Serum tumor markers (carcinoembryonic antigen, carbohydrate antigen 19-9, carbohydrate antigen 72-4, carbohydrate antigen 24-2, ferritin) and gastric cancer prognosis correlation.

BACKGROUND: Gastric cancer is a kind of malignant tumor which is prevalent all over the world. Although some progress has been made in the treatment of gastric cancer, its prognosis is still not optimistic, so it is of great significance to find reliable prognostic indicators to guide the treatment and management of patients with gastric cancer. AIM: To explore the relationship between serum levels of five biomarkers [carcinoembryonic antigen (CEA), carbohydrate antigen (CA) 19-9, CA72-4, CA24-2, and ferritin] and prognosis in patients with gastric cancer. METHODS: This study included 200 patients with gastric adenocarcinoma, and conducted an in-depth analysis of their baseline characteristics, relationship between tumor markers and staging, and prognosis. The study found that CA19-9 has a significant correlation with tumor stage, the average levels of CA24-2, CEA, CA72-4 and ferritin were slightly increased disregarding the stage of tumor. Survival analysis showed that increases in CEA, CA19-9, CA24-2, and ferritin were all associated with shortened overall survival of patients. Further multivariate analysis revealed that elevated serum CA72-4 levels were an independent adverse prognostic factor. RESULTS: This study reveals that there is a significant correlation between the expression levels of serum tumor markers CEA, CA19-9, CA72-4, CA24-2 and ferritin in patients with gastric cancer and prognosis, and can be used as important indicators for prognostic evaluation of gastric cancer. In particular, markers that appear abnormally elevated initially may help identify gastric cancer patients with poor prognosis. CONCLUSION: Serum CEA and CA19-9 play an important role in the prognosis assessment of gastric cancer, and are effective tools to guide clinical practice and optimize individualized treatment strategies for gastric cancer patients.

Resolving tumor evolution: a phylogenetic approach.

The evolutionary dynamics of cancer, characterized by its profound heterogeneity, demand sophisticated tools for a holistic understanding. This review delves into tumor phylogenetics, an essential approach bridging evolutionary biology with oncology, offering unparalleled insights into cancer's evolutionary trajectory. We provide an overview of the workflow, encompassing study design, data acquisition, and phylogeny reconstruction. Notably, the integration of diverse data sets emerges as a transformative step, enhancing the depth and breadth of evolutionary insights. With this integrated perspective, tumor phylogenetics stands poised to redefine our understanding of cancer evolution and influence therapeutic strategies.

Prospectively predicting BPaMZ phase IIb/III trial outcomes using a translational mouse-to-human platform.

Despite known treatments, tuberculosis (TB) remains the world's top infectious killer, highlighting the pressing need for new drug regimens. To prioritize the most efficacious drugs for clinical testing, we previously developed a PK-PD translational platform with bacterial dynamics that reliably predicted short-term monotherapy outcomes in Phase IIa trials from preclinical mouse studies. In this study, we extended our platform to include PK-PD models that account for drug-drug interactions in combination regimens and bacterial regrowth in our bacterial dynamics model to predict cure at the end of treatment and relapse 6 months post-treatment. The Phase III STAND trial testing a new regimen comprised of pretomanid (Pa), moxifloxacin (M), and pyrazinamide (Z) (PaMZ) was suspended after a separate ongoing trial (NC-005) suggested that adding bedaquiline (B) to the PaMZ regimen would improve efficacy. To forecast if the addition of B would, indeed, benefit the PaMZ regimen, we applied an extended translational platform to both regimens. We predicted currently available short- and long-term clinical data well for drug combinations related to BPaMZ. We predicted the addition of B to PaMZ to shorten treatment duration by 2 months and to have similar bacteriological success to standard HRZE treatment (considering only treatment success but not withdrawal from side effects and other adverse events), both at the end of treatment for treatment efficacy and 6 months after treatment has ended in relapse prevention. Using BPaMZ as a case study, we have demonstrated our translational platform can predict Phase II and III outcomes prior to actual trials, allowing us to better prioritize the regimens most likely to succeed.

A foundation model for clinician-centered drug repurposing.

