The activation proteins released by fibroblasts in the tumor microenvironment regulate tumor growth, migration, and treatment response, thereby influencing tumor progression and therapeutic outcomes. Owing to the proliferation and metastasis of tumors, fibroblast activation protein (FAP) is typically highly expressed in the tumor stroma, whereas it is nearly absent in adult normal tissues and benign lesions, making it an attractive target for precision medicine. Radiolabeled agents targeting FAP have the potential for targeted cancer diagnosis and therapy. This comprehensive review aims to describe the evolution of FAPI-based radiopharmaceuticals and their structural optimization. Within its scope, this review summarizes the advances in the use of radiolabeled small molecule inhibitors for tumor imaging and therapy as well as the modification strategies for FAPIs, combined with insights from structure-activity relationships and clinical studies, providing a valuable perspective for radiopharmaceutical clinical development and application.

OBJECTIVES: To investigate the role of the BNIP3-PI3K/Akt signaling pathway in mediating the inhibitory effect of Buyang Huanwu Decoction (BYHWT) on mitochondrial autophagy in human synovial fibroblasts from rheumatoid arthritis patients (FLS-RA) cultured under a hypoxic condition. METHODS: Forty normal Wistar rats were randomized into two groups (n=20) for daily gavage of BYHWT or distilled water for 7 days to prepare BYHWT-medicated or control sera. FLS-RA were cultured in routine condition or exposed to hypoxia (10% O₂) for 24 h wigh subsequent treatment with IL-1β, followed by treatment with diluted BYHWT-medicated serum (5%, 10% and 20%) or control serum. AnnexinV-APC/7-AAD double staining and T-AOC kit were used for detecting apoptosis and total antioxidant capacity of the cells, and the changes in ROS, ATP level, mitochondrial membrane potential and Ca²⁺ homeostasis were analyzed. The changes in mRNA and protein expressions of BNIP3, PI3K and AKT and mRNA expressions of LC3, Beclin-1 and P62 were detected using RT-qPCR and Western blotting. RESULTS: Treatment with BYHWT-medicated serum dose-dependently lowered apoptosis rate of IL-1β-induced FLS-RA with hypoxic exposure. The treatment significantly decreased T-AOC concentration, increased ROS production, autophagosome formation and ATPase levels, and lowered mitochondrial membrane potential and Ca²⁺ level in the cells. In IL-1β-induced FLS-RA with hypoxic exposure, treatment with BYHWT-medicated serum significantly increased BNIP3 protein expression, decreased the protein expressions of PI3K and AKT, increased the mRNA expressions of BNIP3 and P62, and lowered the mRNA expressions of PI3K, AKT, LC3 and Beclin-1 without significantly affecting Beclin-1 protein expression. The cells treated with 5% and 10% BYHWT-medicated serum showed no significant changes in LC3 expression. CONCLUSIONS BYHWT inhibits mitochondrial autophagy in IL-1β-induced FLS-RA with hypoxic exposure possibly by inhibiting BNIP3-mediated PI3K/AKT signaling pathway.
Drugs, Chinese Herbal/pharmacology* ; Arthritis, Rheumatoid/pathology* ; Animals ; Signal Transduction/drug effects* ; Proto-Oncogene Proteins c-akt/metabolism* ; Autophagy/drug effects* ; Humans ; Fibroblasts/cytology* ; Rats, Wistar ; Membrane Proteins/metabolism* ; Rats ; Phosphatidylinositol 3-Kinases/metabolism* ; Mitochondria/metabolism* ; Cells, Cultured ; Proto-Oncogene Proteins/metabolism* ; Apoptosis/drug effects* ; Cell Hypoxia ; Synovial Membrane/cytology* ; Male ; Mitochondrial Proteins

