Phenotyping sarcoidosis: a single institution retrospective analysis by Francesco Rocco Bertuccio, Francesco Rocco Bertuccio, Davide Piloni, Davide Piloni, Marianna Russo, Marianna Russo, Fady Tousa, Fady Tousa, Mariachiara Crescenzi, Mariachiara Crescenzi, Paola Putignano, Paola Putignano, Nicola Baio, Nicola Baio, Ida Maragò, Angelo Guido Corsico, Angelo Guido Corsico, Giulia Maria Stella, Giulia Maria Stella

“…For instance, finding a relationship between symptom burden, race, gender, HRQoL, and pulmonary function could have therapeutic ramifications, influence clinical practice, and aid in selecting patients for specific clinical studies.MethodsA comprehensive statistical evaluation was conducted using the JMP partitioning algorithm which explores all potential divisions to identify the most predictive variables.ResultsWith our analysis, we tried to categorize patients from a single Institution respiratory unit to delineate clinical phenotypes in sarcoidosis.ConclusionsLarger studies using appropriate methodology should surely be carried out to address this issue and help clarify the varying contributions of genetics, socioeconomic status, environmental exposures, and other sociodemographic factors to illness severity and phenotypic presentation. …”
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Preoperative imaging and intraoperative navigation of the parathyroid glands neoplasms in primary hyperparathyroidism by K. Yu. Slashchuk, M. V. Degtyarev, P. O. Rumyantsev, A. K. Eremkina, N. V. Tarbaeva, D. G. Beltsevich, I. V. Kim, G. A. Melnicthhenko, N. G. Mokrysheva

“…The results allowed us to develop an algorithm for preoperative topical diagnosis of parathyroid glands in patients with laboratory-verified primary hyperparathyroidism and indications for surgical treatmen. …”
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SAHVAI‐3D and 4D: Automated AI Volumetric Measurement of Subarachnoid Hemorrhage on Noncontrast Head CT by Melina Wirtz, Saif Salman, Yujia Wei, Vishal N. Patel, Rohan Sharma, Vikash Gupta, Qiangqiang Gu, Benoit Dherin, Sanjana Reddy, Rabih G. Tawk, Bradley J. Erickson, W. David Freeman

“…The SAHVAI deep learning algorithm generated automated SAHV values in cubic centimeters. …”
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Combining Super-Resolution Imaging and Shear Wave Elastography for Enhanced Risk Assessment of Moderate-to-Severe Renal Fibrosis in Chronic Kidney Disease Patients by Huang X, Zhang Y, Hu Y, Pan J, Huang X, Zhang J, Pu H, Chen Y, Deng Q, Zhou Q

“…Furthermore, the nomogram, which integrated clinical factors and ultrasound composite parameters, exhibited excellent predictive performance (AUC = 0.878, 95% CI 0.782– 0.974). …”
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Clinical characteristics of COVID-19 in children and adolescents: insights from an Italian paediatric cohort using a machine-learning approach by Carlo Giaquinto, Daniela Paolotti, Daniele Donà, Stefania Fiandrino, Piero Poletti, Michael Davis Tira, Costanza Di Chiara

“…First, we apply an unsupervised machine-learning algorithm to cluster individuals into groups. Second, we classify new patient risk groups using a random forest classifier model based on sociodemographic information, pre-existing medical conditions, vaccination status and the VOC as predictive variables. …”
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Neuromorphic, physics-informed spiking neural network for molecular dynamics by Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi

“…The results indicate that NP-SNN provides a robust Sci-ML framework that can make accurate predictions across diverse scientific molecular applications. …”
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Pilot Protection of New Energy Transmission Line in Active Distribution Network Based on 5G Communication by Tiecheng LI, Hui FAN, Weiming ZHANG, Xianzhi WANG, Yihong ZHANG, Zhihui DAI

“…Traditional pilot differential protection will have the problem of reliability reduction or even failure to operate after the access of new energy stations. …”
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Development of an interpretable model for foot soft tissue stiffness based on gait plantar pressure analysis by Xiaotian Bai, Xiao Hou, Dazhi Lv, Dazhi Lv, Jialin Wei, Jialin Wei, Yiling Song, Zhengyan Tang, Hongfeng Huo, Hongfeng Huo, Jingmin Liu

“…A backpropagation neural network, optimized by integrating particle swarm optimization and genetic algorithm, was constructed to predict foot soft tissue stiffness using plantar pressure data collected during walking. …”
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Improved Smart Power Socket for Monitoring and Controlling Electrical Home Appliances by Eslam Al-Hassan, Hussain Shareef, Md. Mainul Islam, Addy Wahyudie, Atef Amin Abdrabou

“…A 24-min implementation of the proposed energy management algorithm shows a reduction of 0.811 kWmin (0.0134 kWh) in energy usage after the use of the smart sockets as load controllers. …”
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Identification of water sources of mine water bursts based on the FPS-DT model by Kaide Liu, Yu xia, Xiaolong Li, Chaowei Sun, Wenping Yue, Qiyu Wang, Songxin Zhao, Shufeng Chen

“…The fuzzy C-means (FCM) clustering method is employed to classify water sample data, followed by principal component analysis (PCA) for dimensionality reduction to extract key features. The SMOTE algorithm is then applied to address the issue of class imbalance. …”
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Improved UAV Target Detection Model for RT-DETR by Yong He, Yufan Pang, Guolin Ou, Renfeng Xiao, Yifan Tang

