Prognosis and diagnosis of prostate cancer based on hypergraph regularization sparse least partial squares regression algorithm.

BACKGROUND: Prostate cancer (PCa) is a malignant tumor of the male reproductive system, and its incidence has increased significantly in recent years. This study aimed to further identify candidate biomarkers with prognostic and diagnostic significance by integrating gene expression and DNA methylation data from PCa patients through association analysis. MATERIAL AND METHODS: To this end, this paper proposes a sparse partial least squares regression algorithm based on hypergraph regularization (HR-SPLS) by integrating and clustering two kinds of data. Next, module 2, with the most significant weight, was selected for further analysis according to the weight of each module related to DNA methylation and mRNAs. Based on the DNA methylation sites in module 2, this paper uses multiple machine learning methods to construct a PCa diagnosis-related model of 10-DNA methylation sites. RESULTS: The results of Receiver Operating Characteristic (ROC) analysis showed that the DNA methylation-related diagnostic model we constructed could diagnose PCa patients with high accuracy. Subsequently, based on the mRNAs in module 2, we constructed a prognostic model for 7-mRNAs (MYH11, ACTG2, DDR2, CDC42EP3, MARCKSL1, LMOD1, and MYLK) using multivariate Cox regression analysis. The prognostic model could predict the disease free survival of PCa patients with moderate to high accuracy (area under the curve (AUC) =0.761). In addition, Gene Set EnrichmentAnalysis (GSEA) and immune analysis indicated that the prognosis of patients in the risk group might be related to immune cell infiltration. CONCLUSIONS: Our findings may provide new methods and insights for identifying disease-related biomarkers by integrating DNA methylation and gene expression data.

Clinical Presentations and Intense FDG-avidity in Pulmonary Angiomatoid Fibrous Histiocytoma with Potential Diagnostic Pitfalls: A Case Report and Literature Review.

INTRODUCTION: Angiomatoid fibrous histiocytoma (AFH) is a borderline tumor usually affecting the the children or young adults. 18F-Fluorodexoyglucose (FDG) positron emission tomography/computed tomography (PET/CT) investigations of pulmonary AFH are rare, and there are currently no reports of intense FDG uptake in AFH. CASE REPORT: We report an AFH that occurred in the lung of a 57-year-old woman. She presented with paroxysmal cough and occasional bloodshot sputum. 18FFDG PET/CT revealed a right parahilar nodule with intense FDG-avidity, middle lobe atelectasis, and several bilateral axillary lymph nodes with mild hypermetabolic activity. This patient underwent a right middle lobe lobectomy via video-assisted thoracoscopy. Histopathologically, the diagnosis was pulmonary AFH. She had an uneventful postoperative course, and the bilateral axillary lymph nodes regressed during postoperative follow-up. CONCLUSIONS: The clinical presentation and image findings of patients with primary pulmonary AFH may be potential diagnosis pitfalls. The diagnosis of lymph nodes or distant metastases should be approached with caution. To avoid misdiagnosis, biopsy with histological examination and immunohistochemichal staining should be performed as early as possible.

Multiple reports on the causal relationship between various chronic pain and gut microbiota: a two-sample Mendelian randomization study.

Background: Previous evidence suggests a link between gut microbiota and chronic pain, but the causal relationship is not yet fully understood. Methods: We categorized gut microbiota based on phylum, class, order, family, and genus levels and gathered pain-related information from the UKB and FinnGen GWAS project. Then, we conducted MR analysis to explore the potential causal relationship between gut microbiota and chronic pain at 12 specific locations. Results: We have discovered a direct connection between genetic susceptibility in the gut microbiota (gut metabolites) and pain experienced at 12 specific locations. Notably, Serotonin (5-HT) and Glycine were found to be associated with a higher risk of pain in the extremities. On the other hand, certain microbial families and orders were found to have a protective effect against migraines. Specifically, the family Bifidobacteriaceae (IVW, FDR p = 0.013) was associated with a lower risk of migraines. Furthermore, the genus Oxalobacter (IVW, FDR p = 0.044) was found to be linked to an increased risk of low back pain. Importantly, these associations remained significant even after applying the Benjamini-Hochberg correction test. Our analysis did not find any heterogeneity in the data (p > 0.05), as confirmed by the Cochrane's Q-test. Additionally, both the MR-Egger and MR-PRESSO tests indicated no significant evidence of horizontal pleiotropy (p > 0.05). Conclusion: Our MR analysis demonstrated a causal relationship between the gut microbiota and pain, highlighting its potential significance in advancing our understanding of the underlying mechanisms and clinical implications of microbiota-mediated pain.

