancer subtypes is crucial for clinical diagnosis and treatment. However, the early symptoms of breast cancer may not be apparent. Rapid advances in high-throughput sequencing technology have led to generating large number of multi-omics biological data. Leveraging and integrating the available multi-omics data can effectively enhance the accuracy of identifying breast cancer subtypes. However, few efforts focus on identifying the associations of different omics data to predict the breast cancer subtypes.ResultsIn this paper, we propose a differential sparse canonical correlation analysis network (DSCCN) for classifying the breast cancer subtypes. DSCCN performs differential analysis on multi-omics expression data to identify differentially expressed (DE) genes and adopts sparse canonical correlation analysis (SCCA) to mine highly correlated features between multi-omics DE-genes. Meanwhile, DSCCN uses multi-task deep learning neural network separately to train the correlated DE-genes to predict breast cancer subtypes, which spontaneously tackle the data heterogeneity problem in integrating multi-omics data.ConclusionsThe experimental results show that by mining the associations among multi-omics data, DSCCN is more capable of accurately classifying breast cancer subtypes than the existing methods.
Genome-wide association study for in vitro digestibility and related traits in triticale forage
Authors: De Zutter Anneleen , Piro Maria Chiara , Maenhout Steven , Maurer Hans Peter , De Boever Johan , Muylle Hilde , Roldán-Ruiz Isabel , Haesaert Geert 
Published: March 2024

Abstract
Background
Triticale is making its way on dairy farms as an alternative forage crop. This requires the availability of high-yielding triticale varieties with good digestibility. Triticale forage breeding mainly focussed on biomass yield, but efforts to improve digestibility are increasing. We previously investigated the interrelationships among different quality traits in soft dough triticale: starch, acid detergent fibre and in vitro digestibility of organic matter (IVOMD) and of neutral detergent fibre (IVNDFD) of the total plant, IVNDFD and Klason lignin of the stems, and ear proportion and stem length. Here we determine the genetic control of these traits, using a genome-wide association (GWAS) approach. A total of 33,231 DArTseq SNP markers assessed in a collection of 118 winter triticale genotypes, including 101 varieties and 17 breeding lines, were used.

Results
The GWAS identified a total of 53 significant marker-trait associations (MTAs). The highest number of significantly associated SNP markers (n = 10) was identified for total plant IVNDFD. A SNP marker on chromosome 1A (4211801_19_C/T; 474,437,796 bp) was found to be significantly associated with ear proportion, and plant and stem IVNDFD, with the largest phenotypic variation for ear proportion (R²p = 0.23). Based on MTAs, candidate genes were identified which were of particular relevance for variation in in vitro digestibility (IVD) because they are putatively involved in plasma membrane transport, cytoskeleton organisation, carbohydrate metabolic processes, protein phosphorylation, and sterol and cell wall biogenesis. Interestingly, a xyloglucan-related candidate gene on chromosome 2R, SECCE2Rv1G0126340, was located in close proximity of a SNP significantly associated with stem IVNDFD. Furthermore, quantitative trait loci previously reported in wheat co-localized with significantly associated SNP markers in triticale.

Conclusions
A collection of 118 winter triticale genotypes combined with DArTseq SNP markers served as a source for identifying 53 MTAs and several candidate genes for forage IVD and related traits through a GWAS approach. Taken together, the results of this study demonstrate that the genetic diversity available in this collection can be further exploited for research and breeding purposes to improve the IVD of triticale forage.
Accelerated lymph flow from infusion of crystalloid fluid during general anesthesia
Authors: Hahn Robert G. 
Published: March 2024

Abstract
Background
Kinetic analysis of crystalloid fluid yields a central distribution volume (Vc) of the same size as the expected plasma volume (approximately 3 L) except during general anesthesia during which Vc might be only half as large. The present study examined whether this difference is due to influence of the intravascular albumin balance.

Methods
A population volume kinetic analysis according to a three-compartment model was performed based on retrospective data from 160 infusion experiments during which 1–2.5 L of crystalloid fluid had been infused intravenously over 20–30 min. The plasma dilution based on blood hemoglobin (Hb) and plasma albumin (Alb) was measured on 2,408 occasions and the urine output on 454 occasions. One-third of the infusions were performed on anesthetized patients while two-thirds were given to awake healthy volunteers.

