FFTL-Net: a network for the classification of skin diseases based on feature fusion and transfer learning by Xiaowei Song, Yurong Mei, Zhilei Zhao, Hao Chang, Lina Han, Hui Wang, Xinyi Zhang, Guoqiang Wang

“…This represents an improvement of 1.25% and 0.33% compared to the second-ranked algorithm, respectively. The experimental results demonstrate the efficacy of the model in addressing the data imbalance issue in skin disease datasets, with prediction accuracies of at least 93% being achieved for BCC and other rare samples. …”
Get full text

The relationship between the expression of serum asprosin and miR-21 in patients with osteoporosis and delayed healing after OVCF surgery by Gaoming Song, Silong Lin, Xianqi Zhang, Hong Pan

“…A nomogram model was constructed to predict delayed healing, and its predictive value was evaluated using the receiver operating curve (ROC) analysis. …”
Get full text

Artificial intelligence driven Mid-IR photoimaging device based on van der Waals heterojunctions of black phosphorus by Wang Ziqian, Wang Huide, Wang Chen, Bao Yushuo, Zheng Weiying, Weng Xiaoliang, Zhu Yihan, Liu Yi, Zhang Yule, Tian Xilin, Sun Shuo, Cao Rui, Shi Zhe, Chen Xing, Qiu Meng, Wang Hao, Liu Jun, Chen Shuqing, Zeng Yu-Jia, Liao Wugang, Huang Zhangcheng, Li Haiou, Gao Lingfeng, Li Jianqing, Fan Dianyuan, Zhang Han

Get full text

Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. by Roland F Schwarz, Charlotte K Y Ng, Susanna L Cooke, Scott Newman, Jillian Temple, Anna M Piskorz, Davina Gale, Karen Sayal, Muhammed Murtaza, Peter J Baldwin, Nitzan Rosenfeld, Helena M Earl, Evis Sala, Mercedes Jimenez-Linan, Christine A Parkinson, Florian Markowetz, James D Brenton

“…<h4>Conclusions</h4>This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. …”
Get full text

The role of artificial intelligence in managing hospitalized patients with mental illness: a scoping review by Md Samiun, Moustaq Karim Khan Rony, Sumaiya Yeasmin, Mia Md Tofayel Gonee Manik, Anupom Debnath, Mustakim Bin Aziz, Afia Fairooz Tasnim, Sadia Islam Nilima

“…Machine learning (ML) and natural language processing (NLP) technologies were reported to enhance diagnostic processes by analyzing extensive datasets from electronic health records and clinical documentation, potentially supporting earlier detection and intervention. Predictive analytics effectively assessed patient risks, enhancing proactive care strategies. …”
Get full text

Enhancing TB case detection: experience in offering upfront Xpert MTB/RIF testing to pediatric presumptive TB and DR TB cases for early rapid diagnosis of drug sensitive and drug r... by Neeraj Raizada, Kuldeep Singh Sachdeva, Sreenivas Achuthan Nair, Shubhangi Kulsange, Radhey Shayam Gupta, Rahul Thakur, Malik Parmar, Christen Gray, Ranjani Ramachandran, Bhavin Vadera, Shobha Ekka, Shikha Dhawan, Ameet Babre, Mayank Ghedia, Umesh Alavadi, Puneet Dewan, Mini Khetrapal, Ashwini Khanna, Catharina Boehme, Chinnambedu Nainarappan Paramsivan

“…Upfront Xpert MTB/RIF testing of presumptive PTB and presumptive DR-TB cases resulted in diagnosis of 79 and 12 rifampicin resistance cases, respectively. Positive predictive value (PPV) for rifampicin resistance detection was high (98%, CI 90.1-99.9), with no statistically significant variation with respect to past history of treatment.…”
Get full text

Diagnosis of parotid gland tumors using a ternary classification model based on ultrasound radiomics by Xiaoling Liu, Weihan Xiao, Chen Yang, Zhihua Wang, Dong Tian, Gang Wang, Xiachuan Qin

“…Two-step LASSO-BNB and voting ensemble learning modeling algorithm with recursive feature elimination feature selection method (RFE-Voting) models were then applied for classification. …”
Get full text

MRI-based habitat analysis of vascular and nerve invasion in the tumor microenvironment: an advanced approach for prostate cancer diagnosis by Bo Guan, Cong Huang, Yalei Wang, Jialong Zhang, Xiaowei Li, Zongyao Hao, Zongyao Hao

“…The combined model that integrates habitat scores and Gleason scores had an AUC value of 0.889 (CI: 0.8509-0.9276) in the training set and 0.886 (CI: 0.8183-0.9533) in the test set, outperforming the single models.ConclusionsBy deeply analyzing the tumor microenvironment and combining radiomics models, the diagnostic precision and predictive accuracy of vascular and nerve invasion in prostate cancer can be significantly improved. …”
Get full text

MRI-based 2.5D deep learning radiomics nomogram for the differentiation of benign versus malignant vertebral compression fractures by Wenhua Liang, Hong Yu, Lisha Duan, Xiaona Li, Ming Wang, Bing Wang, Jianling Cui

“…Among all these models, the DLR model that employed ExtraTrees algorithm performed the best, with area under the curve (AUC) values of 0.971 (95% CI: 0.948-0.995) in the training dataset and 0.828 (95% CI: 0.727-0.929) in the testing dataset. …”
Get full text

