bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative proteomic analysis of the ovarian fluid and eggs of Siberian sturgeon
Authors: Kodzik Natalia , Ciereszko Andrzej , Szczepkowska Bożena , Malinowska Agata , Dietrich Mariola Aleksandra 
Published: May 2024

Abstract
Background
Sturgeon species are living fossils that exhibit unique reproductive characteristics, and elucidation of the molecular processes governing the formation and quality of sturgeon eggs is crucial. However, comprehensive data on the protein composition of sturgeon ovarian fluid (OF) and eggs and their functional significance are lacking. To address this knowledge gap, the aim of the present study was to conduct a comprehensive comparative proteomic analysis of Siberian sturgeon OF and eggs using liquid chromatography–mass spectrometry (LC–MS/MS).

Results
A total of 617 proteins were identified in OF, and 565 proteins were identified in eggs. A total of 772 proteins showed differential abundance. Among the differentially abundant proteins, 365 were more abundant in OFs, while 407 were more abundant in eggs. We identified 339 proteins unique to OFs and 287 proteins specific to eggs, and further investigated the top 10 most abundant proteins in each. The functional annotation of the OF proteins highlighted their predominant association with immune system processes, including the complement and coagulation cascade, neutrophil and leukocyte-mediated immunity, cholesterol metabolism, and regulation of the actin cytoskeleton. Analysis of egg proteins revealed enrichment in metabolic pathways, such as oxidative phosphorylation and fatty acid metabolism, and protein ubiquitination and translation. OF-specific proteins included extracellular matrix and secretory vesicles, and eggs were enriched in proteins localized to mitochondria and ribosome components.

Conclusions
This study presents the first comprehensive characterization of the protein composition of sturgeon OF and eggs and elucidates their distinct functional roles. These findings advance our understanding of sturgeon reproduction, OF-egg signaling and the origin of OF proteins. The mass spectrometry proteomics data have been deposited in the ProteomeXchange Consortium with the dataset identifier PXD044168 to ensure accessibility for further research.
Integrative analysis of transcriptome and target metabolites uncovering flavonoid biosynthesis regulation of changing petal colors in Nymphaea ‘Feitian 2’
Authors: Zhou Xian , Wang Xiaohan , Wei Haohui , Zhang Huijin , Wu Qian , Wang Liangsheng 
Published: May 2024

Abstract
Background
Nymphaea (waterlily) is known for its rich colors and role as an important aquatic ornamental plant globally. Nymphaea atrans and some hybrids, including N. ‘Feitian 2,’ are more appealing due to the gradual color change of their petals at different flower developmental stages. The petals of N. ‘Feitian 2’ gradually change color from light blue-purple to deep rose-red throughout flowering. The mechanism of the phenomenon remains unclear.

Results
In this work, flavonoids in the petals of N. ‘Feitian 2’ at six flowering stages were examined to identify the influence of flavonoid components on flower color changes. Additionally, six cDNA libraries of N. ‘Feitian 2’ over two blooming stages were developed, and the transcriptome was sequenced to identify the molecular mechanism governing petal color changes. As a result, 18 flavonoid metabolites were identified, including five anthocyanins and 13 flavonols. Anthocyanin accumulation during flower development is the primary driver of petal color change. A total of 12 differentially expressed genes (DEGs) in the flavonoid biosynthesis pathway were uncovered, and these DEGs were significantly positively correlated with anthocyanin accumulation. Six structural genes were ultimately focused on, as their expression levels varied significantly across different flowering stages. Moreover, 104 differentially expressed transcription factors (TFs) were uncovered, and three MYBs associated with flavonoid biosynthesis were screened. The RT-qPCR results were generally aligned with high-throughput sequencing results.

Conclusions
This research offers a foundation to clarify the mechanisms underlying changes in the petal color of waterlilies.
A high-affinity potassium transporter (MeHKT1) from cassava (Manihot esculenta) negatively regulates the response of transgenic Arabidopsis to salt stress
Authors: Luo Minghua , Chu Jing , Wang Yu , Chang Jingyan , Zhou Yang , Jiang Xingyu 
Published: May 2024

Abstract
Background
High-affinity potassium transporters (HKTs) are crucial in facilitating potassium uptake by plants. Many types of HKTs confer salt tolerance to plants through regulating K+ and Na+ homeostasis under salinity stress. However, their specific functions in cassava (Manihot esculenta) remain unclear.