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks

Metachromatic leukodystrophy (MLD) is an inherited disease caused by a deficiency of the enzyme arylsulfatase A (ARSA). Lentivirus-modified autologous hematopoietic stem cell gene therapy (HSCGT) has recently been approved for clinical use in pre and early symptomatic children with MLD to increase ARSA activity. Unfortunately, this advanced therapy is not available for most patients with MLD who have progressed to more advanced symptomatic stages at diagnosis. Patients with late-onset juvenile MLD typically present with a slower neurological progression of symptoms and represent a significant burden to the economy and healthcare system, whereas those with early onset infantile MLD die within a few years of symptom onset. We conducted a pilot study to determine the safety and benefit of HSCGT in patients with postsymptomatic juvenile MLD and report preliminary results. The safety profile of HSCGT was favorable in this long-term follow-up over 9 years. The most common adverse events (AEs) within 2 months of HSCGT were related to busulfan conditioning, and all AEs resolved. No HSCGT-related AEs and no evidence of distorted hematopoietic differentiation during long-term follow-up for up to 9.6 years. Importantly, to date, patients have maintained remarkably improved ARSA activity with a stable disease state, including increased Functional Independence Measure (FIM) score and decreased magnetic resonance imaging (MRI) lesion score. This long-term follow-up pilot study suggests that HSCGT is safe and provides clinical benefit to patients with postsymptomatic juvenile MLD.
Humans ; Leukodystrophy, Metachromatic/genetics* ; Pilot Projects ; Genetic Therapy/methods* ; Hematopoietic Stem Cell Transplantation ; Male ; Follow-Up Studies ; Female ; Lentivirus/genetics* ; Child ; Child, Preschool ; Hematopoietic Stem Cells/metabolism* ; Cerebroside-Sulfatase/metabolism* ; Adolescent

Radiation-induced thrombocytopenia (RIT) faces a perplexing challenge in the clinical treatment of cancer patients, and current therapeutic approaches are inadequate in the clinical settings. In this research, oxymatrine, a new molecule capable of healing RIT was screened out, and the underlying regulatory mechanism associated with magakaryocyte (MK) differentiation and thrombopoiesis was demonstrated. The capacity of oxymatrine to induce MK differentiation was verified in K-562 and Meg-01 cells in vitro. The ability to induce thrombopoiesis was subsequently demonstrated in Tg (cd41:enhanced green fluorescent protein (eGFP)) zebrafish and RIT model mice. In addition, we carried out network pharmacological prediction, drug affinity responsive target stability assay (DARTS) and cellular thermal shift assay (CETSA) analyses to explore the potential targets of oxymatrine. Moreover, the pathway underlying the effects of oxymatrine was determined by Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses, Western blot (WB), and immunofluorescence. Oxymatrine markedly promoted MK differentiation and maturation in vitro. Moreover, oxymatrine induced thrombopoiesis in Tg (cd41:eGFP) zebrafish and accelerated thrombopoiesis and platelet function recovery in RIT model mice. Mechanistically, oxymatrine directly binds to toll-like receptor 2 (TLR2) and further regulates the downstream pathway stimulator of interferon genes (STING)/nuclear factor-kappaB (NF-κB), which can be blocked by C29 and C-176, which are specific inhibitors of TLR2 and STING, respectively. Taken together, we demonstrated that oxymatrine, a novel TLR2 agonist, plays a critical role in accelerating MK differentiation and thrombopoiesis via the STING/NF-κB axis, suggesting that oxymatrine is a promising candidate for RIT therapy.

