Objective: To investigate the distribution frequency and molecular mechanism of the rare blood type Lu(a-b-) in Shenzhen, and to compare the polymorphisms of the Lutheran blood group system encoding gene LU and the In (Lu) phenotype-related gene KLF1 among Han Chinese, Indian, and Uyghur populations in Xinjiang. Methods: Serological methods were used to screen the Lu(a-b-) phenotype of blood donors in Shenzhen. Third-generation sequencing was employed to sequence the full-length of the LU and KLF1 genes in Lu (a-b-) phenotype samples as well as the samples from the Han Chinese, Indians, and Uyghur population, followed by analysis of gene haplotypes frequencies. Results: Ten individuals with the Lu(a-b-) phenotype were screened out of 14 367 blood donors in Shenzhen, yielding a frequency of approximately 0.07%. Only 2 cases showed mutations in the coding region of the LU gene, while all individuals showed heterozygous mutations in the coding region of the KLF1 gene. The highest mutation frequencies of the LU and KLF1 genes were observed in the Uyghur population in Xinjiang and the Han Chinese in Shenzhen, respectively. Conclusion: All Lu(a-b-) phenotypes are of the In (Lu) type, and their formation mechanism is mainly related to KLF1 gene mutations. Both the LU and KLF1 genes exhibit significant polymorphism in the Han Chinese, Indians, and Uyghur populations.

Objective To study the photodynamic performance and the killing effect of photodynamic therapy on lung cancer of novel chlorin compounds 2-（4-（5,15,20-triphenyl-7H,8H-porphyrin-10-yl） phenoxy） acetic acid（D1）and 4-（4-（5,15,20-triphenyl-7H,8H-porphyrin-10-yl） phenoxy） butanoic acid （D2）. Methods The ultraviolet visible absorption spectrum and fluorescence spectrum of D1 and D2 were determined. The singlet oxygen generation capacity of D1 and D2 was measured by using DPBF as singlet oxygen capture agent. Fluorescence assay was used to detect the cellular phagocytosis rate of the compounds in A549 cells, and MTT assay was used to detect their dark toxicity and phototoxicity. A nude mouse model of lung cancer was established to investigate the antitumor activity of the compounds mediated photodynamic action in vivo, and the blood concentration of D2 in nude mice, its distribution in tumor tissue and skin tissue were further detected. Results D1 and D2 had strong absorption at 652 nm with the best excitation wavelength at 429 nm and 427 nm, and the optimal emission wavelength was at about 659 nm. They also had a higher singlet oxygen generation rate than the control drug m-THPC. D1 and D2 had no dark toxicity at concentrations below 10 μmol/L, and could be ingested by A549 cells, basically reaching saturation in 18~24 hours. After laser irradiation at 650 nm wavelength, D1 and D2 showed significant antitumor activity in vivo and in vitro （P<0.01）. However, D2 could selectively accumulate in tumor tissues after administration, and the optimal treatment time was less than 30 min after administration. Conclusion D2 had excellent photodynamic antitumor activity and could selectively aggregate in tumor tissues, which had the potential to be a candidate drug for photosensitizer and treatment of lung cancer with independent intellectual property rights, and was worth further research.

