Identification of Staphylococcus aureus, Enterococcus faecium, Klebsiella pneumoniae, Pseudomonas aeruginosa and Acinetobacter baumannii from Raman spectra by Artificial Intelligen... by Yu-Tzu Lin, Hsiu-Hsien Lin, Chih-Hao Chen, Kun-Hao Tseng, Pang-Chien Hsu, Ya-Lun Wu, Wei-Cheng Chang, Nai-Shun Liao, Yi-Fan Chou, Chun-Yi Hsu, Yu-Hui Liao, Mao-Wang Ho, Shih-Sheng Chang, Po-Ren Hsueh, Der-Yang Cho

“…This finding confirms the clinical utility of the system while laying a solid foundation for the future development of antimicrobial resistance prediction models.…”
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Serum advanced glycation end products as a putative biomarker in Type2 DKD patients’ prognosis by Ze-Hou Wang, Ze-Hou Wang, Zong-Jin Zhang, Yue-Fen Wang, Jin Xie, Jin Xie, Yi-Min Li, Cun Shen, Yuan Meng, Wen-Jing Zhao, Lu-Ying Sun, Wei Jing Liu

“…This study introduced corrected lgAGEs [novel biomarker derived by adjusting logarithmically transformed AGEs (lgAGEs) levels based on serum albumin (ALB) levels] to enhance the prediction of adverse renal outcomes in patients with type 2 DKD (T2DKD).MethodsIn this prospective cohort study, 196 T2DKD patients were followed up longitudinally. …”
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Can a mechanical circulatory support comprehensive approach to cardiogenic shock at referral centers reduce 30-day mortality? by Marina Pieri, Marina Pieri, Mario Iannaccone, Francesco Burzotta, Francesco Burzotta, Giulia Botti, Giulia Botti, Cristina Aurigemma, Carlo Trani, Carlo Trani, Silvia Ajello, Savino Altizio, Tommaso Sanna, Tommaso Sanna, Enrico Romagnoli, Lazzaro Paraggio, Luigi Cappannoli, Anna Mara Scandroglio, Alaide Chieffo, Alaide Chieffo

“…Although mortality risk prediction in cardiogenic shock (CS) is possible, assessing the impact of the multitude of therapeutic efforts on outcomes is not straightforward. …”
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Introducing the SMAP L4 Products and Investigating the Spatio-Temporal Variability of Soil Moisture in Iran by Ebrahim Asadi Oskouei, Leila Godarzy, Jalil Helali

“…Conclusion: Knowledge of the amount of soil moisture is important in hydrological modeling studies, improving numerical weather prediction (NWP) models, intelligent irrigation in agriculture and climate change. …”
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Application of copy number variation sequencing combined with whole exome sequencing in prenatal left–right asymmetry disorders by Yu Qin, Muon Senglong, Koksear Touch, Juan Xiao, Ruijie Fang, Qingling kang, Lei Fan, Shufang Li, Jing Liu, Jianli Wu, Yuanyuan Wu, Xinwei Shi, Haiyi Liu, Xun Gong, Xingguang Lin, Ling Feng, Suhua Chen, Wei Li

“…Further bioinformatics scrutiny was conducted to assess the protein structure and function prediction of these variants, ultimately indicating their potential pathogenicity. …”
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Efficacy and safety of PD-1/PD-L1 inhibitors in advanced or recurrent endometrial cancer: a meta-analysis with trial sequential analysis of randomized controlled trials by Ji Ren, Jinghe Wang, Yanan Wang, Dongying Yang, Jianming Sheng, Shili Zhu, Yunli Liu, Xiaoqi Li, Wei Liu, Binbin Zhang

“…To evaluate heterogeneity, we employed Cochran’s Q test, I2 statistics, and 95% prediction intervals (PIs). Trial sequential analysis (TSA) was conducted using R Version 4.3.1, STATA Version 12.0, and TSA Version 0.9.5.10 Beta software.ResultsOur analysis incorporated 6 studies, encompassing a total of 2,954 patients. …”
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Machine learning helps reveal key factors affecting tire wear particulate matter emissions by Zhenyu Jia, Jiawei Yin, Tiange Fang, Zhiwen Jiang, Chongzhi Zhong, Zeping Cao, Lin Wu, Ning Wei, Zhengyu Men, Lei Yang, Qijun Zhang, Hongjun Mao