OBJECTIVE: To explore the risk factors affecting the prognosis of patients with sepsis complicated with acute pulmonary embolism, and to construct and validate a nomogram predictive model for in-hospital mortality risk. METHODS: Based on the American Medical Information Mart for Intensive Care (MIMIC-III, MIMIC-IV) databases, the data were collected on patients with sepsis complicated with acute pulmonary embolism from 2001 to 2019, including baseline characteristics, and vital signs, disease scores, laboratory tests within 24 hours of admission to the intensive care unit (ICU), and interventions. In-hospital mortality was the outcome event. The total samples were divided into training and testing sets in a 7:3 ratio by random sampling. Univariate Cox regression analysis was used to verify the impact of all variables on the risk of in-hospital mortality, thereby screen potential influencing factors. Subsequently, a stepwise bi-directional regression method was applied to select factors one by one, leading to the construction of a nomogram prediction model. Collinearity testing was used to demonstrate the absence of strong multicollinearity among the influencing factors in the nomogram prediction model. The discrimination of the nomogram model, sequential organ failure assessment (SOFA), and simplified pulmonary embolism severity index (sPESI) was evaluated using C-index in the test set. Receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of various models for in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. RESULTS: A total of 562 patients with sepsis complicated with acute pulmonary embolism were included, including 393 in the training set and 169 in the testing set. Univariate Cox regression analysis showed that 30 factors associated with in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism. Through stepwise bi-directional regression, 12 variables were ultimately selected, including gender, presence of malignant tumors, body temperature, red cell distribution width (RDW), blood urea nitrogen (BUN), serum potassium, prothrombin time (PT), 24-hour urine output, mechanical ventilation, vasoactive drugs, warfarin use, and sepsis-induced coagulopathy (SIC). Collinearity testing indicated no strong multicollinearity among the influencing factors [all variance inflation factor (VIF) > 10]. A nomogram model was constructed using the 12 variables mentioned above. The nomogram model predicted the C-index and its 95% confidence interval (95%CI) of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism better than SOFA score and sPESI [0.771 (0.725-0.816) vs. 0.579 (0.519-0.639), 0.608 (0.554-0.663)]. The ROC curve showed that the area under the curve (AUC) and its 95%CI of the nomogram model were higher than those of the SOFA score and sPESI [0.811 (0.766-0.857) vs. 0.630 (0.568-0.691), 0.623 (0.566-0.680)]. These findings were consistently replicated in the internal validation of the testing set. In both the training and testing sets, Delong's test showed that the AUC of the nomogram model was significantly higher than the SOFA score and sPESI (both P < 0.05). CONCLUSION The nomogram model demonstrated good predictive effectiveness for the risk of in-hospital mortality in patients with sepsis complicated with acute pulmonary embolism, enabling clinicians to predict mortality risk in advance and take timely interventions to reduce mortality.
Humans ; Pulmonary Embolism/mortality* ; Hospital Mortality ; Nomograms ; Sepsis/complications* ; Prognosis ; Risk Factors ; Intensive Care Units ; Male ; Female ; Middle Aged ; Aged

