ObjectiveTo explore the effect of oral sodium butyrate on skeletal muscle atrophy in CT26 tumor mice through the gut microbiota-skeletal muscle axis and its potential mechanism. MethodsSixty SPF BALB/c male mice aged 8 weeks were randomly divided into a normal control group (NC, n=18) and a ABX-depleted group (ABX, n=42). The ABX mice were pretreated with a quadruple antibiotic cocktail via oral gavage (0.2 ml per administration, once daily, 6 d per week, for 2 weeks), whereas NC received an equal volume of sterile water. The quadruple antibiotic cocktail consisted of metronidazole (1 g/L), vancomycin (0.5 g/L), ampicillin (1 g/L), and gentamicin (1 g/L). Following successful pretreatment, six mice from each group were randomly selected for gut microbiota sequencing analysis and designated as the Abx group and the NC0 group, respectively. Theremaining mice in ABX were subcutaneously inoculated in the dorsum with 0.2 ml of CT26 cell suspension (at a cell density of 1×10⁷/ml). Then these mice were randomly allocated into three subgroups: a control tumor bearing model group (0_NaB, n=12), a tumor-bearing model group receiving low-dose oral sodium butyrate (L_NaB, n=12), a tumor-bearing model group receiving high-dose oral sodium butyrate (H_NaB, n=12). And mice in NC were inoculated at the same site with 0.2 ml of normal saline. The administration dose for L_NaB was 0.3 g/(kg·d), that for H_NaB was 0.5 g/(kg·d), while NC and 0_NaB were given the same volume of normal saline (0.2ml per time, once daily, 6 d per week, for 4 weeks). The general condition of mice was monitored, and forelimb grip strength gastrocnemius muscle mass and its muscle fiber cross-sectional area were measured for each group. The structural changes in gut microbiota were assessed by 16S rRNA sequencing of cecal contents. Pathological alterations in the intestinal wall were examined via HE staining. Serum and gastrocnemius muscle levels of TNF‑α, IL-6, IL-1β, and LPS were quantified using ELISA. The protein expression of ZO-1 and occludin in the small intestine, as well as proteins associated with the TLR4/MyD88/NF-κB signaling pathway in the gastrocnemius muscle, were detected by Western blot analysis. Results(1) The alpha-diversity in Abx was significantly lower than that in NC0 (P<0.01), a significant decrease of the mass and muscle fiber cross-sectional area of the gastrocnemius (P<0.01), with the majority of gut microbiota being effectively depleted. (2) Compared with NC, the subcutaneous tumors of mice in 0_NaB were prominent, a significant increase of the mass and muscle fiber cross-sectional area of the gastrocnemius, accompanied by a significant decrease in body weight at the end of the 3th and 4th week (P<0.05), and a significant weakening of the forelimb grasping strength at the 5th and 6th week (P<0.01). Compared with 0_NaB, the tumor mass of mice in L_NaB and H_NaB showed a significant decreasing trend, and the grip strength of the forelimbs significantly increased at the 5th and 6th week (P<0.05, P<0.01). (3) Compared with 0_NaB, the Shannon and Observed species indices in α diversity of L_NaB and H_NaB were significantly increased (P<0.05). At the genus level, compared with 0_NaB, L_NaB exhibited a significant decrease in the relative abundance of Parasutterella (P< 0.01), while H_NaB showed significant reductions in the relative abundances of both Escherichia-Shigella and Parasutterella (P < 0.01). (4) Compared with 0_NaB, the small intestinal tissue structure in L_NaB and H_NaB was more intact, the infiltration of inflammatory cells was significantly reduced, and the capillaries were slightly dilated. The expression levels of ZO-1 and occludin proteins in L_NaB were significantly increased (P<0.01). (5) The LPS concentration in the gastrocnemius muscle and the protein expression levels of TLR4, MyD88, p-IκBα, and p-NF‑κB p65 in L_NaB and H_NaB were significantly lower than those in 0_NaB (P<0.05). The serum TNF‑α concentration in H_NaB and TNF-α concentration in the gastrocnemius muscle of the L_NaB and H_NaB were significantly lower than those in 0_NaB (P<0.05, P<0.01, P<0.01). ConclusionOral administration of NaB can improve gut microbiota α diversity, adjusting its composition, improving intestinal mucosal barrier function, reducing the LPS-induced pro-inflammatory response, and delaying skeletal muscle atrophy. The underlying mechanism may involve down regulation of TLR4/MyD88/NF-κB signaling in skeletal muscle.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

