Immunogenic Evaluation of Ribosomal P-Protein Antigen P0, P1, and P2 and Pentameric Protein Complex P0-(P1-P2)2 of Plasmodium falciparum in a Mouse Model by Agnieszka Szuster-Ciesielska, Leszek Wawiórka, Dawid Krokowski, Nikodem Grankowski, Łukasz Jarosz, Urszula Lisiecka, Marek Tchórzewski

“…P0 appeared as a poor inducer of cellular response. After the third boost with P1, P2, or P0-(P1-P2)2, the initially high cellular response dropped to the control level accompanied by elevation of the number of activated Treg cells and a high level of suppressive TGF-β. …”
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Determination of the Appropriate Blended Inorganic Fertilizer Rate Recommendation for the Optimal Common Bean (Phaseolus vulgaris L.) Grain Yield and Profitability in the Dawuro Zo... by Berhanu Bilate Daemo, Getahun Bore Wolancho, Dawit Dalga Wakalto

“…The data indicate that raising the NPSB fertilizer rate from 0 to 125 kg·ha−1 would greatly boost grain yield in the common bean in both locations in a similar manner. …”
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Microenvironmental G protein‐coupled estrogen receptor‐mediated glutamine metabolic coupling between cancer‐associated fibroblasts and triple‐negative breast cancer cells governs t... by Chongwu He, Meixi Peng, Xiaoqiang Zeng, Hanzhi Dong, Zhengkui Sun, Jiawei Xu, Manran Liu, Liyan Liu, Yanxiao Huang, Zhiqiang Peng, Yu‐An Qiu, Chunling Jiang, Bin Xu, Tenghua Yu

“…Microenvironmental GPER‐induced glutamine serves as a crucial mediator of metabolic coupling between CAFs and TNBC cells, boosting tumour progression by enhancing mitochondrial function. …”
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Ensemble machine learning models for lung cancer incidence risk prediction in the elderly: a retrospective longitudinal study by Songjing Chen, Sizhu Wu

“…For each subgroup, random forest, extreme gradient boosting, deep neural networks, support vector machine, multiple logistic regression and deep Q network (DQN) models were developed and validated. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…A combination of four machine learning algorithms (XGBoost、Logistic Regression、Random Forest、AdaBoost) was employed to predict NPM recurrence, and the model with the highest Area Under the Curve (AUC) in the test set was selected as the best model. …”
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Tomato yellow leaf curl virus detection based on cross-domain shared attention and enhanced BiFPN by Henghui Mo, Linjing Wei

“…The network’s neck incorporates an enhanced Bidirectional Feature Pyramid Network (BiFPN), integrating feature maps of various scales to boost early symptom detection capabilities. Moreover, the introduction of Inner-CIOU with a scale factor-controlled auxiliary bounding box optimizes the precision and regression efficiency in detecting small target features. …”
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Development and validation of a novel risk-predicted model for early sepsis-associated acute kidney injury in critically ill patients: a retrospective cohort study by Bo Li, Kun Zhang, Cong-Cong Zhao, Zi-Han Nan, Yan-Ling Yin, Li-Xia Liu, Zhen-Jie Hu

“…The external validation cohort was generated from a single-centre ICU database from China.Participants A total of 7179 critically ill patients with sepsis were included in the development cohort and 269 patients with sepsis were included in the external validation cohort.Results A total of 12 risk factors (age, weight, atrial fibrillation, chronic coronary syndrome, central venous pressure, urine output, temperature, lactate, pH, difference in alveolar-arterial oxygen pressure, prothrombin time and mechanical ventilation) were included in the final prediction model. The gradient boosting machine model showed the best performance, and the areas under the receiver operating characteristic curve of the model in the development cohort, internal validation cohort and external validation cohort were 0.794, 0.725 and 0.707, respectively. …”
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Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study by Huanhuan Ren, Haojie Song, Shaoguo Cui, Hua Xiong, Bangyuan Long, Yongmei Li

“…A clinical model was developed using eXtreme Gradient Boosting, an NCCT-based imaging model was created using deep learning, and an ensemble model integrated both models. …”
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Knowledge, attitude and practices regarding vitamin D among adults in Ghana: a cross-sectional study by Abraham Ameyaw Kwabena, Benedicta Appiah, Samuel Ankomah Danso, Samuel Kwame Sopuruchi Agomuo, Samuel Kwarteng, Ebenezer Senu, Alfred Effah, Samuel Asamoah Sakyi, Linda Ahenkorah Fondjo

“…There is need for extensive health educational campaigns to the public to boost the knowledge levels on the importance of Vitamin D.…”
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Construction of a prognostic prediction model for colorectal cancer based on 5-year clinical follow-up data by Boao Xiao, Min Yang, Yao Meng, Weimin Wang, Yuan Chen, Chenglong Yu, Longlong Bai, Lishun Xiao, Yansu Chen

“…Decision tree, random forest, support vector machine, and extreme gradient boosting (XGBoost) models were selected for modeling based on the features identified through recursive feature elimination (RFE). …”
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Research on machine vision online monitoring system for egg production and quality in cage environment by Zhenlong Wu, Hengyuan Zhang, Cheng Fang