OBJECTIVE: To construct machine learning prediction model for sepsis-associated encephalopathy (SAE), and analyze the application value of the model on early identification of SAE risk in elderly septic patients. METHODS: Patients aged over 60 years with a primary diagnosis of sepsis admitted to intensive care unit (ICU) from 2008 to 2023 were selected from Medical Information Mart for Intensive Care-IV 2.2 (MIMIC-IV 2.2). Demographic variables, disease severity scores, comorbidities, interventions, laboratory indicators, and hospitalization details were collected. Key factors associated with SAE were identified using univariate Logistic regression analysis. The data were randomly divided into training and validation sets in a 7 : 3 ratio. Multivariable Logistic regression analysis was conducted in the training set and visualized using a nomogram model for prediction of SAE. The discrimination of the model was evaluated in the validation set using the receiver operator characteristic curve (ROC curve), and its calibration was assessed using calibration curve. Furthermore, multiple machine learning algorithms, including multi-layer perceptron (MLP), support vector machine (SVM), naive bayes (NB), gradient boosting machine (GBM), random forest (RF), and extreme gradient boosting (XGB), were constructed in the training set. Their predictive performance was subsequently evaluated on the validation set. Taking the XGB model as an example, the interpretability of the model through the SHapley Additive exPlanations (SHAP) algorithm was enhanced to identify the key predictive factors and their contributions. RESULTS: A total of 2 204 septic patients were finally enrolled, of whom 840 developed SAE (38.1%). A total of 21 variables associated with SAE were screened through univariate Logistic regression analysis. Multivariable Logistic regression analysis showed that endotracheal intubation [odds ratio (OR) = 0.40, 95% confidence interval (95%CI) was 0.19-0.88, P < 0.001], oxygen therapy (OR = 0.76, 95%CI was 0.53-0.95, P = 0.023), tracheotomy (OR = 0.20, 95%CI was 0.07-0.53, P < 0.001), continuous renal replacement therapy (CRRT; OR = 0.32, 95%CI was 0.15-0.70, P < 0.001), cerebrovascular disease (OR = 0.31, 95%CI was 0.16-0.60, P < 0.001), rheumatic disease (OR = 0.44, 95%CI was 0.19-0.99, P < 0.001), male (OR = 0.68, 95%CI was 0.54-0.86, P = 0.001), and maximum anion gap (AG; OR = 0.95, 95%CI was 0.93-0.97, P < 0.001) were associated with an decreased probability of SAE, and age (OR = 1.05, 95%CI was 1.03-1.06, P < 0.001), acute physiology score III (APSIII; OR = 1.02, 95%CI was 1.01-1.02, P < 0.001), Oxford acute severity of illness score (OASIS; OR = 1.04, 95%CI was 1.03-1.06, P < 0.001), and length of hospital stay (OR = 1.01, 95%CI was 1.01-1.02, P < 0.001) were associated with an increased probability of SAE. A nomogram model was constructed based on these variables. In the validation set, ROC curve analysis showed that the model achieved an area under the ROC curve (AUC) of 0.723, and the calibration curve showed good consistency between the predicted probability of the model and the observed probability. Among the machine learning algorithms, including MLP, SVM, NB, GBM, RF, and XGB, the SVM model and RF model demonstrated relatively good predictive performance, with AUC of 0.748 and 0.739, respectively, and the sensitivity was both exceeding 85%. The predictive performance of the XGB model was explained through SHAP analysis, and the results indicated that APSIII score (SHAP value was 0.871), age (SHAP value was 0.521), and OASIS score (SHAP value was 0.443) were important factors affecting the predictive performance of the model. CONCLUSIONS The machine learning-based SAE prediction model exhibits good predictive capability and holds significant application value for the early identification of SAE risk in elderly septic patients.
Humans ; Machine Learning ; Aged ; Sepsis-Associated Encephalopathy ; Sepsis/complications* ; Intensive Care Units ; Logistic Models ; Middle Aged ; Male ; ROC Curve ; Female ; Bayes Theorem ; Nomograms ; Support Vector Machine ; Algorithms

This study aimed to investigate the associations between the post-wash total progressively motile sperm count (TPMSC) in the first intrauterine insemination (IUI) cycle and pregnancy outcomes of the second IUI cycle. Data were retrieved from the clinical database at the Reproductive Center of Peking University Third Hospital (Beijing, China) between January 2011 and December 2022. Couples were included in this retrospective cohort study if they had unexplained or mild male factor infertility and were treated with IUI for two consecutive cycles using the same protocol. A total of 8290 couples were included in the analysis. The mean ± standard deviation (s.d.) age of women was 32.0 ± 3.5 years. We categorized groups based on the post-wash TPMSC (×10 6 ) levels in the first IUI cycle: group 1 (0 < TPMSC < 1, n = 1290), group 2 (1 ≤ TPMSC < 2, n = 863), group 3 (2 ≤ TPMSC < 3, n = 800), group 4 (3 ≤ TPMSC < 4, n = 783), group 5 (4 ≤ TPMSC < 5, n = 1541), group 6 (5 ≤ TPMSC < 6, n = 522), group 7 (6 ≤ TPMSC < 7, n = 547), group 8 (7 ≤ TPMSC < 8, n = 175), group 9 (8 ≤ TPMSC < 9, n = 556), group 10 (9 ≤ TPMSC < 10, n = 192), and group 11 (TPMSC ≥ 10), n = 1021). The primary outcome was live birth rate of the second IUI cycle. Live birth rates were 7.9%, 5.8%, 7.6%, 7.4%, 7.3%, 8.4%, 7.5%, 7.4%, 8.8%, 8.9%, and 7.6% in each group, respectively. There were no statistically significant differences in clinical pregnancy rates or live birth rates between any groups and those with the post-wash TPMSC <1 × 10 6 . In an IUI program for unexplained and mild male factor infertility, the post-wash TPMSC in the first IUI cycle was not significantly associated with the live birth rate in the second IUI cycle.
Humans ; Female ; Male ; Pregnancy ; Adult ; Retrospective Studies ; Sperm Count ; Pregnancy Rate ; Sperm Motility/physiology* ; Insemination, Artificial/methods* ; Pregnancy Outcome ; Infertility, Male/therapy* ; Insemination, Artificial, Homologous ; Live Birth

