Results 241 to 250 of about 821,961 (316)

ALARUM: Active One Health surveillance in LMICs to monitor and predict Antimicrobial Resistance Using Metagenomics - a cross-sectional study protocol. [PDF]

BMJ Open
van der Sande MAB, Valia D, Tigoi C, Stoesser N, Stamm L, Marten A, Riems B, Musyimi R, Sibidou Y, Schurch AC, Tiendrebeogo EW, Mwaringa S, Kohns Vasconcelos M, Ingelbeen B, Tinto H, Bielicki JA, Cooper BS, Berkley JA, van Kleef E.   +18 more
europepmc   +1 more source

Pathogen‐Centric Activation of an Azoreductase‐Responsive Antibody‐Antibiotic Conjugate for the Targeted Eradication of MRSA

Advanced Science, EarlyView.
A pathogen‐centric antibody‐antibiotic conjugate (AZO‐AAC) releases antibiotics via bacterial azoreductase, independent of host‐lysosomal function. This mechanism achieves nanomolar eradication of both planktonic and intracellular methicillin‐resistant Staphylococcus aureus (MRSA).
Qi Cheng, Lianqi Liu, Chenghua Liu, Fei Xie, Jingwen Dong, Xiaoyu Qin, Shunxiang Huang, Xian Li, Xingyuan Kou, Hongbin Deng, Jiannan Feng, Wu Zhong, Dian Xiao, Xinbo Zhou   +13 more
wiley   +1 more source

Proceedings of the 3rd International Conference on Animal Health Surveillance (ICAHS): Beyond animal health surveillance ; 30 April – 4 May 2017, Rotorua, New Zealand

, 2017

core  

From legacy systems to real-time response: VPD-SMART implementation and its impact on public health surveillance in Paraguay and the Americas.

SSM Health Syst
Ortiz C, Millacura FA, Atavillos C, Bravo-Alcántara P, Espinoza J, Osorio V, Baeza C, Ojeda P, Revilla F, Pacis C, Bobadilla S, Araceli F, Del Medico P, Tejo C, Rossi E, Ordoñez F, Zapata-Bedoya S, Cain E, Tavera P, Magarinos M, Whittembury A, Jean-Baptiste A, Broome K, Ovelar P, Perez T, García M, Villalobos N, Potter R, Cousirat L, Rey-Benito G, Velandia-Gonzalez M.   +30 more
europepmc   +1 more source

Comparative performance of machine learning models in predicting childhood diarrhea: implications for public health surveillance. [PDF]

J Egypt Public Health Assoc
Ashaolu JO, Akanji TS, Ayansola VI, Sunday AJ, Esther OA, Some SYM.   +5 more
europepmc   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

Advanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu, Shuli Sun, Zhengliang Lv, Xinyu Liu, Yihua Wang, Bingqiang Liu   +5 more
wiley   +1 more source

Proceedings of the International Conference on Animal Health Surveillance (ICAHS) ; 17-20 May 2011

, 2011

core  

Promoting stakeholder awareness, collaboration, and engagement in One Health surveillance with a serious game. [PDF]

One Health
Olive MM, Tolno SA, Lafia B, Ndong Bass I, Barry MA, Talla Mba M, Laury E, De Nys H, Ayouba A, Guigma Wendmisida VY, Diaz F, Bourgarel M, Muset S, Peyre M.   +13 more
europepmc   +1 more source

Targeting Golgi–STING Signaling to Reprogram Innate and Adaptive Immunity for the Treatment of Implant‐Associated Infections

Advanced Science, EarlyView.
This study presents an ultrasound‐responsive nanoplatform, CS‐BT@MZ@NEs, with a BaTiO3/Mn‐Zif‐8 core and a chondroitin sulfate coating for Golgi targeting. By leveraging neutrophil hitchhiking, it enables targeted delivery to infection sites. Under ultrasound stimulation, CS‐BT@MZ@NEs generates ROS and modulates Golgi pH to activate cGAS–STING ...
Shicheng Huo, Naifeng Zhu, Zhuocheng Lyu, Yifei Liu, Zhenjiang Xu, Chun Zhou, Yaochao Zhao, Changgui Shi   +7 more
wiley   +1 more source

Divide and recombine approaches for fitting logistic regression to large-scale health surveillance data: application to diabetes risk prediction in BRFSS. [PDF]

Sci Rep
Nayem MMH, Biswas SC.
europepmc   +1 more source