The mediating effect of clinical belongingness on the relationship between anxiety and professional identity in nursing interns: a cross-sectional study by Junhao Zhang, Lijia Wang, Xue Yang, Yuwei Yang, Xuehua Wu, Huaping Huang, Guirong Li

“…The clinical belongingness of nursing interns had a mediating effect on the relationship between anxiety and PI (β = −0.072, 95% confidence interval = −0.133 to −0.013, p < 0.001), accounting for 40% of the total effect.ConclusionThe anxiety level of nursing interns can have a direct impact on the prediction of PI and an indirect influence on PI mediated by clinical belongingness. …”
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Maize crop residue cover mapping using Sentinel-2 MSI data and random forest algorithms by Jia Du, Pierre-Andre Jacinthe, Kaishan Song, Longlong Zhang, Boyu Zhao, Hua Liu, Yan Wang, Weijian Zhang, Zhi Zheng, Weilin Yu, Yiwei Zhang, Dapeng Jiang

“…The tillage indices with the best predictive performance were STI and NDTI (R2 of 0.85 and 0.84, respectively). …”
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Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study by Matthew A. Lee, Kate L. Burley, Emma L. Hazelwood, Sally Moore, Sarah J. Lewis, Lucy J. Goudswaard

“…Future work should explore the utility of these proteins in disease prediction or prevention using proteomic data from patients with MM or precursor conditions.…”
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Imaging Diagnostics, Biomarkers, and Emerging Trends in Orthopedic Research and Treatment by Gabriela Oliwia Trestka, Wiktoria Domino, Urszula Zelik, Maria Przygoda, Joanna Śnieżna, Kamila Stępień, Sabina Adamczyk, Wojciech Florczak, Jagienka Włodyka, Jakub Dziewic, Karol Dzwonnik

“…Biomarkers, such as those used in osteoarthritis, osteoporosis, joint inflammation, and bone regeneration, complement imaging techniques by enabling early diagnosis, monitoring disease progression, and predicting treatment outcomes. The integration of biomarkers with imaging provides a more comprehensive approach to patient care. …”
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A scalable multi-modal learning fruit detection algorithm for dynamic environments by Liang Mao, Liang Mao, Zihao Guo, Mingzhe Liu, Yue Li, Linlin Wang, Jie Li

“…A sliding slice method is then employed to predict image targets, thereby reducing the miss rate of small targets.ResultsExperimental results demonstrate that the proposed model improves accuracy, recall, and mean average precision (mAP) by 9.5, 0.9, and 12.3 percentage points, respectively, compared to the original YOLOv5s model. …”
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Cognitive impairment and psychopathology in out-of-hospital cardiac arrest survivors in Denmark: The REVIVAL cohort study protocol by Jacob Eifer Moller, Christian Hassager, Selina Kikkenborg Berg, Ola Ekholm, Trine Bernholdt Rasmussen, Mette Kirstine Wagner, Sophia Armand, Patrick MacDonald Fisher, Gitte Moos Knudsen, Dea Siggaard Stenbæk

“…Importantly, we face a gap in our knowledge about suitable screening tools in the early post-arrest phase for long-term risk prediction of mental health problems. This study aims to evaluate the efficacy of a novel screening procedure to predict risk of disabling cognitive impairment and psychopathology 3 months after cardiac arrest. …”
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ALKBH5 alleviates lower extremity arteriosclerosis by regulating ITGB1 demethylation and influencing macrophage polarization by Zeyu Guan, Xiaogao Wang, Chao Xu, Ran Lu

“…Additionally, Starbase2.0 was used for downstream target gene prediction of ALKBH5. The half-life of ITGB1 was evaluated using RT-qPCR. …”
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A new strategy for pharmacodynamic substance screening and research on gut microbiota pathway mechanisms based on UPLC-Q-orbitrap-MS and 16S rRNA by Zhiying Yu, Tong Li, Jie Yang, Jianghua He, Weijiang Zhang, Siyuan Li, Yunpeng Qi, Yihui Yin, Ling Dong, Wenjuan Xu

“…The abundance of gut microbiota involved in the metabolism of the prototype components was influenced by the corresponding components. The function prediction results showed that PUE was the most comparable to GQD, with 24 consistent pathways. …”
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The influencing factors of the erythropoietin resistance index and its association with all-cause mortality in maintenance hemodialysis patients by Xinju Zhao, Liangying Gan, Fan Fan Hou, Xinling Liang, Xiaonong Chen, Yuqing Chen, Zhaohui Ni, Li Zuo

“…ERI was associated with increased all-cause mortality in MHD patients, indicating the possibility of death prediction by ERI. Patients with high ERI warrant more attention.…”
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Investigation of a Novel Noninvasive Risk Analytics Algorithm With Laboratory Central Venous Oxygen Saturation Measurements in Critically Ill Pediatric Patients by Sarah A. Teele, MD, MSHPEd, Avihu Z. Gazit, MD, Craig Futterman, MD, William G. La Cava, PhD, David S. Cooper, MD, MPH, MBA, Steven M. Schwartz, MD, MS, Joshua W. Salvin, MD, MPH

