Whole-body tumor segmentation from FDG-PET/CT: Leveraging a segmentation prior from tissue-wise projections by Sambit Tarai, Elin Lundström, Nouman Ahmad, Robin Strand, Håkan Ahlström, Joel Kullberg

“…One significant challenge is the risk for human error, leading to potential omission of especially small tumors and tumors with low FDG uptake.Purpose: In this study, we introduced an automated framework with segmentation prior, from a tissue-wise multi-channel multi-angled based approach, to enhance tumor segmentation in whole-body FDG-PET/CT.Method: The proposed framework utilized a segmentation prior generated from tumor segmentations in tissue-wise multi-channel projections of the standardized uptake value (SUV) from PET. …”
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Predicting the potential distribution areas of Leptotrombidium rubellum under current and future climate change by Qunzheng Mu, Qunzheng Mu, Fengfeng Li, Wenyu Li, Wenyu Li, Xiaoxia Wang, Mingyuan Tang, Kehan Chen, Yihao Jiang, Jingqi Liu, Shirong Zhang, Qiyong Liu, Chuan Wang

“…Projections indicate near current suitable areas are concentrated in southern China, with potential northward expansion under future climate scenarios.ConclusionL. rubellum exhibits broad distribution areas across China, with climate change likely driving suitable areas expansion. …”
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Wireless skin sensors for electrocardiogram and heart rate monitoring in the neonatal intensive care unit: a prospective feasibility, safety, and accuracy study by Eva Senechal, Daniel Radeschi, Shasha Lv, Emilie Jeanne, Ana Saveedra Ruiz, Lydia Tao, Brittany Dulmage, Wissam Shalish, Robert Edward Kearney, Guilherme Sant’Anna

“…Accuracy metrics showed strong agreement, with the Clark Error Grid indicating 97% of paired signals led to the same clinical outcome. …”
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Analysis of the health status of patients with type 1 and type 2 diabetes mellitus living in urban and rural areas of the Saratov region (according to the data of the federal regis... by A. D. Ponomarev, G. Yu. Sazanova, M. A. Kunitsyna, L. M. Terina, A. A. Vojteshak

“…Data were presented as P ± m, where P is the relative value and m is its standard error, and M ± m, where M is the mean value and m is its standard error.RESULTS: A higher average life expectancy was noted for people with type 2 diabetes, regardless of place of residence, in comparison with the same indicator in the Saratov region. …”
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Prediction of drug-drug interactions in clozapine combination therapy based on physiologically based pharmacokinetic model by MOU Fan, HUANG Zhiwei, CHENG Yu, ZHAO Xue, LI Huafang, YU Shunying

“…The models′ accuracy was evaluated by comparing predicted values of the area under the curve (AUC) and peak concentration (Cmax) to observed data, using the mean percentage error (MPE) and mean absolute percentage error (MAPE) as evaluation indicators. …”
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Machine learning using random forest to model heavy metals removal efficiency using a zeolite-embedded sheet in water by N.D. Takarina, N. Matsue, E. Johan, A. Adiwibowo, M.F.N.K. Rahmawati, S.A. Pramudyawardhani, T. Wukirsari

“…The random forest models were then validated using the root mean square error, mean square of residuals, percentage variable explained and graphs depicting out-of-bag error of a random forest.FINDINGS: The results show the heavy metal removal efficiency was 5.51-95.6 percent, 42.71-98.92 percent and 13.39-95.97 percent for copper, lead and zinc, respectively. …”
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Measurement of the interface of external connection implants with metal or zirconia abutments under scanning electron microscopy by Blanca Nieves-Rodriguez, Jair Rodríguez-Ivich, Nana Kwame Ampomah, Fabio Luiz Andretti

“…Error = 8.36, 95% CI 43.48–83.53, and p-value <0.01). …”
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Automatic characterization of stride parameters in canines with a single wearable inertial sensor. by Gregory J Jenkins, Chady H Hakim, N Nora Yang, Gang Yao, Dongsheng Duan

“…A mean error ± standard deviation of 0.000 ± 0.020 and -0.008 ± 0.027 s was obtained for determining toe-off and toe-touch events respectively. …”
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Development of automated phenotyping system for growth traits in bivalves by Xiangfu Kong, Shanhuan Huang, Xuangang Wang, Haoying Liang, Chen Hu, Yujue Wang, Zhenmin Bao, Xiaoli Hu

