bluuebunny/crossref_metadata_2025_split huggingface dataset:
A new species of short unpatterned moray eel (Anguilliformes: Muraenidae) from the northwestern Indian Ocean, including the Socotra Archipelago, with a redescription of Gymnothorax pseudoherrei Böhlke
Authors: SMITH DAVID G. , BOGORODSKY SERGEY V. , DANDAR JAMES , ZAJONZ UWE 
Published: July 2024

A new species of short brown unpatterned moray eel, Gymnothorax. arabicus sp. nov., is described based on specimens collected from the northwestern Indian Ocean, including the Red Sea, Socotra Archipelago, and Arabian Gulf. The new species is characterized by having the dorsal-fin origin before the gill opening, two branchial pores, one or two median intermaxillary teeth, biserial maxillary teeth, total vertebrae 114–120, and a plain brown head and body with dark yellow color at the tip of the tail. The new species formerly has been confused with G. pseudoherrei but is distinguished from it by having slightly more total vertebrae (114–120 vs. 111–116), dark stripes on the throat and side of the head, and a larger maximum size (ca. 300 mm TL vs. ca. 200 mm TL). Gymnothorax pseudoherrei, currently known from the Maldives and Sri Lanka to the Philippines, Solomon Islands, and northern Australia, is redescribed in the present study. A comparison of the COI mtDNA in BOLD, in association with published phylogenetic evidence, confirms that G. arabicus and G. pseudoherrei form two separate genetic lineages, corroborating the morphological data.
Natural variation of domestication-related genes contributed to latitudinal expansion and adaptation in soybean
Authors: Li Jing , Li Yecheng , Agyenim-Boateng Kwadwo Gyapong , Shaibu Abdulwahab Saliu , Liu Yitian , Feng Yue , Qi Jie , Li Bin , Zhang Shengrui , Sun Junming 
Published: July 2024

AbstractSoybean is a major source of protein and edible oil worldwide. Originating from the Huang-Huai-Hai region, which has a temperate climate, soybean has adapted to a wide latitudinal gradient across China. However, the genetic mechanisms responsible for the widespread latitudinal adaptation in soybean, as well as the genetic basis, adaptive differentiation, and evolutionary implications of theses natural alleles, are currently lacking in comprehensive understanding. In this study, we examined the genetic variations of fourteen major gene loci controlling flowering and maturity in 103 wild species, 1048 landraces, and 1747 cultivated species. We found that E1, E3, FT2a, J, Tof11, Tof16, and Tof18 were favoured during soybean improvement and selection, which explained 75.5% of the flowering time phenotypic variation. These genetic variation was significantly associated with differences in latitude via the LFMM algorithm. Haplotype network and geographic distribution analysis suggested that gene combinations were associated with flowering time diversity contributed to the expansion of soybean, with more HapA clustering together when soybean moved to latitudes beyond 35°N. The geographical evolution model was developed to accurately predict the suitable planting zone for soybean varieties. Collectively, by integrating knowledge from genomics and haplotype classification, it was revealed that distinct gene combinations improve the adaptation of cultivated soybeans to different latitudes. This study provides insight into the genetic basis underlying the environmental adaptation of soybean accessions, which could contribute to a better understanding of the domestication history of soybean and facilitate soybean climate-smart molecular breeding for various environments.
Assessment of COVID-19 risk factors of early and long-term mortality with prediction models of clinical and laboratory variables
Authors: Lipski Dawid , Radziemski Artur , Wasiliew Stanisław , Wyrwa Michał , Szczepaniak-Chicheł Ludwina , Stryczyński Łukasz , Olasińska-Wiśniewska Anna , Urbanowicz Tomasz , Perek Bartłomiej , Tykarski Andrzej , Komosa Anna 
Published: July 2024

Abstract
Background
Coronavirus disease (COVID-19) may lead to serious complications and increased mortality. The outcomes of patients who survive the early disease period are burdened with persistent long-term symptoms and increased long-term morbidity and mortality. The aim of our study was to determine which baseline parameters may provide the best prediction of early and long-term outcomes.