ObjectiveTo investigate the value of predictive models established based on machine learning methods in predicting the risk of metabolic associated fatty liver disease （MAFLD）， and to analyze its key risk factors. MethodsA retrospective analysis was performed for the 50 variables of 2 168 healthy individuals who underwent physical examination in Department of Health Assessment， Xiyuan Hospital， China Academy of Chinese Medical Sciences， from January 2021 to December 2024， including body composition， past history， and laboratory tests， and according to whether they were diagnosed with MAFLD or not， they were divided into MAFLD group with 265 individuals and non-MAFLD group with 1 903 individuals. The Mann-Whitney U test was used for comparison of continuous data between two groups， and the chi-square test was used for comparison of categorical data between two groups. Randomly split the research data into a training set and a validation set in a 70% to 30% ratio. Predictive factors were screened from the training set data using univariate analysis， LASSO regression， and multivariate Logistic regression analysis. Predictive models were then constructed using seven machine learning methods： Logistic regression， decision tree， random forest （RF）， eXtreme gradient boosting， light gradient boosting machine， support vector machine， and artificial neural network. Model performance was evaluated by plotting receiver operating characteristic curve for the validation set and calculating the area under the curve （AUC）， sensitivity， specificity， and Youden index for each model. Furthermore， the SHapley Additive exPlanation （SHAP） method was used to analyze the contribution of variables in the optimal model. ResultsThe prevalence rate of MAFLD among the 2 168 subjects was 12.22% （265/2 168）. Smoking， diastolic blood pressure， phase angle， visceral fat area， muscle fat ratio， waist-to-hip ratio， aspartate aminotransferase， non-HDL-C/HDL-C ratio， triglyceride-glucose index， and gallstones were independent risk factors for MAFLD （all P<0.05）. The seven predictive models of support vector machine， eXtreme gradient boosting， decision tree， light gradient boosting machine， artificial neural network， RF， and Logistic regression had an AUC of 0.738， 0.754， 0.757， 0.786， 0.795， 0.796， and 0.815， respectively， in the validation set， among which the RF model had the best discriminatory ability （AUC=0.796， 95% confidence interval： 0.754 — 0.839）， with a sensitivity of 81.01%， a specificity of 63.16%， and a Youden index of 44.17%. The SHAP analysis showed that visceral fat area， waist-to-hip ratio， and diastolic blood pressure were the top three predictive factors in terms of importance. ConclusionThe RF model， constructed based on body composition and clinical indicators， has a good performance in predicting the risk of MAFLD， and its interpretability can help to identify high-risk individuals in the early stage in clinical practice.

Inteins are unique protein insertion sequences capable of self-excision, enabling the covalent ligation of flanking extein peptides via amide bond formation. This process proceeds spontaneously without requiring external enzymes, cofactors, or chemical reagents, granting inteins exceptional biocompatibility and traceless performance in protein engineering applications. Split inteins represent a specialized and versatile subclass whose splicing domains are encoded by two separate gene fragments rather than a single continuous open reading frame. These fragments, known as the N-terminal (IntN) and C-terminal (IntC) split inteins, associate through non-covalent interactions including hydrophobic forces, hydrogen bonds, and van der Waals forces to assemble into an active three-dimensional structure, which then drives efficient extein ligation and enables protein trans-splicing. Protein trans-splicing mediated by split inteins has become a cornerstone for traceless protein ligation owing to its high specificity and irreversibility, fundamentally reshaping strategies for protein modification, assembly, and functional regulation. Compared with traditional chemical ligation methods, split intein systems require no complex chemical derivatization of peptide fragments and can operate efficiently at micromolar concentrations under physiological conditions, thus avoiding structural and functional damage caused by organic reagents. In contrast to enzymatic ligation tools such as sortase, split inteins eliminate the need for additional enzymes or cofactors, simplifying reaction systems, reducing costs, and minimizing non-specific side products. These distinctive advantages render split inteins highly promising for applications in chemical biology, synthetic biology, and biopharmaceutical development. In recent years, deepened mechanistic understanding has established structure-guided rational design as the primary approach to overcoming key limitations of split inteins, including intrinsic aggregation propensity, strict extein sequence dependence, and limited splicing efficiency. Bioinformatic tools have been used to identify aggregation-prone regions in the IntN fragment, and site-directed mutagenesis of hydrophobic residues, relocation of split sites, or removal of misfolding-prone sequences has substantially reduced in vitro aggregation and improved soluble expression and assembly activity. Rational engineering of catalytic residues and adjacent flexible loops has relaxed strict amino acid preferences at extein junctions, enhancing sequence tolerance and