ObjectiveStroke poses a heavy burden due to its high mortality and morbidity rates. Accurate and real-time detection of lesions is pivotal for prompt clinical intervention and favorable prognosis. Electrical impedance tomography (EIT) and microwave tomography (MWT) have emerged as compelling alternatives for stroke screening, owing to their non-ionizing, non-invasive and portable nature. EIT provides information on tissue conductivity, and MWT offers high sensitivity to changes in dielectric properties. However, single-modality imaging is inherently limited, EIT suffers from low sensitivity to deep-seated tissues and severe ill-posedness of inverse problems, whereas MWT is challenged by strong nonlinearity in inverse scattering and susceptibility to modeling errors. Consequently, the clinical utility of standalone EIT or MWT for stroke diagnosis remains constrained by poor spatial resolution and imaging artifacts. To improve the accuracy and robustness of stroke imaging, a dual-modality fusion conditional denoising diffusion probabilistic model (DM-DDPM) was proposed for high-precision dual-modality image reconstruction. MethodsA dual-encoder network with a symmetric architecture and independently trained parameters was constructed to extract heterogeneous features separately from EIT boundary voltage measurements and MWT scattered field signals. Attentional feature fusion (AFF) is employed to integrate complementary information from the two modalities adaptively, generating robust fused priors that suppress redundant noise while preserving key physical characteristics. Subsequently, the fused priors are embedded into a Transformer-based diffusion model via a cross attention mechanism to guide the reverse denoising process. This approach effectively reduces artifacts and enhances the stability of conductivity distribution reconstruction. Time step embedding is introduced to enable the network to perceive the diffusion stage and further improve the accuracy of noise prediction. ResultsSimulated experiments demonstrated that DM-DDPM significantly outperforms single-modality and multi-modality networks under various noise levels. A head model simulation dataset was constructed based on COMSOL Multiphysics, and tests were carried out under 50 dB, 40 dB and 30 dB signal-to-noise ratio levels. At 30 dB, the average relative error (RE) was below 0.20, while the structural similarity index measure (SSIM) and correlation coefficient (CC) remained above 0.90 and 0.89, respectively. Compared with single-modality and multi-modality networks, artifacts were significantly reduced, lesion edges were clearer, and localization was more accurate. The model maintains high reconstruction quality and strong robustness for single, double, and triple lesions simultaneously. Furthermore, physical experiments were conducted using a 16-electrode EIT system and a 16-antenna MWT system with asynchronous data acquisition. These experiments confirmed the feasibility of the method in real-world scenarios and demonstrated that it can robustly reconstruct simulated lesions despite environmental interference and measurement noise, validating its reliability for practical clinical applications. ConclusionThe proposed method effectively combines complementary dual-modality information with a conditional diffusion model. Low accuracy and poor noise resistance in single-modality imaging were effectively addressed, while the noise amplification issue caused by direct multimodal data fusion was avoided. The proposed algorithm exhibits strong anti-noise interference ability and high imaging stability in both simulation and physical experiments. Precise localization of stroke lesions with different quantities was achieved, providing a high-precision, and practical technical support for clinical stroke detection.

