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Background Osteoarthritis (OA) of the knee is a prevalent chronic degenerative joint condition that is having a growing impact on a global scale., posing a challenge in diagnosis which is often reliant on time-consuming and error-prone visual analysis by physicians. There is a critical need for an automated, efficient, and accurate diagnostic method to improve early detection and treatment. Methods We developed a novel Convolutional Neural Network (CNN) module, Dense Multi-Scale (DMS), an advancement over Multi-Scale Convolution (MSC). This module utilizes dense connections in convolutions of varying sizes (1 × 1, 3 × 3, 5 × 5) and across layers, enhancing feature reuse and complexity recognition, thereby improving recognition capabilities. Dense connections also facilitate deeper network architecture and mitigate gradient vanishing problems. We compared our model with a standard baseline model and validated it using an unseen-data test set. Results The DMS model exhibited exceptional performance in unseen-data tests, achieving 73.00% average accuracy (ACC) and 92.73% area under the curve (AUC), surpassing the baseline model’s (DenseNet) 63.52% ACC and 88.76% AUC. This highlights the DMS model’s superior predictive capability for knee OA. Conclusion The DMS model presents a significant advancement in predicting and grading knee OA, holding substantial clinical importance. It promises to aid radiologists in accurate diagnosis and grading, and in choosing appropriate treatments, thereby reducing misdiagnosis and patient burden.

The impact of knee osteoarthritis on individuals' daily functioning is significant. In recent years, Vitamin D supplements cure osteoarthritis has garnered attention from medical professionals and patients due to its simplicity and portability. Several systematic reviews (SRs) and meta-analyses (MAs) have examined the efficacy of vitamin D supplementation for knee osteoarthritis, yet there is variability in their methodology and quality. To search, gather, and analyze data on the characteristics and quantitative results of SR/MA in patients with KOA treated with Vitamin D supplementation, and objectively evaluate the efficacy of supplements. Then, provides clinical evidence and recommendations the clinical use of vitamin D supplementation. Two individuals reviewed and collected data from four databases until October 2023. AMSTAR-2, ROBIS, PRISMA 2020, and GRADE tools were used to evaluate the methodological quality, bias risk, reporting quality, and evidence strength of all SR/MA. Additionally, we applied the corrected covered area (CCA) method to measure overlap in randomized controlled trials (RCTs) cited among the SR/MA. 3 SRs and 6 MAs were included in the analysis: 3 studies were low quality by AMSTAR-2, and 6 studies were very low quality. According to ROBIS, 6 studies were high-risk and 3 were low-risk. In PRISMA 2020 reporting quality, most studies showed deficiencies in comprehensive literature search strategy, reasons for literature exclusion, data preprocessing for meta-analysis, exploration of reasons for heterogeneity, sensitivity analysis, publication bias, and disclosure of funding and conflicts of interest. Grading the quality of evidence in GRADE consisted of 5 items of moderate quality, 14 items of low quality, and 10 items of very low quality. Bias risk and imprecision were the main factors for downgrading. The calculation of RCT overlap between SR/MA using CCA showed a high degree of overlap. Vitamin D supplementation may show potential efficacy in ameliorating symptoms of KOA. The evidence indicates that Vitamin D supplements for knee osteoarthritis can improve patients' Total WOMAC scores and synovial fluid volume in the joints. Nevertheless, due to the generally low quality of current studies, future research should prioritize improving the quality of primary studies to establish the efficacy of vitamin D supplementation for KOA with more robust scientific evidence. The protocol of this overview was registered in the International Prospective Register of Systematic Reviews (PROSPERO) (https://www.crd.york.ac.uk/PROSPERO/) with the registration number CRD42024535841.

Stroke-related hemiplegia often results in significant lower limb dysfunction, severely affecting walking ability, balance, and daily activities. Although various non-pharmacological interventions have shown potential benefits, the optimal rehabilitation strategy remains unclear. To evaluate the efficacy and comparative ranking of non-pharmacological interventions in improving lower limb motor function, balance, walking ability, and activities of daily living in individuals with post-stroke hemiplegia. We conducted a search of PubMed, Embase, Cochrane Library, and Web of Science databases for randomized controlled trials (RCTs) published from January 2010 to August 2025. The Cochrane Risk of Bias Tool and Review Manager 5.4 were used to assess study quality, and evidence was graded with GRADEPro. Using R Studio software, a NMA was carried out to evaluate the clinical efficacy of various treatments in improving lower limb motor function in patients with post-stroke hemiplegia, ranked by the surface under the cumulative ranking curve (SUCRA). The study was officially registered in PROSPERO under the number CRD420251169037. A total of 82 RCTs involving 3514 participants and 16 non-pharmacological interventions were included. The results indicated that repetitive transcranial magnetic stimulation (rTMS) showed favorable effects on lower limb motor function measured by FMA-LE (MD = 3.7, 95% CI 2.5 to 4.9; SUCRA = 88.13%). rTMS also demonstrated positive effects on balance (MD = 8.5, 95% CrI: 5.1 to 11; SUCRA = 98.41%) and activities of daily living (MD = 14, 95% CrI: 11 to 16; SUCRA = 94.68%). For walking independence assessed by FAC, transcranial direct current stimulation (tDCS) showed considerable effects (MD = 1.5, 95% CrI: 0.41 to 2.5; SUCRA = 87.60%). Furthermore, virtual reality combined with robotic rehabilitation showed a relatively marked effect in reducing TUG time (MD = - 6.6, 95% CrI: - 8.9 to - 4.3; SUCRA = 95.27%). Different non-pharmacological interventions may provide distinct benefits for lower limb rehabilitation after stroke. rTMS appears favorable for improving motor function, balance, and daily living ability; tDCS may help enhance walking independence; and virtual reality combined with robotic rehabilitation may be beneficial for functional mobility. Further large-scale, multicenter, standardized RCTs with longer follow-up are needed to confirm these findings.