Results
Herein, an HKT gene (MeHKT1) was cloned from cassava, and its expression is triggered by exposure to salt stress. The expression of a plasma membrane-bound protein functions as transporter to rescue a low potassium (K+) sensitivity of yeast mutant strain, but the complementation of MeHKT1 is inhibited by NaCl treatment. Under low K+ stress, transgenic Arabidopsis with MeHKT1 exhibits improved growth due to increasing shoot K+ content. In contrast, transgenic Arabidopsis accumulates more Na+ under salt stress than wild-type (WT) plants. Nevertheless, the differences in K+ content between transgenic and WT plants are not significant. Additionally, Arabidopsis expressing MeHKT1 displayed a stronger salt-sensitive phenotype.

Conclusion
These results suggest that under low K+ condition, MeHKT1 functions as a potassium transporter. In contrast, MeHKT1 mainly transports Na+ into cells under salt stress condition and negatively regulates the response of transgenic Arabidopsis to salt stress. Our results provide a reference for further research on the function of MeHKT1, and provide a basis for further application of MeHKT1 in cassava by molecular biological means.
Evaluation of blood pressure variation in recovered COVID-19 patients at one-year follow-up: a retrospective cohort study
Authors: Azami Pouria , Vafa Reza Golchin , Heydarzadeh Reza , Sadeghi Mehrdad , Amiri Farhang , Azadian Alireza , Khademolhosseini Amin , Yousefi Mina , Montaseri Mohammad , Hosseini Nazanin , Hosseini Seyed Ali , Kojuri Javad 
Published: May 2024

Abstract
Background
Coronavirus disease 2019 (COVID-19) has various sequelae, one of which might be hypertension. We aimed to evaluate COVID-19’s impact on blood pressure (BP) in non-hospitalized patients at one-year follow-up.

Method
A total of 7,950 consecutive COVID-19 patients regularly visiting our cardiology clinic were retrospectively screened. Patients’ electronic medical records including demographics, comorbidities, vital signs, treatments, and outcomes, were reviewed by two physicians. Individuals with at least one BP measurement in the three months preceding COVID-19 and one measurement in 12 months or more following recovery were included. BP levels before and after COVID-19 were compared using the paired t-test.

Results
5,355 confirmed COVID-19 patients (mean age 55.51 ± 15.38 years) were included. Hypertension (56.9%) and diabetes mellitus (34%) were the predominant comorbidities, and 44.3% had prior major adverse cardiovascular events. Both systolic (126.90 ± 20.91 vs. 139.99 ± 23.94 mmHg, P < 0.001) and diastolic BP (80.54 ± 13.94 vs. 86.49 ± 14.40 mmHg, P < 0.001) were significantly higher post-COVID-19 vs. pre-COVID-19. Notably, 456 (14%) hypertensive patients experienced exacerbated hypertension, while 408 (17%) patients developed new-onset hypertension, overall 864 (16%) of patients had exacerbation or new hypertension. Linear regression analysis revealed that advanced age, smoking, previous cardiovascular events, hypertension, and diabetes mellitus predict increased BP following COVID-19 (P < 0.001).

Conclusion
COVID-19 raised systolic and diastolic BP in the long term in non-hospitalized patients, with over one-sixth developing new-onset or exacerbated hypertension. All patients should be evaluated regarding BP, following COVID-19 recovery, particularly those with the mentioned predictive factors. (clinicaltrial.gov: NCT05798208)
Effectiveness of inactivated influenza vaccine against laboratory-confirmed influenza among Chinese elderly: a test-negative design
Authors: Yang Tianchi , Tang Ling , Li Pingping , Li Baojun , Ye Lixia , Zhou Jifang 
Published: May 2024

Abstract
Background
Evidence on the effectiveness of influenza vaccination in the elderly is limited, and results are controversial. There are also few reports from China.

Methods
We conducted a test-negative case-control study design to estimate influenza vaccine effectiveness (VE) against laboratory-confirmed influenza-associated visits among elderly (aged ≥ 60 years) across four influenza seasons in Ningbo, China, from 2018 to 19 to 2021-22. Influenza-positive cases and negative controls were randomly matched in a 1:1 ratio according to age, sex, hospital, and date of influenza testing. We used logistic regression models to compare vaccination odds ratios (ORs) in cases to controls. We calculated the VE as [100% × (1-adjusted OR)] and calculated the 95% confidence interval (CI) around the estimate.