Drug repurposing-identifying new therapeutic uses for approved drugs-is often a serendipitous and opportunistic endeavour to expand the use of drugs for new diseases. The clinical utility of drug-repurposing artificial intelligence (AI) models remains limited because these models focus narrowly on diseases for which some drugs already exist. Here we introduce TxGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TxGNN uses a graph neural network and metric learning module to rank drugs as potential indications and contraindications for 17,080 diseases. When benchmarked against 8 methods, TxGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TxGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TxGNN's predictive rationales. Human evaluation of TxGNN's Explainer showed that TxGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's new predictions align well with off-label prescriptions that clinicians previously made in a large healthcare system. TxGNN's drug-repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

Lysosomal TFEB-TRPML1 Axis in Astrocytes Modulates Depressive-like Behaviors.

Lysosomes are important cellular structures for human health as centers for recycling, signaling, metabolism and stress adaptation. However, the potential role of lysosomes in stress-related emotions has long been overlooked. Here, it is found that lysosomal morphology in astrocytes is altered in the medial prefrontal cortex (mPFC) of susceptible mice after chronic social defeat stress. A screen of lysosome-related genes revealed that the expression of the mucolipin 1 gene (Mcoln1; protein: mucolipin TRP channel 1) is decreased in susceptible mice and depressed patients. Astrocyte-specific knockout of mucolipin TRP channel 1 (TRPML1) induced depressive-like behaviors by inhibiting lysosomal exocytosis-mediated adenosine 5'-triphosphate (ATP) release. Furthermore, this stress response of astrocytic lysosomes is mediated by the transcription factor EB (TFEB), and overexpression of TRPML1 rescued depressive-like behaviors induced by astrocyte-specific knockout of TFEB. Collectively, these findings reveal a lysosomal stress-sensing signaling pathway contributing to the development of depression and identify the lysosome as a potential target organelle for antidepressants.

KNOWNET: Guided Health Information Seeking from LLMs via Knowledge Graph Integration.

The increasing reliance on Large Language Models (LLMs) for health information seeking can pose severe risks due to the potential for misinformation and the complexity of these topics. This paper introduces KNOWNET a visualization system that integrates LLMs with Knowledge Graphs (KG) to provide enhanced accuracy and structured exploration. Specifically, for enhanced accuracy, KNOWNET extracts triples (e.g., entities and their relations) from LLM outputs and maps them into the validated information and supported evidence in external KGs. For structured exploration, KNOWNET provides next-step recommendations based on the neighborhood of the currently explored entities in KGs, aiming to guide a comprehensive understanding without overlooking critical aspects. To enable reasoning with both the structured data in KGs and the unstructured outputs from LLMs, KNOWNET conceptualizes the understanding of a subject as the gradual construction of graph visualization. A progressive graph visualization is introduced to monitor past inquiries, and bridge the current query with the exploration history and next-step recommendations. We demonstrate the effectiveness of our system via use cases and expert interviews.

Insights into the efficient removal and mechanism of NiFeAl-LDH with abundant hydroxyl to activate peroxymonosulfate for sulfamethoxazole wastewater.

Layered double hydroxide (LDH) material with abundant OH was successfully prepared by co-precipitation method, and a water purification system of Ni2Fe0.25Al0.75-LDH activated peroxymonosulfate (PMS) was constructed to rapidly degrade sulfamethoxazole (SMX) pollutants. The optimal conditions for the degradation of SMX in the system were as follows: 0.30 g/L Ni2Fe0.25Al0.75-LDH, 0.30 mM PMS, pH = 7 and 90 % SMX was removed in 10 min and almost completely in 40 min, which was consistent with the predicted results of response surface methodology (RSM) analysis. The abundant OH in Ni2Fe0.25Al0.75-LDH could form M(O)OSO3 complexes with PMS, accelerating the generation of reactive oxygen species (ROS) and promoting the removal of SMX. Quenching experiments and electron paramagnetic resonance (EPR) spectra showed that SO4-, OH, O2- and 1O2 also existed in the system. The surface-bound SO4- and O2- contributed greatly to the removal of SMX and the electron transfer between metals was also conducive to the production of active substances. The possible degradation pathways and intermediates of SMX were proposed. The toxicity assessment software tool (T.E.S.T) and total organic carbon (TOC) results indicated that the Ni2Fe0.25Al0.75-LDH/PMS system could reduce the overall environmental risk of SMX to some extent. This study provided a new strategy for the practical application of heterogeneous catalysts in sewage treatment.

