Tumors exhibit abnormal glucose metabolism, consuming excessive glucose and excreting lactate, which constructs a tumor microenvironment that facilitates cancer progression and disrupts immunotherapeutic efficacy. Currently, tumor glucose metabolic dysregulation to reshape the immunosuppressive microenvironment and enhance immunotherapy efficacy is emerging as an innovative therapeutic strategy. However, glucose metabolism modulators lack specificity and still face significant challenges in overcoming tumor delivery barriers, microenvironmental complexity, and metabolic heterogeneity, resulting in poor clinical benefit. Nanomedicines, with their ability to selectively target tumors or immune cells, respond to the tumor microenvironment, co-deliver multiple drugs, and facilitate combinatorial therapies, hold significant promise for enhancing immunotherapy through tumor glucose metabolic reprogramming. This review explores the complex interactions between tumor glucose metabolism-specifically metabolite transport, glycolysis processes, and lactate-and the immune microenvironment. We summarize how nanomedicine-mediated reprogramming of tumor glucose metabolism can enhance immunotherapy efficacy and outline the prospects and challenges in this field.

BACKGROUND: Increasing evidence indicates that oxidative stress and interferon γ (IFNγ)-driven cellular immune responses are responsible for the pathogenesis of vitiligo. However, the connection between oxidative stress and the local production of IFNγ in early vitiligo remains unexplored. The aim of this study was to identify the mechanism underlying the production of IFNγ by mast cells and its impact on vitiligo pathogenesis. METHODS: Skin specimens from the central, marginal, and perilesional skin areas of active vitiligo lesions were collected to characterize changes of mast cells, CD8 + T cells, and IFNγ-producing cells. Cell supernatants from hydrogen peroxide (H 2 O 2 )-treated keratinocytes (KCs) were harvested to measure levels of soluble stem cell factor (sSCF) and matrix metalloproteinase (MMP)-9. A murine vitiligo model was established using Mas-related G protein-coupled receptor-B2 (MrgB2, mouse ortholog of human MrgX2) conditional knockout (MrgB2 -/- ) mice to investigate IFNγ production and inflammatory cell infiltrations in tail skin following the challenge with tyrosinase-related protein (Tyrp)-2 180 peptide. Potential interactions between the Tyrp-2 180 peptide and MrgX2 were predicted using molecular docking. The siRNAs targeting MrgX2 and the calcineurin inhibitor FK506 were also used to examine the signaling pathways involved in mast cell activation. RESULTS: IFNγ-producing mast cells were closely aligned with the recruitment of CD8 + T cells in the early phase of vitiligo skin. sSCF released by KCs through stress-enhanced MMP9-dependent proteolytic cleavage recruited mast cells into sites of inflamed skin (Perilesion vs . lesion, 13.00 ± 4.00/high-power fields [HPF] vs . 26.60 ± 5.72/HPF, P <0.05). Moreover, IFNγ-producing mast cells were also observed in mouse tail skin following challenge with Tyrp-2 180 (0 h vs . 48 h post-recall, 0/HPF vs . 3.80 ± 1.92/HPF, P <0.05). The IFNγ + mast cell and CD8 + T cell counts were lower in the skin of MrgB2 -/- mice than in those of wild-type mice (WT vs . KO 48 h post-recall, 4.20 ± 0.84/HPF vs . 0.80 ± 0.84/HPF, P <0.05). CONCLUSION Mast cells activated by MrgX2 serve as a local IFNγ producer that bridges between innate and adaptive immune responses against MCs in early vitiligo. Targeting MrgX2-mediated mast cell activation may represent a new strategy for treating vitiligo.
Vitiligo/metabolism* ; Mast Cells/immunology* ; Animals ; Interferon-gamma/metabolism* ; Mice ; Humans ; Melanocytes/metabolism* ; Receptors, G-Protein-Coupled/genetics* ; Mice, Knockout ; Mice, Inbred C57BL ; Male ; Female ; Matrix Metalloproteinase 9/metabolism* ; Stem Cell Factor/metabolism*

Objective:To conduct a systematic review of risk prediction models for enteral feeding intolerance in ICU patients.Methods:Relevant literature was searched in China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc, VIP, PubMed, Web of Science, Cochrane Library, Embase, CINAHL, and Scopus, with search limits from the establishment of the databases up to July 24, 2024. Two researchers independently screened the literature and extracted data, using Prediction model Risk Of Bias ASsessment Tool to evaluate the quality of the included studies.Results:A total of 12 studies were included, which included 20 prediction models. The area under the receiver operating characteristic curve or C-index for these models ranged from 0.70 to 0.94. The overall bias risk of the 12 studies was high, with three studies having good applicability. The bias risk primarily stemmed from issues such as measurement of prediction factors, variable handling, sample size, outcome definition, and model performance evaluation.Conclusions:Existing risk prediction models for enteral feeding intolerance in ICU patients exhibit a high risk of bias. Further validation, optimization, or development of new models is required in the future.