Automatic segmentation of 15 critical anatomical labels and measurements of cardiac axis and cardiothoracic ratio in fetal four chambers using nnU-NetV2.

BACKGROUND: Accurate segmentation of critical anatomical structures in fetal four-chamber view images is essential for the early detection of congenital heart defects. Current prenatal screening methods rely on manual measurements, which are time-consuming and prone to inter-observer variability. This study develops an AI-based model using the state-of-the-art nnU-NetV2 architecture for automatic segmentation and measurement of key anatomical structures in fetal four-chamber view images. METHODS: A dataset, consisting of 1,083 high-quality fetal four-chamber view images, was annotated with 15 critical anatomical labels and divided into training/validation (867 images) and test (216 images) sets. An AI-based model using the nnU-NetV2 architecture was trained on the annotated images and evaluated using the mean Dice coefficient (mDice) and mean intersection over union (mIoU) metrics. The model's performance in automatically computing the cardiac axis (CAx) and cardiothoracic ratio (CTR) was compared with measurements from sonographers with varying levels of experience. RESULTS: The AI-based model achieved a mDice coefficient of 87.11% and an mIoU of 77.68% for the segmentation of critical anatomical structures. The model's automated CAx and CTR measurements showed strong agreement with those of experienced sonographers, with respective intraclass correlation coefficients (ICCs) of 0.83 and 0.81. Bland-Altman analysis further confirmed the high agreement between the model and experienced sonographers. CONCLUSION: We developed an AI-based model using the nnU-NetV2 architecture for accurate segmentation and automated measurement of critical anatomical structures in fetal four-chamber view images. Our model demonstrated high segmentation accuracy and strong agreement with experienced sonographers in computing clinically relevant parameters. This approach has the potential to improve the efficiency and reliability of prenatal cardiac screening, ultimately contributing to the early detection of congenital heart defects.

Discovery of Selective Proteolysis-Targeting Chimera Degraders Targeting PTP1B as Long-Term Hypoglycemic Agents.

PTP1B, a promising target for insulin sensitizers in type 2 diabetes treatment, can be effectively degraded using proteolysis-targeting chimera (PROTAC). This approach offers potential for long-acting antidiabetic agents. We report potent bifunctional PROTACs targeting PTP1B through the E3 ubiquitin ligase cereblon. Western blot analysis showed significant PTP1B degradation by PROTACs at concentrations from 5 nM to 5 µM after 48 h. Evaluation of five highly potent PROTACs revealed compound 75 with a longer PEG linker (23 atoms), displaying remarkable degradation activity after 48 and 72 h, with DC50 values of 250 nM and 50 nM, respectively. Compound 75 induced selective degradation of PTP1B, requiring engagement with both the target protein and CRBN E3 ligase, in a ubiquitination and proteasome-dependent manner. It significantly reduced blood glucose AUC0-2h to 29% in an oral glucose tolerance test and activated the IRS-1/PI3K/Akt signaling pathway in HepG2 cells, showing promise for long-term antidiabetic therapy.

Analysis of lumbar spine loading during walking in patients with chronic low back pain and healthy controls: An OpenSim-Based study.

Low back pain (LBP) is one of the most prevalent and disabling disease worldwide. However, the specific biomechanical changes due to LBP are still controversial. The purpose of this study was to estimate the lumbar and lower limb kinematics, lumbar moments and loads, muscle forces and activation during walking in healthy adults and LBP. A total of 18 healthy controls and 19 patients with chronic LBP were tested for walking at a comfortable speed. The kinematic and dynamic data of the subjects were collected by 3D motion capture system and force plates respectively, and then the motion simulation was performed by OpenSim. The OpenSim musculoskeletal model was used to calculate lumbar, hip, knee and ankle joint angle variations, lumbar moments and loads, muscle forces and activation of eight major lumbar muscles. In our results, significant lower lumbar axial rotation angle, lumbar flexion/extension and axial rotation moments, as well as the muscle forces of the four muscles and muscle activation of two muscles were found in patients with LBP than those of the healthy controls (p < 0.05). This study may help providing theoretical support for the evaluation and rehabilitation treatment intervention of patients with LBP.

