Endoscopic transforaminal lumbar interbody fusion (TLIF) offers substantial advantages in the management of degenerative spinal diseases, including accelerated postoperative recovery. However, its technical complexity and steep learning curve pose risks for nerve root injury. Optimizing nerve root protection in full-endoscopic facet-sparing TLIF (FE fs-TLIF) and full-endoscopic facet-resecting TLIF (FE fr-TLIF) is essential for enhancing surgical safety. This study aimed to improve the nerve root protection in FE fs-TLIF and FE fr-TLIF by optimizing cage glider selection and insertion techniques based on the specific cage shape—banana-shaped or bullet-shaped. The goal was to ensure safe cage positioning and mitigate nerve root injury during discectomy, endplate preparation, and cage insertion. These strategies were validated through cadaveric simulations and clinical implementation. In FE fr-TLIF utilizing bullet-shaped (straight) cages, one-tip and two-tip cage gliders effectively protected the traversing nerve root by facilitating medial cage entry, thereby minimizing irritation of the exiting nerve root. Conversely, in FE fr-TLIF with banana-shaped cages, the lateral tilt of the cage holder during implantation required the use of a two-tip cage glider to protect the traversing and exiting nerve roots, thereby mitigating the potential risk of nerve irritation. In FE fs-TLIF, a one-tip cage glider is preferred for safeguarding the exiting nerve root, while the traversing root is inherently protected by the medial wall of the facet joint. The use of a two-tip cage glider in FE fs-TLIF can cause injury to the nerve root during glider insertion. In addition to the selection of cage gliders, improper cage insertion steps can also contribute to postoperative neurapraxia. The appropriate selection of cage gliders with corresponding insertion techniques is critical for nerve root protection in endoscopic TLIF. Tailoring these choices to the specific approach (FE fs-TLIF or FE fr-TLIF) and cage type (banana or bullet) enhances surgical safety and clinical outcomes.

Objective To compare the efficacy of five Blastocystis staining method for staining cells with different morphologies and in different developmental stages to select appropriate staining techniques for specific scenarios and experimental purposes. Methods Blastocystis was stained using Wright-Giemsa, periodic acid-Schiff (PAS), Oil Red O, Nile Red, and Lugol's iodine solution respectively. The staining effects of the five method on Blastocystis were compared. Results Wright-Giemsa staining colored the cytoplasm and nucleus of Blastocystis bluish-purple, with other parts lightly stained; PAS staining rendered Blastocystis cells red; Oil Red O staining showed different structures within Blastocystis in varying intensities of orange-red; Nile Red enabled Blastocystis to fluoresce orange-yellow under a fluorescence microscope; Lugol's iodine solution stained the cytoplasm and nucleus a dark brownish-yellow. Conclusions Wright-Giemsa staining is recommended for clinical diagnosis. For metabolic studies, a combination of periodic acid-Schiff (PAS) staining and Oil Red O staining is suggested; PAS + Oil Red O staining can provide comprehensive morphological evidence for cell metabolism research. Nile Red staining is recommended for dynamic observation of live Blastocystis. Lugol's iodine staining is suitable for preliminary screening of large-scale samples.

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.

OBJECTIVE: To investigate the associations between eight serum per- and polyfluoroalkyl substances (PFASs) and regional fat depots, we analyzed the data from the National Health and Nutrition Examination Survey (NHANES) 2011-2018 cycles. METHODS: Multiple linear regression models were developed to explore the associations between serum PFAS concentrations and six fat compositions along with a fat distribution score created by summing the concentrations of the six fat compositions. The associations between structurally grouped PFASs and fat distribution were assessed, and a prediction model was developed to estimate the ability of PFAS exposure to predict obesity risk. RESULTS: Among females aged 39-59 years, trunk fat mass was positively associated with perfluorooctane sulfonate (PFOS). Higher concentrations of PFOS, perfluorohexane sulfonate (PFHxS), perfluorodecanoate (PFDeA), perfluorononanoate (PFNA), and n-perfluorooctanoate (n-PFOA) were linked to greater visceral adipose tissue in this group. In men, exposure to total perfluoroalkane sulfonates (PFSAs) and long-chain PFSAs was associated with reductions in abdominal fat, while higher abdominal fat in women aged 39-59 years was associated with short-chain PFSAs. The prediction model demonstrated high accuracy, with an area under the curve (AUC) of 0.9925 for predicting obesity risk. CONCLUSION PFAS exposure is associated with regional fat distribution, with varying effects based on age, sex, and PFAS structure. The findings highlight the potential role of PFAS exposure in influencing fat depots and obesity risk, with significant implications for public health. The prediction model provides a highly accurate tool for assessing obesity risk related to PFAS exposure.
Humans ; Fluorocarbons/blood* ; Female ; Adult ; Middle Aged ; Male ; Environmental Pollutants/blood* ; Abdominal Fat ; Nutrition Surveys ; Alkanesulfonic Acids/blood* ; Obesity ; Environmental Exposure