Results
A total of 30,630 elderly patients tested for influenza with virus nucleic acid or antigen during the study period. After exclusions, we included 1 825 influenza-positive cases and 1 825 influenza-negative controls. Overall, the adjusted VE for influenza-related visits was 63.5% (95% CI, 56.3–69.5%), but varied by season. Influenza VE was 59.8% (95% CI, 51.5–66.7%) for influenza A and 89.6% (95% CI, 77.1–95.3%) for influenza B. The VE for ages 60–69 and 70–79 was 65.2% (95% CI, 55.4–72.9%) and 69.8% (95% CI, 58.7–77.9%), respectively, but only 45.4% (95% CI, 6.2–68.2%) for ages 80 and over.

Conclusions
Standard-dose inactivated influenza vaccine has shown good protection in the elderly in China. However, protection may not be satisfactory in people aged 80 years and older.
Digital exclusion and cognitive impairment in older people: findings from five longitudinal studies
Authors: Wang Yuge , Wu Zhigang , Duan Lanzhi , Liu Sijia , Chen Ruzhao , Sun Tao , Wang Jiang , Zhou Jianghua , Wang Hongxia , Huang Pan 
Published: May 2024

Abstract
Objectives
Older people are more likely to have digital exclusion, which is associated with poor health. This study investigated the relationship between digital exclusion and cognitive impairment in older adults from 23 countries across five longitudinal surveys.

Design and measurements
Digital exclusion is defined as self-reported non-use of the Internet. We assessed cognitive impairment on three dimensions: orientation, memory, and executive function. We used generalized estimation equations fitting binary logistic regression with exchangeable correlations to study the relationship between digital exclusion and cognitive impairment, and apply the minimum sufficiently adjusted set of causally directed acyclic graphs as the adjusted variable.

Setting and participants
We pooled a nationally representative sample of older adults from five longitudinal studies, including the China Health and Retirement Longitudinal study (CHARLS), the English Longitudinal Study of Ageing (ELSA), the Health and Retirement Study (HRS), the Mexican Health and Ageing Study (MHAS) and the Survey of Health, Ageing and Retirement in European (SHARE).

Results
We included 62,413 participants from five longitudinal studies. Digital exclusion varied by country, ranging from 21.69% (SHARE) in Denmark to 97.15% (CHARLS) in China. In the original model, digital exclusion was significantly associated with cognitive impairment in all five studies. In the adjusted model, these associations remained statistically significant: CHARLS (Odds ratio [OR] = 2.81, 95% confidence interval [CI] 1.84–4.28, ELSA (1.92 [1.70–2.18]), HRS(2.48[2.28–2.71), MHAS (1.92 [1.74–2.12]), and SHARE (2.60 [2.34–2.88]).

Conclusion
Our research shows that a significant proportion of older people suffer from digital exclusion, especially in China. Digital exclusion was positively correlated with cognitive impairment. These findings suggest that digital inclusion could be an important strategy to improve cognitive function and reduce the risk of cognitive impairment in older adults.
Computer-aided designing of a novel multi‑epitope DNA vaccine against severe fever with thrombocytopenia syndrome virus
Authors: Tao Yiran , Zhang Yu , Li Yumeng , Liu Qiao , Zhu Jin , Ji Minjun , Feng Gaoqian , Xu Zhipeng 
Published: May 2024

AbstractSevere fever with thrombocytopenia syndrome (SFTS) is an emerging tick-borne viral disease caused by the SFTS virus (Dabie bandavirus), which has become a substantial risk to public health. No specific treatment is available now, that calls for an effective vaccine. Given this, we aimed to develop a multi-epitope DNA vaccine through the help of bioinformatics. The final DNA vaccine was inserted into a special plasmid vector pVAX1, consisting of CD8+ T cell epitopes, CD4+ T cell epitopes and B cell epitopes (six epitopes each) screened from four genome-encoded proteins——nuclear protein (NP), glycoprotein (GP), RNA-dependent RNA polymerase (RdRp), as well as nonstructural protein (NSs). To ascertain if the predicted structure would be stable and successful in preventing infection, an immunological simulation was run on it. In conclusion, we designed a multi-epitope DNA vaccine that is expected to be effective against Dabie bandavirus, but in vivo trials are needed to verify this claim.
Utilizing machine learning to facilitate the early diagnosis of posterior circulation stroke
Authors: Abujaber Ahmad A. , Imam Yahia , Albalkhi Ibrahem , Yaseen Said , Nashwan Abdulqadir J. , Akhtar Naveed 
Published: May 2024

