Summary: Bacterial flagellar motor is a highly ordered and complex supramolecular structure that powers rotation of flagella and serves as a type III export apparatus for flagellar assembly. Motor biogenesis represents a formidable example of self‐assembly, but little is known about early steps of the motor structure formation. Here we used a combination of fluorescence microscopy techniques to dissect the order of the motor assembly in Escherichia coli cells, to map in vivo the underlying protein interactions and to investigate dynamics of protein exchange in the assembled motor structure. Our data suggest that motor self‐assembly is initiated by oligomerization of the membrane export apparatus protein FlhA, which is followed by the recruitment of the MS ring component FliF and by the ordered association of other motor proteins. The assembly process combines the hierarchy with cooperativity, whereby the association of each subsequent motor structure stabilizes the growing assembly. Our results provide a novel and so far the most complete view of the early steps in flagellar motor assembly and improve understanding of the motor structure and regulation.

Background: The integration of large language models (LLMs) into medical diagnostics has garnered substantial attention due to their potential to enhance diagnostic accuracy, streamline clinical workflows, and address health care disparities. However, the rapid evolution of LLM research necessitates a comprehensive synthesis of their applications, challenges, and future directions. Objective: This scoping review aimed to provide an overview of the current state of research regarding the use of LLMs in medical diagnostics. The study sought to answer four primary subquestions, as follows: (1) Which LLMs are commonly used? (2) How are LLMs assessed in diagnosis? (3) What is the current performance of LLMs in diagnosing diseases? (4) Which medical domains are investigating the application of LLMs? Methods: This scoping review was conducted according to the Joanna Briggs Institute Manual for Evidence Synthesis and adheres to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews). Relevant literature was searched from the Web of Science, PubMed, Embase, IEEE Xplore, and ACM Digital Library databases from 2022 to 2025. Articles were screened and selected based on predefined inclusion and exclusion criteria. Bibliometric analysis was performed using VOSviewer to identify major research clusters and trends. Data extraction included details on LLM types, application domains, and performance metrics. Results: The field is rapidly expanding, with a surge in publications after 2023. GPT-4 and its variants dominated research (70/95, 74% of studies), followed by GPT-3.5 (34/95, 36%). Key applications included disease classification (text or image-based), medical question answering, and diagnostic content generation. LLMs demonstrated high accuracy in specialties like radiology, psychiatry, and neurology but exhibited biases in race, gender, and cost predictions. Ethical concerns, including privacy risks and model hallucination, alongside regulatory fragmentation, were critical barriers to clinical adoption. Conclusions: LLMs hold transformative potential for medical diagnostics but require rigorous validation, bias mitigation, and multimodal integration to address real-world complexities. Future research should prioritize explainable artificial intelligence frameworks, specialty-specific optimization, and international regulatory harmonization to ensure equitable and safe clinical deployment.

Pregnancy is a unique situation, in which placenta‐derived small extracellular vesicles (sEVs) may communicate with maternal and foetal tissues. While relevant to homoeostatic and pathological functions, the mechanisms underlying sEV entry and cargo handling in target cells remain largely unknown. Using fluorescently or luminescently labelled sEVs, derived from primary human placental trophoblasts or from a placental cell line, we interrogated the endocytic pathways used by these sEVs to enter relevant target cells, including the neighbouring primary placental fibroblasts and human uterine microvascular endothelial cells. We found that trophoblastic sEVs can enter target cells, where they retain biological activity. Importantly, using a broad series of pharmacological inhibitors and siRNA‐dependent silencing approaches, we showed that trophoblastic sEVs enter target cells using macropinocytosis and clathrin‐mediated endocytosis pathways, but not caveolin‐dependent endocytosis. Tracking their intracellular course, we localized the sEVs to early endosomes, late endosomes, and lysosomes. Finally, we used coimmunoprecipitation to demonstrate the association of the sEV microRNA (miRNA) with the P‐body proteins AGO2 and GW182. Together, our data systematically detail endocytic pathways used by placental sEVs to enter relevant fibroblastic and endothelial target cells, and provide support for “endocytic escape” of sEV miRNA to P‐bodies, a key site for cytoplasmic RNA regulation.