The drug resistance in the treatment of solid tumors is closely related to abnormalities in the tumor vascular microenvironment (TVM). The TVM is dynamically composed of multiple stromal, vascular, and immune cell populations together with extracellular matrix. Its structural disorder and functional imbalance can lead to abnormal angiogenesis, perfusion disorders, limited drug delivery, and insufficient immune cell infiltration, thereby creating a microenvironment conducive to tumor progression and treatment resistance. Therefore, the coordinated regulation of TVM through vascular normalization and immune remodeling has become an important research direction for improving the treatment effect of solid tumors. Tumor necrosis factor superfamily member 14 (TNFSF14), also known as LIGHT (homologous to lymphotoxin, inducible, competes with herpes simplex virus (HSV) glycoprotein D for herpesvirus entry mediator (HVEM), expressed by T lymphocyte), is an important molecule connecting tumor vascular remodeling and immune regulation. LIGHT can bind to the lymphotoxin β receptor (LTβR) to activate the NF-κB (nuclear factor kappa B)-related signaling pathway, regulate the functions of vascular endothelial cells and stromal cells, and induce the expression of adhesion molecules and chemokines such as VCAM-1(vascular cell adhesion molecule 1), ICAM-1(intercellular adhesion molecule 1), CCL19 (C-C motif chemokine ligand 19), CCL21 (C-C motif chemokine ligand 21), and CXCL13 (C-X-C motif chemokine ligand 13), promoting the formation of high endothelial venules (HEVs). HEVs can improve the vascular access of immune cells into tumor tissues and further promote the formation of tertiary lymphoid structures (TLS), providing a structural basis for the recruitment of effector T cells and sustained anti-tumor immune responses. At the same time, LIGHT interacts with HVEM to provide costimulatory signals to T cells, enhancing their activation, proliferation, and cytotoxic function. Thus, LIGHT can link tumor vascular normalization, immune cell recruitment, and T cell activation through the dual actions mediated by LTβR and HVEM, forming a coordinated regulation model of “vascular remodeling-immune infiltration-immune activation”, providing new molecular targets for the treatment of solid tumors. Despite LIGHT’s good anti-tumor potential, its therapeutic application is still limited by factors such as insufficient stability in vivo, short half-life, and potential off-target effects due to systemic exposure. In recent years, researchers have developed various strategies for the precise delivery of LIGHT, including fusion proteins, bacterial vectors, viral vectors, nanoparticles, chimeric antigen receptor T cells (CAR-T), and mesenchymal stem cells (MSCs), to enhance the enrichment, expression, or sustained release of LIGHT in tumor sites through different approaches, thereby improving its therapeutic efficacy and safety. This article systematically reviews the structural characteristics, biological functions, and molecular mechanisms mediating TVM remodeling by LIGHT, with a focus on the signal transduction mediated by LTβR and HVEM and their roles in tumor immune regulation. Subsequently, it systematically outlines the molecular mechanisms by which LIGHT regulates the tumor vascular microenvironment and its intrinsic connection with immune cell recruitment and activation, from the perspectives of TVM abnormalities, vascular normalization, HEV formation, and TLS induction. Furthermore, it categorizes and summarizes precise delivery strategies such as LIGHT fusion proteins, bacterial vectors, viral vectors, nanoparticles, CAR-T cells, and MSCs according to delivery vectors and modes of action, and compares their functional characteristics, antitumor effects, and potential advantages and limitations. On this basis, the article further discusses the key issues that LIGHT therapeutic strategies still face in terms of targeting, controllability,in vivo stability, safety, and clinical translation, and anticipates the potential value of its combination with immune checkpoint inhibitors, anti-angiogenic therapy, and other tumor treatment methods. This article aims to deepen the understanding of the antitumor mechanism of LIGHT from the perspective of synergistic regulation of tumor vascular microenvironment remodeling and immune activation, providing theoretical basis and new research ideas for the design of solid tumor treatment strategies based on precise regulation of TVM and the clinical translation of LIGHT-related treatment systems.

Non-steroidal anti-inflammatory drugs(NSAIDs),commonly utilized analgesics,are extensively employed for managing pain associated with musculoskeletal disorders or injuries.Recent clinical studies have demonstrated a heightened risk of bone stress injuries(BSI)in soldiers and athletes,particularly during high-intensity training,due to NSAID usage.Furthermore,the impact of NSAIDs on fracture healing is well-documented;however,the precise mechanism by which their use during training contributes to an increased incidence of stress bone injuries remains unclear.This article aims to summarize potential mechanisms through an extensive review of domestic and international literature in order to standardize the utilization and clinical management of NSAIDs,optimize pain management strategies,and prevent stress bone injuries or fractures in specific populations such as soldiers and elite athletes.

Objective To construct a predictive model for high-grade pathological components of early invasive lung adenocarcinoma(ILAC)based on radiomics.Methods Collecting information on total 495 patients who underwent radical operation and were pathologically diagnosed as stage Ⅰ in the cardiothoracic surgery of Zhoushan Hospital from January 2015 to December 2019,including gender,age,pathological findings,tumor markers and preoperative chest CT images.The micropapillary and solid components in postoperative pathology were defined as"high-grade pathological components",while those without high-grade pathological components were classified into the low-grade group and those with high-grade pathological components were classified into the high-grade group.And patients were randomly divided into the training set(343 cases)and the validation set(152 cases)with a ratio of 7∶3 using the simple randomization grouping method.The region of interest of nodules on CT images were delineated layer by layer by scientific research platform and 1950 radiomics features were extracted.And then those features were filtrated by F-test,Pearson correlation coefficient,and L1 based feature selection.A model was built by using Logistic regression machine learning classifier,named mod 2,and radscore was also obtained.Differences between general information and CT features were analyzed.Binary Logistic regression analysis was used to construct a model for statistically significant variables,named mod 1.At the same time,Radscore was added to build the mod and named comb mod.The area under the curve(AUC),sensitivity and specificity of the three models were calculated.A nomogram was also drawn.Results A total of 495 patients were divided into the training set(n=343)and the validation set(n=152).Gender,carcinoma embryonic antigen(CEA),nodule,and maximum diameter were screened out in clinical features and involved in constructing the mod 1.Twelve features were selected from the radiomics features to build mod 2.Comb mod performed best,training set AUC:0.887,validation set AUC:0.875,and had good clinical practicability.Conclusion The model composed of general feature,CT feature and radiomics features could accurately predict high-grade pathological components in early ILAC,and provide references for clinicians to choose surgical method for patients.