Results
The Hb-Alb dilution difference was four times greater during general anesthesia than in the awake state (+ 0.024 ± 0.060 versus − 0.008 ± 0.050; mean ± SD; P < 0.001) which shows that more albumin entered the plasma than was lost by capillary leakage. The Hb-Alb dilution difference correlated strongly and positively with the kinetic parameters governing the rate of fluid transfer through the fast-exchange interstitial fluid compartment (k12 and k21) and inversely with the size of Vc. Simulations suggest that approximately 200 mL of fluid might be translocated from the interstitial space to the plasma despite ongoing fluid administration.

Conclusions
Pronounced plasma volume expansion early during general anesthesia is associated with a positive intravascular albumin balance that is due to accelerated lymphatic flow. This phenomenon probably represents adjustment of the body fluid volumes to anesthesia-induced vasodilatation.
The value of local dexmedetomidine as an adjuvant to ultrasound-guided wide awake local anesthesia no tourniquet (WALANT) in flexor tendon repair surgeries: a randomized controlled trial
Authors: Alseoudy Mahmoud Mohammed , Abdelkarime Elsayed Mohamed , Nour Khaled , Badr May Elsherbiny 
Published: March 2024

Abstract
Background
The Wide-Awake Local Anesthesia No Tourniquet (WALANT) technique allows intraoperative motor assessment of tendon repair integrity of the hand compared with general anesthesia or brachial plexus block. No studies have tested the effect of adding dexmedetomidine to lidocaine on the analgesic properties of the WALANT technique, which is the aim of our study.

Methods
A total of 128 patients aged more than 18 years were scheduled for surgical flexor tendon injury repair using WALANT technique. Patients were divided into two equal groups. Ultrasound-guided subcutaneous injection of lidocaine 1% with dexmedetomidine (1 µg/kg), Group D, or without dexmedetomidine, Group C, was performed at four points: proximal to the wrist joint, the distal forearm, palm region, and proximal phalanges. The primary outcome was total morphine consumption throughout the first postoperative day. Secondary outcomes included number of patients requiring rescue analgesia, time to first analgesic request, and pain score.

Results
Total morphine consumption was significantly (P < 0.001) lower in group D (2.66 ± 0.998) than in group C (3.66 ± 1.144) mg. Number of patients requiring rescue analgesia was significantly (P < 0.001) lower in group D (54.7% (35)) than group C (100.0% (64)). The time for first request for analgesia was significantly (P < 0.001) longer in group D (11.31 ± 6.944) than in group C (5.91 ± 4.839) h. Pain score was significantly higher in group C than D at three (P < 0.001), and six (P = 0.001) hours (P = 0.001) postoperatively.

Conclusion
Dexmedetomidine significantly improves the analgesic quality of WALANT when added to lidocaine with less opioid consumption.

Trial registration
(ID: PACTR202203906027106; Date: 31/07/2023).
Correlation between the cardiometabolic index and arteriosclerosis in patients with type 2 diabetes mellitus
Authors: Tang Chaoyan , Pang Tianjiao , Dang Chaozhi , Liang Hui , Wu Junfeng , Shen Xiaofang , Wang Lielin , Luo Ruiqiong , Lan Haiyun , Zhang Ping 
Published: March 2024

Abstract
Background
The cardiometabolic index (CMI) is a new metric derived from the triglyceride-glucose index and body mass index and is considered a potential marker for cardiovascular risk assessment. This study aimed to examine the correlation between the CMI and the presence and severity of arteriosclerosis in patients with type 2 diabetes mellitus (T2DM).

Methods
This study involved 2243 patients with T2DM. The CMI was derived by dividing the triglyceride level (mmol/L) by the high-density lipoprotein level (mmol/L) and then multiplying the quotient by the waist-to-height ratio. Multivariate logistic regression was used to analyze the correlations between the CMI and BMI blood biomarkers, blood pressure, and brachial-ankle pulse wave velocity (baPWV).

