Effects of Teenage Pregnancy on Girl Child Education in Buhunga Sub-County Uganda. by Twikirize, Edwig

“…School leaders facilitate the teachers, the students, and the community to embrace a hospitable, empathetic, and collaborative learning environment for the different students irrespective of their diversity. …”
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Att lära om naturen från naturen och i naturen by Sara Planting-Bergloo, Emma Rova, Charlotte Lennartsdotter, Per Rohlin, Maria Andrée

“…Studien kan vidare fungera som ett stöd för lärare hur man explicit kan adressera dessa fältpraktiker i undervisningen – inför, under och efter genomförande av fältstudier. Learning about nature from nature in nature: Knowledge interests in lower secondary school Biology field work This study focuses on the role of scientific inquiry in outdoor lower secondary Biology education. …”
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Impaired cognitive function and decreased monoamine neurotransmitters in the DNAJC12 gene knockout mouse model by Shunan Wang, Ming Shen, Bo Pang, Bo Zhou, Yuan Yuan, Mei Lu, Xiangling Deng, Min Yang, Shufang Liu, Qiong Wang, Mei Xue, Qisheng Xia, Zhixin Zhang

“…These knockout mice showed significant deficits in learning and memory performance as assessed by the Morris water maze test. …”
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Comprehensive analysis of scRNA-seq and bulk RNA-seq reveals the non-cardiomyocytes heterogeneity and novel cell populations in dilated cardiomyopathy by Siyu He, Chunyu Li, Mingxin Lu, Fang Lin, Sangyu Hu, Junfang Zhang, Luying Peng, Li Li

“…Based on gene-specific expression and prior marker genes, we identified 9 distinct subtypes, including fibroblasts, endothelial cells, myeloid cells, pericytes, T/NK cells, smooth muscle cells, neuronal cells, B cells, and cardiomyocytes. Using machine learning methods to quantify bulk RNA-seq data, we found significant differences in fibroblasts, T cells, and macrophages between DCM and normal samples. …”
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Accessible model predicts response in hormone receptor positive HER2 negative breast cancer receiving neoadjuvant chemotherapy by Luca Mastrantoni, Giovanna Garufi, Giulia Giordano, Noemi Maliziola, Elena Di Monte, Giorgia Arcuri, Valentina Frescura, Angelachiara Rotondi, Armando Orlandi, Luisa Carbognin, Antonella Palazzo, Federica Miglietta, Letizia Pontolillo, Alessandra Fabi, Lorenzo Gerratana, Sergio Pannunzio, Ida Paris, Sara Pilotto, Fabio Marazzi, Antonio Franco, Gianluca Franceschini, Maria Vittoria Dieci, Roberta Mazzeo, Fabio Puglisi, Valentina Guarneri, Michele Milella, Giovanni Scambia, Diana Giannarelli, Giampaolo Tortora, Emilio Bria

“…We developed a framework to predict pCR using clinicopathological characteristics widely available at diagnosis. The machine learning (ML) models were trained to predict pCR (n = 463), evaluated in an internal validation cohort (n = 109) and validated in an external validation cohort (n = 151). …”
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Lightweight Tea Shoot Picking Point Recognition Model Based on Improved DeepLabV3+ by HU Chengxi, TAN Lixin, WANG Wenyin, SONG Min

“…In this study, based on the actual scenario of the Xiqing Tea Garden in Hunan Province, proposes a novel deep learning algorithm was proposed to solve the precise segmentation challenge of famous and high-quality tea picking points.…”
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An interactive course program on nutrition for medical students: interdisciplinary development and mixed-methods evaluation by Gonza B. Ngoumou, Daniela A. Koppold, Laetitia Wenzel, Anne Schirmaier, Carolin Breinlinger, Lisa M. Pörtner, Stefan Jordan, Julia K. Schiele, Etienne Hanslian, Annika Koppold, Beate Stock-Schröer, Dimitra M. Varvarezou, Michael Jeitler, Miriam Ortiz, Andreas Michalsen, Wiebke Stritter, Georg Seifert, Christian S. Kessler

“…The conceptualisation resulted in a highly participative two-week course of 37.5 h. The main learning objectives aimed at the acquisition of technical competences and personal competencies in four areas: (1) the role of nutrition in health and disease; (2) the exploration of global food systems; (3) communication on subjects of nutrition; (4) culinary medicine. …”
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Exploring sex differences in Alzheimer’s disease: a comprehensive analysis of a large patient cohort from a memory unit by Maitee Rosende-Roca, Fernando García-Gutiérrez, Yahveth Cantero-Fortiz, Montserrat Alegret, Vanesa Pytel, Pilar Cañabate, Antonio González-Pérez, Itziar de Rojas, Liliana Vargas, Juan Pablo Tartari, Ana Espinosa, Gemma Ortega, Alba Pérez-Cordón, Mariola Moreno, Sílvia Preckler, Susanna Seguer, Miren Jone Gurruchaga, Lluís Tárraga, Agustín Ruiz, Sergi Valero, Mercè Boada, Marta Marquié