reducing the risk of functional impairment in target proteins. Consensus design based on multiple sequence alignments has yielded ultra-fast splicing variants such as Cfa DnaE and Cat-TerL, which exhibit significantly accelerated kinetics and improved tolerance to denaturing conditions. Meanwhile, advances in structural biology have further clarified the conformational dynamics and catalytic mechanisms of splicing, supporting the precise design of high-performance intein modules. On this basis, electrostatic interaction tuning and metagenomic screening have yielded multiple mutually orthogonal split intein pairs, enabling selective multi-fragment protein ligation and providing new routes for the efficient synthesis of large multi-domain functional proteins. With these engineered split inteins offering continuously improved performance and expanded applicability, protein trans-splicing has been widely applied in numerous cutting-edge areas of protein research and biomedicine. In gene delivery, split intein-based systems overcome the packaging limit of adeno-associated viral vectors, enabling the accurate reconstitution of large therapeutic proteins and base editors in target cells, thereby enhancing the efficacy and scope of gene therapy for genetic diseases. In internal protein sequence editing, split inteins mediate precise sequence replacement and modification in flexible regions or loops of target proteins, without the need for complex multi-step ligation and protein refolding involved in traditional protein semisynthesis. In protein-protein interaction studies, intein-mediated splicing covalently captures transient and weak intracellular complexes, enabling sensitive, high-throughput interaction detection and drug screening. In synthetic biology, conditionally controllable splicing systems support the construction of diverse intracellular and cell-surface biological logic gates for the precise regulation of cellular behavior. In mechanistic biochemical research, split inteins enable photocatalytic proximity labeling and site-specific tagging, allowing the preparation of homogeneous protein samples carrying precise post-translational modifications such as ubiquitination and polyglutamylation for chromatin interactome analysis and epigenetic studies. Moreover, covalent trapping strategies using split inteins stabilize transient enzymatic intermediates, providing unprecedented insights into molecular mechanisms such as nucleosome ubiquitination that are difficult to elucidate using conventional methods. This review systematically summarizes key technological advances in split inteins over the past decade, highlighting engineering strategies, mechanistic insights, and the development of orthogonal components. It comprehensively surveys emerging applications at the frontiers of protein research, analyzes current core challenges, and proposes future directions, particularly emphasizing artificial intelligence-driven de novo design and novel splicing pathways to break existing technical bottlenecks. By enabling traceless, efficient, and versatile protein manipulation, split inteins continue to serve as indispensable tools that drive innovation in protein engineering and fundamental life science research.

ObjectiveTo investigate the current awareness and optimization recommendations on pneumonia with unknown etiology (PUE) surveillance among Shanghai healthcare professionals, and to provide evidence for the improvement of the surveillance system. MethodsIn December 2024, healthcare professional participants from diverse medical and health institutions were randomly selected within each stratum using a stratified cluster sampling method in all 16 districts of Shanghai for questionnaire surveys on their knowledge and suggestions for optimization of the PUE surveillance system. Descriptive statistics analyses, multivariate logistic regressions, and content analyses were used for data analyses. ResultsA total of 385 survey subjects were involved in the study, including 119 professional staffs from disease prevention and control centers and 266 from medical institutions. Those who understood the case definition for PUE correctly accounted for 67.01% (258/385). Multivariate regression model analyses showed that female gender (aOR=2.22, 95%CI: 1.36‒3.66), professionals from disease control institutions (aOR=2.02, 95%CI: 1.12‒3.72), and those who self-assessed as being familiar with the monitoring system (aOR=2.18, 95%CI:1.23‒3.89) had better understanding of the definition. Overall, 89.61% (345/385) of the survey subjects acknowledged the necessity of the surveillance system. Small number of cases meeting the case definition (54.46%, 214/379), insufficient diagnostic awareness (39.84%, 151/379), and lack of incentives for case reporting (29.29%, 111/379) were major problems for the current surveillance system, with variations observed across different types of institutions. Suggested segments for optimizing the surveillance system included monitoring purpose, case definition, form of monitoring and reporting procedure, organization mechanism, and epidemiological investigation and response. ConclusionThe necessity of PUE surveillance is commonly recognized among healthcare workers. However, the existing system falls short in terms of mastery of case definition and overall surveillance adaptability. There is an urgent need for optimization and improvement to enhance the capacity for preventing and controlling emerging and sudden respiratory infectious diseases.

