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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The Extended CG Method for Non-Quadratic Models by Abbas Al-Bayati, Basim Hassan, Sawsan Ismael

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Advanced water quality assessment using machine learning: Source identification and probabilistic health risk analysis by Amin Mohammadpour, Ehsan Gharehchahi, Mohammad Golaki, Majid Amiri Gharaghani, Fahime Ahmadian, Soroush Abolfathi, Mohammad Reza Samaei, Md Galal Uddin, Agnieszka I. Olbert, Amin Mousavi Khaneghah

“…The Extremely Randomized Trees (ExT) algorithm demonstrated strong predictive capability for RMS-WQI, with sensitivity analysis identifying electrical conductivity (EC) and chloride (Cl−) as the most influential parameters. …”
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Study of the interval width of features for improving forecasting efficiency of the transport flow intensity by I. N. Pugachev, N. G. Sheshera, D. Yе. Grigorov

“…There are a lot of methods for increasing the accuracy of predictive models, but this method has been used for the first time. …”
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Fatness, fitness and the aging brain: A cross sectional study of the associations between a physiological estimate of brain age and physical fitness, activity, sleep, and body comp... by David Wing, Lisa T. Eyler, Eric J. Lenze, Julie Loebach Wetherell, Jeanne F. Nichols, Romain Meeusen, Job G. Godino, Joshua S. Shimony, Abraham Z. Snyder, Tomoyuki Nishino, Ginger E. Nicol, Guy Nagels, Bart Roelands

“…Methods: Using T1 weighted MRI images, we applied a novel algorithm to determine the physiological age of the brain (brain-predicted age) and the predicted age difference between this physiologically based estimate and chronological age (BrainPAD) to 551 sedentary adults aged 65 to 84 with self-reported cognitive complaint measured at baseline as part of a larger study. …”
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Automated ejection fraction and risk stratification in cardiomyopathy patients with diverse LV geometry using 2D echocardiography by Ziwei Zhu, Ke Fan, Shuyuan Zhang, Tingting Hu, Jingyi Li, Ze Zhao, Ye Jin, Shuyang Zhang

“…LASSO regression identified predicted LVEF, E/e′ ratio, and age as significant predictors, with AUC values of 0.833 (CR), 0.695 (CH), and 0.938 (EH) for adverse outcomes prediction. …”
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Identification of the intestinal type gastric adenocarcinoma transcriptomic markers using bioinformatic and gene expression analysis by V. V. Volkomorov, E. S. Grigor’eva, G. S. Krasnov, N. V. Litvyakov, N. A. Lisitsyn, M. I. Voevoda, A. V. Belkovets, S. G. Afanas’ev, N. V. Cherdyntseva

“…These markers can be used for early tumor diagnosis and prognosis as well as for prediction of therapeutic response, estimation of tumor volume or to assess disease recurrence through monitoring. …”
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Artificial Intelligence Approach in Hip Prosthesis Identification and Addressing Radiographic Outcome Measures by Omar Musbahi, MSc, ChM, Savvas Hadjixenophontos, MEng, Saran S. Gill, Iris Soteriou, Bsc, Kyriacos Pouris, Bsc, Takuro Ueno, PhD, Justin P. Cobb, MCh

“…By incorporating additional features such as the lateral NSA, the model can provide an accurate prediction of the AP radiographic NSA, closely approximating the ground truth.…”
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Genome-wide profiling of circulating microRNAs in adolescent idiopathic scoliosis and their relation to spinal deformity severity, and disease pathophysiology by Nasrin Khatami, Iurie Caraus, Mahamuda Rahaman, Evguenia Nepotchatykh, Mohamed Elbakry, Wesam Elremaly, Anita Franco, Marie Beauséjour, Anne-Marie Laberge, Stefan Parent, Hubert Labelle, Carl-Éric Aubin, Jean Lachaine, Alain Moreau

“…After optimization and the application of Random Forest Models a panel of six miRNAs (miR-1-3p, miR-19a-3p, miR-19b-3p, miR-133b, miR-143-3p, and miR-148b-3p) out of 15 led us to develop an algorithm predicting the risk of developing a severe scoliosis with great accuracy (100%), sensitivity (100%) and specificity (100%). …”
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A Tile with Nested Chain Abacus by Eman F. Mohammed

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The Spatiotemporal Evolution and Driving Forces of the Urban Heat Island in Shijiazhuang by Xia Zhang, Yue Liu, Ruohan Chen, Menglin Si, Ce Zhang, Yiran Tian, Guofei Shang

“…This study focuses on Shijiazhuang city, using Landsat ETM+/OLI data from 2000 to 2020 to analyze the spatiotemporal traits of the UHI effect. The mono-window algorithm (MW) was used to retrieve land surface temperatures (LSTs), and the seasonal autoregressive integrated moving average (SARIMA) model was used to predict LST trends. …”
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INFLAMMATION's cognitive impact revealed by a novel "Line of Identity" approach. by Donald R Royall, Raymond F Palmer

“…The Afflicted groups were predicted to have adverse levels of inflammation-related blood-based biomarkers, greater dementia severity and greater risk of prospective conversion.…”
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Clinical features and serum biomarkers in HIV immune reconstitution inflammatory syndrome after cryptococcal meningitis: a prospective cohort study. by David R Boulware, David B Meya, Tracy L Bergemann, Darin L Wiesner, Joshua Rhein, Abdu Musubire, Sarah J Lee, Andrew Kambugu, Edward N Janoff, Paul R Bohjanen

“…At the time of IRIS onset, multiple proinflammatory cytokine responses were present, including CRP and IL-6. Mortality was predicted by pre-ART increasing IL-17, decreasing GM-CSF, and CRP level >32 mg/l (highest quartile). …”
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