Results
Patients were categorized into three groups based on their CMI: Group C1 (CMI < 0.775; n = 750), Group C2 (CMI: 0.775–1.355; n = 743), and Group C3 (CMI > 1.355; n = 750). Increased BMI, fasting glucose, insulin (at 120 min), total cholesterol (TC), and baPWV values were observed in Groups C2 and C3, with statistically significant trends (all trends P < 0.05). The CMI was positively correlated with systolic blood pressure (r = 0.74, P < 0.001). Multivariate analysis revealed that an increased CMI contributed to a greater risk for arteriosclerosis (OR = 1.87, 95%CI: 1.66–2.10, P < 0.001). Compared to the C1 group, the C2 group and C3 group had a greater risk of developing arteriosclerosis, with ORs of 4.55 (95%CI: 3.57–5.81, P<0.001) and 5.56 (95%CI: 4.32–7.17, P<0.001), respectively. The association was notably stronger in patients with a BMI below 21.62 kg/m² than in those with a BMI of 21.62 kg/m² or higher (OR = 4.53 vs. OR = 1.59).

Conclusions
These findings suggest that the CMI is a relevant and independent marker of arteriosclerosis in patients with T2DM and may be useful in the risk stratification and management of these patients.
Methodological insights into ChatGPT’s screening performance in systematic reviews
Authors: Issaiy Mahbod , Ghanaati Hossein , Kolahi Shahriar , Shakiba Madjid , Jalali Amir Hossein , Zarei Diana , Kazemian Sina , Avanaki Mahsa Alborzi , Firouznia Kavous 
Published: March 2024

Abstract
Background
The screening process for systematic reviews and meta-analyses in medical research is a labor-intensive and time-consuming task. While machine learning and deep learning have been applied to facilitate this process, these methods often require training data and user annotation. This study aims to assess the efficacy of ChatGPT, a large language model based on the Generative Pretrained Transformers (GPT) architecture, in automating the screening process for systematic reviews in radiology without the need for training data.

Methods
A prospective simulation study was conducted between May 2nd and 24th, 2023, comparing ChatGPT’s performance in screening abstracts against that of general physicians (GPs). A total of 1198 abstracts across three subfields of radiology were evaluated. Metrics such as sensitivity, specificity, positive and negative predictive values (PPV and NPV), workload saving, and others were employed. Statistical analyses included the Kappa coefficient for inter-rater agreement, ROC curve plotting, AUC calculation, and bootstrapping for p-values and confidence intervals.

Results
ChatGPT completed the screening process within an hour, while GPs took an average of 7–10 days. The AI model achieved a sensitivity of 95% and an NPV of 99%, slightly outperforming the GPs’ sensitive consensus (i.e., including records if at least one person includes them). It also exhibited remarkably low false negative counts and high workload savings, ranging from 40 to 83%. However, ChatGPT had lower specificity and PPV compared to human raters. The average Kappa agreement between ChatGPT and other raters was 0.27.

Conclusions
ChatGPT shows promise in automating the article screening phase of systematic reviews, achieving high sensitivity and workload savings. While not entirely replacing human expertise, it could serve as an efficient first-line screening tool, particularly in reducing the burden on human resources. Further studies are needed to fine-tune its capabilities and validate its utility across different medical subfields.
Patients with chronic hepatitis B who have persistently normal alanine aminotransferase or aged < 30 years may exhibit significant histologic damage
Authors: Wei Sufang , Xie Qiuli , Liao Guichan , Chen Hongjie , Hu Meixin , Lin Xiaoli , Li Hong , Peng Jie 
Published: March 2024

Abstract
Background
The timing of antiviral therapy for chronic hepatitis B (CHB) patients with normal alanine transaminase (ALT) or aged < 30 years is still undetermined. We aimed to elucidate the correlation between liver histology, age, and ALT level in CHB patients and analyze the histological characteristics of the liver among patients with persistently normal ALT or aged < 30 years.

Methods
A retrospective analysis was conducted on 697 treatment-naive CHB patients. Liver biopsies were performed, and significant histological damage was defined as the grade of liver inflammation ≥ G2 and/or fibrosis ≥ S2 based on the Scheuer scoring system.

Results
The liver inflammation grades and fibrosis stages correlated positively with age, ALT, AST, GGT levels and negatively with the counts of PLT (all p < 0.050) in HBeAg-positive patients. Higher ALT levels and lower PLT counts were independently associated with significant liver inflammation and fibrosis in both HBeAg-positive and HBeAg-negative patients. Furthermore, among those with persistently normal ALT levels, the incidence of significant liver inflammation and fibrosis were 66.1% and 53.7% in HBeAg-positive groups, and 63.0% and 55.5% in HBeAg-negative groups. Moreover, there was no significant difference in the prevalence of significant liver damage between patients aged < 30 years and those aged ≥ 30 years, in both HBeAg-positive (≥ G2 or ≥ S2: 63.8% vs. 75.8%, p = 0.276) and HBeAg-negative (≥ G2 or ≥ S2: 65.9% vs. 72.5%, p = 0.504) groups, among patients with persistently normal ALT levels.