Aims: As data on the use of circulating tumor cells (CTCs) to predict patient outcomes in extensive-stage small-cell lung cancer (ES-SCLC) remain inconclusive, we investigated the clinical value of CTC determination in an open-label, multicenter study of 91 patients with newly diagnosed ES-SCLC. Materials & methods: Blood CTC counts were determined using the CellSearch® system at baseline, after the second cycle of chemotherapy, and on disease progression. Results & conclusion: Following the second cycle of treatment, CTC numbers and the change in CTCs were strong, significant and independent indicators for both progression-free survival and overall survival in ES-SCLC. The CTC change was associated with both refractory disease (response to initial therapy ≤3 months) and sensitive disease (response to initial therapy >3 months).

Background: Acute ischemic stroke (AIS) is the main cause of worldwide death and disability. Early neurological deterioration (END) can further increase the probability of death and disability in patients with ischemic stroke. Therefore, it is essential to find biomarkers to predict END early. Inflammatory response plays a crucial role in determining the course, outcome, and prognosis of END. Earlier studies focused on the relationship between routine hematological inflammatory markers and END, which limited the results. At present, relatively new and comprehensive markers of inflammatory response are relatively scarce. In this study, we investigate the predictive value of inflammatory markers in acute ischemic stroke cases for END which include systemic inflammatory response index (SIRI), platelet/lymphocyte ratio (PLR), lymphocyte/monocyte ratio (LMR), neutrophil/lymphocyte ratio (NLR), and then to establish a nomogram model. Methods: A total of 375 patients with AIS were analyzed who were admitted to the Second Affiliated Hospital of Harbin Medical University from September 2019 to June 2021. The associations between END and inflammatory markers were studied by employing the analysis of univariate. Following that, through regression models of the least absolute shrinkage and selection operator, the END risk model’s feature selection was optimized. The development of the model of prediction was carried out by applying the multivariable logistic regression analysis. The calibration, discrimination, and clinical efficacy of the prediction model were studied via calibration plot, C-index, and decision curve analysis (DCA). The bootstrapping validation method was used for the evaluation of internal validation. Results: We constructed a nomogram consisting of CRP, monocytes, NIHSS and SIRI. This model had desirable calibration and discrimination, with a C-index of 0.757 (95% confidence interval: 0.702–0.805). Interval validation could still achieve the higher C-index value of 0.747. When the risk threshold for END was greater than 13% but less than 84%, DCA proved to be clinically useful. Conclusions: Our research shows that SIRI can be used as a new predictor of END, as well as a monitor of treatment response. Compared with the traditional single inflammatory indicator, the integration of SIRI nomogram can predict the occurrence of END more objectively and reliably.

Purpose: The purpose of this study was to characterize alterations in mucosa-associated microbiota in different anatomical locations of the stomach during gastric cancer progression and to identify associations between Helicobacter pylori infection and gastric microbial changes in patients with gastric cancer. Methods: Twenty-five H. pylori negative subjects with chronic gastritis and thirty-four subjects with gastric cancer were recruited, including H. pylori negative and positive patients with tumors in the antrum and the corpus. Gastric mucosa-associated microbiota were determined by 16S ribosomal RNA gene sequencing using a 454 sequencing platform. Results: We found that individuals with chronic gastritis from three different anatomical sites exhibited different microbiota compositions, although the microbial alpha diversity, richness and beta diversity were similar. Compared to patients with chronic gastritis, the gastric microbiota compositions were significantly different at the order level in the antrum and the corpus of patients with gastric cancer, which was dependent on the H. pylori infection status. Microbial alpha diversity and species richness, however, were similar between chronic gastritis and gastric cancer cases and independent of H. pylori status. The microbial community structure in patients with gastric cancer was distinct from that in patients with chronic gastritis. In addition, we found that the presence of H. pylori markedly altered the structure in gastric corpus cancer, but only mildly affected the antrum. Conclusion: Our data revealed distinct niche-specific microbiota alterations during the progression from gastritis to gastric cancer. These alterations may reflect adaptions of the microbiota to the diverse specific environmental habitats in the stomach, and may play an important, as yet undetermined, role in gastric carcinogenesis.