Objective To construct and compare logistic regression and decision tree models for predicting systemic inflammatory response syndrome (SIRS) in patients with type B aortic dissection (TBAD) after interventional surgery. Methods A retrospective analysis was conducted on clinical data of TBAD patients at Peking University Shenzhen Hospital from 2020 to 2024. The patients were divided into a SIRS group and a non SIRS group based on whether SIRS occurred within 24 hours after surgery. Multivariate logistic regression was used to analyze the influencing factors of SIRS occurrence in TBAD intervention patients, and a decision tree model was constructed using SPSS Modeler to compare the predictive performance of the two models. Results A total of 742 patients with TBAD were included, including 579 males and 163 females, aged 27-97 (58.85±10.79) years. Within 24 hours after intervention, a total of 506 patients developed SIRS, with an incidence rate of 68.19%. Logistic regression analysis showed that the extensive involvement of the dissection, the surgical time≥2 hours, PET coated stents implanted, serum creatinine, white blood cell count, C-reactive protein, monocyte (MONO), neutrophil count levels elevated, estimated glomerular filtration rate and decreased albumin levels were independent risk factors for SIRS (P<0.05). The decision tree model selected a total of 10 explanatory variables and 6 layers with 37 nodes, among which MONO was the most important predictor. The area under the decision tree model curve was 0.829 [95%CI (0.800, 0.856)], which was better than the logistic regression model's 0.690 [95%CI (0.655, 0.723)], and the difference was statistically significant (P<0.001). Conclusion The incidence of SIRS after TBAD intervention is high, and the decision tree model has better predictive performance than logistic regression. It can identify high-risk patients with higher accuracy and provide a practical tool for early clinical intervention.

ObjectiveThis study aimed to investigate the anti-Mycoplasma pneumoniae (MP) activity of luteolin and elucidate its underlying mechanisms. MethodsLuteolin was identified as the primary active compound from the polyphenol extract ofF. diotrys using network pharmacology. Its efficacy was evaluated against two MP strains: the standard strain M129 and the multidrug-resistant strain M19. A modified culture medium with visual characteristics was employed to determine the minimum inhibitory concentration (MIC) of luteolin. The expression of key proteins involved in MP growth and pathogenicity was assessed by qRT-PCR following luteolin treatment. Additionally, the viability of A549 cells infected with MP was compared between luteolin-treated and untreated groups. In vivo anti-MP activity was evaluated using a mouse model, and the expression of inflammatory cytokines in lung tissues was analyzed. ResultsLuteolin effectively inhibited both MP strains, with MIC₉₀ values of 100 mg/L for M19 and M129. Treatment with luteolin significantly downregulated the expression of adhesion proteins P1 and P30 in both strains. However, the expression of P65, HMW3, TrmB, and CARDS TX was reduced only in the M19 strain following luteolin intervention. Luteolin also enhanced the growth and viability of A549 cells infected with MP. In the mouse model, luteolin treatment resulted in steady weight gain and was well tolerated. The bacteriostatic rate of luteolin in lung tissues was 50.7%, significantly higher than the 25.2% observed in the roxithromycin group. Furthermore, luteolin reduced the expression of inflammatory factors, including IL-6, TNF-α, and HMGB1, in MP-infected mice. ConclusionLuteolin effectively and safely inhibits the proliferation and pathogenicity of MP, particularly the drug-resistant M19 strain, by downregulating the expression of toxicity-associated proteins (P1, P30, P65, HMW3, TrmB, CARDS TX) and modulating host inflammatory responses. These findings suggest that luteolin may offer a novel therapeutic strategy for treating MP infections, especially those caused by drug-resistant strains.

