Purpose: This study aimed to evaluate the safety and efficacy of gemcitabine and cisplatin (GP) regimen in combination with immune checkpoint inhibitor sintilimab as neoadjuvant therapy for muscle-invasive bladder cancer (MIBC) patients and the feasibility of the following selective bladder sparing surgery. Materials and Methods: Patients with histopathologically confirmed urothelial carcinoma without distant metastases (T2-4a, N ≤ 1, M0, American Joint Committee of Cancer 8th) and with adequate organ function will be enrolled. The therapeutic regimen was sintilimab 200 mg once on day 8, gemcitabine 1,000 mg/m2 and cisplatin 35 mg/m2 once on days 1 and 8, every 21 days for four cycles. The primary endpoint was pathologic complete response (pCR, pT0N0) rate. The secondary end points were ypT < 2 rate, R0 resection rate, event-free survival, and safety. Results: From May 4, 2020, to May 20, 2023, 55 patients were enrolled. Forty-six patients were evaluated for efficacy. Among the 42 patients who underwent surgery, 16 patients (38.0%) achieved pCR. Thirty-three patients (78.6%) achieved pT < 2. With a median follow-up of 15.7 months, the 1-year event-free survival was 91.3%. Notwithstanding the poor pathological baseline characteristic of a high T3-T4a proportion (39.1%), a promising bladder preservation (including 22 patients transurethral resection of bladder tumor, 5 patients partial cystectomy, and 4 surveillances) rate was achieved (67.4%). The most common grade ≥ 3 treatment-related adverse events was neutropenia (n=15, 27.3%), which was related to chemotherapy. There were no grade 3 immune-related adverse events. Conclusion Neoadjuvant GP plus sintilimab is a promising regimen for MIBC patients, with relatively high pT < 2 rate and triggering the emerging roles for the multi-disciplinary team decision-making for bladder sparing surgery.

Gene regulation relies on the precise binding of transcription factors (TFs) at regulatory elements, but simultaneously detecting hundreds of TFs on chromatin is challenging. We developed cFOOT-seq, a cytosine deaminase-based TF footprinting assay, for high-resolution, quantitative genome-wide assessment of TF binding in both open and closed chromatin regions, even with small cell numbers. By utilizing the dsDNA deaminase SsdAtox, cFOOT-seq converts accessible cytosines to uracil while preserving genomic integrity, making it compatible with techniques like ATAC-seq for sensitive and cost-effective detection of TF occupancy at the single-molecule and single-cell level. Our approach enables the delineation of TF footprints, quantification of occupancy, and examination of chromatin influences on TF binding. Notably, cFOOT-seq, combined with FootTrack analysis, enables de novo prediction of TF binding sites and tracking of TF occupancy dynamics. We demonstrate its application in capturing cell type-specific TFs, analyzing TF dynamics during reprogramming, and revealing TF dependencies on chromatin remodelers. Overall, cFOOT-seq represents a robust approach for investigating the genome-wide dynamics of TF occupancy and elucidating the cis-regulatory architecture underlying gene regulation.
Transcription Factors/genetics* ; Humans ; Chromatin/genetics* ; Animals ; Binding Sites ; Mice ; DNA Footprinting/methods*