Abstract
Background
Posterior Circulation Syndrome (PCS) presents a diagnostic challenge characterized by its variable and nonspecific symptoms. Timely and accurate diagnosis is crucial for improving patient outcomes. This study aims to enhance the early diagnosis of PCS by employing clinical and demographic data and machine learning. This approach targets a significant research gap in the field of stroke diagnosis and management.

Methods
We collected and analyzed data from a large national Stroke Registry spanning from January 2014 to July 2022. The dataset included 15,859 adult patients admitted with a primary diagnosis of stroke. Five machine learning models were trained: XGBoost, Random Forest, Support Vector Machine, Classification and Regression Trees, and Logistic Regression. Multiple performance metrics, such as accuracy, precision, recall, F1-score, AUC, Matthew’s correlation coefficient, log loss, and Brier score, were utilized to evaluate model performance.

Results
The XGBoost model emerged as the top performer with an AUC of 0.81, accuracy of 0.79, precision of 0.5, recall of 0.62, and F1-score of 0.55. SHAP (SHapley Additive exPlanations) analysis identified key variables associated with PCS, including Body Mass Index, Random Blood Sugar, ataxia, dysarthria, and diastolic blood pressure and body temperature. These variables played a significant role in facilitating the early diagnosis of PCS, emphasizing their diagnostic value.

Conclusion
This study pioneers the use of clinical data and machine learning models to facilitate the early diagnosis of PCS, filling a crucial gap in stroke research. Using simple clinical metrics such as BMI, RBS, ataxia, dysarthria, DBP, and body temperature will help clinicians diagnose PCS early. Despite limitations, such as data biases and regional specificity, our research contributes to advancing PCS understanding, potentially enhancing clinical decision-making and patient outcomes early in the patient’s clinical journey. Further investigations are warranted to elucidate the underlying physiological mechanisms and validate these findings in broader populations and healthcare settings.
Diabetes mellitus in pregnancy across Canada
Authors: Nelson Chantal RM , Dzakpasu Susie , Moore Aideen M. , Darling Elizabeth K. , Edwards Wesley , Murphy Phil , Scott Heather , Van Den Hof Michiel , Ray Joel G. 
Published: May 2024

Abstract
Background
Contemporary estimates of diabetes mellitus (DM) rates in pregnancy are lacking in Canada. Accordingly, this study examined trends in the rates of type 1 (T1DM), type 2 (T2DM) and gestational (GDM) DM in Canada over a 15-year period, and selected adverse pregnancy outcomes.

Methods
This study used repeated cross-sectional data from the Canadian Institute of Health Information (CIHI) hospitalization discharge abstract database (DAD). Maternal delivery records were linked to their respective birth records from 2006 to 2019. The prevalence of T1DM, T2DM and GDM were calculated, including relative changes over time, assessed by a Cochrane-Armitage test. Also assessed were differences between provinces and territories in the prevalence of DM.

Results
Over the 15-year study period, comprising 4,320,778 hospital deliveries in Canada, there was a statistically significant increase in the prevalence of GDM and T1DM and T2DM. Compared to pregnancies without DM, all pregnancies with any form of DM had higher rates of hypertension and Caesarian delivery, and also adverse infant outcomes, including major congenital anomalies, preterm birth and large-for-gestational age birthweight.

Conclusion
Among 4.3 million pregnancies in Canada, there has been a rise in the prevalence of DM. T2DM and GDM are expected to increase further as more overweight women conceive in Canada.
Adverse maternal outcomes and associated factors among mothers of advanced age delivering at a tertiary hospital, southwestern Uganda: a cross-sectional study
Authors: Masembe Sezalio , Migisha Richard , Turyasingura Godwin , Aheisibwe Hillary , Nzabandora Emmanuel , Lule John C. 
Published: May 2024

Abstract
Background
Mothers of advanced age, defined as pregnant women aged ≥ 35 years at the time of giving birth, are traditionally known to be associated with increased risks of adverse maternal outcomes. We determined the prevalence of adverse maternal outcomes and associated factors among mothers of advanced age who delivered at Kabale Regional Referral Hospital (KRRH), in Southwestern Uganda.