“…In the domain of egg production, the application of automation technologies is essential for boosting productivity and quality. This study introduces an online monitoring system designed for egg quality assessment within caged environments, incorporating a robotic patrol system for egg localization and a fixed video stream for quality analysis. …”
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Factors associated with the formation of neutralizing antibodies post-vaccination: a lesson from COVID-19 vaccination by Hana Ratnawati, Fen Tih, Grace Puspasari, Allen A. Pelapelapon, Steven Felim

“…Sufficient vitamin D levels in the elderly significantly boost post-vaccination NAb levels. Maintaining a healthy body weight is also vital, as studies have revealed a significant and negative correlation between BMI and the level of NAbs, suggesting a possible need for adjusted vaccine doses in obese individuals.…”
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Unraveling the Beneficial Role of Resveratrol in Fructose-Induced Non-Alcoholic Steatohepatitis with a Focus on the AMPK/Nrf2 Signaling Axis by Soha S. Zakaria, Safaa M. Hanafy

“…Resveratrol reduces NASH by boosting autophagy and activating the AMPK/Nrf2 pathway. …”
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Predicting pregnancy loss and its determinants among reproductive-aged women using supervised machine learning algorithms in Sub-Saharan Africa by Tirualem Zeleke Yehuala, Sara Beyene Mengesha, Nebebe Demis Baykemagn

“…Python software was used to process the data, and machine learning techniques such as Random Forest, Decision Tree, Logistic Regression, Extreme Gradient Boosting, and Gaussian were applied. The performance of the predictive models was evaluated using several standard metrics, including the ROC curve, accuracy score, precision, recall, and F-measure.ResultThe final experimental results indicated that the Random Forest model performed the best in predicting pregnancy loss, achieving an accuracy of 98%, precision of 98%, F-measure of 83%, ROC curve of 94%, and recall of 77%. …”
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Anxiety, Depression, and Their Related Factors Among Parents/Guardians of Students Affected by Nigerian University Staff Strikes by Patrick Ayi Ewah, Idoo Womboh, Peter Agba Awhen, Felicia Agbor-Obun Dan

“…We recommend parents to regularly engage in aerobic exercise, and jobs should be provided to them during strikes to boost their family income.…”
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Risk factors and machine learning prediction models for intrahepatic cholestasis of pregnancy by Yingchun Ren, Xiaoying Shan, Gengchao Ding, Ling Ai, Weiying Zhu, Ying Ding, Fuzhou Yu, Yun Chen, Beijiao Wu

“…The AUCs of the selected top five models ranged from 0.9509 to 0.9614. The CatBoost model achieved the best performance, with an AUC of 0.9614 (95% confidence interval, 0.9377–0.9813), an accuracy of 0.9085, a precision of 0.8930, a recall of 0.9059, and a F1-score of 0.8981. …”
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Machine learning models reveal microbial signatures in healthy human tissues, challenging the sterility of human organs by Anargyros Skoulakis, Anargyros Skoulakis, Giorgos Skoufos, Giorgos Skoufos, Armen Ovsepian, Armen Ovsepian, Artemis G. Hatzigeorgiou, Artemis G. Hatzigeorgiou

“…Unaligned sequencing reads with the human genome were processed using AGAMEMNON, an algorithm for metagenomic microbial quantification, with a reference database comprising bacterial, archaeal, and viral genomes, alongside fungal transcriptomes. Gradient-boosting ML models were trained to classify each tissue against all others based on its microbial profile. …”
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Effectiveness of Non-Governmental Organizations on Social Service Delivery: A Case Study of Uganda Red Cross Society Kabale District. by Aharizira, Zackaria

“…It concludes with recommendations like environmental protection should be ensured in the study area as it has backward and forward linkages with development like boosting agriculture and improving health conditions, ensuring the health of the population is the basement for development. …”
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SACCOs and Agricultural Production in Uganda: A Case Study of Kitenga Village in Kebisoni Sub-County Rukungiri District. by Nitwikiriza, Racheal

“…The study revealed the effects of the SACCOS loans on agricultural development in the Kebisoni sub-county, Rukungiri district and these included helping farmers to send their children to school and empowering the marginalized families, boosting food production, offering advisory services to farmers, SACCOS offering educative programs to farmers about better methods of farming, provision of emergency housing loans, SACCOS helps in Saving and mobilization, poverty reduction and strengthening of economic resilience, promote agribusiness enterprise. …”
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Impact of Ecobiol plus ® feed additive on growth performance, physiological response, oxidative status and immunological status of Nile tilapia (Oreochromis niloticus) fingerlings... by Amira A. Omar, Doaa H. Assar, Mustafa Shukry, Aya Abo El-Ezz, Foad A. Farrag, Wesam E. Abd El-Aziz, Eman M. Moustafa

“…Additionally, lysozyme activity and phagocytic response were markedly enhanced, demonstrating the probiotic’s immune-boosting potential. Histopathological evaluations revealed reduced gut, gills, and hepatopancreas lesions, especially in groups with higher supplementation levels. …”
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