bluuebunny/crossref_metadata_2025_split huggingface dataset:
Radiomics-based infarct features on CT predict hemorrhagic transformation in patients with acute ischemic stroke
Authors: Xie Gang , Li Ting , Ren Yitao , Wang Danni , Tang Wuli , Li Junlin , Li Kang 
Published: September 2022

ObjectiveTo develop and validate a model based on the radiomics features of the infarct areas on non-contrast-enhanced CT to predict hemorrhagic transformation (HT) in acute ischemic stroke.Materials and methodsA total of 118 patients diagnosed with acute ischemic stroke in two centers from January 2019 to February 2022 were included. The radiomics features of infarcted areas on non-contrast-enhanced CT were extracted using 3D-Slicer. A univariate analysis and the least absolute shrinkage and selection operator (LASSO) were used to select features, and the radiomics score (Rad-score) was then constructed. The predictive model of HT was constructed by analyzing the Rad-score and clinical and imaging features in the training cohort, and it was verified in the validation cohort. The model was evaluated with the receiver operating characteristic curve, calibration curve and decision curve, and the prediction performance of the model in different scenarios was further discussed hierarchically.ResultsOf the 118 patients, 52 developed HT, including 21 cases of hemorrhagic infarct (HI) and 31 cases of parenchymal hematoma (PH). The Rad-score was constructed from five radiomics features and was the only independent predictor for HT. The predictive model was constructed from the Rad-score. The area under the curve (AUCs) of the model for predicting HT in the training and validation cohorts were 0.845 and 0.750, respectively. Calibration curve and decision curve analyses showed that the model performed well. Further analysis found that the model predicted HT for different infarct sizes or treatment methods in the training and validation cohorts with 78.3 and 71.4% accuracy, respectively. For all samples, the model predicted an AUC of 0.754 for HT in patients within 4.5 h since stroke onset, and predicted an AUC of 0.648 for PH.ConclusionThis model, which was based on CT radiomics features, could help to predict HT in the setting of acute ischemic stroke for any infarct size and provide guiding suggestions for clinical treatment and prognosis evaluation.
Population-based input function (PBIF) applied to dynamic whole-body 68Ga-DOTATOC-PET/CT acquisition
Authors: Thuillier Philippe , Bourhis David , Pavoine Mathieu , Metges Jean-Philippe , Le Pennec Romain , Schick Ulrike , Blanc-Béguin Frédérique , Hennebicq Simon , Salaun Pierre-Yves , Kerlan Véronique , Karakatsanis Nicolas A. , Abgral Ronan 
Published: September 2022

RationalTo validate a population-based input function (PBIF) model that alleviates the need for scanning since injection time in dynamic whole-body (WBdyn) PET.MethodsThirty-seven patients with suspected/known well-differentiated neuroendocrine tumors were included (GAPETNET trial NTC03576040). All WBdyn 68Ga-DOTATOC-PET/CT acquisitions were performed on a digital PET system (one heart-centered 6 min-step followed by nine WB-passes). The PBIF model was built from 20 image-derived input functions (IDIFs) obtained from a respective number of patients’ WBdyn exams using an automated left-ventricle segmentation tool. All IDIF peaks were aligned to the median time-to-peak, normalized to patient weight and administrated activity, and then fitted to an exponential model function. PBIF was then applied to 17 independent patient studies by scaling it to match the respective IDIF section at 20–55 min post-injection time windows corresponding to WB-passes 3–7. The ratio of area under the curves (AUCs) of IDIFs and PBIF3–7 were compared using a Bland–Altman analysis (mean bias ± SD). The Patlak-estimated mean Ki for physiological uptake (Ki-liver and Ki-spleen) and tumor lesions (Ki-tumor) using either IDIF or PBIF were also compared.ResultsThe mean AUC ratio (PBIF/IDIF) was 0.98 ± 0.06. The mean Ki bias between PBIF3–7 and IDIF was −2.6 ± 6.2% (confidence interval, CI: −5.8; 0.6). For Ki-spleen and Ki-tumor, low relative bias with low SD were found [4.65 ± 7.59% (CI: 0.26; 9.03) and 3.70 ± 8.29% (CI: −1.09; 8.49) respectively]. For Ki-liver analysis, relative bias and SD were slightly higher [7.43 ± 13.13% (CI: −0.15; 15.01)].ConclusionOur study showed that the PBIF approach allows for reduction in WBdyn DOTATOC-PET/CT acquisition times with a minimum gain of 20 min.
Association between the empirical dietary inflammatory index and cardiorespiratory fitness in Tehranian adults in 2017–2018
Authors: Payandeh Nastaran , Shahinfar Hossein , Babaei Nadia , Davarzani Samira , Ebaditabar Mojdeh , Djafarian Kurosh , Shab-Bidar Sakineh 
Published: September 2022

