Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

This study summarized Professor Su Peiji's clinical experience in differenting and treating lumbago and leg pain.He contends that the disease involves the liver and kidney,manifesting as a syndrome of deficiency in origin and excess in superficiality,with liver-kidney deficiency and insufficiency of essence and blood as the root cause,and exogenous wind,cold,and dampness invading the meridians and then causing obstruction as the superficial manifestation.The principal pathological factors include wind,cold,dampness,blood stasis,and deficiency.In clinical practice,he follows the therapeutic principle of expelling pathogens and supporting healthy qi in accordance with the abundance or deficiency of pathogens and healthy qi,takes the therapy of tonifying the liver and kidney as the primary approach,and applies the adjuvant therapies of dispelling wind,cold,and dampness as well as relaxing tendons and activating collaterals.By incorporating the theory of collateral diseases,Professor Su Peiji proposes that the deficiency-excess of collateral diseases is closely related to the onset of lumbago and leg pain.His therapeutic approach centers around Shuji Formula,a self-formulated formula which is composed of Taxilli Herba,Achyranthis Bidentatae Radix,Stephaniae Tetrandrae Radix,Angelicae Pubescentis Radix,Clematidis Radix et Rhizoma,Aconiti Radix Cocta,Flemingiae Radix et Caulis,Millettiae Speciosae Radix,Poria,Angelicae Sinensis Radix,Paeoniae Radix Alba,Chuanxiong Rhizoma,Spatholobi Caulis,Tinosporae sinensis Caulis,and Glycyrrhizae Radix et Rhizoma Praeparata cum Melle.Modified use of herbs was performed according to the predominance of wind,cold,dampness,blood stasis,or deficiency,so as to achieve the effects of tonifying the liver and kidney,strengthening bones and tendons,dispelling wind,cold and dampness,and relaxing tendons and activating collaterals.

Psychological factors have become significant contributors to the onset and progression of insomnia. This article explored the treatment of insomnia from the perspective of “liver governs wei qi （defensive qi）”. The concept of “liver governs wei qi （defensive qi）” is summarized in three aspects， firstly， the liver assists the spleen and stomach in transformation and transportation， governing the generation of wei qi； secondly， the liver aids lung qi diffusion and dispersion， governing the distribution of wei qi； thirdly， the liver regulates circadian rhythms， governing the circulation of wei qi. It is proposed that the clinical treatment of insomnia should focus on the following methods： for regulating the liver to harmonize the five viscera， and facilitate the circulation of wei qi， medicinals entering the liver channel include Chaihu （Bupleuri radix）， Baishao （Paeoniae Radix Alba）， Zhizi （Gardeniae Fructus）， and Suanzaoren （Ziziphi Spinosae Semen） could be commonly used； for nourishing the liver， the treatment should align with the day-night rhythm， and herbs such as Baihe （Lilium）， Hehuan （Albizia julibrissin）， and Yejiaoteng （Polygoni multiflori caulis） are commonly used； for soothing the liver and address both mental and physical health to calm wei qi， treatment should advocate verbal counseling， psychological regulation， and health education. Ultimately， this treatment approach can free liver qi to flow， soothe qi movement， restore the motion of wei qi， regulate during day and night， balance yin and yang， and resolve insomnia effectively.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Background: Gestational diabetes mellitus (GDM) is a metabolic disorder posing significant risks to maternal and infant health, with a lack of effective early screening markers. Therefore, identifying early screening biomarkers for GDM with higher sensitivity and specificity is urgently needed. Methods: High-throughput sequencing was employed to screen for key circular RNAs (circRNAs), which were then evaluated using reverse transcription quantitative polymerase chain reaction. Logistic regression analysis was conducted to examine the relationship between clinical characteristics, circRNA expression, and adverse pregnancy outcomes. The diagnostic accuracy of circRNAs for early and mid-pregnancy GDM was assessed using receiver operating characteristic curves. Pearson correlation analysis was utilized to explore the relationship between circRNA levels and oral glucose tolerance test results. A predictive model for early GDM was established using logistic regression. Results: Significant alterations in circRNA expression profiles were detected in GDM patients, with hsa_circ_0031560 and hsa_ circ_0000793 notably upregulated during the first and second trimesters. These circRNAs were associated with adverse pregnancy outcomes and effectively differentiated GDM patients, with second trimester cohorts achieving an area under the curve (AUC) of 0.836. In first trimester cohorts, these circRNAs identified potential GDM patients with AUCs of 0.832 and 0.765, respectively. The early GDM prediction model achieved an AUC of 0.904, validated in two independent cohorts. Conclusion Hsa_circ_0031560, hsa_circ_0000793, and the developed model serve as biomarkers for early prediction or midterm diagnosis of GDM, offering clinical tools for early GDM screening.