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

OBJECTIVE: To identify the key features of facial and tongue images associated with anemia in female populations, establish anemia risk-screening models, and evaluate their performance. METHODS: A total of 533 female participants (anemic and healthy) were recruited from Shuguang Hospital. Facial and tongue images were collected using the TFDA-1 tongue and face diagnosis instrument. Color and texture features from various parts of facial and tongue images were extracted using Face Diagnosis Analysis System (FDAS) and Tongue Diagnosis Analysis System version 2.0 (TDAS v2.0). Least Absolute Shrinkage and Selection Operator (LASSO) regression was used for feature selection. Ten machine learning models and one deep learning model (ResNet50V2 + Conv1D) were developed and evaluated. RESULTS: Anemic women showed lower a-values, higher L- and b-values across all age groups. Texture features analysis showed that women aged 30-39 with anemia had higher angular second moment (ASM)and lower entropy (ENT) values in facial images, while those aged 40-49 had lower contrast (CON), ENT, and MEAN values in tongue images but higher ASM. Anemic women exhibited age-related trends similar to healthy women, with decreasing L-values and increasing a-, b-, and ASM-values. LASSO identified 19 key features from 62. Among classifiers, the Artificial Neural Network (ANN) model achieved the best performance [area under the curve (AUC): 0.849, accuracy: 0.781]. The ResNet50V2 model achieved comparable results [AUC: 0.846, accuracy: 0.818]. CONCLUSION Differences in facial and tongue images suggest that color and texture features can serve as potential TCM phenotype and auxiliary diagnostic indicators for female anemia.
Humans ; Female ; Tongue/diagnostic imaging* ; Adult ; Anemia/diagnosis* ; Middle Aged ; Face/diagnostic imaging* ; Young Adult ; Machine Learning

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*

Objective:To construct a tripartite game model involving the government,the public,and the pharmaceutical industry alliance during public health emergencies,revealing the dynamic mechanisms of health-related information quality regulation and exploring effective strategies to optimize the informa-tion dissemination environment through reward-punishment mechanisms.Methods:Based on evolutionary game theory,a tripartite evolutionary game model was established,integrating strategy spaces,payoff functions,and parameter definitions for each stakeholder.The pharmaceutical industry alliance's strate-gies included publishing high-or low-quality information(α),the public's strategies encompassed ration-al analysis or passive response(β),and the government's strategies involved regulatory enforcement or inaction(γ).Key parameters,such as economic benefits(Iyy),regulatory costs(Czf),penalties(Fyy),and incentives(Pyy),were quantified to reflect real-world scenarios.Replicator dynamic equa-tions and Jacobian matrices were derived to analyze the stability of equilibrium points,while MATLAB 2016a simulations were conducted to validate the model under varying initial conditions(e.g.,Iyy=100,150,200;Pyy=0,20,35;Fyy=0,10,20).Sensitivity analyses examined the impact of critical pa-rameters on system evolution,by 50 iterative simulations to observe convergence patterns.Results:The study revealed three key findings:(1)Public rational discernment(β)significantly influenced the phar-maceutical industry's strategy.Simulations demonstrated that increasing Iqz(benefits of information acqui-sition)reduced Cqz(cognitive costs),elevating β from 0.4 to 0.8 and driving α(high-quality information probability)to stabilize at 1.(2)Government regulatory intensity(γ)correlated positively with the so-cial hazards of low-quality information.When Fyy+Pyy＞Iyy,speculative behaviors decreased,achieving equilibrium at α=1.(3)Dual stable equilibria emerged:a high-quality equilibrium(α=1,β=1,γ=0)with lower regulatory costs and a low-quality equilibrium(α=0,β=0,γ=1)associated with higher social risks.Phase diagrams illustrated path dependency,where initial α＜0.5 led to the low-quality equilibrium unless dynamic penalties(Fyy＞20)and incentives(Pyy＞30)were enforced.Conclusion:A"carrot-stick"collaborative governance framework is proposed,emphasizing categorized regulation,AI-enabled auditing,and dynamic penalty systems.Future research should integrate emotional utility func-tions to address irrational decision-making impacts,thereby enhancing the adaptability of health informa-tion regulatory systems.