BackgroundInflammatory-related chronic diseases are increasing in Iran with high consumption of a diet containing pro-inflammatory potential and a sedentary lifestyle. The empirical dietary inflammatory index (EDII) was developed as a tool to assess dietary effects on systemic inflammation. We examined the hypothesis that specific dietary patterns reflecting systemic inflammation are associated with cardiorespiratory fitness (CRF) in Tehranian adults.MethodsThis cross-sectional study was carried out on 270 adults who are residents of Tehran. Dietary intake was assessed using a 168-item valid and reliable food frequency questionnaire. The EDII score was developed according to participant dietary intakes of 21-item pre-defined food groups. CRF was assessed by using a graded exercise treadmill test. Anthropometric measurements were assessed using standard methods. To discover the association between CRF and EDII, we used multivariable logistic regression analysis.ResultsThose who were in the third tertile of the EDII had 57% lower odds of having better VO2Max (ml/kg/min) than those in the first tertile (OR: 0.43; 95% CI: 0.16, 1.12, p = 0.01). There were no significant differences between tertiles of the EDII score in terms of VO2 (L·min) and VO2 (LBM) before and after adjusting for confounders. There was a significant decrease in VO2Max (ml/kg/min) across tertiles of the EDII after controlling for covariates (p-value = 0.04). There was a significant inverse association between the EDII score and VO2Max (ml/kg/min) (β = −0.35, p = 0.001).ConclusionsOur finding demonstrated that a higher EDII might be associated with lower CRF in Tehranian adults. Prospective studies are needed to shed light on the causal link between the EDII and CRF.
Multi-task learning-based feature selection and classification models for glioblastoma and solitary brain metastases
Authors: Huang Ya , Huang Shan , Liu Zhiyong 
Published: September 2022

PurposeTo investigate the diagnostic performance of feature selection via a multi-task learning model in distinguishing primary glioblastoma from solitary brain metastases.MethodThe study involved 187 patients diagnosed at Xiangya Hospital, Yunnan Provincial Cancer Hospital, and Southern Cancer Hospital between January 2010 and December 2018. Radiomic features were extracted from conventional magnetic resonance imaging including T1-weighted, T2-weighted, and contrast-enhanced T1-weighted sequences. We proposed a new multi-task learning model using these three sequences as three tasks. Multi-series fusion was performed to complement the information from different dimensions in order to enhance model robustness. Logical loss was used in the model as the data-fitting item, and the feature weights were expressed in the logical loss space as the sum of shared weights and private weights to select the common features of each task and the characteristics having an essential impact on a single task. A diagnostic model was constructed as a feature selection method as well as a classification method. We calculated accuracy, recall, precision, and area under the curve (AUC) and compared the performance of our new multi-task model with traditional diagnostic model performance.ResultsA diagnostic model combining the support vector machine algorithm as a classification algorithm and our model as a feature selection method had an average AUC of 0.993 in the training set, with AUC, accuracy, precision, and recall rates respectively of 0.992, 0.920, 0.969, and 0.871 in the test set. The diagnostic model built on our multi-task model alone, in the training set, had an average AUC of 0.987, and in the test set, the AUC, accuracy, precision, and recall rates were 0.984, 0.895, 0.954, and 0.838.ConclusionIt is feasible to implement the multi-task learning model developed in our study using logistic regression to differentiate between glioblastoma and solitary brain metastases.
Artificial intelligence-based preoperative prediction system for diagnosis and prognosis in epithelial ovarian cancer: A multicenter study
Authors: Wu Meixuan , Zhao Yaqian , Dong Xuhui , Jin Yue , Cheng Shanshan , Zhang Nan , Xu Shilin , Gu Sijia , Wu Yongsong , Yang Jiani , Yao Liangqing , Wang Yu 
Published: September 2022