“…The 3D laser imaging platform enables accurate measurement of body size parameters (shell height, length, width, area and circumference) within 1.2 s per individual, with measurement error ranging from 0.01 to 0.30 mm. The weighing platform demonstrates an accuracy exceeding 99 % for bivalves moving at 1.0 m/s. …”
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Robotic urologic applications of the hinotori™ Surgical Robot System by Shunsuke Miyamoto, Tomoya Hatayama, Hiroyuki Shikuma, Kazuma Yukihiro, Kyohsuke Iwane, Ryo Tasaka, Yuki Kohada, Takafumi Fukushima, Kenshiro Takemoto, Miki Naito, Kohei Kobatake, Yohei Sekino, Hiroyuki Kitano, Kenichiro Ikeda, Keisuke Goto, Akihiro Goriki, Keisuke Hieda, Nobuyuki Hinata

“…Surgical outcomes for cases with the hinotori were comparable to those with the da Vinci. Conclusion: This study demonstrated that the hinotori is a safe and feasible tool for robotic surgeries in the field of urology.…”
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Psychological Barriers to the Use of Opioid Analgesics for Treating Pain in Patients With Advanced Recurrent Cancer: A Multicenter Cohort Study by Takehiko Tsuno, Takashi Kawaguchi, Ryota Yanaizumi, Junichi Kondo, Keiko Kojima, Takashi Igarashi, Masaki Inoue, Tomofumi Miura, Akime Miyasato, Kanako Azuma, Hiroshi Hamada, Tomoya Saeki, Hironori Mawatari, Hiroyuki Ogura, Akira Kotani, Takuhiro Yamaguchi, Hideki Hakamata

“…Conclusion: This study showed that patients with advanced recurrent cancer have psychological barriers to opioid induction. …”
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The association of Trisomy 13 and 18 and hospital discharge outcomes among neonates in California: A retrospective cohort study by Edith Haghnazarian, Jiaqi Hu, Ashley Y. Song, Philippe S. Friedlich, Ashwini Lakshmanan

“…In patients with Trisomy 13, after adjusting for gender, ethnicity, advanced directive (DNR), insurance and co-morbidities on multivariate analysis, the provision of more than 96 h of mechanical ventilation was associated with significantly increased LOS (standard error, SE) by 18.0 ± 5.3 days and THC (SE) by $399,000 ± $85,000. …”
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CRISPR.BOT an autonomous platform for streamlined genetic engineering and molecular biology applications by Fatmanur Erkek, Rana Kizilkaya, Seyma Baybara, Gamze Gulden, Berranur Sert, Muzeyyen Sena Yazicilar, Menekse Cagla Yilmaz, Sibel Pinar Odabas, Enes Bal, Gamze Yelgen, Tarik Teymur, Yasin Ay, Solen Dogdu, Nilufer Neslihan Tiryaki, Buse Baran, Beste Gelsin, Hasret Araz, Ilayda Cavrar, Cihan Tastan

“…Traditional workflows, laden with laborious and error-prone stages, incur prolonged timelines and escalating costs. …”
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Pneumorrhachis with spontaneous pneumomediastinum in pediatric patients: An 11-year retrospective study in Southern Taiwan by Yu-Tang Chang, Chieh-Ni Kao, Yu-Ling Huang, Hung-Hsing Chiang, Jui-Ying Lee, Hsien-Pin Li, Po-Chih Chang, Shah-Hwa Chou, Yu-Wei Liu

“…On multivariable regression analysis, the SPM plus PR group exhibited more predisposing factors than did the SPM group (coefficient: 0.514, standard error: 0.136, p < 0.001). All patients were successfully treated without morbidity and mortality. …”
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Community participation in community-based surveillance of infectious diseases: A structural equation modeling approach based on the theory of reasoned action by Ahmed Azeez Hasan, Anis Kausar Ghazali, Norsa’adah Bachok, Najib Majdi Yacoob, Suhaily Mohd Hairon, Nur Amira M. Nadir, Fatimah Muhd Shukri

“…Model fit was assessed using comparative fit index (CFI), Tucker-Lewis index (TLI), root mean square error of approximation (RMSEA), and standardized root mean square residual (SRMR) indices. …”
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An automation framework for clinical codelist development validated with UK data from patients with multiple long-term conditions by A. Aslam, L. Walker, M. Abaho, H. Cant, M. O’Connell, A. S. Abuzour, L. Hama, P. Schofield, F. S. Mair, R. A. Ruddle, O. Popoola, M. Sperrin, J. Y. Tsang, E. Shantsila, M. Gabbay, A. Clegg, A. A. Woodall, I. Buchan, S. D. Relton