Transcription factor networks and novel immune biomarkers reveal key prognostic and therapeutic insights in ovarian cancer by Aiqin Zhao, Sufang Zhou, Xiaoyi Yang, Haiying Lu, Dan Zou, Xuan Zhang, Li Liu

“…Abstract Background Understanding the tumor microenvironment (TME) is essential for the advancement of immunotherapy for ovarian cancer (OC). Nonetheless, predicting transcription factor (TF) regulation from the TME using single-cell RNA sequencing (scRNA-seq) data is challenging. …”
Get full text

SERLogic: A Logic-Integrated Framework for Enhancing Sequential Recommendations by Lihang Fan

“…Sequential recommendation models are used to predict users&#x2019; next top-K preferred items based on their historical interactions. …”
Get full text

Decoding the diagnostic biomarkers of mitochondrial dysfunction related gene variants in pediatric T cell acute lymphoblastic leukemia by Yunfeng Lu, Xi Yang, Liying Liu, Xiaojun Sun, Xiuli Li, Ping Zhao, Guotao Guan, Yunpeng Dai

“…Additionally, A mitochondrial dysfunction-based signature was created to predict pediatric T-ALL. The mRNA-miRNA and mRNA-TF Regulatory Network were constructed, and the molecular distributions of key genes were calculated. …”
Get full text

An in-depth exploration of the association between olanzapine, quetiapine and acute pancreatitis based on real-world datasets and network toxicology analysis by Shuang Wang, Liying Song, Yuanjie Sun, Haonan Zhou, Jia Yao

“…First, the reports of antipsychotics were extracted from the US FDA Adverse Event Reporting System (FAERS), and the signals of AP were detected by four pharmacovigilance algorithms. The gene targets of drugs were predicted using multiple databases. …”
Get full text

Applications of molecular dynamics in nanomaterial design and characterization - A review by Md. Aminul Islam, S M Maksudur Rahman, Juhi Jannat Mim, Safiullah Khan, Fardin Khan, Md. Ahadul Islam Patwary, Nayem Hossain

“…In addition, MD is a powerful method for predicting mechanical, thermal, and electronic properties of nanomaterials in different environments. …”
Get full text

Population status and impact of climate change on the distribution of vulnerable multipurpose plant Hippophae rhamnoides ssp. turkestanica for conservation in Trans-Himalaya, India by Shiv Paul, Shiv Paul, Khilendra Singh Kanwal, Anil Kumar, Sher Singh Samant, Sher Singh Samant, Indra Dutt Bhatt, Rakesh Chandra Sundriyal, Rakesh Chandra Sundriyal, Swaran Lata

“…The machine learning algorithm showed that Bio_6 (minimum temperature of the coldest month), elevation, and slope were the best suitable variables for the habitat prediction along with the CMIP6 project’s MIROC6 and CMCC-ESM2 climate change models to identify the potential distribution area of the species for the future under the SSP245 (middle of the way) and SSP585 (fossil-fueled development) scenarios, respectively. …”
Get full text

A quantitative evaluation method of laser treatment efficacy for pigmentary dermatosis based on image segmentation technology by Haopu Jian, Qi Chen, Youjun Yu, Cheng Wang, Peiru Wang, Xiuli Wang

Get full text

Machine learning approach for Studying the multifunctionality of soil against global climate changes by Xiangng Hu, Yingying Xie, Qixing Zhou, Li Mu

Get full text

Prospects of application of artificial neural networks for forecasting of cargo transportation volume in transport systems by D. T. Yakupov, O. N. Rozhko

“…The authors consider the possibility of forecasting to use ANN for these economic indicators not as an alternative to the traditional methods of statistical forecasting, but as one of the available simple means for solving complex problems.Materials and methods. When predicting the ANN, three methods of learning were used: 1) the Levenberg-Marquardt algorithm-network training stops when the generalization ceases to improve, which is shown by the increase in the mean square error of the output value; 2) Bayes regularization method - network training is stopped in accordance with the minimization of adaptive weights; 3) the method of scaled conjugate gradients, which is used to find the local extremum of a function on the basis of information about its values and gradient. …”
Get full text

Optimized Tile Quality Selection in Multi-User 360&#x00B0; Video Streaming by Moatasim Mahmoud, Stamatia Rizou, Andreas S. Panayides, Nikolaos V. Kantartzis, George K. Karagiannidis, Pavlos I. Lazaridis, Zaharias D. Zaharis

“…To simulate our solution in practical settings, we employ a long short-term memory (LSTM) model to perform viewport prediction and make the assessment based on real-life viewing data. …”
Get full text

Potential of solar-induced chlorophyll fluorescence for monitoring long-term dynamics of soil salinity in Central Asia the Xinjiang Region China by Kuangda Cui, Kuangda Cui, Jianli Ding, Jianli Ding, Jinjie Wang, Jinjie Wang, Jiao Tan, Jiao Tan, Lijing Han, Jiangtao Li, Jiangtao Li

“…Solar-induced chlorophyll fluorescence (SIF), which reflects plant photosynthetic status and stress, shows promise for monitoring salinity but remains underutilized in this region.MethodsThis study integrated SIF-derived indices (SIFI) with soil salinity data to build a region-specific prediction model. Using a random forest algorithm, soil salinity was classified into five levels based on satellite data and ground references from 2000–2020. …”
Get full text