Obesity is a chronic low-grade inflammation and a risk factor for diseases such as diabetes， hypertension， dyslipidemia， and malignant tumors， demonstrating an increasingly grim development situation. The nuclear factor-kappa B （NF-κB） signaling pathway is a key signaling pathway involved in the immune response and inflammatory response. In obese individuals， the expression of NF-κB is overactivated， which leads to abnormal inflammatory responses in the body. Therefore， it is expected to alleviate inflammation and treat obesity by regulating the NF-κB signaling pathway， which has been proven effective by a large number of studies. The available studies on the NF-κB signaling pathway mostly focus on tumors， and there is no systematic review of the mechanism of this pathway in mediating obesity and the traditional Chinese medicine （TCM） treatment. We reviewed the research progress in the pathological and physiological processes of obesity mediated by NF-κB signaling pathway and TCM treatment， aiming to give insights into the clinical treatment of obesity with TCM and provide reference targets and research directions for exploring the biological foundations and the development of new TCM preparations.

Objective To explore the value of oral contrast-enhanced ultrasonography (OCEUS) combined with contrast-enhanced CT in predicting preoperative T staging in patients with gastric cancer. Methods A retrospective analysis was conducted on 80 patients with gastric cancer confirmed via endoscopic biopsy or postoperative pathology at the First People’s Hospital of Jining from January 2021 to November 2024. The cohort included 56 males and 24 females, aged 38-79 years, with a median age of 55.9 years. All patients underwent both OCEUS and contrast-enhanced CT within one week prior to surgery. T staging of gastric cancer was determined using OCEUS, contrast-enhanced CT, or their combination. The results were compared with pathological T staging, and statistical differences in accuracy were analyzed. Results Pathological T staging identified T1 in 9 cases, T2 in 16 cases, T3 in 42 cases, and T4 in 13 cases. OCEUS indicated T1 in 6 cases, T2 in 14 cases, T3 in 50 cases, and T4 in 10 cases, with an accuracy rate of 80.0%. Contrast-enhanced CT indicated T1 in 4 cases, T2 in 12 cases, T3 in 52 cases, and T4 in 12 cases, with an accuracy rate of 75.0%. The combination of OCEUS and contrast-enhanced CT indicated T1 in 6 cases, T2 in 15 cases, T3 in 47 cases, and T4 in 12 cases, with an accuracy rate of 87.5%. The combined approach demonstrated significantly higher accuracy in preoperative T staging compared to either method alone (P < 0.05). Conclusion The combination of OCEUS and contrast-enhanced CT improves the accuracy of preoperative T staging in gastric cancer patients, providing valuable support for their diagnosis and treatment.

ObjectiveTo investigate the triglyceride-glucose (TyG) index and the level of serum homocysteine (Hcy) in association with the incidence of stroke in type 2 diabetes mellitus (T2DM) patients. MethodsBased on the chronic disease risk factor surveillance cohort in Pudong New Area, Shanghai, excluding those with stroke in baseline survey, T2DM patients who joined the cohort from January 2016 to October 2020 were selected as the research subjects. During the follow-up period, a total of 318 new-onset ischemic stroke patients were selected as the case group, and a total of 318 individuals matched by gender without stroke were selected as the control group. The Cox proportional hazards regression model was used to adjust for confounding factors and explore the serum TyG index and the Hcy biochemical indicator in association with the risk of stroke. ResultsThe Cox proportional hazards regression results showed that after adjusting for confounding factors, the risk of stroke in T2DM patients with 10 μmol·L⁻¹-¹ and Hcy>15 μmol·L⁻¹ was 1.532 times (95%CI: 1.017‒2.309 times, P=0.041) and 1.738 times (95%CI: 1.119‒2.699 times, P=0.014) higher respectively, compared to T2DM patients with Hcy≤10 μmol·L⁻¹. T2DM patients with TyG>9.074 had 1.396 times higher risk of stroke (95%CI: 1.091‒1.787, P=0.008) compared to those with TyG≤9.074. The study found that urea and α1-antitrypsin (α1-AT) were independent risk factors for the incidence of stroke in T2DM patients. For every unit increase in urea andα1-AT, the risk of stroke in T2DM patients increased by 233.6% (HR=3.336, 95%CI: 1.588‒7.009, P=0.001) and 11.3% (HR=1.113, 95%CI: 1.028‒1.205, P=0.008), respectively. Cumulative risk curves showed that Hcy>10 μmol·L⁻¹ and TyG>9.074 accelerated the time to stroke occurrence in T2DM patients. ConclusionElevated levels of Hcy>10 μmol·L⁻¹ and a higher TyG index are risk factors for stroke in T2DM patients. Effective interventions for Hcy and TyG should be conducted for diabetic patients to reduce the incidence of stroke.