AIM To investigate the effects of Jisuishang Formula on neurological function and ferroptosis in a rat model of cervical spondylotic myelopathy(CSM).METHODS The CSM rat models were established and randomly assigned to the model group,the Fer-1 group(2 g/kg Ferrostatin-1 via intraperitoneal injection),the low-dose(9.7 g/kg,intragastrically),medium-dose(19.4 g/kg,intragastrically)and high-dose(38.8 g/kg,intragastrically)Jisuishang Formula groups,and the sham operation group,with 6 rats in each group.Following 4 weeks of treatment administration,BBB locomotor scores and oblique plate test result were recorded to assess their neurological function in rats.Histopathological evaluation utilized HE staining for spinal cord tissue pathology,Nissl staining for Nissl body visualization,and Prussian blue staining for iron ion deposition analysis.Protein expressions of Nrf2,SLC7A11,GPX4,HO-1,TFRC and Cox2 in spinal cord tissues was detected by immunofluorescence and Western blot,while mRNA expressions were quantified using RT-qPCR.RESULTS Compared to the sham group,the CSM model group exhibited significantly reduced BBB locomotor scores and inclined plane test performance at 1,2 and 4 weeks post-operation(P＜0.05);obvious tissue cavitation,cellular edema and Prussian blue positive iron deposition in spinal cord tissues;downregulated protein and mRNA expressions of Nrf2,SLC7A11,GPX4,HO-1(P＜0.05);and upregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).Compared to the model group,the Jisuishang Formula and Fer-1 intervention groups showed significantly improved BBB scores and inclined plane test result at 1,2 and 4 weeks post-operation(P＜0.05);reduced tissue cavitation,attenuated cellular edema and decreased Prussian blue positive iron deposition in spinal cord tissues;upregulated protein and mRNA expression of Nrf2,SLC7A11,GPX4 and HO-1 in spinal cord tissues(P＜0.05);and downregulated protein and mRNA expressions of TFRC and Cox2(P＜0.05).CONCLUSION Targeting the Nrf2/SLC7A11/GPX4 signaling pathway,Jisuishang Formula potentially suppresses ferroptosis and alleviates iron accumulation in spinal cord neurons,thereby improving neurological recovery in CSM rats.

Objective To develop the value of machine learning(ML)models based on contrast-transthoracic echocardiography(cTTE)and transesophageal echocardiography(TEE)combined with clinical and laboratory indicators for predicting patent foramen ovale-associated stroke(PFO-AS).Methods Totally 313 patients with PFO diagnosed with cTTE and TEE were retrospectively enrolled.Among them,65 cases were found complicated with ischemic stroke and confirmed as PFO-AS(PFO-AS group),and the rest 248 cases without ischemic stroke were classified as non-PFO-AS group.The patients were divided into training set(n=219,including 48 cases of PFO-AS and 171 cases of non-PFO-AS)and test set(n=94,including 17 cases of PFO-AS and 77 cases of non-PFO-AS)at the ratio of 7∶3.Univariable and multivariable logistic regression(LR)were used to analyze clinical and laboratory indicators as well as cTTE and TEE parameters in training set to screen independent predictive factors of PFO-AS.ML models,including LR,K-nearest neighbor(KNN),support vector machine(SVM),random forest(RF),decision tree(DT),back propagation neural network(BPNN)and gradient boosting machine(GBM)were constructed,and the predictive efficacy of the models for predicting PFO-AS was evaluated,then the optimal model was selected.Results Patient's age＞49-69 years,with smoking history,plasma albumin≥43.8 g/L,significant right-to-left shunt at rest shown on cTTE and complicated atrial septal aneurysm shown on TEE were all independent predictors of PFO-AS,which were used to construct ML models.The area under the curve(AUC)of LR,KNN,SVM,RF,DT,BPNN and GBM models in training set was 0.779-0.853,while in test set was 0.730-0.877.RF model had relatively high and comparable sensitivity,specificity and AUC in both training and test sets,also higher precision and smaller Brier score in test set,hence was regarded as the optimal ML model.Conclusion RF model based on cTTE and TEE combined with clinical and laboratory indicators could be used to effectively predict PFO-AS.