BackgroundOvarian cancer (OC) is the most lethal gynecological malignancy, with limited early screening methods and poor prognosis. Artificial intelligence technology has made a great breakthrough in cancer diagnosis.PurposeWe aim to develop a specific interpretable machine learning (ML) prediction model for the diagnosis and prognosis of epithelial ovarian cancer (EOC) based on a variety of biomarkers.MethodsA total of 521 patients with EOC and 144 patients with benign gynecological diseases were enrolled including derivation datasets and an external validation cohort. The predicted information was acquired by 9 supervised ML methods, through 34 parameters. Behind predicted reasons for the best ML were improved by using the SHapley Additive exPlanations (SHAP) algorithm. In addition, the prognosis of EOC was analyzed by unsupervised clustering and Kaplan–Meier (KM) survival analysis.ResultsML technology was superior to conventional logistic regression in predicting EOC diagnosis and XGBoost performed best in the external validation datasets. The AUC values of distinguishing EOC and benign disease patients, determining pathological type, grade and clinical stage were 0.958 (0.926-0.989), 0.792 (0.701-0.8834), 0.819 (0.687-0.950) and 0.68 (0.573-0.788) respectively. For negative CA-125 EOC patients, the AUC performance of XGBoost model was 0.835(0.763-0.907). We used unsupervised cluster analysis to identify EOC subgroups with significantly poor overall survival (p-value &lt;0.0001) and recurrence-free survival (p-value &lt;0.0001).ConclusionsBased on the preoperative characteristics, we proved that ML algorithm can provide an acceptable diagnosis and prognosis prediction model for EOC patients. Meanwhile, SHAP analysis can improve the interpretability of ML models and contribute to precision medicine.
Lenvatinib combined with anti-PD-1 antibodies plus transcatheter arterial chemoembolization for neoadjuvant treatment of resectable hepatocellular carcinoma with high risk of recurrence: A multicenter retrospective study
Authors: Wu Jun-Yi , Wu Jia-Yi , Li Yi-Nan , Qiu Fu-Nan , Zhou Song-Qiang , Yin Zhen-Yu , Chen Yu-Feng , Li Bin , Zhou Jian-Yin , Yan Mao-Lin 
Published: September 2022

BackgroundEarly recurrence is common after surgical resection (SR) for hepatocellular carcinoma (HCC) with high risk of recurrence and is associated with poor prognosis. The combinations of lenvatinib (LEN), anti-PD-1 antibodies (PD-1) and transcatheter arterial chemoembolization (TACE) (triple therapy) has shown better trend in tumor response and survival outcomes on unresectable HCC. It is unknown whether triple therapy for neoadjuvant treatment of resectable HCC with high risk of recurrence is effective. This article aimed to compare the outcomes of surgery alone and neoadjuvant combination treatment with triple therapy before SR in patients with HCC with high risk of recurrence.MethodsA retrospective study was conducted on patients diagnosed with HCC with high risk of recurrence who received treatment with or without triple therapy. The records of 24 patients in the triple therapy group and 76 patients in the surgery-alone group were analyzed. Propensity score matching (PSM) was performed to minimize the influence of potential confounders.ResultsOne hundred patients were enrolled. In the triple therapy group, 8 (33.3%) and 12 (50.0%) patients had complete and partial responses, respectively, as assessed by an investigator. Before PSM, the overall survival (OS) rates for the triple therapy group at 6, 12, 18, and 24 months were 100.0%, 100.0%, 100.0%, and 85.7%, respectively, compared with corresponding 92.1%, 73.7%, 53.9%, and 48.7% for the surgery-alone group (P&lt;0.001). The disease-free survival (DFS) rates were 82.2%, 66.95%, 48.8%, and 48.8% for the triple therapy and 41.92%, 28.34%, 27.05%, and 22.99% for the surgery-alone group (P=0.003). After PSM, DFS and OS were significantly longer in the triple therapy group than in the surgery-alone group (DFS, p=0.019; OS, p=0.003).ConclusionsNeoadjuvant combination treatment before SR had a high rate of tumor response and provided significantly better postoperative survival outcomes than surgery alone in patients with HCC with high risk of recurrence.
Developmental adverse effects of trace amounts of lead: Evaluation using zebrafish model
Authors: Komoike Yuta , Matsuoka Masato 
Published: September 2022

