AI-powered literature search: some observations and concerns by Farooq Azam Rathore, Fareeha Farooq

“…A brief overview of some tools that perform these functions is mentioned in Table I. …”
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Use of ethylene-oxide gas sterilisation to terminate multidrug-resistant bacterial outbreaks linked to duodenoscopes by Lawrence F Muscarella

“…Most of these 23 cases were associated with exposure to a duodenoscope model featuring a sealed elevator-wire channel. Six of the cases reported adopting (or in one case supplementing high-level disinfection with) ethylene oxide (EO) gas sterilisation of the duodenoscope, with at least three reporting this measure terminated the outbreak. …”
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A Polysorbate-Based Lipid Nanoparticle Vaccine Formulation Induces In Vivo Immune Response Against SARS-CoV-2 by Aishwarya Saraswat, Alireza Nomani, Lin-Kin Yong, Jimmy Chun-Tien Kuo, Heather Brown, Muralikrishna Narayanareddygari, Avery Peace, Rizan Fazily, Timothy Blake, Christopher D. Petro, Bindhu Rayaprolu, Johanna Hansen, Amardeep Singh Bhalla, Mohammed Shameem

“…Various analytical assays were performed to assess particle size, encapsulation efficiency, mRNA purity, and in vitro potency of the developed formulation and a humanized mouse model was used to measure its immunogenicity over six months of storage at −80 °C. …”
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Quantum ensemble learning with a programmable superconducting processor by Jiachen Chen, Yaozu Wu, Zhen Yang, Shibo Xu, Xuan Ye, Daili Li, Ke Wang, Chuanyu Zhang, Feitong Jin, Xuhao Zhu, Yu Gao, Ziqi Tan, Zhengyi Cui, Aosai Zhang, Ning Wang, Yiren Zou, Tingting Li, Fanhao Shen, Jiarun Zhong, Zehang Bao, Zitian Zhu, Zixuan Song, Jinfeng Deng, Hang Dong, Pengfei Zhang, Wei Zhang, Hekang Li, Qiujiang Guo, Zhen Wang, Ying Li, Xiaoting Wang, Chao Song, H. Wang

“…We experimentally demonstrate the versatility of our approach on a programmable superconducting processor, where we observe notable performance enhancements across various quantum machine learning models, including quantum neural networks and quantum convolutional neural networks. …”
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Análisis de imágenes mediante el método de los momentos usando funciones de base continuas a intervalos (PCBF) by Sergio Domínguez

“…The use of discontinuous basis can be justified by the own discontinuous nature of the object of such analysis, namely images: it is thoroughly known that the contours of visible objects are modeled as discontinuities in the series of luminance values as we go from one side of the border to the other. …”
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Maternal plasma cell-free RNA as a predictive test for fetal lung maturation by Sean W. D. Carter, Kay Yi Michelle Seah, Si En Poh, Winston Koh, Haruo Usuda, Erin L. Johnson, Yusaku Kumagai, Tsukasa Takahashi, Lara J. Monteiro, Reyna Peñailillo, Gino Nardocci, Hannah R. S. Watson, Masatoshi Saito, Mahesh A. Choolani, Sebastián E. Illanes, Matthew W. Kemp

“…A Random Forest algorithm identified genes that separated mature from immature fetal lung subgroups and determined AUC values for maternal and fetal cell-free RNA (cfRNA) feature sets to predict fetal lung maturation. Results We demonstrate that the analysis of 21 human cfRNA targets in maternal plasma is highly predictive of fetal lung maturation status across antenatal steroid induced (Dexamethasone AUC = 0.93; Betamethasone AUC = 1) and physiological (AUC = 1) lung development models. …”
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A hybrid approach to predicting and classifying dental impaction: integrating regularized regression and XG boost methods by Asok Mathew, Pradeep K. Yadalam, Ahmed Radeideh, Shrouk Hady, Rona Swed, Reyyan Cheema, Majd Mousa AL-Mohammad, Mohammed Alsaegh, SR Shetty

“…Enhancing data quality, refining feature selection, and using advanced modeling techniques are crucial for improving predictive capabilities. …”
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Navigating Ethical Dilemmas Of Generative AI In Medical Writing by Qurrat Ulain Hamdan, Waleed Umar, Mahnoor Hasan

“…Privacy and Security Concerns The innate programming of generative AI chatbots to store the information that is provided to them also raises concerns regarding their safety and security. While this feature serves to improve the performance of these tools according to user input and requirements, it also fuels the debate about privacy breaches and cybersecurity issues that could arise if malicious forces override these tools.6 Although ChatGPT has allowed users to turn off their chat history and prevent the bot from storing their data as of April 2023, the extent to which AI-driven tools uphold the pledge of privacy and confidentiality of user data is not quite transparent. …”
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Wisdom of the Established Pattern by Joel Janhonen

“…Most of our choices we do not base on precise rational modeling but on a far more ancient response mechanism. …”
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SLIT3-mediated intratumoral crosstalk induces neuroblastoma differentiation via a spontaneous regression-like program by Meiling Liu, Dekang Lv, Wenjing Yan, Yi Wu, Shulan Wang, Luoxuan Wang, Jie Lei, Deshun Zeng, Zifeng Wang, Fang Liu, Bing Deng, Quentin Liu, Bin He, Min Yan

“…Orthotopic and subcutaneous xenograft models were established to verify SLIT3's in vivo function. …”
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Mechanism of Influence of Spatial Perception on Residents’ Emotion in Child-Friendly Urban Streets of Fuzhou City by Shaofeng CHEN, Zhengyan CHEN, Yuhan XU, Zheng DING