“…In light of the shortcomings pertaining to UAV small target detection, the detection of complex scenes, and the detection of multi-scale targets, a time-frequency dual-domain feature extraction algorithm, TF-DETR, has been proposed. This algorithm has been optimized for RT-DETR.Firstly, a time-frequency domain feature extraction module, TF-CSPNet, has been introduced into the backbone network. …”
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In-Silico discovery of novel cephalosporin antibiotic conformers via ligand-based pharmacophore modelling and de novo molecular design by Rayhan Chowdhury, Samia Akter Saima, Md. Al Amin, Md. Kawsar Habib, Ramisa Binti Mohiuddin, Ali Mohamod Wasaf Hasan, Roksana Khanam, Shahin Mahmud

“…This research aims to predict new compounds using ligand-based pharmacophore models while optimizing existing drugs. …”
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DEPDC1B, CDCA2, APOBEC3B, and TYMS are potential hub genes and therapeutic targets for diagnosing dialysis patients with heart failure by Wenwu Tang, Wenwu Tang, Zhixin Wang, Xinzhu Yuan, Liping Chen, Haiyang Guo, Zhirui Qi, Ying Zhang, Xisheng Xie

“…We constructed a ceRNA regulatory network, and found that 4 hub genes (TYMS, CDCA2 and DEPDC1B) might be regulated by 4 miRNAs (hsa-miR-1297, hsa-miR-4465, hsa-miR-27a-3p, hsa-miR-129-5p) and 21 lncRNAs (such as HCP5, CAS5, MEG3, HCG18). 24 small molecule drugs were predicted based on TYMS through DrugBank website. Finally, qRT-PCR experiments showed that the expression trend of biomarkers was consistent with the results of transcriptome sequencing.DiscussionOverall, our results reveal the molecular mechanism of HF in patients with MHD and provide insights into potential diagnostic markers and therapeutic targets.…”
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Optimal vaccination model of airborne infection under variable humidity and demographic heterogeneity for hybrid fractional operator technique by Saima Rashid, Ilyas Ali, Nida Fatima, Tehreem Fatima, Fekadu Tesgera Agam, Sayed K. Elagan

“…Our system’s best-fit parameter settings were detected using the Markov Chain Monte Carlo (M-C-M-C) technique with influenza information collected in Spain. We predict a basic reproduction number of 1.3645 (96% C.I: (1.3644, 1.3646)). …”
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At-Home Evaluation of Both Wearable and Touchless Digital Health Technologies for Measuring Nocturnal Scratching in Atopic Dermatitis: Analytical Validation Study by Stefan Avey, Mark Morris, Davit Sargsyan, Molly V Lucas, Andrea O'Brisky, Kenneth Mosca, Andrew Elias, Nicholas Fountoulakis, Mehdi Boukhechba, Xuen Hoong Kok, Saiyam Jain, Mehrnoosh Oghbaie, Nikolay V Manyakov, Miao Wang, Zuleima Aguilar, Lynn Yieh

“…Within-night agreement of DHT-predicted scratching events versus the Reference was assessed by sensitivity, precision, and F1 ResultsCharacterization of human-annotated scratching revealed a basal level of scratching in both healthy volunteers (13.1 seconds per hour) and in participants with AD on nights when no itch was reported (10.2 seconds per hour). …”
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Bioinformatics‑Based Analysis Reveals Diagnostic Biomarkers and Immune Landscape in Atopic Dermatitis by Yang M, Zhang X, Zhou C, Du Y, Zhou M, Zhang W

“…Potential microRNA (miRNA)-messenger RNA (mRNA) and miRNA-long non-coding RNA (lncRNA) interactions were predicted using miRanda and TargetScan tools.Results: We identified 381 DEGs (217 upregulated, 164 downregulated). …”
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The underlying molecular mechanisms and biomarkers of Hip fracture combined with deep vein thrombosis based on self sequencing bioinformatics analysis by Guanghua Shi, Xiaocui Shi, Meng Zhang, Rui Cheng, Mengqing Hu, Yu Zhao, Shimei Li, Xiuxiu Li, Haiyun Ma, Pengcui Li

“…Conclusions In conclusion, five feature genes (RGS1, HSF2, ARL4A, AAED1, and WDR81) were identified, and functional enrichment analyses were conducted, providing a foundation for predicting the diagnosis of fractures associated with thrombosis.…”
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Single cell transcriptomic analysis reveals tumor immune infiltration by macrophage cells gene signature in lung adenocarcinoma by Xiaotong Guo, Youjun Deng, Wenjun Jiang, Heng Li, Yisheng Luo, Huachuan Zhang, Hao Wu

“…High TGS tumors exhibited enrichment in TGF-β signaling and hypoxia pathways, suggesting their potential utility in predicting prognosis and immune responses in patients with LUAD. …”
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Identifying mating events of group-housed broiler breeders via bio-inspired deep learning models by Venkat U.C. Bodempudi, Guoming Li, J. Hunter Mason, Jeanna L. Wilson, Tianming Liu, Khaled M. Rasheed

“…The DLM framework included a bird detection model, data filtering algorithms based on mating duration, and logic frameworks for mating identification based on bird count changes. …”
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Comparative performance evaluation of quartz and snail shell powders modified concrete: Mechanical, machine learning, and microstructural assessments by Md. Habibur Rahman Sobuz, Md. Kanan Chowdhury Tilak, SM Arifur Rahman, Fahim Shahriyar Aditto, Faiz Uddin Ahmed Shaikh, Md. Kawsarul Islam Kabbo, M Jameel, Md. Munir Hayet Khan

“…Later, the extreme gradient boosting ML algorithm provided a nominal coefficient of determination of 0.988 in the prediction of compressive strength of concrete compared to the gradient boosting and random forest model. …”
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