“…The random forest (RF) model was developed and displayed good prediction performance with an R2 of 0.84 and 0.78 for PM2.5 and PM2.5-10 on the test set, respectively. …”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…Ultimately, through a sequence of convolutional operations and upsampling procedures, a prediction map congruent in resolution with the original image was generated, enabling the precise demarcation of tea shoot harvesting points.…”
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Immunological composition of human milk before and during subclinical and clinical mastitis by Irma Castro-Navarro, Ryan M. Pace, Ryan M. Pace, Ryan M. Pace, Janet E. Williams, Christina D. W. Pace, Harpreet Kaur, Julia Piaskowski, Alberto Aragón, Alberto Aragón, Juan M. Rodríguez, Juan M. Rodríguez, Mark A. McGuire, Leonides Fernandez, Leonides Fernandez, Michelle K. McGuire

“…Further research is needed to determine if these differences in milk’s immune profiles can be used to improve mastitis risk prediction prior to onset of symptoms.…”
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BIJAKAWEB: Platform Berbasis Web Untuk Deteksi Hate Speech Pada Komentar Berita Bahasa Indonesia by Moh. Firdaus, Permata Nur Miftahur Rizki

“…This research has produced several significant contributions, including the availability of a new dataset for relevant research, a fine-tuned IndoBERT model accessible to the public on HuggingFace, and the development of the BijakaWeb platform using the full-stack Django framework, capable of real-time scraping and hate speech prediction. It is hoped that this research can assist news portals in moderating online news comments to combat hate speech and provide a model that can be used and adapted by other online news platforms to prevent the spread of hate speech on the internet.   …”
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Influencing factors of occupational stress and health effect among grassroots medical and health personnel in Xiong’an New Area, Hebei Province based on Bayesian network by Huixia LI, Junqin ZHAO, Lixin YANG, Qiuying DONG, Jinmei SHI, Jianguo LI, Chunxiang ZHAO, Yan GAO

“…The final Bayesian network contained 14 nodes and 18 directed edges, and its prediction accuracy was 85.4%. .Job title, daily working hours, overcommitment and exercise directly associated with occupational stress, and other work and personal factors associated with occupational stress indirectly. …”
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Predictors of Noncompliance to Antihypertensive Therapy among Hypertensive Patients Ghana: Application of Health Belief Model by Yaa Obirikorang, Christian Obirikorang, Emmanuel Acheampong, Enoch Odame Anto, Daniel Gyamfi, Selorm Philip Segbefia, Michael Opoku Boateng, Dari Pascal Dapilla, Peter Kojo Brenya, Bright Amankwaa, Evans Asamoah Adu, Emmanuel Nsenbah Batu, Adjei Gyimah Akwasi, Beatrice Amoah

“…Overall, the five HBM constructs explained 31.7% of the variance in noncompliance to AHT with a prediction accuracy of 77.5%, after adjusting for age, gender, and duration of condition. …”
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Evaluation of the Prognostic Value of Red Cell Distribution Width to Total Serum Calcium Ratio in Patients with Acute Pancreatitis by Tian-Yong Han, Tao Cheng, Bo-Fu Liu, Ya-Rong He, Pan Pan, Wen-Tao Yang, Qiang-Lai, Yu Cao

“…The objective of the study was to evaluate whether RDW/TSC can be used to predict the prognosis of patients with AP at an early stage. …”
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Differentiation between glioblastoma and solitary brain metastases using perfusion and amide proton transfer weighted MRI by Malte Knutsson, Tim Salomonsson, Faris Durmo, Emelie Ryd Johansson, Anina Seidemo, Jimmy Lätt, Anna Rydelius, Sara Kinhult, Elisabet Englund, Johan Bengzon, Peter C. M. van Zijl, Peter C. M. van Zijl, Linda Knutsson, Linda Knutsson, Linda Knutsson, Pia C. Sundgren, Pia C. Sundgren, Pia C. Sundgren