ObjectiveThis paper aims to investigate the mechanism by which Erchentang improves body weight in obese mice by regulating the AMP‑activated protein kinase （AMPK）/peroxisome proliferator‑activated receptor γ coactivator‑1α （PGC‑1α） signaling pathway and inducing browning of inguinal white adipose tissue （iWAT）. MethodsObese mouse models were established by feeding a high‑fat diet. After successful modeling， mice were randomly divided into a model group and low‑， medium‑， and high‑dose Erchentang groups （7.5， 15， 30 g·kg^-1）， with six mice in each group. Another six normal mice were set as the normal group. Mice in the treatment groups were administered with corresponding doses of the drug by gavage， while those in the normal and model groups were administered with an equal volume of pure water by gavage for four consecutive weeks. Obesity was evaluated by body weight and Lee's index. The levels of low‑density lipoprotein cholesterol （LDL‑C） and high‑density lipoprotein cholesterol （HDL‑C） in serum were detected by biochemical assays. The leptin content in serum was measured by enzyme‑linked immunosorbent assay （ELISA）. Hematoxylin and eosin （HE） staining was used to observe the pathological morphology of the liver and iWAT. Immunofluorescence staining was applied to detect the protein expression levels of glucose transporter 4 （GLUT4） in the liver and iWAT. Molecular docking was performed to simulate the binding affinity between the key components of Erchentang （nobiletin， diosmetin， naringenin） and the key pathway proteins AMPK and PGC‑1α. Western blot was used to detect the protein expression levels of uncoupling protein‑1 （UCP‑1）， AMPK， phosphorylated AMP-activated protein kinase （p‑AMPK）， and PGC‑1α in iWAT. ResultsCompared with those in the normal group， the mice in the model group showed significantly increased body weight and Lee's index， elevated levels of HDL‑C， LDL‑C， and leptin in serum， enlarged adipocytes in iWAT， down‑regulated protein expression levels of GLUT4 in iWAT and liver， and decreased protein expression levels of UCP‑1 and PGC‑1α in iWAT（P<0.05， P<0.01）， the expression level of p-AMPK / AMPK protein was up-regulated， but the difference was not statistically significant. Compared with those in the model group， the mice in the Erchentang groups with different doses exhibited significantly reduced body weight and Lee's index， decreased levels of HDL‑C， LDL‑C， and leptin in serum， smaller adipocytes in iWAT， up‑regulated GLUT4 protein expression levels in iWAT and liver， and increased protein expression levels of UCP‑1， p‑AMPK/AMPK， and PGC‑1α in iWAT （P<0.05， P<0.01）. Molecular docking results show that nobiletin， diosmetin， and naringenin have strong binding energies with both AMPK and PGC‑1α. ConclusionErchentang may improve body weight in obese mice by regulating the AMPK/PGC‑1α signaling pathway and inducing iWAT browning.

BACKGROUND:Extracellular vesicles mediate intercellular signal transduction through the proteins,nucleic acids,and lipids they carry,thereby influencing the function of target cells.This vesicle-mediated communication mechanism is involved in regulating female reproductive development.OBJECTIVE:To summarize and analyze the regulatory roles of extracellular vesicle-mediated intercellular communication in female reproductive development.METHODS:A search was conducted in the PubMed database using the search terms"extracellular vesicles,exosomes,reproduction,maternal-embryo communication,maternal-fetal crosstalk,embryo implantation,endometrium,oviduct,follicle."The initial screening was carried out by reading the titles and abstracts of the literature,and then the literature with poor relevance to the research purpose,outdated content,and duplication was excluded according to the inclusion and exclusion criteria.Ultimately,69 relevant articles were included for comprehensive analysis.RESULTS AND CONCLUSION:Extracellular vesicles play a crucial role in key processes of female reproductive development,from folliculogenesis to implantation.(1)Extracellular vesicles mediate intercellular communication within the follicle,particularly the interactions between oocytes and follicular cells,which are essential for follicle development and maturation.(2)Extracellular vesicles and their contents facilitate interactions between the embryo and the fallopian tube,influencing the trajectory of embryonic development.(3)Extracellular vesicles and their contents promote implantation by mediating bidirectional communication between the embryo and the endometrium.Uterine-derived extracellular vesicles regulate processes such as embryo adhesion,invasion,and decidualization,while embryo-derived extracellular vesicles modulate endometrial receptivity,convey embryonic signals,and adjust the endometrial microenvironment.Studying the roles of extracellular vesicles in female reproductive development can provide valuable insights into the mechanisms of infertility and support the development of new therapeutic strategies.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Depressive disorder is a prevalent mental illness characterized by pronounced and enduring symptoms of depression and cognitive impairment. The escalating pressures of modern society have led to a corresponding rise in the number of depressive disorder patients, particularly those exposed to adverse social, economic, political, and environmental factors which exacerbate the risk of this disorder. The pathogenesis of depressive disorder is multifaceted, encompassing oxidative stress, neuroplasticity alterations, neuroinflammation, neurotransmitter system imbalances, and intestinal microecological disruptions, among others. Clinically, conventional antidepressants are primarily predicated on the monoamine neurotransmitter hypothesis. This theory posits that depressive disorder can be ameliorated by regulating the levels of neurotransmitters within the body through a singular mechanism. However, the complex and multifaceted pathogenesis of depressive disorder results in limited selectivity for these drugs. Mitogen-activated protein kinase (MAPK) is a conserved serine/threonine kinase that plays a crucial role in various cellular physiological and pathological processes, including cell growth, differentiation, stress adaptation, and inflammatory response. It is instrumental in maintaining cellular homeostasis and regulating cellular responses. Numerous studies indicate that MAPK is involved in the pathogenesis and progression of depressive disorder through various pathogenesis. However, what deserves attention is that the interaction between the pathogenesis and dynamics of regulatory process remains unclear. Modulating MAPK has been shown to influence