Metastasis remains the primary cause of cancer-related mortality worldwide. Circulating tumor cells (CTCs) represent critical targets for metastasis prevention and treatment. Traditional Chinese medicine may prevent lung cancer metastasis through long-term intervention in CTC activity. Tiao-Shen-Zhi-Ai Formular (TSZAF) represents a Chinese medicine compound prescription utilized clinically for lung cancer treatment. This study combined three principal active ingredients from TSZAF into a novel TSZAF monomer combination (TSZAF mc) to investigate its anti-metastatic effects and mechanisms. TSZAF mc demonstrated significant inhibition of proliferation, migration, and invasion in CTC-TJH-01 and LLC cells, while inducing cellular apoptosis in vitro. Moreover, TSZAF mc substantially inhibited LLC cell growth and metastasis in vivo. Mechanistically, TAZSF mc significantly suppressed the Wnt/β-catenin signaling pathway and CXCL5 expression in lung cancer cells and tissues. Additionally, TAZSF mc notably reduced neutrophil infiltration in metastatic lesions. These findings indicate that TSZAF mc inhibits lung cancer growth and metastasis by suppressing the Wnt/β-catenin signaling pathway and reducing CXCL5 secretion, thereby decreasing neutrophil recruitment and infiltration. TSZAF mc demonstrates potential as an effective therapeutic agent for lung cancer metastasis.
Lung Neoplasms/genetics* ; Wnt Signaling Pathway/drug effects* ; Animals ; Humans ; Drugs, Chinese Herbal/pharmacology* ; Mice ; Neoplasm Metastasis/prevention & control* ; Cell Proliferation/drug effects* ; Cell Line, Tumor ; Neutrophil Infiltration/drug effects* ; Down-Regulation/drug effects* ; Cell Movement/drug effects* ; beta Catenin/genetics* ; Apoptosis/drug effects* ; Mice, Inbred C57BL ; Male ; Neoplastic Cells, Circulating/drug effects*

Osteoarthritis is a chronic inflammatory disease characterized by damage to the articular cartilage, synovitis, and subchondral bone remodeling. Its pathological mechanisms involve extracellular matrix degradation, cell apoptosis, autophagy, and inflammatory responses. Among these, dysregulated apoptosis is a central driver of disease progression, making chondrocyte apoptosis a critical therapeutic target. This review summarizes current understanding of OA pathogenesis. Pro-inflammatory cytokines [e.g., interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and IL-6] exacerbate cartilage catabolism by activating signaling pathways like NF-κ B and MAPK. Chemokines, including the C-C motif chemokine ligand (CCL) family and the C-X-C motif chemokine ligand (CXC) family, amplify the inflammatory cascade by recruiting inflammatory cells, thereby contributing to the pathological process of osteoarthritis. In the study of programmed cell death, apoptosis is divided into extrinsic (death receptor pathway) and intrinsic (mitochondrial pathway) types. Both pathways induce chondrocyte apoptosis by activating the caspase cascade. Reactive oxygen species and inflammatory factors can promote excessive chondrocyte apoptosis through these pathways. Therapeutic strategies targeting apoptosis are diverse and include non-coding RNAs (miRNA, lncRNA, circRNA) that inhibit apoptosis by regulating related signaling pathways; phytochemicals that exert anti-inflammatory and anti-apoptotic effects; exosomes that suppress apoptosis by modulating immune responses and metabolism; and proteins/cytokines as well as melatonin, which protect chondrocytes by regulating specific signaling pathways. Clinical studies suggest these approaches hold promise for precision and personalized therapy, though challenges such as high cost, off-target effects, and drug resistance remain. In addition, drug delivery systems based on biomaterials (hydrogels) and nanotechnology can improve drug bioavailability and targeting. For example, drug-loaded hydrogels enable sustained release, and nanoparticles enhance drug stability and delivery efficiency, offering new perspectives for the treatment of osteoarthritis.