“…We employed various statistical techniques to assess the impact of sex on cognitive evolution in these dementia patients, accounting for other sex-related risk factors identified through Machine Learning methods. Results The study cohort comprised a total of 6108 individuals diagnosed with AD dementia during the study period (28.4% males and 71.6% females). …”
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Risk Factors for Gastrointestinal Bleeding in Patients With Acute Myocardial Infarction: Multicenter Retrospective Cohort Study by Yanqi Kou, Shicai Ye, Yuan Tian, Ke Yang, Ling Qin, Zhe Huang, Botao Luo, Yanping Ha, Liping Zhan, Ruyin Ye, Yujie Huang, Qing Zhang, Kun He, Mouji Liang, Jieming Zheng, Haoyuan Huang, Chunyi Wu, Lei Ge, Yuping Yang

“…ObjectiveThis study aimed to develop and validate a machine learning (ML)–based model for predicting in-hospital GIB in patients with AMI, identify key risk factors, and evaluate the clinical applicability of the model for risk stratification and decision support. …”
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Fatty Acids of Erythrocyte Membranes and Blood Serum in Differential Diagnosis of Inflammatory Bowel Diseases by M. V. Kruchinina, I. O. Svetlova, M. F. Osipenko, N. V. Abaltusova, A. A. Gromov, M. V. Shashkov, A. S. Sokolova, I. N. Yakovina, A. V. Borisova

“…The study of FA levels in groups with different nosological forms of IBDs using complex statistical analysis, including machine learning methods, made it possible to create diagnostic models that differentiate CD, UC and UCC in the acute stage with high accuracy. …”
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Meibomian gland alterations in allergic conjunctivitis: insights from a novel quantitative analysis algorithm by Jingting Wei, Jingting Wei, Kunhong Xiao, Qingyuan Cai, Qingyuan Cai, Shenghua Lin, Xiangjie Lin, Yujie Wang, Yujie Wang, Jiawen Lin, Kunfeng Lai, Yunxi Ye, Yuhan Liu, Li Li

“…MG images were analyzed using a deep learning-based a quantitative analysis algorithm to evaluate gland length, area, dropout ratio, and deformation. …”
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Therapeutic effect of dihydroartemisinin on Alzheimer’s disease model mice with senile macular degeneration by Gao Han, Gong Xuewu, Zhang Meng, Wang Yuejing, Wang Yuchun, Zhang Keshuang, Yao Hongbo

“…Morris water maze was used to detect the spatial learning and memory ability of AD mice. Hematoxylin eosin (he) staining was used to observe the structural changes of cerebral cortical neurons and retina, and transmission electron microscope was used to observe the structural changes of mitochondria and synapses. …”
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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

“…Pathopticon demonstrates a better prediction performance than solely cheminformatic measures as well as state-of-the-art network and deep learning-based methods. Top predictions made by Pathopticon have high chemical structural diversity, suggesting their potential for building compound libraries. …”
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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

“…Although state-of-the-art deep learning-based OD methods achieve high detection rates, their large model size and high computational demands often hinder deployment on resource-constrained edge devices. …”
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Dealing with Complexity – Knowledge, design, and management of the built environment by Cesare Sposito, Francesca Scalisi

“…How do we place knowledge and learning into a system to better understand the current era’s multidimensional, fundamental, and global issues in their irreducible complexity? …”
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A consensus statement on child and family health during the COVID-19 pandemic and recommendations for post-pandemic recovery and re-build by Caroline A. B. Redhead, Sergio A. Silverio, Sergio A. Silverio, Elana Payne, Elana Payne, Mari Greenfield, Mari Greenfield, Sara M. Barnett, Anna Chiumento, Beth Holder, Helen Skirrow, Ofelia Torres, Carmen Power, Staci M. Weiss, Laura A. Magee, Soo Downe, Lucy Frith, Claire Cameron

“…We have identified and agreed priorities areas for both action and learning, which we present as recommendations for research, healthcare practice, and policy.ResultsThe synthesis led to immediate recommendations grouped around what to retain and what to remove from “pandemic” provision and what to reinstate from pre-pandemic, healthcare provision in these services. …”
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“¿Sabés qué es…?” Explicaciones de niños tutores en torno a vocabulario no familiar. Un estudio con niños de poblaciones urbano marginadas de Argentina/ “Do you know what it is…?”... by Florencia Alam, Maia Julieta Migdalek, María Laura Ramirez, Alejandra Stein, Celia Renata Rosemberg

“…The goal of this program is to promote reading and writing learning among children living in urban-marginalized populations in Argentina. …”
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Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers‐Based Model by Maria Colomba Comes, Annarita Fanizzi, Samantha Bove, Luca Boldrini, Agnese Latorre, Deniz Can Guven, Serena Iacovelli, Tiziana Talienti, Alessandro Rizzo, Francesco Alfredo Zito, Raffaella Massafra

“…Aims This study aimed to develop an ensemble deep learning‐based model, exploiting a Vision Transformer (ViT) architecture, which merges features automatically extracted from five segmented slices of both pre‐ and mid‐treatment exams containing the maximum tumor area, to predict and monitor pCR to NAC. …”
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A novel α-conotoxin [D1G, ΔQ14] LvIC decreased mouse locomotor activity by Wen Wang, Wen Wang, Meiting Wang, Huanbai Wang, Weifeng Xu, Conggang Wang, Jie Pei, Xiaodan Li, Dongting Zhangsun, Dongting Zhangsun

“…Despite these changes, results from the Morris water maze (MWM) indicated that [D1G, ΔQ14] LvIC did not impair the learning and memory abilities of the mice.ConclusionOur findings indicate that α-conotoxin [D1G, ΔQ14] LvIC significantly decreased locomotor activity in mice. …”
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