Celastrol mediates CAV1 to attenuate pro-tumorigenic effects of senescent cells.

BACKGROUND: Cellular senescence is an emerging hallmark of cancers, primarily fuels cancer progression by expressing senescence-associated secretory phenotype (SASP). Caveolin-1 (CAV1) is a key mediator of cell senescence. Previous studies from our group have evidenced that the expression of CAV1 is downregulated by Celastrol (CeT). PURPOSE: To investigate the impact of CeT on cellular senescence and its subsequent influence on post-senescence-driven invasion, migration, and stemness of clear cell renal cell carcinoma (ccRCC). STUDY DESIGN AND METHODS: The expression levels of CAV1, canonical senescence markers, and markers associated with epithelial-mesenchymal transition (EMT) and stemness in clinical samples were assessed through Pearson correlation analysis. Senescent cell models were induced using DOX, and their impact on migration, invasion, and stemness was evaluated. The effects of CeT treatment on senescent cells and their pro-tumorigenic effects were examined. Subsequently, the underlying mechanism of CeT were explored using lentivirus transfection and CRISPR/Cas9 technology to silence CAV1. RESULTS: In human ccRCC clinical samples, the expression of the canonical senescence markers p53, p21, and p16 are associated with ccRCC progression. Senescent cells facilitated migration, invasion, and enhanced stemness in both ccRCC cells and ccRCC tumor-bearing mice. As expected, CeT treatment reduced senescence markers (p16, p53, p21, SA-ß-gal) and SASP factors (IL6, IL8, CXCL12), alleviating cell cycle arrest. However, it did not restore the proliferation of senescent cells. Additionally, CeT suppressed senescence-driven migration, invasion, and stemness. Further investigations into the underlying mechanism demonstrated that CAV1 is a critical mediator of cell senescence and represents a potential target for CeT to attenuate cellular senescence. CONCLUSIONS: This study presents a pioneering investigation into the intricate interplay between cellular senescence and ccRCC progression. We unveil a novel mechanism of CeT to mitigate cellular senescence by downregulating CAV1, thereby inhibiting the migration, invasion and stemness of ccRCC driven by senescent cells. These findings provide valuable insights into the underlying mechanisms of CeT and its potential as a targeted therapeutic approach for alleviating the aggressive phenotypes associated with senescent cells in ccRCC.

Association between pain interference and motoric cognitive risk syndrome in older adults: a population-based cohort study.

OBJECTIVES: Motoric cognitive risk syndrome (MCR) is a pre-dementia condition characterized by subjective complaints in cognition and slow gait. Pain interference has previously been linked with cognitive deterioration; however, its specific relationship with MCR remains unclear. We aimed to examine how pain interference is associated with concurrent and incident MCR. METHODS: This study included older adults aged ≥ 65 years without dementia from the Health and Retirement Study. We combined participants with MCR information in 2006 and 2008 as baseline, and the participants were followed up 4 and 8 years later. The states of pain interference were divided into 3 categories: interfering pain, non-interfering pain, and no pain. Logistic regression analysis was done at baseline to examine the associations between pain interference and concurrent MCR. During the 8-year follow-up, Cox regression analysis was done to investigate the associations between pain interference and incident MCR. RESULTS: The study included 7120 older adults (74.6 ± 6.7 years; 56.8% females) at baseline. The baseline prevalence of MCR was 5.7%. Individuals with interfering pain had a significantly increased risk of MCR (OR = 1.51, 95% CI = 1.17-1.95; p = 0.001). The longitudinal analysis included 4605 participants, and there were 284 (6.2%) MCR cases on follow-up. Participants with interfering pain at baseline had a higher risk for MCR at 8 years of follow-up (HR = 2.02, 95% CI = 1.52-2.69; p < 0.001). CONCLUSIONS: Older adults with interfering pain had a higher risk for MCR versus those with non-interfering pain or without pain. Timely and adequate management of interfering pain may contribute to the prevention and treatment of MCR and its associated adverse outcomes.