Objective To explore the distribution of common TCM syndromes and symptoms of insomnia;To prepare for the construction of the theoretical framework and item pool of syndrome diagnosis and efficacy evaluation scale.Methods TCM guideline standards of insomnia,textbooks and journals over the years were retrieved,the information of TCM syndromes,syndrome elements and symptoms was extracted,the guideline textbook and journal database were established,and descriptive statistics,association rules,systematic clustering,factor analysis,potential categories and implicit structure analysis were carried out.Results Totally 116 guide standards and textbooks over the years were included,and 454 articles of journals were included.The high-frequency symptoms accounted for≥3%of the guide textbooks and journal databases were 87 and 79 categories,respectively,and the cumulative proportion was 87.48%and 87.75%,respectively.According to the analysis results,five common TCM syndromes and their characteristic symptom classification of insomnia were finally deduced.According to the frequency/person time distribution,they were heart and spleen deficiency syndrome,yin deficiency and fire hyperactivity syndrome,liver fire disturbing heart syndrome,phlegm heat disturbing heart syndrome,heart and gallbladder qi deficiency syndrome.Conclusion There are five common TCM syndromes of insomnia,and the characteristic symptoms of each TCM syndrome provide a reference source for the theoretical framework of syndrome diagnosis and efficacy evaluation scale and the establishment of item pool.

Osteoarthritis is a chronic inflammatory disease characterized by damage to the articular cartilage, synovitis, and subchondral bone remodeling. Its pathological mechanisms involve extracellular matrix degradation, cell apoptosis, autophagy, and inflammatory responses. Among these, dysregulated apoptosis is a central driver of disease progression, making chondrocyte apoptosis a critical therapeutic target. This review summarizes current understanding of OA pathogenesis. Pro-inflammatory cytokines [e.g., interleukin-1β (IL-1β), tumor necrosis factor-α (TNF-α), and IL-6] exacerbate cartilage catabolism by activating signaling pathways like NF-κ B and MAPK. Chemokines, including the C-C motif chemokine ligand (CCL) family and the C-X-C motif chemokine ligand (CXC) family, amplify the inflammatory cascade by recruiting inflammatory cells, thereby contributing to the pathological process of osteoarthritis. In the study of programmed cell death, apoptosis is divided into extrinsic (death receptor pathway) and intrinsic (mitochondrial pathway) types. Both pathways induce chondrocyte apoptosis by activating the caspase cascade. Reactive oxygen species and inflammatory factors can promote excessive chondrocyte apoptosis through these pathways. Therapeutic strategies targeting apoptosis are diverse and include non-coding RNAs (miRNA, lncRNA, circRNA) that inhibit apoptosis by regulating related signaling pathways; phytochemicals that exert anti-inflammatory and anti-apoptotic effects; exosomes that suppress apoptosis by modulating immune responses and metabolism; and proteins/cytokines as well as melatonin, which protect chondrocytes by regulating specific signaling pathways. Clinical studies suggest these approaches hold promise for precision and personalized therapy, though challenges such as high cost, off-target effects, and drug resistance remain. In addition, drug delivery systems based on biomaterials (hydrogels) and nanotechnology can improve drug bioavailability and targeting. For example, drug-loaded hydrogels enable sustained release, and nanoparticles enhance drug stability and delivery efficiency, offering new perspectives for the treatment of osteoarthritis.