Clinical significance of peripheral blood immune cells in patients with gastric cancer after surgery.

BACKGROUND: Gastric cancer is one of the most common malignant tumors worldwide, and surgical resection is one of the main ways to treat gastric cancer. However, the immune status of postoperative patients is crucial for prognosis and survival, and immune cells play an important role in this process. Therefore, it is helpful to understand the immune status of postoperative patients by evaluating the levels of peripheral blood immune cells, especially total T cells (CD3+), helper T cells (CD3+CD4+), and suppressor T cells (CD3+CD8+), and its relationship to survival. AIM: To analyzed the immune cells in peripheral blood of patients with gastric cancer after surgery, detect the levels of total T cells, helper T cells and suppressor T cells. METHODS: A total of 58 patients with gastric cancer who received surgical treatment were included in the retrospective study. Flow cytometry was used to detect the level of peripheral blood immune cells and analyze the correlation between total T cells, helper T cells and inhibitory T cells. To explore the relationship between these immune markers and patient survival. RESULTS: The results showed that the levels of total T cells, helper T cells, and suppressor T cells changed in patients after gastric cancer surgery. There was a significant positive correlation between total T cells, helper T cells and suppressor T cells (r = 0.35, P < 0.01; r = 0.56, P < 0.01). However, there was a negative correlation between helper T cells and suppressor T cells (r = -0.63, P < 0.01). Follow-up showed that the survival rate of patients in the high-level total T cell group was significantly higher than that in the low-level group (28.87 ± 24.98 months vs 18.42 ± 16.21 months). The survival curve shows that the curve of patients in the high-level group is shifted to the upper right, and that of the low-level group is shifted downward. There was no significant difference between the levels of helper T cells and suppressor T cells and patient survival time. CONCLUSION: By detecting peripheral blood immune cells with flow cytometry, we can initially evaluate the immune status of patients after gastric cancer surgery and initially explore its relationship with patient survival.

A foundation model for clinician-centered drug repurposing.

Drug repurposing - identifying new therapeutic uses for approved drugs - is often serendipitous and opportunistic, expanding the use of drugs for new diseases. The clinical utility of drug repurposing AI models remains limited because the models focus narrowly on diseases for which some drugs already exist. Here, we introduce TXGNN, a graph foundation model for zero-shot drug repurposing, identifying therapeutic candidates even for diseases with limited treatment options or no existing drugs. Trained on a medical knowledge graph, TXGNN utilizes a graph neural network and metric-learning module to rank drugs as potential indications and contraindications across 17,080 diseases. When benchmarked against eight methods, TXGNN improves prediction accuracy for indications by 49.2% and contraindications by 35.1% under stringent zero-shot evaluation. To facilitate model interpretation, TXGNN's Explainer module offers transparent insights into multi-hop medical knowledge paths that form TXGNN's predictive rationales. Human evaluation of TXGNN's Explainer showed that TXGNN's predictions and explanations perform encouragingly on multiple axes of performance beyond accuracy. Many of TxGNN's novel predictions align with off-label prescriptions clinicians make in a large healthcare system. TXGNN's drug repurposing predictions are accurate, consistent with off-label drug use, and can be investigated by human experts through multi-hop interpretable rationales.

Vitamin B-6 Prevents Heart Failure with Preserved Ejection Fraction Through Downstream of Kinase 3 in a Mouse Model.