Celastrol nano-emulsions selectively regulate apoptosis of synovial macrophage for alleviating rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic autoimmune inflammation. Excessive proliferation and inadequate apoptosis of synovial macrophages are the crucial events of RA. Therefore, delivering therapeutic molecules to synovial macrophages specifically to tackle apoptotic insufficiency probably can be an efficient way to reduce joint inflammation and bone erosion. Based on the characteristics of dextran sulphate (DS) specifically binding scavenger receptor A (SR-A) on macrophage and celastrol (CLT) inducing apoptosis, we designed synovial macrophage-targeted nano-emulsions encapsulated with CLT (SR-CLTNEs) and explored their anti-RA effect. After intravenous injection, fluorescence-labelled SR-CLTNEs successfully targeted inflammatory joints and synovial macrophages in a mouse model of RA, with the macrophage targeting efficiency of SR-CLTNEs, CLTNEs and free DID was 20.53%, 13.93% and 9.8%, respectively. In vivo and in vitro studies showed that SR-CLTNEs effectively promoted the apoptosis of macrophages, reshaped the balance between apoptosis and proliferation, and ultimately treated RA in a high efficiency and low toxicity manner. Overall, our work demonstrates the efficacy of using SR-CLTNEs as a novel nanotherapeutic approach for RA therapy and the great translational potential of SR-CLTNEs.

A prognostic model for triple-negative breast cancer patients with liver metastasis: A population-based study.

However, it is still difficult for clinicians to establish prognostic stratifications and therapeutic strategies because of the lack of tools for predicting the survival of triple-negative breast cancer patients with liver metastases (TNBC-LM). Based on clinical data from large populations, a sensitive and discriminative nomogram was developed and validated to predict the prognosis of TNBC patients with LM at initial diagnosis or at the later course. Introduction/background: Liver metastasis (LM) in TNBC patients is associated with significant morbidity and mortality. The objective of this study was to construct a clinical model to predict the survival of TNBC-LM patients. Materials and methods: Clinicopathologic data were retrieved from the Surveillance, Epidemiology, and End Results (SEER) database and the Fifth Affiliated Hospital of Sun Yat-Sen University (FAFSYU). Based on patients with newly diagnosed TNBC with LM (nTNBC-LM) from the SEER database, a predictive nomogram was established and validated. Its predictive effect on TNBC patients with LM at later disease course by enrolling TNBC patients from FAFSYU who developed LM later. The prognostic effect of different treatment for nTNBC-LM was further assessed. Results: A prognostic model was developed and validated to predict the prognosis of TNBC-LM patients. For LM patients diagnosed at the initial or later treatment stage, the C-index (0.712, 0.803 and 0.699 in the training, validation and extended groups, respectively) and calibration plots showed the acceptable prognostic accuracy and clinical applicability of the nomogram. Surgical resection on the primary tumour and chemotherapy were found to be associated with significantly better overall survival (OS). Conclusion: A sensitive and discriminative model was developed to predict OS in TNBC-LM patients both at and after initial diagnosis.

The impact of ankle-foot orthoses on mobility of dual-task walking in stroke patients? A cross-sectional two-factor factorial design clinical trial.

ABSTRACTTo assess the impact of ankle-foot orthoses (AFOs) on mobility and gait during dual-task walking in post-stroke survivors. In this cross-sectional, factorial design trial, stroke survivors performed four randomized tasks: (1) dual-task walking with AFOs, (2) single-task walking with AFOs, (3) dual-task walking without AFOs, and (4) single-task walking without AFOs. Primary outcome was the Timed Up and Go (TUG) test, with secondary outcomes including gait metrics, Tinetti scores, and auditory N-back tests. In the results, 48 subjects (38 males and 10 females; 19-65 years) completed the trial. Patients had a greater TUG score with AFOs compared with non-AFOs conditions (95% CI: 7.22-14.41, P < 0.001) in single-task and dual-task conditions. Secondary outcomes showed marked enhancement with AFOs during dual-task walking, with significant interaction effects in gait metrics, balance, and cognitive function (P < 0.05). Although not statistically significant, dual-task effects of TUG and walking speed were more pronounced during dual-task walking. In conclusion, AFOs enhance mobility and gait during both single and dual-task walking in post-stroke survivors.