Dormant tumor cells constitute a population of cancer cells that reside in a non-proliferative or low-proliferative state, typically arrested in the G0/G1 phase and exhibiting minimal mitotic activity. These cells are commonly observed across multiple cancer types, including breast, lung, and ovarian cancers, and represent a central cellular component of minimal residual disease (MRD) following surgical resection of the primary tumor. Dormant cells are closely associated with long-term clinical latency and late-stage relapse. Due to their quiescent nature, dormant cells are intrinsically resistant to conventional therapies—such as chemotherapy and radiotherapy—that preferentially target rapidly dividing cells. In addition, they display enhanced anti-apoptotic capacity and immune evasion, rendering them particularly difficult to eradicate. More critically, in response to microenvironmental changes or activation of specific signaling pathways, dormant cells can re-enter the cell cycle and initiate metastatic outgrowth or tumor recurrence. This ability to escape dormancy underscores their clinical threat and positions their effective detection and elimination as a major challenge in contemporary cancer treatment. Hypoxia, a hallmark of the solid tumor microenvironment, has been widely recognized as a potent inducer of tumor cell dormancy. However, the molecular mechanisms by which tumor cells sense and respond to hypoxic stress—initiating the transition into dormancy—remain poorly defined. In particular, the lack of a systems-level understanding of the dynamic and multifactorial regulatory landscape has impeded the identification of actionable targets and constrained the development of effective therapeutic strategies. Accumulating evidence indicates that hypoxia-induced dormancy tumor cells are accompanied by a suite of adaptive phenotypes, including cell cycle arrest, global suppression of protein synthesis, metabolic reprogramming, autophagy activation, resistance to apoptosis, immune evasion, and therapy tolerance. These changes are orchestrated by multiple converging signaling pathways—such as PI3K-AKT-mTOR, Ras-Raf-MEK-ERK, and AMPK—that together constitute a highly dynamic and interconnected regulatory network. While individual pathways have been studied in depth, most investigations remain reductionist and fail to capture the temporal progression and network-level coordination underlying dormancy transitions. Systems biology offers a powerful framework to address this complexity. By integrating high-throughput multi-omics data—such as transcriptomics and proteomics—researchers can reconstruct global regulatory networks encompassing the key signaling axes involved in dormancy regulation. These networks facilitate the identification of core regulatory modules and elucidate functional interactions among key effectors. When combined with dynamic modeling approaches—such as ordinary differential equations—these frameworks enable the simulation of temporal behaviors of critical signaling nodes, including phosphorylated AMPK (p-AMPK), phosphorylated S6 (p-S6), and the p38/ERK activity ratio, providing insights into how their dynamic changes govern transitions between proliferation and dormancy. Beyond mapping trajectories from proliferation to dormancy and from shallow to deep dormancy, such dynamic regulatory models support topological analyses to identify central hubs and molecular switches. Key factors—such as NR2F1, mTORC1, ULK1, HIF-1α, and DYRK1A—have emerged as pivotal nodes within these networks and represent promising therapeutic targets. Constructing an integrative, systems-level regulatory framework—anchored in multi-pathway coordination, omics-layer integration, and dynamic modeling—is thus essential for decoding the architecture and progression of tumor dormancy. Such a framework not only advances mechanistic understanding but also lays the foundation for precision therapies targeting dormant tumor cells during the MRD phase, addressing a critical unmet need in cancer management.