BACKGROUND: There is an urgent need to develop an efficient therapeutic strategy for heart failure with preserved ejection fraction (HFpEF), which is mediated by phenotypic changes in cardiac macrophages. We previously reported that vitamin B-6 inhibits macrophage-mediated inflammasome activation. OBJECTIVES: We sought to examine whether the prophylactic use of vitamin B-6 prevents HFpEF. METHODS: HFpEF model was elicited by a combination of high-fat diet and Nω-nitro-l-arginine methyl ester supplement in mice. Cardiac function was assessed using conventional echocardiography and Doppler imaging. Immunohistochemistry and immunoblotting were used to detect changes in the macrophage phenotype and myocardial remodeling-related molecules. RESULTS: Co-administration of vitamin B-6 with HFpEF mice mitigated HFpEF phenotypes, including diastolic dysfunction, cardiac macrophage phenotypic shifts, fibrosis, and hypertrophy. Echocardiographic improvements were observed, with the E/E' ratio decreasing from 42.0 to 21.6 and the E/A ratio improving from 2.13 to 1.17. The exercise capacity also increased from 295.3 to 657.7 min. However, these beneficial effects were negated in downstream of kinase (DOK) 3-deficient mice. Mechanistically, vitamin B-6 increased DOK3 protein concentrations and inhibited macrophage phenotypic changes, which were abrogated by an AMP-activated protein kinase inhibitor. CONCLUSIONS: Vitamin B-6 increases DOK3 signaling to lower risk of HFpEF by inhibiting phenotypic changes in cardiac macrophages.

Characterization of D1R and D2R neuronal subpopulations in the globus pallidus interna: Implications for Parkinson's disease pathogenesis.

Parkinson's disease (PD) ranks as the second most prevalent and rapidly growing neurodegenerative disorder. As a primary output nucleus within the basal ganglia (BG), the globus pallidus interna (GPi) is a key structure in BG information processing. It is also a key target for deep brain stimulation (DBS) to alleviate motor symptoms of PD. Previous studies have identifiedPD patients exhibiting abnormal neuronal activity in the GPi. On the other hand, various types of dopamine receptor (DR)-positive neurons have been identified within the GPi. However, the electrophysiological properties of specific DR-positive neurons within the GPi and their alterations in PD have not been addressed. In the present study, we used whole-cell patch-clamp recordings to identify two neuronal subpopulations within the GPi, dopamine D1 receptor (D1R)-positive, and dopamine D2 receptor (D2R)-positive neurons, which exhibited distinct electrophysiological properties. Additionally, significant alterations of electrophysiological properties of D2R-positive neurons within the GPi were observed in 6-hydroxydopamine (6-OHDA)-lesioned mice. These data suggest that the distinct electrophysiological properties of specific DR-positive neurons and their abnormal alteration in the GPi may be associated with PD's pathogenesis.

Dose optimization of TBI-223 for enhanced therapeutic benefit compared to linezolid in antituberculosis regimen.

TBI-223, a novel oxazolidinone for tuberculosis, is designed to provide improved efficacy and safety compared to linezolid in combination with bedaquiline and pretomanid (BPaL). We aim to optimize the dosing of TBI-223 within the BPaL regimen for enhanced therapeutic outcomes. TBI-223 is investigated in preclinical monotherapy, multidrug therapy, and lesion penetration experiments to describe its efficacy and safety versus linezolid. A translational platform incorporating linezolid and BPaL data from preclinical experiments and 4 clinical trials (NCT00396084, NCT02333799, NCT03086486, NCT00816426) is developed, enabling validation of the framework. TBI-223 preclinical and Phase 1 data (NCT03758612) are applied to the translational framework to predict clinical outcomes and optimize TBI-223 dosing in combination with bedaquiline and pretomanid. Results indicate that daily doses of 1200-2400 mg TBI-223 may achieve efficacy comparable to the BPaL regimen, with >90% of patients predicted to reach culture conversion by two months.

Platelet count as a prognostic marker for acute respiratory distress syndrome.