OBJECTIVE To investigate the effects of the split formulas of Biyuan Heji(BYHJ)on paranasal sinus mucosal in-flammation in rats with acute rhinosinusitis(ARS)based on the pathogenesis of"wind-cold transforming into lung-heat".METHODS Unilateral nasal cavity occlusion combined with nasal dripping of Staphylococcus aureus were performed to establish a rat model of ARS.SD rats were randomly divided into blank,model,BYHJ(wind-cold removal+lung-heat removal),lung-heat removal,wind-cold removal,and positive drug groups,with 6 rats in each group.The rats were treated with the corresponding drugs for 7 d and then the samples were collected.HE staining was used to observe the pathological changes of rat paranasal sinus mucosa tissues,ELISA was employed to determine the levels of interleukin-1β(IL-1β),IL-6,IL-8,IL-9,IL-10,and IL-12 in serum,immunohistochemis-try(IHC)was adopted to measure the protein expression of tumor necrosis factor-α(TNF-α)and intercellular adhesion molecule(ICAM-1)in paranasal sinus mucosa tissues,and Western blot was used to detect the protein expression of phosphorylated p38 mito-gen-activated protein kinase(p38 MAPK),nuclear transcription factor-κB p50(NF-κB p50),and NF-κB p65 in paranasal sinus mucosa tissues.RESULTS The acute sinusitis rat inflammation model was successfully established.Compared with the model group,the water drinking,diet eating,and body weight of rats in the BYHJ group,wind-cold removal,lung-heat removal,and positive drug groups were significantly improved,the aggregation of inflammatory cells in the paranasal sinus mucosal tissue was reduced,and the levels of IL-1β,IL-6,IL-8,IL-9,and IL-12 in the serum were significantly reduced(P<0.01),IL-10 content significantly in-creased(P<0.01),the protein expression of TNF-α,ICAM-1,p38 MAPK,NF-κB p50,and NF-κB p65 in the paranasal sinus mucosa was significantly decreased(P<0.01).The comparison between various traditional Chinese medicine groups showed that the decrease of IL-1β,IL-6,IL-8,IL-9,IL-12,TNF-α,ICAM-1,p38 MAPK,NF-κB p50,and NF-κB p65 and the increase of IL-10 in the BYHJ group were better than those in the split formula groups(P<0.01),and the lung-heat removal group was better than the wind-cold removal group(P<0.01).CONCLUSION BYHJ and its split formulas can effectively inhibit the inflammatory response in rats with ARS.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43％(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38％,9.73％,and 8.47％,respectively.The prevalence of CRE strains in Escherichia coli was 1.99％.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)％,followed by blood(20.88±3.40)％and urine(18.40±3.45)％.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)％.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.

Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6％of the isolates were Gram-negative bacteria and 17.4％were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)％and(94.0±0.1)％,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P＜0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9％.The prevalence of penicillin-resistant S.pneumoniae was 1.4％.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8％and 41.3％,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7％,20.8％,9.4％,29.8％,and 73.3％,respectively.The prevalence of β-lactamase was 96.1％in Moraxella catarrhalis and 60.0％in Haemophilus influenzae.The H.influenzae isolates from children(＜18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P＜0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.

ObjectiveThis study sought to investigate the impact of exosomes derived from LoVo cells (LoVo-Exos) in colorectal cancer (CRC) on tumor angiogenesis, as well as to elucidate the potential molecular mechanisms underlying their pro-angiogenic effects. MethodsLoVo-Exos were isolated via ultracentrifugation, and their internalization into recipient human umbilical vein endothelial cells (HUVECs) was visualized using confocal microscopy. The influence of LoVo-Exos on angiogenesis was assessed through an in vitro tube formation assay. Additionally, the pro-angiogenic effects of LoVo-Exos were evaluated in vivo using a matrix gluing assay in mice. To investigate the molecular mechanisms through which LoVo-Exos facilitate angiogenesis, Western blot analysis was employed to examine the transfer of pEGFR by LoVo-Exos into recipient cells. Both Western blot and ELISA were utilized to assess the expression levels of key signaling proteins within the EGFR-ERK pathway, as well as the expression of downstream angiogenic core molecules. Furthermore, the impact of EGFR knockdown and ERK inhibitor treatment on angiogenesis was evaluated, with subsequent analysis of the expression of downstream angiogenic core molecules following these interventions. ResultsConfocal microscopy demonstrated the internalization of LoVo-Exos into HUVECs. In vitro angiogenesis assays further indicated that LoVo-Exos significantly enhanced the formation of tubular structures in HUVECs. Additionally, macroscopic examination of subcutaneous matrix plug formation in mice revealed a substantial increase in vascular-like structures within the matrix plugs following the administration of LoVo-Exos, compared to the PBS control group. Hematoxylin and eosin (HE) staining revealed the presence of erythrocyte-filled microvessels within the matrix plugs combined with LoVo-Exos. Furthermore, immunohistochemical analysis demonstrated the expression of the endothelial cell marker CD31 in these matrix plugs. The presence of CD31-positive cells in the LoVo-Exos-treated matrix plugs was associated with a significant enhancement in the formation of luminal structures. These findings suggest that LoVo-Exos facilitate the in vivo development of vascular-like structures. Subsequent investigations demonstrated that LoVo-Exos facilitated the delivery of pEGFR to HUVEC, thereby enhancing angiogenesis. Conversely, LoVo-Exos with EGFR knockdown exhibited a diminished capacity to promote angiogenesis, an effect that was further attenuated by the ERK phosphorylation inhibitor U0126. Western blot analysis assessing the activation of the EGFR-ERK signaling pathway in HUVEC indicated that LoVo-Exos augmented angiogenesis through the activation of this pathway. Furthermore, analysis of the impact of LoVo-Exos on the expression of downstream angiogenic core molecules revealed an increase in interleukin-8 (IL-8) secretion in HUVEC. The enhancement observed was diminished in LoVo-Exos following EGFR knockdown, and this reduction was counteracted by the ERK phosphorylation inhibitor U0126. ConclusionThe underlying mechanism may involve the delivery of pEGFR in LoVo-Exos to HUVECs, leading to increased IL-8 secretion via the EGFR-ERK signaling pathway, thereby enhancing the angiogenic potential of HUVECs. This finding may offer new insights into the mechanisms underlying cancer metastasis.