Methods
We conducted a cross-sectional study at the Maternity Ward of KRRH from April to September 2023. We consecutively enrolled pregnant women aged ≥ 35 years during their immediate post-delivery period and before discharge. We obtained data on their socio-demographic, obstetric, medical characteristics and their maternal outcomes using interviewer-administered questionnaires. We defined adverse maternal outcome as any complication sustained by the mother that was related to pregnancy, delivery and immediate post-partum events (obstructed labour, antepartum haemorrhage, mode of delivery [cesarean or vacuum extraction], postpartum haemorrhage, hypertensive disorders of pregnancy, preterm or postdate pregnancy, anemia, premature rupture of membranes, multiple pregnancy, and maternal death). A participant was considered to have an adverse outcome if they experienced any one of these complications. We identified factors associated with adverse outcomes using modified Poisson regression.

Results
Out of 417 participants, most were aged 35–37 years (n = 206; 49.4%), and had parity ≥ 5 (65.5%). The prevalence of adverse maternal outcomes was 37.6% (n = 157, 95%CI: 33.1–42.4%). Common adverse maternal outcomes included caesarian delivery (23%), and obstructed labour (14.4%). Other complications included anemia in pregnancy (4.5%), chorioamnionitis (4.1%), preterm prelabour rupture of membranes (3.9%), and chronic hypertension and preeclampsia (both 2.4%). Factors associated with adverse maternal outcomes were precipitate labour (adjusted prevalence ratio [aPR] = 1.95, 95%CI: 1.44–2.65), prolonged labour, lasting > 12 h (aPR = 2.86, 95%CI: 1.48–3.16), and chronic hypertension (aPR = 2.01, 95%CI: 1.34–3.9).

Conclusion
Approximately two-fifth of the advanced-aged mothers surveyed had adverse outcomes. Mothers with prolonged labour, precipitate labour and chronic hypertension were more likely to experience adverse outcomes. We recommend implementation of targeted interventions, emphasizing proper management of labor as well as close monitoring of hypertensive mothers, and those with precipitate or prolonged labor, to mitigate risks of adverse outcomes within this study population.
Prognostic significance of preoperative lymphocytes, albumin, and neutrophils (LANR) index in resectable pancreatic ductal adenocarcinoma
Authors: Zhuang Jiaru , Wang Shan , Wang Yuan , Wu Yibo , Hu Renjing 
Published: May 2024

Abstract
Purpose
The index composed of preoperative lymphocytes, albumin, and neutrophils (LANR), a new composite score based on inflammatory response and nutritional status, has been reported to be associated with the prognosis of multiple types of cancer, but the role of LANR in the prognosis of resectable pancreatic ductal adenocarcinoma (PDAC) has not yet been elucidated.

Patients and methods
The data of 142 patients with PDAC who underwent radical resection in the Affiliated Hospital of Jiangnan University from January 2015 to December 2018 were retrospectively analyzed. Receiver Operating Characteristic (ROC) curves were generated to determine the optimal cut-off values for these parameters, as well as the sensitivity and specificity of LANR in predicting survival. The Kaplan–Meier method was used to draw the survival curves. Log rank test was used for univariate analysis, and Cox proportional hazards regression model was used for multivariate analysis.

Results
The optimal cut-off value of LANR was 18.145, and a low preoperative LANR was significantly correlated with the location of the tumor (p = 0.047). Multivariate analysis showed that tumor differentiation degree (HR:2.357, 95%CI:1.388–4.003,p = 0.002), lymph node metastasis (HR:1.755, 95%CI: 1.115–2.763, p = 0.015), TNM stage (HR:4.686, 95%CI: 2.958–7.425, p < 0.001), preoperative cancer antigen 19 − 9 levels (HR:1.001, 95%CI: 1.000-1.001, p < 0.001) and preoperative LANR (HR:0.221, 95%CI: 0.111–0.441, p < 0.001) were independent risk factors for a poor prognosis in patients undergoing radical resection of PDAC.

Conclusion
This study found that preoperative LANR can be used to assess the prognosis of radical resection in patients with PDAC; those with low preoperative LANR had a worse outcome.