Methods
The study group comprised 141 patients hospitalized for COVID-19. Demographic data, clinical data and laboratory parameters were collected. The main study endpoints were defined as in-hospital mortality and 1-year mortality. The associations between the baseline data and the study endpoints were evaluated. Prediction models were created.

Results
The in-hospital mortality rate was 20.5% (n = 29). Compared with survivors, nonsurvivors were significantly older (p = 0.001) and presented comorbidities, including diabetes (0.027) and atrial fibrillation (p = 0.006). Assessment of baseline laboratory markers and time to early death revealed negative correlations between time to early death and higher IL-6 levels (p = 0.032; Spearman rho − 0.398) and lower lymphocyte counts (p = 0.018; Pearson r -0.438). The one-year mortality rate was 35.5% (n = 50). The 1-year nonsurvivor subgroup was older (p < 0.001) and had more patients with arterial hypertension (p = 0.009), diabetes (p = 0.023), atrial fibrillation (p = 0.046) and active malignancy (p = 0.024) than did the survivor subgroup. The model composed of diabetes and atrial fibrillation and IL-6 with lymphocyte count revealed the highest value for 1-year mortality risk prediction.

Conclusions
Diabetes and atrial fibrillation, as clinical factors, and LDH, IL-6 and lymphocyte count, as laboratory determinants, are the best predictors of COVID-19 mortality risk.
An interpretable artificial intelligence model based on CT for prognosis of intracerebral hemorrhage: a multicenter study
Authors: Zhang Hao , Yang Yun-Feng , Song Xue-Lin , Hu Hai-Jian , Yang Yuan-Yuan , Zhu Xia , Yang Chao 
Published: July 2024

Abstract
Objectives
To develop and validate a novel interpretable artificial intelligence (AI) model that integrates radiomic features, deep learning features, and imaging features at multiple semantic levels to predict the prognosis of intracerebral hemorrhage (ICH) patients at 6 months post-onset.

Materials and methods
Retrospectively enrolled 222 patients with ICH for Non-contrast Computed Tomography (NCCT) images and clinical data, who were divided into a training cohort (n = 186, medical center 1) and an external testing cohort (n = 36, medical center 2). Following image preprocessing, the entire hematoma region was segmented by two radiologists as the volume of interest (VOI). Pyradiomics algorithm library was utilized to extract 1762 radiomics features, while a deep convolutional neural network (EfficientnetV2-L) was employed to extract 1000 deep learning features. Additionally, radiologists evaluated imaging features. Based on the three different modalities of features mentioned above, the Random Forest (RF) model was trained, resulting in three models (Radiomics Model, Radiomics-Clinical Model, and DL-Radiomics-Clinical Model). The performance and clinical utility of the models were assessed using the Area Under the Receiver Operating Characteristic Curve (AUC), calibration curve, and Decision Curve Analysis (DCA), with AUC compared using the DeLong test. Furthermore, this study employs three methods, Shapley Additive Explanations (SHAP), Grad-CAM, and Guided Grad-CAM, to conduct a multidimensional interpretability analysis of model decisions.

Results
The Radiomics-Clinical Model and DL-Radiomics-Clinical Model exhibited relatively good predictive performance, with an AUC of 0.86 [95% Confidence Intervals (CI): 0.71, 0.95; P < 0.01] and 0.89 (95% CI: 0.74, 0.97; P < 0.01), respectively, in the external testing cohort.

Conclusion
The multimodal explainable AI model proposed in this study can accurately predict the prognosis of ICH. Interpretability methods such as SHAP, Grad-CAM, and Guided Grad-Cam partially address the interpretability limitations of AI models. Integrating multimodal imaging features can effectively improve the performance of the model.