Long-term exposure to major constituents of fine particulate matter and neurodegenerative diseases: A population-based survey in the Pearl River Delta Region, China.

BACKGROUND: Exposure to PM2.5 has been linked to neurodegenerative diseases, with limited understanding of constituent-specific contributions. OBJECTIVES: To explore the associations between long-term exposure to PM2.5 constituents and neurodegenerative diseases. METHODS: We recruited 148,274 individuals aged ≥ 60 from four cities in the Pearl River Delta region, China (2020 to 2021). We calculated twenty-year average air pollutant concentrations (PM2.5 mass, black carbon (BC), organic matter (OM), ammonium (NH4+), nitrate (NO3-) and sulfate (SO42-)) at the individuals' home addresses. Neurodegenerative diseases were determined by self-reported doctor-diagnosed Alzheimer's disease (AD) and Parkinson's disease (PD). Generalized linear mixed models were employed to explore associations between pollutants and neurodegenerative disease prevalence. RESULTS: PM2.5 and all five constituents were significantly associated with a higher prevalence of AD and PD. The observed associations generally exhibited a non-linear pattern. For example, compared with the lowest quartile, higher quartiles of BC were associated with greater odds for AD prevalence (i.e., the adjusted odds ratios were 1.81; 95% CI, 1.45-2.27; 1.78; 95% CI, 1.37-2.32; and 1.99; 95% CI, 1.54-2.57 for the second, third, and fourth quartiles, respectively). CONCLUSIONS: Long-term exposure to PM2.5 and its constituents, particularly combustion-related BC, OM, and SO42-, was significantly associated with higher prevalence of AD and PD in Chinese individuals. ENVIRONMENTAL IMPLICATION: PM2.5 is a routinely regulated mixture of multiple hazardous constituents that can lead to diverse adverse health outcomes. However, current evidence on the specific contributions of PM2.5 constituents to health effects is scarce. This study firstly investigated the association between PM2.5 constituents and neurodegenerative diseases in the moderately to highly polluted Pearl River Delta region in China, and identified hazardous constituents within PM2.5 that have significant impacts. This study provides important implications for the development of targeted PM2.5 prevention and control policies to reduce specific hazardous PM2.5 constituents.

Systemic immune-inflammation index and all-cause and cause-specific mortality in sarcopenia: a study from National Health and Nutrition Examination Survey 1999-2018.

Background: Sarcopenia, common in the elderly, often linked to chronic diseases, correlates with inflammation.The association between SII and mortality in sarcopenia patients is underexplored, this study investigates this relationship in a U.S. adult cohort. Methods: We analyzed 1999-2018 NHANES data, focusing on 2,974 adults with sarcopenia. Mortality outcomes were determined by linking to National Death Index (NDI) records up to December 31, 2019. Using a weighted sampling design, participants were grouped into three groups by the Systemic Immune-Inflammation Index (SII). We used Cox regression models, adjusting for demographic and clinical variables, to explore SII's association with all-cause and cause-specific mortality in sarcopenia, performing sensitivity analyses for robustness. Results: Over a median follow-up of 9.2 years, 829 deaths occurred. Kaplan-Meier analysis showed significant survival differences across SII groups. The highest SII group showed higher hazard ratios (HRs) for all-cause and cause-specific mortality in both crude and adjusted models. The highest SII group had a higher HR for all-cause(1.57, 1.25-1.98), cardiovascular(1.61, 1.00-2.58), cancer(2.13, 1.32-3.44), and respiratory disease mortality(3.21, 1.66-6.19) in fully adjusted models. Subgroup analyses revealed SII's association with all-cause mortality across various demographics, including age, gender, and presence of diabetes or cardiovascular disease. Sensitivity analyses, excluding participants with cardiovascular diseases, those who died within two years of follow-up, or those under 45 years of age, largely reflected these results, with the highest SII group consistently demonstrating higher HRs for all types of mortality in both unadjusted and adjusted models. Conclusion: Our study is the first to demonstrate a significant relationship between SII and increased mortality risks in a sarcopenia population.

Cytochrome b5 diversity in green lineages preceded the evolution of syringyl lignin biosynthesis.