Conclusions
A considerable proportion of CHB patients with persistently normal ALT, including those below the age of 30 years, exhibited significant histological damage. This highlights the importance of initiating early antiviral therapy for HBV-infected individuals, even in the absence of elevated ALT levels.
Associations between cooking fuel use, its transitions, and worsening sensory impairments among Chinese middle-aged and older adults: a cohort study
Authors: Li Shaojie , Cui Guanghui , Hu Mingzheng , Hu Yang , Ren Longbing , Jiang Yuling , Sun Jing , Luan Zhe , Hu Kejia , Zhang Yunquan , Sun Gang , Yao Yao 
Published: March 2024

Abstract
Background
This study aimed to explore the associations between household air pollution (HAP), measured by cooking fuel use, sensory impairments (SI), and their transitions in Chinese middle-aged and older adults.

Methods
Participants were recruited from the 2011 China Health and Retirement Longitudinal Study (CHARLS) and were subsequently followed up until 2018. Data on SI were collected by self-reported hearing and vision impairments, which were divided into three categories: non-SI, single SI (hearing or vision impairment), and dual SI (DSI). Cooking fuels, including solid and clean fuels, are proxies for HAP. The transitions of cooking fuels and SI refer to the switching of the fuel type or SI status from baseline to follow-up. Cox proportional hazard regression models were used to explore associations, and hazard ratios (HRs) and 95% confidence intervals (CI) were used to evaluate the strength of the association.

Results
The prevalence of non-SI, single SI, and DSI was 59.6%, 31.8%, and 8.6%, respectively, among the 15,643 participants at baseline in this study. Over a median follow-up of 7.0 years, 5,223 worsening SI transitions were observed. In the fully adjusted model, solid fuel use for cooking was associated with a higher risk of worsening SI transitions, including from non-SI to single SI (HR = 1.08, 95% CI = 1.01–1.16) and from non-SI to DSI (HR = 1.26, 95% CI = 1.09–1.47), but not from single SI to DSI. In addition, compared to those who always used solid fuels, participants who switched from solid to clean fuel for cooking appeared to have attenuated the risk of worsening SI transitions. The statistical significance of the associations remained in the set of sensitivity analyses.

Conclusion
Solid fuel use was associated with higher risks of worsening SI transitions, while converting the type of cooking fuel from solid to clean fuels may reduce the risk of worsening SI transitions. Our study suggests that tailored clean fuel interventions, especially in developing countries, should be implemented to prevent sensory impairments and hence reduce the burden of sensory impairment-related disability.
The symptoms and interval of Omicron SARS-CoV-2 reinfection among healthcare workers in a hospital of Southern China: a cross-sectional study
Authors: Ma Xiaoju , Wang Zheng , Chen Youpeng , Li Zhanjie 
Published: March 2024

Abstract
Background
The prevalence and distinction between first Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection and reinfection with the Omicron variant among healthcare workers (HCWs) remain unclear.

Methods
A cross-sectional study was conducted at a hospital in Southern China. The study included 262 HCWs who were infected with SARS-CoV-2 between April and June 2023, with 101 cases of first infection and 161 ones of reinfection. Student’s t-test, Analysis of Variance (ANOVA), and Mann-Whitney U tests were used based on the distribution of quantitative variables. Pearson’s chi-square and Fisher’s exact tests were used based on the expected frequencies of categorical variables.

