Skip to content

PREreview of "Mycobacterial Lipoarabinomannan: Major Trail of Immune Evasion and Drug resistance in host"

Oct 2026 · Zenodo (CERN European Organization for Nuclear Research) · 1 citation
Tuberculosis Research and Epidemiology

Abstract

This Zenodo record is a permanently preserved version of a PREreview. You can view the complete PREreview at https://prereview.org/reviews/23181910. Major issues From the title: The phrase "drug resistance in host" is also unclear. It needs to be revised so as to reflect the paper's actual focus. The manuscript mainly discusses immune modulation, persistence, and LAM as a biomarker, rather than explaining how LAM contributes to antimicrobial drug resistance. For the Abstract: The objective and scope are not clearly stated. The claim that LAM is linked to "drug resistance" needs to be supported and distinguished from immune evasion and bacterial persistence. Introduction: Section 1, The section contains extensive background on TB pathogenesis, but does not clearly identify the knowledge gap or explain how the paper is organized to address it. Some statements about granulomas, MDR-TB, and bacterial persistence are broad and need more precise qualification. Section 2, "LAM and Antimicrobial Response": This section combines immune evasion, oxidative stress, phagosome maturation, diagnostic testing, and treatment monitoring without a clear progression. The authors should distinguish host immune evasion from antimicrobial drug resistance and explain the relevance of each subsection to the central topic. Section 3, "LAM as Yardstick…": The claim that LAM indicates bacterial identity, burden, virulence, treatment response, and prognosis is too broad. The authors should distinguish established diagnostic or prognostic evidence from proposed applications, and clarify the populations and settings in which the cited findings apply. For the Conclusion: The suggestion that LAM can help manage latent or MDR-TB and serve as a target for host-directed therapy is not sufficiently developed in the preceding discussion. These should be presented as potential research directions, not established applications. Overall structure and evidence should be more critically looked into, acknowledge conflicting evidence, and identify key knowledge gaps. Minor issues Throughout, authors should standardize capitalization and terminology, including "drug resistance," "Mycobacterium tuberculosis," "M. tuberculosis," "ManLAM," and "PI-LAM." Figure 1's caption is repeated, and several abbreviations should be checked for accuracy and defined consistently. References: Author should correct the duplicated numbering in reference 2, the incomplete publication year in reference 20, and the duplicated reference entries (references 9 and 11). As well, check all in-text citations against the reference list. Authors should break up long, information-dense sentences and define abbreviations at first use. In addition, there are some grammatical issues within the paper e.g "predisposing patient asymptomatic". Authors should review such issues. Competing interests The authors declare that they have no competing interests. Use of Artificial Intelligence (AI) The authors declare that they did not use generative AI to come up with new ideas for their review.

View source

Similar papers

#artificial intelligence Open access May 2023

Evaluating the Performance of Large Language Models on GAOKAO Benchmark

GAOKAO-Bench is introduced, an intuitive benchmark that employs questions from the Chinese GAOKAO examination as test samples, including both subjective and objective questions that contribute a robust evaluation benchmark for future large language models and offers valuable insights into the advantages and limitations...

Xiaotian Zhang, Chun-yan Li, Yi Zong et al. · 216 citations · ⚡17
#artificial intelligence Open access Jul 2024

Gender, Race, and Intersectional Bias in Resume Screening via Language Model Retrieval

This work investigates the possibilities of using LLMs in a resume screening setting via a document retrieval framework that simulates job candidate selection and finds that the MTEs are biased, significantly favoring White-associated names in 85% of cases and female-associated names in only 11.1% of cases.

Kyra Wilson, Aylin Caliskan · 131 citations · ⚡8
#artificial intelligence Review Oct 2025

Ultralytics YOLO Evolution: An Overview of YOLO26, YOLO11, YOLOv8 and YOLOv5 Object Detectors for Computer Vision and Pattern Recognition

This paper presents a comprehensive overview of the Ultralytics YOLO family, emphasizing architectural evolution, benchmarking, deployment, and emerging directions from YOLOv5 through YOLO27, and examines detection, segmentation, depth, classification, pose, oriented detection, tracking, export, quantization, and deplo...

Ranjan Sapkota, Manoj Karkee · 112 citations · ⚡10

BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models

A novel threat is unveiled in which attackers steer the RAG system's response by injecting malicious passages into its knowledge base, enabling the attacker to steer the response without altering the user input or modifying the RAG weights.

Jiaqi Xue, Meng Zheng, Yebowen Hu et al. · 109 citations · ⚡8

The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models

This work revisits schema linking when using the latest generation of large language models (LLMs) and finds empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones.

Karime Maamari, Fadhil Abubaker, Daniel Jaroslawicz et al. · 109 citations · ⚡19

PRISM: Self-Pruning Intrinsic Selection Method for Training-Free Multimodal Data Selection

Empirically, PRISM reduces the end-to-end time for data selection and model tuning to just 30% of conventional pipelines, and achieves this efficiency while simultaneously enhancing performance, surpassing models fine-tuned on the full dataset across eight multimodal and three language understanding benchmarks.

Jinhe Bi, Yifan Wang, Danqi Yan et al. · 73 citations · ⚡4

Related blog posts

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.