Lead (Pb) is widely used as a raw material for various daily necessities in human civilization. However, Pb is a major toxicant and Pb poisoning has long been a global health concern. A large body of evidence has revealed that exposure to Pb causes a variety of adverse health effects. Meanwhile, experimental studies on the developmental effects caused by trace amounts of Pb remain to be fully conducted. Therefore, we aimed to provide direct experimental evidence of the adverse developmental effects of Pb exposure below the occupational regulatory standard concentrations using a zebrafish model. We also attempted to investigate the cellular stress response caused by such a trace amount of Pb at the individual level. Fertilized zebrafish eggs were exposed to 100 ppb Pb from 6 to 72 h post fertilization (hpf), the developmental period included within the mammalian implantation to birth. The embryos exposed to Pb did not show superficially evident morphological alterations or differences in viability compared with the controls until 72 hpf; however, they hatched earlier and were significantly shorter in body length than the controls at 48 and 72 hpf. Larvae that were exposed to Pb until 72 hpf and then cultured until 7 days post fertilization without Pb exhibited edema and inflation defects in the swim bladder. The reactive oxygen species level in the Pb-exposed embryos was similar at 24 hpf, slightly but significantly higher at 48 hpf, and lower than half that of the control at 72 hpf. Accordingly, the expression levels of oxidative stress response-related genes were analyzed, and five out of seven tested genes were upregulated in Pb-exposed embryos at 48 and 72 hpf. In addition, the endoplasmic reticulum (ER) stress related genes were upregulated at 48 hpf. These results indicate that exposure of embryos to trace amounts of Pb induces a transient increase in oxidative- and ER-stresses and results in weak hypotrophy and subsequent abnormalities later in development. Our findings may be key to understanding the total health effects of Pb exposure, and indicate that the zebrafish model is suitable for the investigation of developmental toxicity of pollutants such as Pb.
Dehydromiltirone inhibits osteoclast differentiation in RAW264.7 and bone marrow macrophages by modulating MAPK and NF-κB activity
Authors: Deng Wei , Huang YanBo , Li HaiShang , Chen ChiWei , Lin YueWei , Wang Min , Huang HuaSheng , Liu Teng , Qin QiuLi , Shao Yang , Tang YongChao , Yuan Kai , Ding JinYong , Xu LiangLiang , Li YongXian , Zhang ShunCong 
Published: September 2022

Background: Osteoporosis is a type of systematic metabolic bone disease caused by the decrease in osteogenic activity or excessive resorption of bone with the relative enhancement of osteoclast function. As osteoporosis seriously affects the quality of patients’ life, effective drugs are needed to treat this disease. Based on the combination of network pharmacology and cellular studies, this study aimed to investigate the probable mechanism of Dehydromiltirone (DHT) in the treatment of osteoporosis.Method: The targets of DHT in osteoporosis were searched using the PharmGKB, OMIM, and Genecard platforms. The PPI core targets, and the GO and KEGG enrichment analysis results were obtained using Cytoscape software, and the David and Metascape databases, respectively. The network pharmacology results were also verified via in vitro cellular experiments.Results: Through network pharmacology and docking analysis, we found DHT was involved in peptide tyrosine phosphorylation, cell surface receptor tyrosine kinase signaling pathways, and MAPK signaling pathways. According to the molecular docking results, the binding of DHT to MAPK14 was more stable than other proteins, which suggests that DHT may affect osteoclast formation through the MAPK signaling pathway. Moreover, DHT was found to inhibit the expression of osteoclast-associated genes, including NFATc1, CTSK, c-Fos, Acp5, and MMP9; as well as the phosphorylation of P38, ERK, and JNK of the MAPK signaling pathway; and the degradation of IκB-α of NF-κB signaling pathway.Conclusion: DHT exhibited an anti-osteoclastogenesis effect by reducing the expression of related genes, ultimately inhibiting bone resorption in vitro.
The role of autophagy in calcium oxalate kidney stone: A systematic review of the literature
Authors: Li Hao , Zhou Yingjian , Xu Wenchao , Liu Jihong , Wang Shaogang , Jiang Hongyang 
Published: September 2022