the onset and progression of depressive disorder, though the precise mechanism remains elusive. Within the MAPK family, aberrant activity of extracellular signal-regulated kinase (ERK) can damage hippocampal neurons and overactivate microglia, precipitating depressive disorder. Excessive activation of c-Jun N-terminal kinase (JNK) results in heightened neuronal apoptosis in the hippocampus and prefrontal cortex, and suppresses the expression of neurotrophic factors. p38, a key regulator in inflammatory reactions, can induce neuroinflammation when overactive, leading to depressive disorder. ERK, JNK, and p38 sub-pathways do not function in isolation but rather interact synergistically and/or antagonistically through shared activators and common target molecules. Consequently, these sub-pathways form a complementary and coordinated regulatory network. In addition, MAPK family members can jointly influence the process of depressive disorder by sharing upstream factors and regulating common downstream targets, and there is a lack of identification of their markers and screening for subgroups. The collective abnormal activities of these MAPK family members illuminate the underlying mechanisms of depressive disorder, suggesting that MAPK could serve as a potential therapeutic target for this disorder. As for the study of ERK, different models of depressive disorder have contradictory effects on its activity. The primary cause of these differences can be attributed to the distinct pathological environments utilized in the creation of depressive disorder models. In the future, it is suggested that we use the inducement of depressive disorder as a modeling standard to accurately simulate the onset of depressive disorder to carry out accurate treatment according to the causes of depressive disorder. Research shows that classic clinical drugs, novel MAPK inhibitors and certain traditional Chinese medicines can prevent and treat depressive disorder by regulating the MAPK signaling pathway. Research on MAPK remains limited, particularly concerning the permeability and cellular specificity across the blood-brain barrier and the identification of objective predictive markers. Although inhibitors face challenges, they also possess significant advantages and developmental potential. This paper systematically summarizes the current status of MAPK in the treatment of depressive disorder, in order to provide insights for researching the pathogenesis of depressive disorder and developing new antidepressant drugs.

Organoid-on-a-chip technology represents a promising interdisciplinary advancement that merges two cutting-edge biomedical platforms: stem cell-derived organoids and microfluidics-based organ-on-a-chip systems. Organoids are self-organizing three-dimensional (3D) cell cultures that mimic the key structural and functional features of in vivo organs. However, traditional organoid culture systems are often static, lacking dynamic environmental cues and suffering from limitations such as batch-to-batch variability, low stability, and low throughput. Organ-on-a-chip platforms, by contrast, utilize microfluidic technologies to simulate the dynamic physiological microenvironment of human tissues and organs, enabling more controlled cell growth and differentiation. By integrating the advantages of organoids and organ-on-a-chip technologies, organoid-on-a-chip systems transcend the limitations of conventional 3D culture models, offering a more physiologically relevant and controllable in vitro platform. In organoid-on-a-chip systems, stem cells or pre-formed organoids are cultured in micro-engineered environments that mimic in vivo conditions, enabling precise control over fluid flow, mechanical forces, and biochemical cues. Specifically, these platforms employ advanced strategies including bio-inspired 3D scaffolds for structural support, precise spatial cell patterning via 3D bioprinting, and integrated biosensors for real-time monitoring of metabolic activities. These synergistic elements recreate complex extracellular matrix signals and ensure high structural fidelity. Based on structural complexity, organoid-on-a-chip systems are classified into single-organoid and multi-organoid types, forming a trajectory from unit biomimicry to systemic simulation. Single-organoid chips focus on highly biomimetic units by integrating vascular, immune, or neural functions. Multi-organoid chips simulate inter-organ crosstalk and systemic homeostasis, advancing complex disease modeling and PK/PD evaluation. This emerging technology has demonstrated broad application potential in multiple fields of biomedicine. Organoid-on-a-chip systems can recapitulate organ developmentin vitro, facilitating research in developmental biology. They mimic organ-specific physiological activities and mechanisms, showing promising applications in regenerative medicine for tissue repair or replacement. In disease modeling, they support the reconstruction of models for neurodegenerative, inflammatory, infectious, metabolic diseases, and cancers. These platforms also enable in vitro drug testing and pharmacokinetic studies (ADME). Patient-derived chips preserve genetic and pathological features, offering potential for precision medicine. Additionally, they reduce species differences in toxicology, providing human-relevant data for environmental, food, cosmetic, and drug safety assessments. Despite progress, organoid-on-a-chip systems face challenges in dynamic simulation, extracellular matrix (ECM) variability, and limited real-time 3D imaging, requiring improved materials and the integration of developmental signals. Current bottlenecks also include the high technical threshold for automation and the lack of standardized validation frameworks for regulatory adoption. Meanwhile, the concept of a “human-on-a-chip” has been proposed to mimic whole-body physiology by integrating multiple organoid modules. This approach enables systemic modeling of drug responses and toxicity, with the potential to reduce animal testing and revolutionize drug development. Future advancements in bio-responsive hydrogels and flexible biosensors will further empower these platforms to bridge the gap between bench-side research and personalized clinical interventions. In conclusion, organoid-on-a-chip technology offers a transformative in vitro model that closely recapitulates the complexity of human tissues and organ systems. It provides an unprecedented platform for advancing biomedical research, clinical translation, and pharmaceutical innovation. Continued development in biomaterials, microengineering, and analytical technologies will be essential to unlocking the full potential of this powerful tool.