“…Collected data included vital signs, ventilator data, laboratory data, and demographics. PREDICTION MODEL:. The ability of the IDo2 index to predict Svo2 below a preselected threshold (30%, 40%, or 50%) was evaluated for discriminatory power, range utilization, and robustness. …”
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Magnetic resonance imaging quantification of left ventricular mechanical dispersion and scar heterogeneity optimize risk stratification after myocardial infarction by Xiaoying Zhao, Li Zhang, Lujing Wang, Wanqiu Zhang, Yujiao Song, Xinxiang Zhao, Yanli Li

“…This study aims to evaluate the predictive value of cardiac magnetic resonance (CMR)-derived LVMD for adverse clinical outcomes and to explore its correlation with myocardial scar heterogeneity. …”
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Knowledge discovery from database: MRI radiomic features to assess recurrence risk in high-grade meningiomas by Chen Chen, Lifang Hao, Bin Bai, Guijun Zhang

“…Conclusion Knowledge discovery from MRI radiomic features can slightly help predict recurrence risk in HGMs. T2WI or T1C yielded better efficiency than T2WI + T1C. …”
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Mapping knowledge landscapes and emerging trends in artificial intelligence for antimicrobial resistance: bibliometric and visualization analysis by Zhongli Wang, Zhongli Wang, Gaopei Zhu, Shixue Li, Shixue Li

“…Citation analysis highlighted two major breakthroughs: AlphaFold’s protein structure prediction (6,811 citations) and deep learning approaches to antibiotic discovery (4,784 citations). …”
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Discovery of a heparan sulfate binding domain in monkeypox virus H3 as an anti-poxviral drug target combining AI and MD simulations by Bin Zheng, Meimei Duan, Yifen Huang, Shangchen Wang, Jun Qiu, Zhuojian Lu, Lichao Liu, Guojin Tang, Lin Cheng, Peng Zheng

“…Using AI-based structural prediction tools and molecular dynamics (MD) simulations, we identified a novel, positively charged α-helical domain in H3 that is essential for HS binding. …”
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The significance of risk stratification through nomogram-based assessment in determining postmastectomy radiotherapy for patients diagnosed with pT1 − 2N1M0 breast cancer by Chao Wei, Jie Kong, Huina Han, Xue Wang, Zimeng Gao, Danyang Wang, Andu Zhang, Jun Zhang, Zhikun Liu

“…Abstract Objective To explore the high-risk factors affecting the prognosis of pT1 − 2N1M0 patients after mastectomy, establish a nomogram prediction model, and screen the radiotherapy benefit population. …”
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Identification of Potential Type II Diabetes in a Chinese Population with a Sensitive Decision Tree Approach by Dongmei Pei, Chengpu Zhang, Yu Quan, Qiyong Guo

“…With a strict data filtration, 10,436 records from the eligible participants were utilized to develop a prediction model using the J48 decision tree algorithm. …”
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Penerapan Decision Tree J48 dan Reptree dalam Menentukan Prediksi Produksi Minyak Kelapa Sawit menggunakan Metode Fuzzy Tsukamoto by Tundo Tundo, Shofwatul 'Uyun

“…From the data used the accuracy of the J48 decision tree is 95.2381%, while the REPTree accuracy is 90.4762%, but in this case the REPTree decision tree is more appropriate to be used in the prediction process of palm oil production, because it is tested with actual data in March 2019 uses REPTree obtained 16355835 liters, while using J48 obtained 11844763 liters, where the actual production data is 179,20000 liters. …”
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AMH Is a Good Predictor of Metabolic Risk in Women with PCOS: A Cross-Sectional Study by Miaoxian Ou, Pei Xu, Han Lin, Kaichi Ma, Mingxing Liu

“…The values of AMH that best predicted the risk of HOMA-IR were also analyzed by ROC curves. …”
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Metabolite profiling in assessing ulcerative colitis activity: A systematic review by Danyang Cui, Xu Han, Jiazhu Jin, Yanhong Wang, Zijia Chen, Yang Gong, Miao Jiang

“…This finding will identify small molecule biomarkers that may potentially replace colonoscopy in the assessment and prediction of UC disease activity in the future, which indicates a significant potential for biomarker development.…”
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Identification of a distinctive immunogenomic gene signature in stage-matched colorectal cancer by Pankaj Ahluwalia, Ashis K. Mondal, Ashutosh Vashisht, Harmanpreet Singh, Ahmet Alptekin, Kalyani Ballur, Nivin Omar, Meenakshi Ahluwalia, Kimya Jones, Amanda Barrett, Vamsi Kota, Ravindra Kolhe

“…The development of prognostic and predictive indicators, particularly in the context of personalized medicine, could significantly improve CRC patient management. …”
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