Background: Calcium oxalate kidney stone is one of the common diseases in the urinary system and has a high recurrence rate. Currently, the pathogenesis of kidney stone and the methods to prevent recurrence are still being investigated. Autophagy, as an event of cellular self-repair, has received attention in the field of kidney stone in recent years. In some current studies, autophagy has shown destructiveness and protectiveness in the pathogenesis of kidney stone. The inhibition or promotion of autophagy may be a key target for future kidney stone therapy. This systematic literature review discusses the function of autophagy in kidney stone pathogenesis in the context of current research and synthesizes the evidence analysis to provide a basis for new future therapies.Method: We systematically reviewed the literature during September 2021 according to the Preferred Reporting Items for Systematic Evaluation and Meta-Analysis (PRISMA) guidelines. Articles on studying the role of autophagy in the pathogenesis of calcium oxalate kidney stone were extracted from PubMed, MEDLINE, Embase and Scopus, including in vivo versus in vitro experiments. The study topic, language and publication date were not restricted. Two authors (Li and Zhou) searched and screened the literature.Results: We screened 18 articles from the 33 collected articles, of which 6 conducted in vitro cellular studies, four conducted animal studies, eight conducted cellular studies with animal studies, and five studied human specimens. In early studies, the literature generally concluded that autophagy is deleterious in the development of kidney stone. In 2020, the idea of the protectiveness of autophagy associated with kidney stone was first proposed and focused on targeting transcription factor EB. In addition, the interaction of autophagy with other cellular events and the regulation of signaling molecules are focused on in this paper.Conclusion: This systematic review provides advances in research on the role of autophagy in renal calculi. The current studies suggest that both upregulation and downregulation of autophagy may ameliorate injury in kidney stone models. The authors prefer the upregulation of autophagy as a future research direction for kidney stone treatment.
Beyond a biased binary: A perspective on the misconceptions, challenges, and implications of studying females in avian behavioral endocrinology
Authors: Smiley Kristina O. , Lipshutz Sara E. , Kimmitt Abigail A. , DeVries M. Susan , Cain Kristal E. , George Elizabeth M. , Covino Kristen. M. 
Published: September 2022

For decades, avian endocrinology has been informed by male perspectives and male-focused research, leaving significant gaps in our understanding of female birds. Male birds have been favored as research subjects because their reproductive behaviors are considered more conspicuous and their reproductive physiology is presumably less complex than female birds. However, female birds should not be ignored, as female reproductive behavior and physiology are essential for the propagation of all avian species. Endocrine research in female birds has made much progress in the last 20 years, but a substantial disparity in knowledge between male and female endocrinology persists. In this perspective piece, we provide examples of why ornithology has neglected female endocrinology, and we propose considerations for field and laboratory techniques to facilitate future studies. We highlight recent advances that showcase the importance of female avian endocrinology, and we challenge historic applications of an oversimplified, male-biased lens. We further provide examples of species for which avian behavior differs from the stereotypically described behaviors of male and female birds, warning investigators of the pitfalls in approaching endocrinology with a binary bias. We hope this piece will inspire investigators to engage in more comprehensive studies with female birds, to close the knowledge gap between the sexes, and to look beyond the binary when drawing conclusions about what is ‘male’ versus ‘female’ biology.
Performance evaluation of automatic speech recognition systems on integrated noise-network distorted speech
Authors: Kumalija Elhard , Nakamoto Yukikazu 
Published: September 2022

In VoIP applications, such as Interactive Voice Response and VoIP-phone conversation transcription, speech signals are degraded not only by environmental noise but also by transmission network quality, and distortions induced by encoding and decoding algorithms. Therefore, there is a need for automatic speech recognition (ASR) systems to handle integrated noise-network distorted speech. In this study, we present a comparative analysis of a speech-to-text system trained on clean speech against one trained on integrated noise-network distorted speech. Training an ASR model on noise-network distorted speech dataset improves its robustness. Although the performance of an ASR model trained on clean speech depends on noise type, this is not the case when noise is further distorted by network transmission. The model trained on noise-network distorted speech exhibited a 60% improvement rate in the word error rate (WER), word match rate (MER), and word information lost (WIL) over the model trained on clean speech. Furthermore, the ASR model trained with noise-network distorted speech could tolerate a jitter of less than 20% and a packet loss of less than 15%, without a decrease in performance. However, WER, MER, and WIL increased in proportion to the jitter and packet loss as they exceeded 20% and 15%, respectively. Additionally, the model trained on noise-network distorted speech exhibited higher robustness compared to that trained on clean speech. The ASR model trained on noise-network distorted speech can also tolerate signal-to-noise (SNR) values of 5 dB and above, without the loss of performance, independent of noise type.