BACKGROUND: This study aimed to evaluate the role of platelet count (PLT) in the prognosis of patients with acute respiratory distress syndrome (ARDS). METHODS: The data were extracted from the Medical Information Mart for Intensive Care database (version 2.2). Patients diagnosed with ARDS according to criteria from Berlin Definition and had the platelet count (PLT) measured within the first day after intensive care unit admission were analyzed. Based on PLT, ARDS patients were divided into four groups: PLT ≤ 100 × 109/L, PLT 101-200 × 109/L, PLT 201-300 × 109/L, and PLT > 300 × 109/L. The primary outcome was 28-day mortality. Survival probabilities were analyzed using Kaplan-Meier. Furthermore, the association between PLT and mortality in ARDS patients was assessed using a univariate and multivariable Cox proportional hazards model. RESULTS: Overall, the final analysis included 3,207 eligible participants with ARDS. According to the Kaplan-Meier curves for 28-day mortality of PLT, PLT ≤ 100 × 109/L was associated with a higher incidence of mortality (P = 0.001), the same trends were observed in the 60-day (P = 0.001) and 90-day mortality (P = 0.001). In the multivariate model adjusted for the potential factors, the adjusted hazard ratio at PLT 101-200 × 109/L group, PLT 201-300 × 109/L, and PLT > 300 × 109/L was 0.681 [95% confidence interval (CI): 0.576-0.805, P < 0.001], 0.733 (95% CI: 0.604-0.889, P = 0.002), and 0.787 (95% CI: 0.624-0.994, P = 0.044) compared to the reference group (PLT ≤ 100 × 109/L), respectively. Similar relationships between the PLT ≤ 100 × 109/L group and 28-day mortality were obtained in most subgroups. CONCLUSION: PLT appeared to be an independent predictor of mortality in critically ill patients with ARDS.

The combined detection of carcinoembryonic antigen, carcinogenic antigen 125, and carcinogenic antigen 19-9 in colorectal cancer patients.

BACKGROUND: Hepatic metastases are common and difficult to treat after colorectal cancer (CRC) surgery. The predictive value of carcinoembryonic antigen (CEA), cancer antigen (CA) 125 and CA19-9 combined tests for liver metastasis is unclear. AIM: To evaluate predictive value of combined tests for CEA, CA125, and CA19-9 levels in patients with liver metastases of CRC. METHODS: The retrospective study included patients with CRC alone (50 cases) and patients with CRC combined with liver metastases (50 cases) who were hospitalized between January 2021 and January 2023. Serum CEA, CA125 and CA19-9 levels were compared between the two groups, and binary logistic regression was used to analyze the predictive value of the combination of these tumor markers in liver metastasis. In addition, we performed receiver operating characteristic (ROC) curve analysis to assess its diagnostic accuracy. RESULTS: The results showed that the serum CEA, CA125 and CA19-9 levels in the CRC with liver metastasis group were significantly higher than those in the CRC alone group. Specifically, the average serum CEA level in the CRC with liver metastasis group was 162.03 ± 810.01 ng/mL, while that in the CRC alone group was 5.71 ± 9.76 ng/mL; the average serum CA125 levels were 43.47 ± 83.52 U/mL respectively. and 13.5 ± 19.68 U/mL; the average serum CA19-9 levels were 184.46 ± 473.13 U/mL and 26.55 ± 43.96 U/mL respectively. In addition, binary logistic regression analysis showed that CA125 was significant in predicting CRC liver metastasis (P < 0.05). ROC curve analysis results showed that the areas under the ROC curves of CEA, CA125 and CA19-9 were 0.607, 0.692 and 0.586. CONCLUSION: These results suggest that combined detection of these tumor markers may help early detection and intervention of CRC liver metastasis, thereby improving patient prognosis.

A systematic review of allometric scaling exponents for IgG mAbs.

Increasing complexity of mAbs in development creates challenges in predicting human pharmacokinetic (PK) parameters from preclinical data. The aim of this analysis was to identify optimal allometric scaling exponents.Data were extracted from literature to create a central database (currently the largest available published database) of two-compartment model parameters for mAbs (n = 59) in cynomolgus monkey (CM) and human.Global allometric exponents were calculated and drug-dependent factors were investigated as potential variables in determining the optimal scaling factor.The global exponents for scaling CM mAb PK data were 0.74 (CL), 0.80 (CL with Fc-modified mAbs excluded), 0.44 (CL with Fc-modified mAbs only), 0.71 (Q), 1.12 (V1), and 0.99 (V2). These values are in line with previously published literature values.

Chromosome-level genome assembly of Hippophae rhamnoides variety.