Results
The reinfection rate among HCWs was 11.5% (161/1406). The majority of the infected HCWs were female (212/262, 80.9%, first infection vs. reinfection: 76.2% vs. 83.9%). The nursing staff, had the highest percentage of SARS-CoV-2 infection (42.0%), especially of its reinfection (47.8%). Out of the 262 infected individuals, 257 had received SARS-CoV-2 vaccination, primarily inactivated vaccines (243/257, 91.1%). The first infection group, which received four doses (24, 23.8%), was significantly higher than that in the reinfection group (6, 3.7%) (P < 0.001). The proportion of asymptomatic infections among HCWs in the two groups was 1.0% and 1.2%. The main symptoms during the first infection and reinfection were fever (83.2% and 50.9%) and sore throat (78.2% and 77.0%). There were significant differences in the prevalence of fever (83.2% vs. 50.9%), rhinorrhea (45.5% vs. 60.9%) and myalgia (56.4% vs. 37.9%) between the first infection and reinfection (P < 0.05). The average interval for SARS-CoV-2 reinfection was 149.9 (range: 114–182, SD = 11.9) days. Notably, physicians had the shortest average interval of 142.8 (8.8) days, while management and administrative staff had the longest average interval of 153.8 (13.5) days.

Conclusions
Although the symptoms of HCWs during reinfection with SARS-CoV-2 were milder, the high reinfection rate and short interval between infections indicate the need to enhance monitoring and protective measures for HCWs during the epidemic.
Collateral effects of COVID-19 countermeasures on hepatitis E incidence pattern: a case study of china based on time series models
Authors: Qin Yajun , Peng Haiyang , Li Jinhao , Gong Jianping 
Published: March 2024

Abstract
Background
There are abundant studies on COVID-19 but few on its impact on hepatitis E. We aimed to assess the effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence and explore the application of time series models in analyzing this pattern.

Methods
Our pivotal idea was to fit a pre-COVID-19 model with data from before the COVID-19 outbreak and use the deviation between forecast values and actual values to reflect the effect of COVID-19 countermeasures. We analyzed the pattern of hepatitis E incidence in China from 2013 to 2018. We evaluated the fitting and forecasting capability of 3 methods before the COVID-19 outbreak. Furthermore, we employed these methods to construct pre-COVID-19 incidence models and compare post-COVID-19 forecasts with reality.

Results
Before the COVID-19 outbreak, the Chinese hepatitis E incidence pattern was overall stationary and seasonal, with a peak in March, a trough in October, and higher levels in winter and spring than in summer and autumn, annually. Nevertheless, post-COVID-19 forecasts from pre-COVID-19 models were extremely different from reality in sectional periods but congruous in others.

Conclusions
Since the COVID-19 pandemic, the Chinese hepatitis E incidence pattern has altered substantially, and the incidence has greatly decreased. The effect of the COVID-19 countermeasures on the pattern of hepatitis E incidence was temporary. The incidence of hepatitis E was anticipated to gradually revert to its pre-COVID-19 pattern.
The value of a neural network based on multi-scale feature fusion to ultrasound images for the differentiation in thyroid follicular neoplasms
Authors: Chen Weiwei , Ni Xuejun , Qian Cheng , Yang Lei , Zhang Zheng , Li Mengdan , Kong Fanlei , Huang Mengqin , He Maosheng , Yin Yifei 
Published: March 2024

Abstract
Objective
The objective of this research was to create a deep learning network that utilizes multiscale images for the classification of follicular thyroid carcinoma (FTC) and follicular thyroid adenoma (FTA) through preoperative US.

Methods
This retrospective study involved the collection of ultrasound images from 279 patients at two tertiary level hospitals. To address the issue of false positives caused by small nodules, we introduced a multi-rescale fusion network (MRF-Net). Four different deep learning models, namely MobileNet V3, ResNet50, DenseNet121 and MRF-Net, were studied based on the feature information extracted from ultrasound images. The performance of each model was evaluated using various metrics, including sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, F1 value, receiver operating curve (ROC), area under the curve (AUC), decision curve analysis (DCA), and confusion matrix.

Results
Out of the total nodules examined, 193 were identified as FTA and 86 were confirmed as FTC. Among the deep learning models evaluated, MRF-Net exhibited the highest accuracy and area under the curve (AUC) with values of 85.3% and 84.8%, respectively. Additionally, MRF-Net demonstrated superior sensitivity and specificity compared to other models. Notably, MRF-Net achieved an impressive F1 value of 83.08%. The curve of DCA revealed that MRF-Net consistently outperformed the other models, yielding higher net benefits across various decision thresholds.

Conclusion
The utilization of MRF-Net enables more precise discrimination between benign and malignant thyroid follicular tumors utilizing preoperative US.