Depressive disorder is a prevalent mental illness characterized by pronounced and enduring symptoms of depression and cognitive impairment. The escalating pressures of modern society have led to a corresponding rise in the number of depressive disorder patients, particularly those exposed to adverse social, economic, political, and environmental factors which exacerbate the risk of this disorder. The pathogenesis of depressive disorder is multifaceted, encompassing oxidative stress, neuroplasticity alterations, neuroinflammation, neurotransmitter system imbalances, and intestinal microecological disruptions, among others. Clinically, conventional antidepressants are primarily predicated on the monoamine neurotransmitter hypothesis. This theory posits that depressive disorder can be ameliorated by regulating the levels of neurotransmitters within the body through a singular mechanism. However, the complex and multifaceted pathogenesis of depressive disorder results in limited selectivity for these drugs. Mitogen-activated protein kinase (MAPK) is a conserved serine/threonine kinase that plays a crucial role in various cellular physiological and pathological processes, including cell growth, differentiation, stress adaptation, and inflammatory response. It is instrumental in maintaining cellular homeostasis and regulating cellular responses. Numerous studies indicate that MAPK is involved in the pathogenesis and progression of depressive disorder through various pathogenesis. However, what deserves attention is that the interaction between the pathogenesis and dynamics of regulatory process remains unclear. Modulating MAPK has been shown to influence the onset and progression of depressive disorder, though the precise mechanism remains elusive. Within the MAPK family, aberrant activity of extracellular signal-regulated kinase (ERK) can damage hippocampal neurons and overactivate microglia, precipitating depressive disorder. Excessive activation of c-Jun N-terminal kinase (JNK) results in heightened neuronal apoptosis in the hippocampus and prefrontal cortex, and suppresses the expression of neurotrophic factors. p38, a key regulator in inflammatory reactions, can induce neuroinflammation when overactive, leading to depressive disorder. ERK, JNK, and p38 sub-pathways do not function in isolation but rather interact synergistically and/or antagonistically through shared activators and common target molecules. Consequently, these sub-pathways form a complementary and coordinated regulatory network. In addition, MAPK family members can jointly influence the process of depressive disorder by sharing upstream factors and regulating common downstream targets, and there is a lack of identification of their markers and screening for subgroups. The collective abnormal activities of these MAPK family members illuminate the underlying mechanisms of depressive disorder, suggesting that MAPK could serve as a potential therapeutic target for this disorder. As for the study of ERK, different models of depressive disorder have contradictory effects on its activity. The primary cause of these differences can be attributed to the distinct pathological environments utilized in the creation of depressive disorder models. In the future, it is suggested that we use the inducement of depressive disorder as a modeling standard to accurately simulate the onset of depressive disorder to carry out accurate treatment according to the causes of depressive disorder. Research shows that classic clinical drugs, novel MAPK inhibitors and certain traditional Chinese medicines can prevent and treat depressive disorder by regulating the MAPK signaling pathway. Research on MAPK remains limited, particularly concerning the permeability and cellular specificity across the blood-brain barrier and the identification of objective predictive markers. Although inhibitors face challenges, they also possess significant advantages and developmental potential. This paper systematically summarizes the current status of MAPK in the treatment of depressive disorder, in order to provide insights for researching the pathogenesis of depressive disorder and developing new antidepressant drugs.