Metal-halide perovskite thin monocrystals featuring efficient carrier collection and transport capabilities are well suited for radiation detectors, yet their growth in a generic, well-controlled manner remains challenging. Here, we reveal that mass transfer is one major limiting factor during solution growth of perovskite thin monocrystals. A general approach is developed to overcome synthetic limitation by using a high solute flux system, in which mass diffusion coefficient is improved from 1.7×10 –10 to 5.4×10 –10 m 2 s –1 by suppressing monomer aggregation. The generality of this approach is validated by the synthesis of 29 types of perovskite thin monocrystals at 40–90 °C with the growth velocity up to 27.2 μm min –1 . The as-grown perovskite monocrystals deliver a high X-ray sensitivity of 1.74×10 5 µC Gy −1 cm −2 without applied bias. The findings regarding limited mass transfer and high-flux crystallization are crucial towards advancing the preparation and application of perovskite thin monocrystals. Liu et al. report a universal solution growth method for perovskite thin monocrystals by improving the mass transfer in the high solute flux system. The approach is applied to 29 types of perovskites with growth velocity up to 27.2 µm min -1 and enables efficient self-driven X-ray detectors.

Growth arrest and DNA damage-inducible 45 (GADD45) proteins are critical stress sensors rapidly induced in response to genotoxic/physiological stress and regulate many cellular functions. Even though the primary function of the proteins is to block the cell cycle, inhibit cell proliferation, promote cell apoptosis, and repair DNA damage to cope with the damage caused by internal and external stress on the body, evidence has shown that GADD45 also has the function to modulate innate and adaptive immunity and plays a broader role in inflammatory and autoimmune diseases. In this review, we focus on the immunomodulatory role of GADD45 in inflammatory and autoimmune diseases. First, we describe the regulatory factors that affect the expression of GADD45. Then, we introduce its immunoregulatory roles on immune cells and the critical signaling pathways mediated by GADD45. Finally, we discuss its immunomodulatory effects in various inflammatory and autoimmune diseases.

Background Osteoarthritis (OA) is a prevalent degenerative joint disorder affecting over 240 million people worldwide, yet no disease-modifying therapies currently exist, with clinical management limited to symptomatic relief or joint replacement. Exosomes (Exos) from bone marrow mesenchymal stem cells (Exo BMSC ) play positive role in the treatment of cartilage damage. Parathyroid hormone (PTH) (1–34) can enhance cartilage repair. Here, We found Exos from Exo BMSC reduces cartilage damage during treatment. Meanwhile, the Exos of PTH(1–34)-preconditioned BMSCs (Exo PTH ) can alleviate OA better than Exo BMSC . Through MicroRNA (miRNA) sequencing analysis, this study aims to reveal the effects and potential mechanism of miRNA (let-7a-5p) in Exo PTH to repair OA cartilage. Methods Differential centrifugation was used for isolating Exo BMSC and Exo PTH . Extract bone marrow mesenchymal stem cells from rats and utilize the C28/I2 chondrocytes line, the OA model was established using lipopolysaccharide (LPS; 1 µg/mL) in vitro. OA was induced in rats with intra-articular injection with collagenase-2. By performing a miRNA array, RNA-seq, in addition to bioinformatic analysis, the miRNA and the potential regulatory mechanism were detected. We compared in vitro let-7a-5p effects on the ability of OA chondrocytes to proliferate, migrate, apoptosis, and form the extracellular matrix (ECM). Histological and immunohistochemical assessments were used for evaluating cartilage pathology in vivo. Results We extracted Exo BMSC and Exo PTH and established the OA model in vitro. Compared with Exo BMSC group, Exo PTH group has a stronger effect on promoting the proliferation and migration of chondrocytes. Exo BMSC and Exo PTH can inhibit the apoptosis of chondrocytes, but there was no significant difference between the two groups. The two most significant differences in groups Exo BMSC and Exo PTH are let-7a-5p. Let-7a-5p promotes OA chondrocytes proliferation and migration by inhibiting the expression of IL-6 in vitro experiments. For in vivo experiments, let-7a-5p delays the progression of OA. Conclusion Our study shows that Exo PTH may improve the regulatory inflammatory responses to delays the progression of OA by shuttling let-7a-5p. Let-7a-5p promoted chondrocytes migration and proliferation to suppress OA pathology by inhibiting IL-6/STAT3 pathway.