ObjectiveThe essence of syndrome manifestation recognition in traditional Chinese medicine (TCM) is to infer the body’s latent pathogenesis state from clinical observational information, rather than to perform simple label matching. However, previous studies have largely modeled this task as syndrome pattern classification within a fixed label space, which does not adequately reflect the cognition process of TCM syndrome differentiation centered on pathogenesis reasoning, and is also insufficient to capture the openness, semantic variability, and cross-disease reusability of syndrome manifestation expression. This study aimed to investigate whether introducing pathogenesis reasoning chain-of-thought (PR-CoT) supervision into large language models (LLMs) could improve the quality and cognitive consistency of syndrome manifestation recognition and support cross-disease transfer. MethodsSyndrome manifestation recognition was formulated as a conditional generation task under the framework of clinical observational information (X)→pathogenesis structure (Z)→syndrome pattern output (Y), where Z serves as an explicit intermediate structural variable linking the clinical evidence and syndrome judgment. Within this framework, a PR-CoT-supervised dataset for syndrome manifestation recognition was constructed based on medical case records of spleen-stomach disorders. After preprocessing, information extraction, manual proofreading, and data cleaning, the dataset comprised 4 800 training cases, 400 development cases, and 400 test cases. Each sample was annotated with a structured PR-CoT consisting of three progressive levels: clinical information summarization, comprehensive pathogenesis analysis, and syndrome pattern output. Supervised fine-tuning was conducted on open-source LLMs, with an end-to-end model serving as the baseline. Qwen3-32B was used as the primary experimental model, and Qwen3-14B as the scale comparison model. A progressive multidimensional evaluation framework was further established, comprising a structural parsing level, a semantic similarity level, and an expert blind review level. At the structural parsing level, syndrome pattern expressions were decomposed into structural elements and evaluated using Precision, Recall, F1 score, and Jaccard similarity. At the semantic similarity level, independent LLMs scored the theoretical proximity between predicted and reference syndrome patterns. At the expert blind review level, three TCM experts independently evaluated model outputs on two dimensions: syndrome differentiation consistency and terminology standardization of syndrome patterns. In addition, zero-shot cross-disease transfer evaluation was conducted on gynecological and heart-system disorder test sets. ResultsAt the structural parsing level, PR-CoT supervision did not lead to a stable improvement in the element-wise overlap of syndrome pattern structural components. Compared with the corresponding baselines, neither Qwen3-32B nor Qwen3-14B showed consistent advantages in structural matching metrics after the introduction of PR-CoT supervision. In contrast, at the semantic similarity level, PR-CoT supervision produced stable positive gains across different model scales and evaluation systems. The average semantic score of Qwen3-32B increased from 6.425 8 in the baseline model to 6.585 0 after PR-CoT supervision, and that of Qwen3-14B increased from 5.870 0 to 5.964 2. At the expert blind review level, the overall score of Qwen3-32B (PR-CoT) was 7.026 0±0.107 7, higher than 6.416 3±0.288 9 for its baseline. In zero-shot cross-disease testing, the PR-CoT model still showed advantages in semantic evaluation and expert evaluation on both gynecological and heart-system disorder test sets, indicating a certain degree of transferability. ConclusionThe benefits of PR-CoT supervision are mainly reflected in TCM semantic consistency and clinical plausibility, rather than in improved hard matching of structural elements. These findings support understanding syndrome manifestation recognition as a process of generating and expressing latent pathogenesis structures, rather than as a classification task within a traditional fixed label space. By introducing pathogenesis reasoning as an explicit intermediate structure into the modeling process and combining it with a progressive multidimensional evaluation framework, this study provides a methodological pathway for intelligent TCM syndrome differentiation that integrates theoretical alignment, interpretability, and multi-level evaluation.