“…Combinations of nAPTwmax + ncCBVmax, nAPTwmean + ncCBVmean, nAPTwmax + nCBFmax, nAPTwmax + K2max and nAPTwmax + ncCBVmax + K2max in ET showed significant prediction in differentiating GBM and MET (AUC = 0.92, 0.82, 0.92, 0.85, and 0.92 respectively).ConclusionWhen assessed in Gd-enhanced tumor areas, nAPTw MRI signal intensity alone or combined with DSC-MRI parameters, was an excellent predictor for differentiating GBM and MET. …”
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Integrating pharmacogenomics and cheminformatics with diverse disease phenotypes for cell type-guided drug discovery by Arda Halu, Sarvesh Chelvanambi, Julius L. Decano, Joan T. Matamalas, Mary Whelan, Takaharu Asano, Namitra Kalicharran, Sasha A. Singh, Joseph Loscalzo, Masanori Aikawa

“…We validate the drugs predicted in our proofs-of-concept using real-time polymerase chain reaction (qPCR) experiments. …”
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Knowledge Distillation in Object Detection for Resource-Constrained Edge Computing by Arief Setyanto, Theopilus Bayu Sasongko, Muhammad Ainul Fikri, Dhani Ariatmanto, I. Made Artha Agastya, Rakandhiya Daanii Rachmanto, Affan Ardana, In Kee Kim

“…YOLOv4 is a widely used OD model with a backbone for image feature extraction and a prediction layer. Originally, YOLOv4 was designed to use CSPDarkNet53 as its backbone, which requires significant computational power. …”
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Technical note: Towards atmospheric compound identification in chemical ionization mass spectrometry with pesticide standards and machine learning by F. Bortolussi, H. Sandström, F. Partovi, F. Partovi, J. Mikkilä, P. Rinke, P. Rinke, P. Rinke, P. Rinke, M. Rissanen, M. Rissanen

“…Our best classification model reaches a prediction accuracy of 0.85 <span class="inline-formula">±</span> 0.02 and a receiver operating characteristic curve area of 0.91 <span class="inline-formula">±</span> 0.01. …”
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Low-grade systemic inflammation, but not neuroinflammation, is associated with 12-month postoperative outcome after total hip arthroplasty in patients with painful osteoarthritis:... by Morten R. Blichfeldt-Eckhardt, Claus Varnum, Jørgen T. Lauridsen, Lasse E. Rasmussen, Winnie C. P. Mortensen, Hanne I. Jensen, Henrik B. Vaegter, Kate L. Lambertsen

“…Aims: Better prediction of outcome after total hip arthroplasty (THA) is warranted. …”
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Modelling the seasonal dynamics of Aedes albopictus populations using a spatio-temporal stacked machine learning model by Daniele Da Re, Giovanni Marini, Carmelo Bonannella, Fabrizio Laurini, Mattia Manica, Nikoleta Anicic, Alessandro Albieri, Paola Angelini, Daniele Arnoldi, Federica Bertola, Beniamino Caputo, Claudio De Liberato, Alessandra della Torre, Eleonora Flacio, Alessandra Franceschini, Francesco Gradoni, Përparim Kadriaj, Valeria Lencioni, Irene Del Lesto, Francesco La Russa, Riccardo Paolo Lia, Fabrizio Montarsi, Domenico Otranto, Gregory L’Ambert, Annapaola Rizzoli, Pasquale Rombolà, Federico Romiti, Gionata Stancher, Alessandra Torina, Enkelejda Velo, Chiara Virgillito, Fabiana Zandonai, Roberto Rosà

“…The meta-learner, in turn, assimilates these predictions and generates a final prediction by combining the information from all the base learners. …”
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Phenotypic and genotypic characterization of Mycobacterium tuberculosis pyrazinamide resistance—India, 2018–2020 by Sembulingam Tamilzhalagan, Evanslin Santus Justin, Ashok Selvaraj, Karthick Venkateswaran, Arun Kumar Sivakumar, Suganthi Chittibabu, Heather P. McLaughlin, Patrick K. Moonan, Jonathan P. Smith, Sakthi Suba, Mukesh Kumar Sathya Narayanan, Christine S. Ho, Nishant Kumar, Srikanth P. Tripathy, Srikanth P. Tripathy, Siva K. Shanmugam, Patricia J. Hall-Eidson, Uma Devi Ranganathan

“…This nationwide study was conducted to explore the prevalence of mutations conferring PZA resistance, catalogue mutation diversity, investigate the associations of PZA resistance with specific lineages, examine co-resistance to 13 first- and second-line drugs, and evaluate the diagnostic accuracy of sequencing pncA and panD genes for predicting PZA resistance. Whole genome sequencing was performed on 2,207 M. tuberculosis isolates from 25 States and 4 Union Territories of India. …”
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