Clinical relevance statement
Predicting the prognosis of patients with ICH is a key objective in emergency care. Accurate and efficient prognostic tools can effectively prevent, manage, and monitor adverse events in ICH patients, maximizing treatment outcomes.
Analyses of eye movement parameters in children with anisometropic amblyopia
Authors: Fan Yunwei , Zuo Huaxin , Chu Ping , Wu Qian , Li Li , Wang Yuan , Cao Wenhong , Zhou Yunyu , Huang Lijuan , Li Ningdong 
Published: July 2024

Abstract
Objective
To investigate the characteristics of eye movement in children with anisometropic amblyopia, and to compare those characteristics with eye movement in a control group.

Methods
31 children in the anisometropic amblyopia group (31 amblyopic eyes in group A, 31 contralateral eyes in group B) and 24 children in the control group (48 eyes in group C). Group A was subdivided into groups Aa (severe amblyopia) and Ab (mild-moderate amblyopia). The overall age range was 6–12 years (mean, 7.83 ± 1.79 years). All children underwent ophthalmic examinations; eye movement parameters including saccade latency and amplitude were evaluated using an Eyelink1000 eye tracker. Data Viewer and MATLAB software were used for data analysis.

Results
Mean and maximum saccade latencies, as well as mean and maximum saccade amplitudes, were significantly greater in group A than in groups B and C before and after treatment (P < 0.05). Mean and maximum saccade latencies were significantly different among groups Aa, Ab, and C (P < 0.05). Pupil trajectories in two detection modes suggested that binocular fixation was better than monocular fixation.

Conclusions
Eye movement parameters significantly differed between contralateral normal eyes and control eyes. Clinical evaluation of children with anisometropic amblyopia should not focus only on static visual acuity, but also on the assessment of eye movement.
Association between 29 food groups of diet quality questionnaire and perceived stress in Chinese adults: a prospective study from China health and nutrition survey
Authors: Zhou Jia , Wang Huan , Pao Christine , Zhou Jingjing , Zou Zhiyong 
Published: July 2024

Abstract
Purpose
Diet plays a fundamental role in promoting resilience against stress-related disorders. We aimed to examine the overall and sex-specific association between food groups and perceived stress in adults.

Methods
We analyzed the prospective data of 7,434 adults who completed both the 2011 and 2015 surveys of the China Health and Nutrition Survey (CHNS). The Diet Quality Questionnaire (DQQ) was used to code all the food items of 2011 dietary intake into 29 food groups, and perceived stress in 2015 was measured using a 14-item perceived stress scale (PSS-14). Univariate analysis and logistic regression models were used to examine the relationship between food groups and perceived stress.

Results
People who perceived a higher level of stress (PSS-14 total score > 25) made up 41.5% and 45.1% of the male and female groups, respectively (χ2 = 9.605, p = 0.002). Individuals with increased intake of food groups such as legumes, other vegetables, other fruits, yogurt, poultry, fish & seafood, fluid milk, and fruit juice were less likely to experience a higher level of psychological stress (OR range: 0.544–0.892, p < 0.05). Additionally, we found sex-specific associations between food groups and perceived stress. The difference in the proportion of food groups, such as fluid milk and fish & seafood, between the two stress groups in men was statistically significant (p < 0.025). In the female group, the distribution of eight food groups, like legumes and nuts & seeds, between the two stress groups was statistically significant (p < 0.025).

Conclusion
This study indicated that food groups were differentially associated with perceived stress.
Psychological distress mediates the association between COVID-19-related discrimination and subsequent PTSD symptom severity in healthcare workers: a two-year follow-up study
Authors: Narita Zui , Okubo Ryo , Sasaki Yohei , Takeda Kazuyoshi , Takao Masaki , Komaki Hirofumi , Oi Hideki , Miyama Takeshi , Kim Yoshiharu 
Published: July 2024

Abstract
Background
Past research has suggested a cross-sectional association between COVID-19-related discrimination and PTSD symptom severity. However, no cohort study has examined the longitudinal association that better supports causal interpretation. Also, even if such an association genuinely exists, the specific pathway remains unclear.