Lignin production marked a milestone in vascular plant evolution, and the emergence of syringyl (S)-lignin is lineage-specific. S-lignin biosynthesis in angiosperms, mediated by ferulate 5-hydroxylase (F5H, CYP84A1), has been considered a recent evolutionary event. F5H uniquely requires the cytochrome b5 protein CB5D as an obligatory redox partner for catalysis. However, it remains unclear how CB5D functionality originated and whether it co-evolved with F5H. We reveal here the ancient evolution of CB5D-type function supporting F5H-catalyzed S-lignin biosynthesis. CB5D emerged in charophyte algae, the closest relatives of land plants, and is conserved and proliferated in embryophytes, especially in angiosperms, suggesting functional diversification of the CB5 family before terrestrialization. A sequence motif containing acidic amino residues in helix 5 of the CB5 heme-binding domain contributes to the retention of CB5D function in land plants but not in algae. Notably, CB5s in the S-lignin-producing lycophyte Selaginella lack these residues, resulting in no CB5D-type function. An independently evolved S-lignin biosynthetic F5H (CYP788A1) in Selaginella relies on NADPH-dependent cytochrome P450 reductase as sole redox partner, distinct from angiosperms. These results suggest that angiosperm F5Hs co-opted the ancient CB5D, forming a modern cytochrome P450 monooxygenase system for aromatic ring meta-hydroxylation, enabling the re-emergence of S-lignin biosynthesis in angiosperms.

Lipids and lipid metabolism in cellular senescence: Emerging targets for age-related diseases.

Cellular senescence is a kind of cellular state triggered by endogenous or exogenous stimuli, which is mainly characterized by stable cell cycle arrest and complex senescence-associated secretory phenotype (SASP). Once senescent cells accumulate in tissues, they may eventually accelerate the progression of age-related diseases, such as atherosclerosis, osteoarthritis, chronic lung diseases, cancers, etc. Recent studies have shown that the disorders of lipid metabolism are not only related to age-related diseases, but also regulate the cellular senescence process. Based on existing research evidences, the changes in lipid metabolism in senescent cells are mainly concentrated in the metabolic processes of phospholipids, fatty acids and cholesterol. Obviously, the changes in lipid-metabolizing enzymes and proteins involved in these pathways play a critical role in senescence. However, the link between cellular senescence, changes in lipid metabolism and age-related disease remains to be elucidated. Herein, we summarize the lipid metabolism changes in senescent cells, especially the senescent cells that promote age-related diseases, as well as focusing on the role of lipid-related enzymes or proteins in senescence. Finally, we explore the prospect of lipids in cellular senescence and their potential as drug targets for preventing and delaying age-related diseases.

Synthesis and Neuroprotective Evaluation of Substituted Indanone/Benzofuranone and Piperidine Hybrids.

Based on the neuroprotection of butylphthalide and donepezil, a series of indanone/benzofuranone and piperidine hybrids were designed and synthesized for assessment of their neuroprotective activities, aiming to enhance the bioavailability and therapeutic efficacy of natural phthalide analogues. Within this study, it was observed that most indanone derivatives bearing 1-methylpiperidine in the tail segment demonstrated superior neuroprotective effects on the oxygen glucose deprivation/reperfusion (OGD/R)-induced rat primary neuronal cell injury model in vitro compared to benzofuranone compounds. Among the synthesized compounds, 11 (4, 14, 15, 22, 26, 35, 36, 37, 48, 49, and 52) displayed robust cell viabilities in the OGD/R model, along with favorable blood-brain barrier permeability as confirmed by the parallel artificial membrane permeability assay. Notably, compound 4 showed significant neuronal cell viabilities within the concentration range of 3.125 to 100 µM, without inducing cytotoxicity. Further results from in vivo middle cerebral artery occlusion/R experiments revealed that 4 effectively ameliorated ischemia-reperfusion injury, reducing the infarct volume to 18.45% at a dose of 40 mg/kg. This outcome suggested a superior neuroprotective effect compared to edaravone at 20 mg/kg, further highlighting the potential therapeutic efficacy of compound 4 in addressing neurological disorders.

Identifying changes in dynamic plantar pressure associated with radiological knee osteoarthritis based on machine learning and wearable devices.