ObjectiveThis study aims to explore the potential of different orders of magnitude single-nucleotide polymorphism (SNP) locus combinations for predicting distant kinship relationships. A high-density SNP locus set was constructed, and a comprehensive assessment of its inference capability was conducted. MethodsFirstly, we selected three commercial chip panels, CGA (Chinese genotyping array, Illumina), GSA (Global screening array, Illumina), Affy (23MF_V2 high-density SNP array, Affymetrix) and merged them after quality control, forming a high-density SNP locus panel(1 180 k). Secondly, we selected 161 samples and collected their peripheral blood samples by using whole-genome sequencing technology. Within this sample population, the levels of kinship relationships fully covered the range from level 1 to level 9, and the number of kinship pairs at each level was consistently maintained at over 50 pairs. From 161 samples data of whole-genome sequencing, the 1 180 k locus set was extracted, which is referred to as the high-density SNP locus set in the following text. The kinship inference was conducted using the identity-by-descent (IBD) algorithm with the selected optimal parameters. To comprehensively evaluate the performance of the high-density SNP locus set in kinship inference, we compared it with the three commercial chip panels, the intersection of these three chip loci, and the control sets constructed by randomly reducing the number of the high-density SNP locus set. Based on the changes in the IBD lengths, as well as the dynamic trends in prediction accuracy, we conducted a scientific assessment of the kinship inference capability of the high-density SNP locus set. ResultsAfter screening, a set of 1 184 334 autosomal SNPs was obtained. During the process of screening the optimal IBD length threshold, the result revealed that 0 cM, 1 cM, and 2 cM all demonstrated good applicability. However, to avoid the issue of a large amount of redundant information caused by setting a too low IBD length threshold, this study ultimately selected 2 cM as the optimal threshold. Compared with the average results of three chip panels, the high-density SNP locus set increased the total IBD length and the average IBD length across levels 1-9; the accuracy of the confidence interval for level 8 was 70.97%, which represented a 3.50% improvement; the average confidence interval accuracy for levels 1-8 was 91.39%, representing a 1.00% increase; and the false negative rates at levels 8 and 9 were reduced by 2.42% and 6.76%, respectively. The system efficacy of the high-density SNP locus set for kinship inference of first to eighth degree relationships reached 98.91%. Through random reduction of the high-density SNP locus set results, it is found that increasing the number of SNPs with the panel, the detection efficiency of IBD length showed a significant upward trend. At the same time, the overall trend in the accuracy of kinship relationship prediction as well as the confidence interval accuracy also indicated that both metrics steadily increased with the addition of more loci. ConclusionThe results show that the high-density SNPs panel significantly enhances the efficacy of distant kinship inference, accurately covering kinship degrees, with the average confidence interval accuracy for first to eighth degree relationships stably above 90%. The study finds that increasing the number of SNPs panel can improve the ability to predict distant kinship.

Objective To explore the clinical values of non-invasive myocardial work (MW) and tissue motion annular displacement (TMAD) in evaluation of anthracycline therapy-related cardiac dysfunction in patients with non-Hodgkin lymphoma. Methods A total of 62 patients with non-Hodgkin lymphoma who received standardized chemotherapy based on doxorubicin. Two and three dimensional transthoracic echocardiography, along with two dimensional speckle tracking echocardiography, were performed one day before chemotherapy and at 3, 6, and 9 months after chemotherapy to assess left ventricular ejection fraction, global longitudinal strain (GLS), MW parameters, and TMAD. Logistic regression analysis was used to evaluate the risk factors for cancer therapy-related cardiac dysfunction (CTRCD). The receiver operating characteristic curve was used to assess the diagnostic values of MW- and TMAD-related parameters for CTRCD. Results Compared to baseline, GLS, global work index (GWI), global constructive work (GCW), global work efficiency (GWE), TMAD at midpoint (TMADmid), and TMADmid percentage of left ventricular long-axis diameter (TMADmid%) decreased at 3 months after chemotherapy, while global wasted work (GWW) increased at 6 months after chemotherapy (P＜0.05). Logistic regression analysis showed that the relative reduction in GLS and TMADmid% at 3 months after chemotherapy were independent predictors for CTRCD （P＜0.05), while MW parameters were not independent predictors for CTRCD. GLS reduction≥10.3% and TMADmid% reduction≥15.8% at 3 months after chemotherapy predicted CTRCD with 0.866 and 0.824 of area under the curve (AUC), 92% and 75% of sensitivity, and 74% and 80% of specificity, respectively. AUC of combination of two indexes improved to 0.905, with 75% of sensitivity and 90% of specificity. Conclusions In non-Hodgkin lymphoma patients, the combination of GLS and TMADmid% is helpful of predicting CTRCD early, TMAD may be a novel diagnostic index for CTRCD, and GLS has superior predictive performance than MW for CTRCD.