Methods
We conducted a two-year follow-up study, obtaining data from healthcare workers in a hospital setting. We first evaluated how COVID-19-related discrimination in 2021 was associated with subsequent PTSD symptom severity in 2023. Thereafter, we conducted causal mediation analysis to examine how this association was mediated by psychological distress in 2022, accounting for exposure-mediator interaction. Missing data were handled using random forest imputation.

Results
A total of 660 hospital staff were included. The fully adjusted model showed greater PTSD symptom severity in individuals who experienced any COVID-19-related discrimination compared with those without such experiences (β, 0.44; 95% CI, 0.04–0.90). Regarding each type of discrimination, perceived discrimination was associated with greater PTSD symptom severity (β, 0.52; 95% CI, 0.08–0.96), whereas verbal discrimination did not reach statistical significance. Psychological distress mediated 28.1%–38.8% of the observed associations.

Conclusions
COVID-19-related discrimination is associated with subsequent PTSD symptom severity in healthcare workers. Psychological distress may serve as an important mediator, underscoring the potential need for interventions targeting this factor.
Effects and mechanisms of puerarin against neuroblastoma: insights from bioinformatics and in vitro experiments
Authors: Sui Xiaohui , Liu Tingting , Zou Zhiyun , Zhang Baoqing , Zhang Guiju 
Published: July 2024

Abstract
Background
Neuroblastoma, a prevalent solid tumor in children, often manifests with hidden onset sites, rapid growth, and high metastatic potential. The prognosis for children with high-risk neuroblastoma remains poor, highlighting the urgent need for novel prognostic models and therapeutic avenues. In recent years, puerarin, as a kind of small molecule drug extracted from Chinese medicine Pueraria lobata, has demonstrated significant anticancer effects on various cancer cell types. In this study, through bioinformatics analysis and in vitro experiments, the potential and mechanism of puerarin in the treatment of neuroblastoma were investigated, and a prognostic model was established.

Methods
A total of 9 drug-disease related targets were observed by constructing a database of drug targets and disease genes. Besides, GO and KEGG enrichment analysis was performed to explore the potential mechanism of its therapeutic effect. To construct the prognostic model, risk regression analysis and LASSO analysis were carried out for validation. Finally, the prognostic genes were identified. Parachute test and immunofluorescence staining were performed to verify the potential mechanism of puerarin in neuroblastoma treatment.

Results
Three prognostic genes, i.e., BIRC5, TIMP2 and CASP9, were identified. In vitro studies verified puerarin's impact on BIRC5, TIMP2, and CASP9 expression, inhibiting proliferation in neuroblastoma SH-SY5Y cells. Puerarin disrupts the cytoskeleton, boosts gap junctional communication, curtailing invasion and migration, and induces mitochondrial damage in SH-SY5Y cells.

Conclusions
Based on network pharmacology and bioinformatics analysis, combined with in vitro experimental verification, puerarin was hereby observed to enhance GJIC in neuroblastoma, destroy cytoskeleton and thus inhibit cell invasion and migration, cause mitochondrial damage of tumor cells, and inhibit cell proliferation. Overall, puerarin, as a natural medicinal compound, does hold potential as a novel therapy for neuroblastoma.
Improve the efficiency and accuracy of ophthalmologists’ clinical decision-making based on AI technology
Authors: Guo Yingxuan , Huang Changke , Sheng Yaying , Zhang Wenjie , Ye Xin , Lian Hengli , Xu Jiahao , Chen Yiqi 
Published: July 2024

Abstract
Background
As global aging intensifies, the prevalence of ocular fundus diseases continues to rise. In China, the tense doctor-patient ratio poses numerous challenges for the early diagnosis and treatment of ocular fundus diseases. To reduce the high risk of missed or misdiagnosed cases, avoid irreversible visual impairment for patients, and ensure good visual prognosis for patients with ocular fundus diseases, it is particularly important to enhance the growth and diagnostic capabilities of junior doctors. This study aims to leverage the value of electronic medical record data to developing a diagnostic intelligent decision support platform. This platform aims to assist junior doctors in diagnosing ocular fundus diseases quickly and accurately, expedite their professional growth, and prevent delays in patient treatment. An empirical evaluation will assess the platform’s effectiveness in enhancing doctors’ diagnostic efficiency and accuracy.