OBJECTIVE: To investigate the early clinical efficacy of single-position O-arm navigation-assisted oblique lateral interbody fusion(OLIF) combined with minimally invasive percutaneous pedicle screw fixation(PPS) in the treatment of lumbar spondylolisthesis. METHODS: A retrospective analysis was conducted on 22 patients with lumbar spondylolisthesis who underwent OLIF-PPS surgery including 11 males and 11 females with a mean age of (64.6±1.5) years old ranging from 49 to 80 years old between April 2021 and June 2023. All patients presented with lumbosacral pain, lower limb radiating pain, numbness, and had poor responses to conservative treatment. Surgical time, intraoperative blood loss, hospital stay, and postoperative complications were recorded. Clinical outcomes were evaluated using the visual analogue scale(VAS) and Oswestry disability index(ODI) preoperatively at 3 days after operation and the final follow-up. Standing lumbar anteroposterior and lateral X-rays were performed to measure disc height(DH), slippage degree, vertebral reduction rate, pedicle screw accuracy, and cage subsidence. RESULTS: All surgeries were successfully completed with a mean follow-up of (27.1±2.2) months (range 18 to 36 months). The mean surgical time was (76.1±12.2) min (range 60 to 93 min), intraoperative blood loss was (86.3±32.2) ml (range 40 to 113 ml), and hospital stay was (7.1±1.2) days. Postoperative VAS significantly improved from (7.2±0.7) preoperatively to (2.3±0.5) at 3 days after operation and (1.7±0.2) at the final follow-up (P<0.05). ODI decreased from (68.5±7.2)% preoperatively to (30.3±3.1)% at 3 days after operation and (16.6±1.6)% at the final follow-up (P<0.05). DH increased from (8.5±1.7) mm preoperatively to (18.1±1.4) mm at 3 days after operation and (17.2±1.1) mm at the final follow-up (P<0.05). Slippage degree improved from (24.1±4.6)% preoperatively to (10.3±4.2)% at 3 days after operation and (10.1±3.2)% at the final follow-up (P<0.05). A total of 88 pedicle screws were implanted with an excellent rate of 98% (86/88). Complications included transient left hip flexion weakness (2 cases) and left anteromedial thigh pain (1 case), all resolved during follow-up. No incision hematoma, infection, screw loosening, or cage subsidence occurred. CONCLUSION Single-position O-arm navigation-assisted OLIF combined with PPS demonstrates satisfactory early clinical efficacy for lumbar spondylolisthesis, with advantages including minimal invasiveness, significant pain relief, effective vertebral reduction, and low complication rates.
Humans ; Male ; Female ; Spondylolisthesis/diagnostic imaging* ; Middle Aged ; Aged ; Spinal Fusion/methods* ; Lumbar Vertebrae/diagnostic imaging* ; Minimally Invasive Surgical Procedures/methods* ; Pedicle Screws ; Aged, 80 and over ; Retrospective Studies

Bacterial biofilms can make traditional antibiotics impenetrable and even promote the development of antibiotic-resistant strains. Therefore, non-antibiotic strategies to effectively penetrate and eradicate the formed biofilms are urgently needed. Here, we demonstrate the development of self-propelled biohybrid microrobots that can enhance the degradation and penetration effects for Pseudomonas aeruginosa biofilms in minimally invasive strategy. The biohybrid microrobots (CR@Alg) are constructed by surface modification of Chlamydomonas reinhardtii (CR) microalgae with alginate lyase (Alg) via biological orthogonal reaction. By degrading the biofilm components, the number of CR@Alg microrobots with fast-moving capability penetrating the biofilm increases by around 2.4-fold compared to that of microalgae. Massive reactive oxygen species are subsequently generated under laser irradiation due to the presence of chlorophyll, inherent photosensitizers of microalgae, thus triggering photodynamic therapy (PDT) to combat bacteria. Our algae-based microrobots with superior biocompatibility eliminate biofilm-infections efficiently and tend to suppress the inflammatory response in vivo, showing huge promise for the active treatment of biofilm-associated infections.

Quality marker (Q-Marker) is an innovative concept and model for quality control of Traditional Chinese medicines (TCMs), which will navigate the new direction of quality development of TCMs. Yet, how to characterize the overall quality attributes of TCMs and their biological effects is still debating. In view of this key scientific issue, this paper proposes a research method based on mass spectrometry imaging (MSI) technology for the discovery and confirmation of TCMs Q-Marker. MSI is powerful in investigating the spatial distribution of molecules in a variety of samples, and visualizing the information obtained from MS. On this basis, combine with the five principles of TCMs Q-Marker validation, i.e., specificity, transmission and traceability, testability, prescription compatibility, and validity, were applied to confirm the finalized Q-Marker. It will lead the new direction of quality development of TCMs.