OBJECTIVE: Pseudomonas aeruginosa( P. aeruginosa) is a prevalent pathogenic bacterium involved in meningitis; however, the virulence factors contributing to this disease remain poorly understood. METHODS: The virulence of the P. aeruginosa A584, isolated from meningitis samples, was evaluated by constructing in vitro blood-brain barrier and in vivo systemic infection models. qPCR, whole-genome sequencing, and drug efflux assays of A584 were performed to analyze the virulence factors. RESULTS: Genomic sequencing showed that A584 formed a phylogenetic cluster with the reference strains NY7610, DDRC3, Pa58, and Pa124. Its genome includes abundant virulence factors, such as hemolysin, the Type IV secretion system, and pyoverdine. A584 is a multidrug-resistant strain, and its wide-spectrum resistance is associated with enhanced drug efflux. Moreover, this strain caused significantly more severe damage to the blood-brain barrier than the standard strain, PAO1. qPCR assays further revealed the downregulation of the blood-brain barrier-associated proteins Claudin-5 and Occludin by A584. During systemic infection, A584 exhibited a higher capacity of brain colonization than PAO1 (37.1 × 10 ⁶ CFU/g brain versus 2.5 × 10 ⁶ CFU/g brain), leading to higher levels of the pro-inflammatory factors IL-1β and TNF-α. CONCLUSION This study sheds light on the virulence factors of P. aeruginosa involved in meningitis.
Pseudomonas aeruginosa/genetics* ; Virulence Factors/metabolism* ; Animals ; Virulence ; Mice ; Pseudomonas Infections/microbiology* ; Blood-Brain Barrier/microbiology* ; Humans ; Female

OBJECTIVE To investigate the prognostic factors of chondrosarcoma of the larynx,deeply analyze its clinical data,and provide a theoretical basis for better treatment of chondrosarcoma of the larynx.METHODS A retrospective analysis was conducted on the complete clinical data of patients with primary chondrosarcoma of the larynx admitted to the Department of Otolaryngology Head and Neck Surgery,Beijing Tongren Hospital,Capital Medical University from January 2010 to December 2024.RESULTS A total of 15 patients were included,including 11 males and 4 females,with a gender ratio of 11∶4.The average age of onset was 57.3 years,and the average clinical symptom duration was 12.2 months.The tumors were mainly located in the cricoid cartilage in 11 patients,in the arytenoid cartilage in 2 patients,and in the thyroid cartilage in 2 patients.Tumor grading showed that 7 patients were grade I and 8 were grade II.Four patients underwent transoral laser minimally invasive surgery,2 patients underwent partial laryngectomy+tracheotomy,and 9 patients underwent total laryngectomy/cervical lymph node dissection+tracheostomy.The 5-year overall survival rate was 85.7%,the 5-year disease-specific survival rate was 100%,and the 5-year local-regional control rate was 90.9%.Gender,tumor location,tumor grade,Ki-67,tumor size,and whether larynx preservation surgery was performed did not affect the local-regional control rate or disease-specific survival rate.CONCLUSION Laryngeal chondrosarcoma generally has a longer disease history and is difficult to detect.The pathological type is mostly well-differentiated.Regional or distant metastasis is rare,and the long-term survival rate is good.Surgical resection is the preferred treatment option.On the basis of not reducing the tumor control rate,surgery that prioritizes preserving laryngeal function should be given priority,while comprehensive treatment is generally not recommended.