Methods
In this study, eight Chinese Named Entity Recognition (NER) models were compared, and the SoftLexicon-Glove-Word2vec model, achieving a high F1 score of 93.02%, was selected as the optimal recognition tool. This model was then used to extract key information from electronic medical records (EMRs) and generate feature variables based on diagnostic rule templates. Subsequently, an XGBoost algorithm was employed to construct an intelligent decision support platform for diagnosing ocular fundus diseases. The effectiveness of the platform in improving diagnostic efficiency and accuracy was evaluated through a controlled experiment comparing experienced and junior doctors.

Results
The use of the diagnostic intelligent decision support platform resulted in significant improvements in both diagnostic efficiency and accuracy for both experienced and junior doctors (P < 0.05). Notably, the gap in diagnostic speed and precision between junior doctors and experienced doctors narrowed considerably when the platform was used. Although the platform also provided some benefits to experienced doctors, the improvement was less pronounced compared to junior doctors.

Conclusion
The diagnostic intelligent decision support platform established in this study, based on the XGBoost algorithm and NER, effectively enhances the diagnostic efficiency and accuracy of junior doctors in ocular fundus diseases. This has significant implications for optimizing clinical diagnosis and treatment.
Cost-effectiveness of approaches to cervical cancer screening in Malawi: comparison of frequencies, lesion treatment techniques, and risk-stratified approaches
Authors: Rasmussen Petra W. , Hoffman Risa M. , Phiri Sam , Makwaya Amos , Kominski Gerald F. , Bastani Roshan , Moses Agnes , Moucheraud Corrina 
Published: July 2024

Abstract
Background
Recently-updated global guidelines for cervical cancer screening incorporated new technologies—most significantly, the inclusion of HPV DNA detection as a primary screening test—but leave many implementation decisions at countries’ discretion. We sought to develop recommendations for Malawi as a test case since it has the second-highest cervical cancer burden globally and high HIV prevalence. We incorporated updated epidemiologic data, the full range of ablation methods recommended, and a more nuanced representation of how HIV status intersects with cervical cancer risk and exposure to screening to model outcomes of different approaches to screening.

Methods
Using a Markov model, we estimate the relative health outcomes and costs of different approaches to cervical cancer screening among Malawian women. The model was parameterized using published data, and focused on comparing “triage” approaches—i.e., lesion treatment (cryotherapy or thermocoagulation) at differing frequencies and varying by HIV status. Health outcomes were quality-adjusted life years (QALYs) and deaths averted. The model was built using TreeAge Pro software.

Results
Thermocoagulation was more cost-effective than cryotherapy at all screening frequencies. Screening women once per decade would avert substantially more deaths than screening only once per lifetime, at relatively little additional cost. Moreover, at this frequency, it would be advisable to ensure that all women who screen positive receive treatment (rather than investing in further increases in screening frequency): for a similar gain in QALYs, it would cost more than four times as much to implement once-per-5 years screening with only 50% of women treated versus once-per-decade screening with 100% of women treated. Stratified screening schedules by HIV status was found to be an optimal approach.

Conclusions
These results add new evidence about cost-effective approaches to cervical cancer screening in low-income countries. At relatively infrequent screening intervals, if resources are limited, it would be more cost-effective to invest in scaling up thermocoagulation for treatment before increasing the recommended screening frequency. In Malawi or countries in a similar stage of the HIV epidemic, a stratified approach that prioritizes more frequent screening for women living with HIV may be more cost-effective than population-wide recommendations that are HIV status neutral.