BACKGROUND: Knee osteoarthritis (KOA) is an irreversible degenerative disease that characterized by pain and abnormal gait. Radiography is typically used to detect KOA but has limitations. This study aimed to identify changes in plantar pressure that are associated with radiological knee osteoarthritis (ROA) and to validate them using machine learning algorithms. METHODS: This study included 92 participants with variable degrees of KOA. A modified Kellgren-Lawrence scale was used to classify participants into non-ROA and ROA groups. The total feature set included 210 dynamic plantar pressure features captured by a wearable in-shoe system as well as age, gender, height, weight, and body mass index. Filter and wrapper methods identified the optimal features, which were used to train five types of machine learning classification models for further validation: k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), AdaBoost, and eXtreme gradient boosting (XGBoost). RESULTS: Age, the standard deviation (SD) of the peak plantar pressure under the left lateral heel (f_L8PPP_std), the SD of the right second peak pressure (f_Rpeak2_std), and the SD of the variation in the anteroposterior displacement of center of pressure (COP) in the right foot (f_RYcopstd_std) were most associated with ROA. The RF model with an accuracy of 82.61% and F1 score of 0.8000 had the best generalization ability. CONCLUSION: Changes in dynamic plantar pressure are promising mechanical biomarkers that distinguish between non-ROA and ROA. Combining a wearable in-shoe system with machine learning enables dynamic monitoring of KOA, which could help guide treatment plans.

Comparative effects of exercise interventions and mindfulness-based interventions for cognitive impairment and quality of life in breast cancer survivors during or after cancer treatment: A systematic review and Bayesian network meta-analysis.

OBJECTIVE: The aim of this network meta-analysis was to compare the improvement effects of various exercise interventions and mindfulness-based interventions to determine the best interventions for the improvement of cognitive impairment. DESIGN: 7 databases were searched to screen RCTs of exercise interventions and mindfulness-based interventions to improve cognitive impairment. The network meta-analysis was performed using Revman 5.3, R 4.2.1 and ADDIS 1.16.8 software. RESULTS: 34 RCTs involving 14 interventions were included in the study. In terms of cognitive function, except for mindfulness-based stress reduction, all interventions showed significantly greater improvement in cognitive function compared with conventional therapy. Physical activity and Qigong showed better effect in improving executive function. In terms of improving verbal memory, compensatory cognitive training, neurofeedback training, Qigong and sham Qigong were more effective than other interventions. On performing surface under the cumulative ranking curve analysis, acceptance and commitment therapy, neurofeedback training, Qigong, and mediation had the best effects on cognitive function, quality of life, executive function, and processing speed, respectively. CONCLUSIONS: Mindfulness-based interventions were found to be more effective than exercise interventions for alleviating cognitive impairment. More robust RCTs focusing on acceptance and commitment therapy for cognitive impairment are required to support the current evidence.

Subspace Identification of Bridge Frequencies Based on the Dimensionless Response of a Two-Axle Vehicle.

As an essential reference to bridge dynamic characteristics, the identification of bridge frequencies has far-reaching consequences for the health monitoring and damage evaluation of bridges. This study proposes a uniform scheme to identify bridge frequencies with two different subspace-based methodologies, i.e., an improved Short-Time Stochastic Subspace Identification (ST-SSI) method and an improved Multivariable Output Error State Space (MOESP) method, by simply adjusting the signal inputs. One of the key features of the proposed scheme is the dimensionless description of the vehicle-bridge interaction system and the employment of the dimensionless response of a two-axle vehicle as the state input, which enhances the robustness of the vehicle properties and speed. Additionally, it establishes the equation of the vehicle biaxial response difference considering the time shift between the front and the rear wheels, theoretically eliminating the road roughness information in the state equation and output signal effectively. The numerical examples discuss the effects of vehicle speeds, road roughness conditions, and ongoing traffic on the bridge identification. According to the dimensionless speed parameter Sv1 of the vehicle, the ST-SSI (Sv1 < 0.1) or MOESP (Sv1 ≥ 0.1) algorithm is applied to extract the frequencies of a simply supported bridge from the dimensionless response of a two-axle vehicle on a single passage. In addition, the proposed methodology is applied to two types of long-span complex bridges. The results show that the proposed approaches exhibit good performance in identifying multi-order frequencies of the bridges, even considering high vehicle speeds, high levels of road surface roughness, and random traffic flows.