“…For FCN-RF Semantic Segmentation, street view images are processed by fully convolutional networks to quantify 10 spatial metrics, validated against human-scored safety perceptions via random forest-based adversarial training; for XGBoost-SHAP Interpretability Framework, the nonlinear relationships between 12 street environment indicators and emotional indices are modeled through extreme gradient boosting, with SHapley additive explanations (SHAP) decoding feature contributions and interaction effects. …”
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EZH2 loss promotes gastric squamous cell carcinoma by Mengsha Zhang, Ailing Zhong, Hongyu Liu, Lei Zhao, Yingjie Wang, Zhenghao Lu, Lanxin Zhang, Xiangyu Pan, Zuoyu Liang, Limin Gao, Xuelan Chen, Jian Wang, Siqi Dai, Xudong Wan, Manli Wang, Jingyao Chen, Jiajia Du, Shuang Chen, Tingfa Peng, Jingyang Cai, Liuxiang Chen, Yang Yang, Chaoxin Xiao, Menglin Yao, Jing Xu, Runkuan Qin, Chan Yang, Baohong Wu, Qi Zhang, Shengzhuo Liu, Chengjian Zhao, Hongxin Deng, Yuan Wang, Lu Chen, Jiankun Hu, Bing Hu, Shengyong Yang, Feifei Na, Linjie Guo, Yu Liu, Chong Chen

“…Ezh2 loss induces squamous feature both in gastric organoids in vitro and in vivo mouse model. …”
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Natural lung-tropic TH9 cells: a sharp weapon for established lung metastases by Tao Chen, Hao Zhou, Shuming Li, Nong Lin, Yixuan Feng, Zhijian Cai, Chenxiao Qiao, Eloy Yinwang, Shengdong Wang, Xuehuan Wen, Xiangang Jin, Yucheng Xue, Wenkan Zhang, Xianchang Zeng, Zenan Wang, Hangxiang Sun, Lifeng Jiang, Hengyuan Li, Binghao Li, Zhaoming Ye

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Integrated Multiomics Analyses of the Molecular Landscape of Sarcopenia in Alcohol‐Related Liver Disease by Nicole Welch, Pugazhendhi Kannan, Saurabh Mishra, Annette Bellar, Vandana Agrawal, Grahame Kidd, Emily Benson, Ryan Musich, Raya Tabbalat, Ling Li, J. Mark Brown, Belinda Willard, Karyn A. Esser, Laura E. Nagy, Srinivasan Dasarathy

“…Studies were done in a comprehensive array of models including ethanol‐treated (ET) murine and human‐induced pluripotent stem cell–derived myotubes (hiPSCm), skeletal muscle from a mouse model of ALD (mALD) and human patients with alcohol‐related cirrhosis and controls. …”
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Impact of antiparasitic therapy on cardiovascular outcomes in chronic Chagas disease. A systematic review and meta-analysisResearch in context by Anis Rassi, Jr, Alyssa Grimshaw, Ashwin Sarwal, Ranjit Sah, Sangam Shah, Nelson I. Agudelo Higuita, Fabio Mahamed Rassi, Michaele Francesco Corbisiero, Hannah M. Kyllo, Jordan Stellern, Samantha Kaplan, Luis A. Marcos, Edgar A. Ramírez-García, Martin Casapia, Peter Hotez, Maria Elena Bottazzi, Shital Patel, Carlos Franco-Paredes, José Antonio Marin-Neto, Andrés F. Henao-Martínez

“…A customized risk of bias scale assessed the methodological quality of studies, and a random-effects model estimated the pooled risk ratios. This investigation was registered in PROSPERO (CRD42023495755). …”
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Investigating the Location-Spatial Effects of Urban Tourism on the Stability of the Urban Spatial System (Case Study: Region 3 of Isfahan City) by Mina Kazemi, Aboozar Vafaei, Rasool Heydari Sureshjani

“…Findings from the structural model further demonstrated the significance of these effects on the spatial system's stability. …”
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The Impact of a Deep Learning Self-Adaptive Colour Restoration Pipeline for Deep Underwater Images in 3D Reconstruction by M. Vlachos, D. Skarlatos, S. Demesticha

“…The proposed approach leverages deep learning to correct colour distortions without relying on physical models of underwater light propagation. Evaluation was performed using established feature detection algorithms, such as SIFT and SURF, applied to multiple underwater datasets capturing diverse imaging conditions. …”
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Apple Detection via Near-Field MIMO-SAR Imaging: A Multi-Scale and Context-Aware Approach by Yuanping Shi, Yanheng Ma, Liang Geng

“…DSPP employs a learnable adaptive mechanism to dynamically adjust multi-scale feature representations, enhancing sensitivity to apple targets of varying sizes and distributions; RFN uses a multi-round iterative feature fusion strategy to gradually refine semantic consistency and stability, improving the robustness of feature representation under weak texture and high noise scenarios; and the CAFE module, based on attention mechanisms, explicitly models global and local associations, fully utilizing the scene context in texture-poor SAR conditions to enhance the discriminability of apple targets. …”
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Implementation and Evaluation of Machine Learning Algorithms in Ball Bearing Fault Detection by Stepanić Pavle, Dučić Nedeljko, Vidaković Jelena, Baralić Jelena, Popović Marko

“…For each recorded vibration signal, a feature extraction was performed by digital processing in the time domain. …”
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Stereo Matching of High-Resolution Satellite Images via Hierarchical ViT and Self-Supervised DINO by X. He, M. Yang, S. Jiang, S. Jiang, W. Jiang, Q. Li

“…In the feature extraction module, we have performed attention-based in-depth learning on the smallest-scale feature using the self-supervised DINO. …”
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