Fructus hippophae (Hippophae rhamnoides spp. mongolica×Hippophae rhamnoides sinensis), a hybrid variety of sea buckthorn that Hippophae rhamnoides spp. mongolica serves as the female parent and Hippophae rhamnoides sinensis serves as the male parent, is a traditional plant with great potentials of economic and medical values. Herein, we gained a chromosome-level genome of Fructus hippophae about 918.59 Mb, with the scaffolds N50 reaching 83.65 Mb. Then, we anchored 440 contigs with 97.17% of the total genome sequences onto 12 pseudochromosomes. Next, de-novo, homology and transcriptome assembly strategies were adopted for gene structure prediction. This predicted 36475 protein-coding genes, of which 36226 genes could be functionally annotated. Simultaneously, various strategies were used for quality assessment, both the complete BUSCO value (98.80%) and the mapping rate indicated the high assembly quality. Repetitive elements, which occupied 63.68% of the genome, and 1483600 bp of non-coding RNA were annotated. Here, we provide genomic information on female plants of a popular variety, which can provide data for pan-genomic construction of sea buckthorn and for the resolution of the mechanism of sex differentiation.

Using clusterProfiler to characterize multiomics data.

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

Reducing language barriers, promoting information absorption, and communication using fanyi.

ABSTRACT: Interpreting genes of interest is essential for identifying molecular mechanisms, but acquiring such information typically involves tedious manual retrieval. To streamline this process, the fanyi package offers tools to retrieve gene information from sources like National Center for Biotechnology Information (NCBI), significantly enhancing accessibility. Additionally, understanding the latest research advancements and sharing achievements are crucial for junior researchers. However, language barriers often restrict knowledge absorption and career development. To address these challenges, we developed the fanyi package, which leverages artificial intelligence (AI)-driven online translation services to accurately translate among multiple languages. This dual functionality allows researchers to quickly capture and comprehend information, promotes a multilingual environment, and fosters innovation in academic community. Meanwhile, the translation functions are versatile and applicable beyond biomedicine research to other domains as well. The fanyi package is freely available at https://github.com/YuLab-SMU/fanyi .

The predictors to self-reported and performance-based physical function in knee osteoarthritis patients: a cross-sectional study.

Background: Osteoarthritis (OA) knee patients have limited ability in physical function, or difficulties with physical tasks and activities may develop disability. This study aimed to observe the predictors of self-reported and performance-based physical function in patients with knee OA by analyzing the impacts of demographic, pathological, and muscle impairment factors. Methods: 135 knee OA patients participated in this study to complete self-reported questionnaires using Knee Injury and Osteoarthritis Outcome Score (KOOS). When measuring performance-based physical function, a 6-meter gait speed (6MGS) test was measured to evaluate their mobility, and a 5-time Sit-to-Stand test (5STS) was assessed to evaluate their balance. Pain intensity, knee extensor and flexor muscle strength, age, body mass index (BMI), durations of symptoms, and radiographic severity were also collected. Spearman correlation and stepwise multiple linear regression were used to explore the association and predictors in self-reported and performance-based physical function. Results: BMI and durations of symptoms did not indicate any significant correlation with either self-reported or performance-based physical function. Age is significantly negatively associated with 6MGS (r 2 = -0.383, p < 0.01), while knee extensor muscle strength has a moderate correlation with 5STS (r 2 = -0.528, p < 0.01). In the stepwise multiple linear regression models, pain intensity (ß = 0.712, p < 0.001), knee flexor muscle strength (ß = 0.112, p = 0.042) were significantly associated with self-reported physical function in daily activities and contributed to 55.0% of the variance in KOOS-PF score. Knee muscle strength, including knee extensor (5STS: ß = -0.428, p < 0.001) and flexor muscle strength (6MGS: ß = 0.367, p < 0.001), were the main predictors with performance-based physical function. Conclusion: Pain intensity was the leading risk factor of self-reported physical function, and knee flexor muscle strength contributed as well. The severity of knee OA, durations of symptoms and BMI did not contribute to physical function. However, knee extensor and flexor muscle strength were the main predictors of performance-based performance. Our results show that strengthening of weak knee muscles in both quadriceps and hamstring muscle strength should be considered a priory consideration in knee OA no matter if people are in the early or end-stage of knee OA.