OBJECTIVE: To develop a digital intelligent multimodal shift handover system for the intensive care unit (ICU) and evaluate its application effect in ICU shift handovers. METHODS: A research and development team was established, consisting of 1 department director, 1 head nurse, 3 information technology engineers, 3 nurses, and 2 doctors. Team members were assigned responsibilities including overall coordination and planning, platform design and maintenance, pre-application training, collection and organization of clinical feedback, and research investigation respectively. A digital intelligent multimodal shift handover system was developed for ICU based on the Shannon-Weaver linear transmission model. This innovative system integrated automated data collection, intelligent dynamic monitoring, multidimensional condition analysis and visual reporting functions. A cloud platform was used to gather data from multi-parameter vital signs monitors, infusion pumps, ventilators and other devices. Artificial intelligence algorithms were employed to standardize and analyze the data, providing personalized recommendations for healthcare professionals. A self-controlled before-after method was adopted. Before the application of the ICU digital intelligent multimodal shift handover system (from December 2023 to March 2024), the traditional verbal bedside handover was used; from June 2024 to March 2025, the ICU digital intelligent multimodal shift handover system was applied for shift handovers. Questionnaires before the application of the shift handover system were collected in April 2024, and those after the application were collected in April 2025. The shift handover time, handover quality (scored by the nursing handover evaluation scale), satisfaction with doctor-nurse communication (scored by the ICU doctor-nurse scale) before and after the application of the handover system were compared, and nurses' satisfaction with the shift handover system (scored by the clinical nursing information system effectiveness evaluation scale) was investigated. RESULTS: After the application of the ICU digital intelligent multimodal shift handover system, the shift handover time was significantly shorter than that before the application [minutes: 20 (15, 25) vs. 30 (22, 40)], the handover quality was significantly higher than that before the application [score: 84.0 (78.0, 88.5) vs. 71.0 (55.0, 79.0)], and the satisfaction with doctor-nurse communication was also significantly higher than that before the application (score: 84.58±6.79 vs. 74.50±11.30). All differences were statistically significant (all P < 0.05). In addition, the nurses' system effectiveness evaluation scale score was 102.30±10.56, which indicated that nurses had a very high level of satisfaction with the ICU digital intelligent multimodal shift handover system. CONCLUSIONS The application of the ICU digital intelligent multimodal shift handover system can shorten the shift handover time, improve the handover quality, and enhance the satisfaction with doctor-nurse communication. Nurses have a high level of satisfaction with this system.
Intensive Care Units ; Humans ; Patient Handoff ; Artificial Intelligence ; Algorithms

Objective: To establish a machine learning-based precision blood donor recruitment model at the county level and assess its generalizability and applicability. Methods: A retrospective study was conducted using blood donation and SMS recruitment data from the Taicang Branch of the Suzhou Blood Center between 2019 and 2024. Multiple machine learning algorithms were employed, including extreme gradient boosting, support vector machine, k-nearest neighbor, logistic regression, decision tree, random forest, and multilayer perceptron. These were combined with techniques such as synthetic minority oversampling, undersampling, and cost-sensitive learning (using MFE and MSFE loss functions). Model parameters were optimized through grid search to identify the best-performing model. Results: In a prospective comparative study against conventional methods, the machine learning models increased the recruitment success rate among high-willingness donors by an average of 129.15%, and the recruitment efficiency per SMS improved by 125.02% compared with the traditional method. Under full-scale SMS sending, the recruitment rate per SMS increased by 42.61%, and SMS sending efficiency improved by 31.77%, significantly enhancing recruitment performance. Conclusion: This study represents the first application of a machine learning-based precision donor recruitment model at the county-level in China. The precise recruitment framework not only improves recruitment efficiency and reduces recruitment costs but also demonstrates strong scalability and generalizability. It provides a scientific and feasible intelligent pathway to ensure the safety and sustainability of the blood supply.