“…A key benefit of this approach is its emphasis on automation and reliance on trusted sources, which significantly lowers the workload, minimizes human error, and saves substantial time, particularly the time needed from clinical experts.…”
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Rapid estimation of DON content in wheat flour using close‐range hyperspectral imaging and machine learning by Dinesh Kumar Saini, Anshul Rana, Jyotirmoy Halder, Mohammad Maruf Billah, Harsimardeep S. Gill, Jinfeng Zhang, Subash Thapa, Shaukat Ali, Brent Turnipseed, Karl Glover, Maitiniyazi Maimaitijiang, Sunish K. Sehgal

“…However, the one‐dimensional convolutional neural network (1D‐CNN) achieved the highest prediction accuracies (R2P = 0.90 and = 0.96 for original and augmented datasets, respectively) compared to all tested models and demonstrated the lowest error. In conclusion, integration of advanced hyperspectral imaging with ML approaches exhibits significant potential for high‐throughput and cost‐effective estimation of DON content in wheat, thereby accelerating wheat breeding efforts for reduced DON levels.…”
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Leveraging Comprehensive Echo Data to Power Artificial Intelligence Models for Handheld Cardiac Ultrasound by D.M. Anisuzzaman, PhD, Jeffrey G. Malins, PhD, John I. Jackson, PhD, Eunjung Lee, PhD, Jwan A. Naser, MBBS, Behrouz Rostami, PhD, Grace Greason, BA, Jared G. Bird, MD, Paul A. Friedman, MD, Jae K. Oh, MD, Patricia A. Pellikka, MD, Jeremy J. Thaden, MD, Francisco Lopez-Jimenez, MD, MSc, MBA, Zachi I. Attia, PhD, Sorin V. Pislaru, MD, PhD, Garvan C. Kane, MD, PhD

“…Conclusion: Robust TTE datasets can be used to effectively power HCU deep learning models, which in turn demonstrates focused diagnostic images can be obtained with handheld devices.…”
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Prediction of PD-L1 tumor positive score in lung squamous cell carcinoma with H&E staining images and deep learning by Qiushi Wang, Xixiang Deng, Pan Huang, Qiang Ma, Lianhua Zhao, Yangyang Feng, Yiying Wang, Yuan Zhao, Yan Chen, Peng Zhong, Peng He, Mingrui Ma, Peng Feng, Hualiang Xiao

“…Therefore, the application of deep learning models to segment and quantitatively predict PD-L1 expression in digital sections of Hematoxylin and eosin (H&amp;E) stained lung squamous cell carcinoma is of great significance.MethodsWe constructed a dataset comprising H&amp;E-stained digital sections of lung squamous cell carcinoma and used a Transformer Unet (TransUnet) deep learning network with an encoder-decoder design to segment PD-L1 negative and positive regions and quantitatively predict the tumor cell positive score (TPS).ResultsThe results showed that the dice similarity coefficient (DSC) and intersection overunion (IoU) of deep learning for PD-L1 expression segmentation of H&amp;E-stained digital slides of lung squamous cell carcinoma were 80 and 72%, respectively, which were better than the other seven cutting-edge segmentation models. The root mean square error (RMSE) of quantitative prediction TPS was 26.8, and the intra-group correlation coefficients with the gold standard was 0.92 (95% CI: 0.90–0.93), which was better than the consistency between the results of five pathologists and the gold standard.ConclusionThe deep learning model is capable of segmenting and quantitatively predicting PD-L1 expression in H&amp;E-stained digital sections of lung squamous cell carcinoma, which has significant implications for the application and guidance of immune checkpoint inhibitor treatments. …”
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Neutrophil-lymphocyte ratio: A novel outcome prognosticator following traumatic brain injury: A retrospective analysis by Siddharth Srinivasan, Ajay Hegde, Veeradithya Ballal, Sarah Johnson, Rajesh Nair, Bharat Raju, Yasaswi Kanneganti, Udgam Baxi, Susanth Subramanian, Raghavendra Nayak, Ashwin Pai, Girish Menon

“…Receiver operating curve (ROC) analysis of NLR for prediction of outcome after 6 months in patients without polytrauma, revealed an NLR cut-off value of >6.24 with an area under the curve (AUC) ± standard error (SE) of 0.717 ± 0.0340 with a corresponding p-value of <0.0001, which correlated with poor outcome. …”
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