Neuronal PAS domain protein 1 (NPAS1) is a protein-coding gene expressed mainly in the central nervous system and plays a key role in nervous system development. The expression and prognostic value of NPAS1 in colorectal adenocarcinoma (COAD) are unknown. The expression and clinicopathological data of COAD from Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) were analyzed. NPAS1 gene expression in colon cancer tissues was validated by Western blotting and immunohistochemical staining. Function and immune infiltration were established using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Set Enrichment Analysis (GSEA), and single sample Gene Set Enrichment Analysis (ssGSEA). Methylation levels were analyzed utilizing the UALCAN database. NPAS1-related mutations were analyzed using cBioPortal. Single-cell and tissue-specific expression of NPAS1 was examined using the Human Protein Atlas. Protein-protein interactions and transcription factor networks of NPAS1-associated genes were analyzed with STRING and Network Analyst. Our study found that the expression level of NPAS1 was higher in normal tissues compared to COAD tissues, higher NPAS1 expression was associated with better survival outcomes. The bioinformatics analysis confirmed that NPAS1 co-expressed genes were linked to diverse signalling pathways and cellular functions. NPAS1 is correlated with CD56bright, cytotoxic and NK cells, and negatively correlated with helper T cells and Tcm cells. The DNA methylation level of NPAS1 in tumor tissues was elevated compared to normal tissues. We analyzed the mutation characteristics of NPAS1, single-cell expression profiling of NPAS1, and protein-protein interactions involving genes associated with NPAS1. These discoveries offered perspectives on the etiology, identification, and management of COAD. This study revealed a significant decrease of NPAS1 expression in COAD tissues, exhibiting associations with the clinical stage, prognosis, immune infiltration, and DNA methylation of COAD.

Objectives To explore the diagnostic value of radiomics in differentiating between lung adenocarcinomas appearing as ground‐glass opacity nodules (GGO) with high‐ and low Ki‐67 expression levels. Materials and Methods From January 2018 to January 2021, patients with pulmonary GGO who received lung resection were evaluated for potential enrollment. The included GGOs were then randomly divided into a training cohort and a validation cohort with a ratio of 7:3. Logistic regression (LR), decision tree (DT), support vector machines (SVM), and adaboost (AB) were applied for radiomic model construction. Area under the curve (AUC) of the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of the established models. Results Seven hundred and sixty‐nine patients with 769 GGOs were included in this study. Two hundred and forty‐five GGOs were confirmed to be of high Ki‐67 labeling index (LI). In the training cohort, gender, age, spiculation sign, pleural indentation sign, bubble sign, and maximum 2D diameter of the nodule were found to be significantly different between high‐ and low Ki‐67 LI groups (p < 0.05), and spiculation sign and maximum 2D diameter of the nodule were further confirmed to be risk factors for Ki‐67 LI. The radiomic model established using SVM exhibited an AUC of 0.731 in the validation cohort, which was higher than that of the clinical‐radiographic model (AUC = 0.675). Moreover, radiomic model combining both intra‐ and peri‐nodular features showed better diagnostic efficacy than using intra‐nodular features alone (AUC = 0.731 and 0.720, respectively). Conclusions The established radiomic model exhibited good diagnostic efficacy in differentiating between lung adenocarcinoma GGOs with high and low Ki‐67 LI, which was higher than the clinical‐radiographic model. Peri‐nodular radiomic features showed added benefits to the radiomic model. As a novel noninvasive method, radiomics have the potential to be applied in the preliminary classification of Ki‐67 expression level in lung adenocarcinoma GGOs. By nodule segmentation, feature selection, modeling, and validation, a radiomic model was established to differentiate between lung adenocarcinomas appearing as ground‐glass opacity nodules with high‐ and low Ki‐67 expression levels, which exhibited good diagnostic efficacy.

Remote Sensing Image Change Captioning (RSICC) aims to describe changes between bi-temporal remote sensing images and holds significant research and application value. However, most existing methods rely on conventional deep learning architectures, and the limited model capacity constrains performance. Although large-model post-training techniques have achieved great success in general domains, their direct transfer to RSICC remains challenging due to data scarcity and the need for fine-grained change understanding. To address this, we propose RSICCLLM, the first post-training framework for large vision-language models in RSICC. Specifically, we design a data generation paradigm, release the instruction dataset RSICI, and establish a task-specific RSICC benchmark. We further introduce Difference-aware Supervised Fine-tuning to explicitly extract change representations and guide the model in perceiving and understanding temporal differences. In addition, we propose Dual-Negative Preference Optimization (DNPO), which employs two complementary negative-sample construction strategies to construct the preference dataset RSICP and further refine model performance. Extensive experiments validate the superior capability of RSICCLLM, which achieves outstanding results with only 7B parameters, surpassing models of substantially larger scales. The code and dataset will be made publicly available at https://github.com/keaill/RSICCLLM.