In recent years， the exploration and development of artificial intelligence （AI） technology in clinical trials for liver diseases have promoted the continuous innovation of research methods and processes in this field. AI has gradually become an important technical tool for various links of clinical trial including patient selection， risk stratification， endpoint evaluation， and result interpretation. Nevertheless， the standardized integration of AI into clinical trials still faces the methodological challenges such as data quality control， model interpretability， and causal inference. From the perspective of methodology， this article systematically reviews the principal application scenarios of AI as an object under investigation （validation trials） and as a research tool （supportive trials） in clinical trials for liver diseases， as well as the major methodological challenges of AI-related clinical trials along and the corresponding solution strategies， in order to provide methodological guidance for promoting the scientific and standardized implementation of AI technologies.

Objective:To retrospectively analyze the distribution and drug resistance of pathogenic bacteria in neonatal infection, in order to provide evidence for rational use of antibiotics in clinic.Methods:Pathogenic bacteria were collected from 497 newborn patients in the Neonatal Intensive care Unit (NICU) of the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China from January 2020 to June 2024, and the pathogen identification and drug susceptibility test were conducted.Results:A total of 569 strains of pathogenic bacteria were detected, including 195 gram-positive strains (34.27%). 332 Gram-negative strains (58.35%); There were 42 fungal strains, accounting for 7.38%. The top 3 gram-positive bacteria were: 63 strains (11.07%), 44 strains (7.73%) of Staphylococcus epidermidis and 18 strains (3.16%) of Staphylococcus aureus, all of which were sensitive to linezolid, vancomycin, tigecycline, rifampicin and amikacin (83.33%-100.00%). The top three gram negative bacteria detection rates were: Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii. Among them, the resistance rate of Klebsiella pneumoniae to penicillins and cephalosporins was 73.33%-95.00%, the resistance rate of Escherichia coli to cotrimoxazole was the highest (71.88%), and the resistance rate of Acinetobacter baumannii to cefoxitine and cefotaxime was over 70.00%. The sensitivity of the detected fungi to amphotericin, 5-fluorocytosine, voriconazole, itraconazole and fluconazole were all over 80.00%.Conclusions:There are many kinds of pathogens detected in NICU in our hospital, mainly gram-negative bacteria, and the resistance rate to penicillin and cephalosporin antibiotics is high.

Objective:To retrospectively analyze the distribution and drug resistance of pathogenic bacteria in neonatal infection, in order to provide evidence for rational use of antibiotics in clinic.Methods:Pathogenic bacteria were collected from 497 newborn patients in the Neonatal Intensive care Unit (NICU) of the Affiliated Changsha Central Hospital, Hengyang Medical School, University of South China from January 2020 to June 2024, and the pathogen identification and drug susceptibility test were conducted.Results:A total of 569 strains of pathogenic bacteria were detected, including 195 gram-positive strains (34.27%). 332 Gram-negative strains (58.35%); There were 42 fungal strains, accounting for 7.38%. The top 3 gram-positive bacteria were: 63 strains (11.07%), 44 strains (7.73%) of Staphylococcus epidermidis and 18 strains (3.16%) of Staphylococcus aureus, all of which were sensitive to linezolid, vancomycin, tigecycline, rifampicin and amikacin (83.33%-100.00%). The top three gram negative bacteria detection rates were: Klebsiella pneumoniae, Escherichia coli, Acinetobacter baumannii. Among them, the resistance rate of Klebsiella pneumoniae to penicillins and cephalosporins was 73.33%-95.00%, the resistance rate of Escherichia coli to cotrimoxazole was the highest (71.88%), and the resistance rate of Acinetobacter baumannii to cefoxitine and cefotaxime was over 70.00%. The sensitivity of the detected fungi to amphotericin, 5-fluorocytosine, voriconazole, itraconazole and fluconazole were all over 80.00%.Conclusions:There are many kinds of pathogens detected in NICU in our hospital, mainly gram-negative bacteria, and the resistance rate to penicillin and cephalosporin antibiotics is high.

Recording the highly diverse and dynamic activities in large populations of neurons in behaving animals is crucial for a better understanding of how the brain works. To meet this challenge, extensive efforts have been devoted to developing functional fluorescent indicators and optical imaging techniques to optically monitor neural activity. Indeed, optical imaging potentially has extremely high throughput due to its non-invasive access to large brain regions and capability to sample neurons at high density, but the readout speed, such as the scanning speed in two-photon scanning microscopy, is often limited by various practical considerations. Among different imaging methods, light field microscopy features a highly parallelized 3D fluorescence imaging scheme and therefore promises a novel and faster strategy for functional imaging of neural activity. Here, we briefly review the working principles of various types of light field microscopes and their recent developments and applications in neuroscience studies. We also discuss strategies and considerations of optimizing light field microscopy for different experimental purposes, with illustrative examples in imaging zebrafish and mouse brains.
Animals ; Mice ; Microscopy/methods* ; Zebrafish ; Neurons/physiology* ; Brain/physiology* ; Neurosciences