With the advancements in artificial intelligence (AI), computational power, and surgical robotics, the analysis of surgical performance at the granular level of individual surgical gestures has become feasible. Surgical gestures, defined as the smallest independent units of inter-action between surgical instruments and tissues, offer a quantifiable framework for surgical skill assessment. Evidence suggests that the selection and execution of surgical gestures are strongly correlated with the expertise of the surgeon and patient outcomes, underscoring their significance in both surgical training and clinical practice. Moreover, the establishment of a standardized classifi-cation system for surgical gestures and the adoption of uniform terminology have the potential to improve communication efficiency during surgical education and training. The authors synthesize existing classification systems for surgical gestures, with a focus on their applications in diverse tasks such as suturing, exposure and dissection. By examining the latest advancements in AI models applied to surgical gesture, as well as the current research landscape of surgical gesture recognition in digestive surgery, the authors explore the potential applications of such technologies in assisting surgeons during operations in the future.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

Objective:To investigate the characteristics of visual and auditory sustained attention and sex differences in pupils of different grades.Method:Totally 231 students from grades 1 to 6 participated in this stud-y.The development and sex differences of sustained attention were measured by average reaction times,coefficients of variation for reaction times,omission error rates and commission error rates in visual and auditory two-choice re-action time(CRT)tasks.Result:The students in grade 5 had significantly slower visual average reaction times and coefficients of variation for reaction times(P＜0.001 or P＜0.05)than those in grade 3.The students in grade 5 had significantly lower auditory average reaction times,coefficients of variation for reaction times,omission error rates and commission error rates(P＜0.001 or P＜0.01)than those in grade 1.There was no significant sex differ-ences in visual or auditory average reaction times,coefficients of variation for reaction times or omission error rates(Ps＞0.05).Conclusion:There would be age differences in characteristics of both visual and auditory sustained at-tention in pupils,and may be no sex difference in visual or auditory sustained attention.

Objective:To investigate the characteristics of visual and auditory sustained attention and sex differences in pupils of different grades.Method:Totally 231 students from grades 1 to 6 participated in this stud-y.The development and sex differences of sustained attention were measured by average reaction times,coefficients of variation for reaction times,omission error rates and commission error rates in visual and auditory two-choice re-action time(CRT)tasks.Result:The students in grade 5 had significantly slower visual average reaction times and coefficients of variation for reaction times(P＜0.001 or P＜0.05)than those in grade 3.The students in grade 5 had significantly lower auditory average reaction times,coefficients of variation for reaction times,omission error rates and commission error rates(P＜0.001 or P＜0.01)than those in grade 1.There was no significant sex differ-ences in visual or auditory average reaction times,coefficients of variation for reaction times or omission error rates(Ps＞0.05).Conclusion:There would be age differences in characteristics of both visual and auditory sustained at-tention in pupils,and may be no sex difference in visual or auditory sustained attention.

With the advancements in artificial intelligence (AI), computational power, and surgical robotics, the analysis of surgical performance at the granular level of individual surgical gestures has become feasible. Surgical gestures, defined as the smallest independent units of inter-action between surgical instruments and tissues, offer a quantifiable framework for surgical skill assessment. Evidence suggests that the selection and execution of surgical gestures are strongly correlated with the expertise of the surgeon and patient outcomes, underscoring their significance in both surgical training and clinical practice. Moreover, the establishment of a standardized classifi-cation system for surgical gestures and the adoption of uniform terminology have the potential to improve communication efficiency during surgical education and training. The authors synthesize existing classification systems for surgical gestures, with a focus on their applications in diverse tasks such as suturing, exposure and dissection. By examining the latest advancements in AI models applied to surgical gesture, as well as the current research landscape of surgical gesture recognition in digestive surgery, the authors explore the potential applications of such technologies in assisting surgeons during operations in the future.

With the rapid development of minimally invasive techniques in surgery, arti-ficial intelligence (AI), particularly deep learning, is playing an increasingly important role in mini-mally invasive surgery. By automated analysis of surgical videos, AI can efficiently perform key tasks such as instrument recognition, surgical phase identification, action analysis, anatomical structure recognition, intraoperative diagnosis, adverse event monitoring and smart desmoking. These appli-cations provide essential support for real-time monitoring, surgical navigation and skill assessment during surgery. The authors summarize the current research progress of AI in minimally invasive surgery, including its applications in the fields of hepatobiliary and pancreatic surgery, as well as gastrointestinal surgery. It also explores the potential of AI in enhancing surgical safety, efficiency and skill assessment. By synthesizing the latest research achievements of AI technology in the field of surgery, as well as analyzing its technical challenges and risks, it aims to provide guidance for future innovations and clinical applications, promoting the advancement and implementation of AI in minimally invasive surgery.

Objective:To conduct a systematic review of risk prediction models for enteral feeding intolerance in ICU patients.Methods:Relevant literature was searched in China National Knowledge Infrastructure, Wanfang Data, China Biology Medicine disc, VIP, PubMed, Web of Science, Cochrane Library, Embase, CINAHL, and Scopus, with search limits from the establishment of the databases up to July 24, 2024. Two researchers independently screened the literature and extracted data, using Prediction model Risk Of Bias ASsessment Tool to evaluate the quality of the included studies.Results:A total of 12 studies were included, which included 20 prediction models. The area under the receiver operating characteristic curve or C-index for these models ranged from 0.70 to 0.94. The overall bias risk of the 12 studies was high, with three studies having good applicability. The bias risk primarily stemmed from issues such as measurement of prediction factors, variable handling, sample size, outcome definition, and model performance evaluation.Conclusions:Existing risk prediction models for enteral feeding intolerance in ICU patients exhibit a high risk of bias. Further validation, optimization, or development of new models is required in the future.