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

AIM To evaluate the quality of Gegen Formula Granules.METHODS Linear calibration with two reference substances(LCTRS)was adopted in the predicting of retention time with puerarin and daidzein as internal standards.UPLC characteristic chromatograms were established.The contents of 3'-hydroxy puerarin,puerarin(internal standard),3'-methoxy puerarin,puerarin 6"-O-xyloside,puerarin apioside and daidzin were determined by quantitative determination analysis multi-components by a single marker(QAMS),after which their transfer rates were calculated.RESULTS Compared with relative retention time method,LCTRS demonstrated higher positional accuracy for characteristic peaks and wider application range for columns.There were 9 characteristic peaks in the characteristic chromatograms for 14 batches of formula granules and 15 batches of standard decoctions with the similarities of more than 0.95.The contents and transfer rates of various constituents in formula granules and standard decoctions were basically consistent.CONCLUSION The chemical constituents in formula granules and their standard decoctions of Puerariae lobatae Radix display good consistency,reliable preparation process is observable in the former.

Objective:To elucidate the genomic characteristics of the immunoglobulin (IG) heavy-chain variable region and light-chain variable region, the expression of subclones, and the prognostic significance in patients with CLL.Methods:Blood and/or bone marrow specimens were gathered from a cohort of 36 patients with CLL diagnosed at Jiangsu Province Hospital from December 2018 to May 2023, including 12 cases of B cell receptor (BCR) stereotyped patients. IG heavy-chain (IGH) and light-chain (IG Kappa [IGK] and IG lambda [IGL]) gene rearrangements were performed using next-generation sequencing (NGS) technology to analyze the characteristics and prognostic value in CLL.Results:NGS detection of IG variable region (IGHV) demonstrated a significant correlation and superior consistency with Sanger sequencing ( r=0.957, P < 0.001). Among the 36 patients, the IGH variant (IGHV) was observed in 9 (25.0%) but not in 27 (75.0%) participants. The incidence of the MYD88 mutation was higher among patients with mutated IGHV [1/27 (3.7%) vs 4/9 (44.4%), P=0.00]. A high incidence of trisomy 12 was observed in the IGHV #8/#8B subset [4/11 (36.4%) vs 1/25 (4.0%), P=0.023], which were more likely to develop Richter transformation [8/11 (72.7%) vs 4/25 (16.0%), P=0.002]. In the patient cohort, 36 individuals (36/36, 100.0%) used the IGK variable, whereas 15 individuals (15/36, 41.7%) employed the IGL variable (IGLV). IGLV3 - 21 reported the highest utilization rate in IGLV (5/15, 33.3%). Remarkably, patients with CLL with IGLV3-21 fragments were exclusively observed in the Binet C stage and Rai Phase Ⅲ-Ⅳ, with an incidence of del (13) (q14) at 60.0% (3/5). The median time to first treatment (TTFT) of patients with or without IGLV3 - 21 fragments was 5.2 (1.1 - 41.5) and 9.9 (0.1 - 94.4) months, respectively. Using the total reads threshold of 2.5%, 4 (4/36, 11.1%) samples were detected to have two IGHV productive clones. The median TTFT and overall survival (OS) time were 2.8 (0.9-72.7) and 12.8 months in patients with one mutated clone and 57.5 (32.0-120.7) and 51.8 months in those with two mutated clones, respectively. The median TTFT and OS time were 10.9 (0.3-94.4) and 6.3 (0.1 - 12.5) months in patients with one unmutated clone and 49.9 (22.2 - 211.1) and 30.0 (9.6 - 50.3) months in those with multiple unmutated clones, respectively ( P>0.05) . Conclusions:Detection of IG gene rearrangements using NGS technology not only facilitates the analysis of the IGHV mutation status, dominant clones, and prognostic value but also contributes to the exploration of IGK/IGL gene rearrangement fragments and the utilization of subclones. Further, it provides information about the poor prognosis of IGLV3 - 21 CLL. The shortened survival of the two unmutated clone groups in the IGHV unmutated group may indicate a poor prognosis.

Biochemistry,as a fundamental course for science and engineering majors related to biology and chemistry,holds a significant position in the curriculum.The course team at Dalian Minzu University is committed to teaching innovation,adopting the outcome-based education(OBE)concept for teaching de-sign and incorporating ideological and political elements,in order to achieve the dual goals of knowledge transmission and value guidance.The team has established a three-dimensional teaching goal of"knowl-edge,morality,and ability",covering"consolidating core knowledge,cultivating moral sentiment,and enhancing innovation ability".Through a multi-dimensional integrated teaching method of"three integra-tions and five combinations",multiple rounds of teaching practice have been carried out in the applied chemistry major using"classification and specificity of enzyme"as an example.The output of teaching re-sults and survey questionnaires show that students highly recognize the teaching design and its"process-based learning"evaluation method,fully reflecting the student-centered teaching idea.Research has shown that OBE design combined with ideological and political elements can effectively promote students' knowl-edge acquisition,moral growth,and innovation ability improvement in the course of Biochemistry.This teaching design not only helps students construct correct worldviews,outlooks on life,and values,but also significantly enhances their innovative thinking and practical abilities.This teaching design can not only ef-fectively improve the teaching quality of the course,but also provide new perspectives and ideas for the teaching design of Biochemistry,realizing the organic integration of professional knowledge imparting and i-deological and political education,and has certain innovation and practical significance.

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.