Objectives: This study aimed to present the Asia-Pacific consensus on long-term and sequential therapy for osteoporosis, offering evidence-based recommendations for the effective management of this chronic condition.The primary focus is on achieving optimal fracture prevention through a comprehensive, individualized approach. Methods: A panel of experts convened to develop consensus statements by synthesizing the current literature and leveraging clinical expertise. The review encompassed long-term anti-osteoporosis medication goals, first-line treatments for individuals at very high fracture risk, and the strategic integration of anabolic and anti resorptive agents in sequential therapy approaches. Results: The panelists reached a consensus on 12 statements. Key recommendations included advocating for anabolic agents as the first-line treatment for individuals at very high fracture risk and transitioning to anti resorptive agents following the completion of anabolic therapy. Anabolic therapy remains an option for in dividuals experiencing new fractures or persistent high fracture risk despite antiresorptive treatment. In cases of inadequate response, the consensus recommended considering a switch to more potent medications. The consensus also addressed the management of medication-related complications, proposing alternatives instead of discontinuation of treatment. Conclusions This consensus provides a comprehensive, cost-effective strategy for fracture prevention with an emphasis on shared decision-making and the incorporation of country-specific case management systems, such as fracture liaison services. It serves as a valuable guide for healthcare professionals in the Asia-Pacific region, contributing to the ongoing evolution of osteoporosis management.

Objective: Lynch syndrome (LS) is a hereditary cancer predisposition syndrome with a significantly increased risk of colorectal and endometrial cancers. Current standard practice involves universal screening for LS in patients with newly diagnosed colorectal or endometrial cancer using a multi-step screening protocol (MSP). However, MSP may not always accurately identify LS cases. To address this limitation, we compared the diagnostic performance of immediate germline sequencing (IGS) with MSP in a high-risk group. Methods: A total of 31 Taiwanese women with synchronous or metachronous endometrial and colorectal malignancies underwent MSP which included immunohistochemical staining of DNA mismatch repair (MMR) proteins, MLH1 promoter hypermethylation analysis, and germline sequencing to identify pathogenic variants. All patients who were excluded during MSP received germline sequencing for MMR genes to simulate IGS for the detection of LS. Results: Our findings indicate that IGS surpassed MSP in terms of diagnostic yield (29.0% vs.19.4%, respectively) and sensitivity (90% vs. 60%, respectively). Specifically, IGS successfully identified nine LS cases, which is 50% more than the number detected through MSP.Additionally, germline methylation analysis revealed one more LS case with constitutional MLH1 promoter hypermethylation, bringing the total LS cases to ten (32.3%). Intriguingly, we observed no significant differences in clinical characteristics or overall survival between patients with and without LS in our cohort. Conclusion Our study suggests that IGS may potentially offer a more effective approach compared to MSP in identifying LS among high-risk patients. This advantage is evident when patients have been pre-selected utilizing specific clinical criteria.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.

Objective: This study aims to overcome challenges in lumbar spine imaging, particularly lumbar spinal stenosis, by developing an automated segmentation model using advanced techniques. Traditional manual measurement and lesion detection methods are limited by subjectivity and inefficiency. The objective is to create an accurate and automated segmentation model that identifies anatomical structures in lumbar spine magnetic resonance imaging scans. Methods: Leveraging a dataset of 539 lumbar spinal stenosis patients, the study utilizes the residual U-Net for semantic segmentation in sagittal and axial lumbar spine magnetic resonance images. The model, trained to recognize specific tissue categories, employs a geometry algorithm for anatomical structure quantification. Validation metrics, like Intersection over Union (IOU) and Dice coefficients, validate the residual U-Net’s segmentation accuracy. A novel rotation matrix approach is introduced for detecting bulging discs, assessing dural sac compression, and measuring yellow ligament thickness. Results: The residual U-Net achieves high precision in segmenting lumbar spine structures, with mean IOU values ranging from 0.82 to 0.93 across various tissue categories and views. The automated quantification system provides measurements for intervertebral disc dimensions, dural sac diameter, yellow ligament thickness, and disc hydration. Consistency between training and testing datasets assures the robustness of automated measurements. Conclusion Automated lumbar spine segmentation with residual U-Net and deep learning exhibits high precision in identifying anatomical structures, facilitating efficient quantification in lumbar spinal stenosis cases. The introduction of a rotation matrix enhances lesion detection, promising improved diagnostic accuracy, and supporting treatment decisions for lumbar spinal stenosis patients.