ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

ObjectiveDiscriminating atypical hepatocellular carcinoma (HCC) from other malignancies in liver nodules classified as Liver Imaging Reporting and Data System category M (LR-M) remains a significant diagnostic challenge on conventional ultrasound examination. The LR-M category, originally intended to capture non-HCC malignancies, paradoxically contains up to 63% of atypical HCCs that deviate from classic enhancement patterns, leading to potential misdiagnosis and suboptimal treatment planning. While deep learning has shown promise in HCC diagnosis, most existing models rely exclusively on single-modality ultrasound, overlooking the diagnostic benefits of integrating complementary information from multiple imaging sources. To address this gap, we propose a novel attention-weighted tri-modal ultrasound network (TUS-Net) that integrates contrast-enhanced ultrasound (CEUS), B-mode ultrasound (BUS), and time-intensity curves (TICs) to improve diagnostic accuracy for these clinically challenging lesions. MethodsOur framework incorporates a three-dimensional convolutional neural network (C3D) backbone to extract spatiotemporal features from CEUS videos, capturing dynamic vascular patterns critical for lesion characterization. To effectively fuse complementary modalities, we introduce a dual-channel feature fusion module (DCFFM) that adaptively combines features from CEUS and BUS through channel-wise attention mechanisms, allowing the model to dynamically weigh the contribution of each modality based on diagnostic relevance. Additionally, we propose a temporal intensity feature fusion module (TIFFM) that leverages quantitative hemodynamic information from TICs to guide the model’s attention toward diagnostically critical temporal phases, such as arterial wash-in and portal venous washout. The model is further enhanced by automated lesion localization using YOLOX and class activation mapping for interpretability, ensuring that predictions align with clinically meaningful imaging features. ResultsEvaluated on a tri-modal ultrasound dataset comprising 161 patients with pathologically confirmed LR-M nodules (131 atypical HCC and 30 non-HCC malignancies), our model achieved an accuracy of 86.83%, a sensitivity of 92.50%, a specificity of 75.50%, and an AUC of 89.32% in screening atypical HCC. Compared to single-modality baselines, TUS-Net demonstrated superior specificity, a clinically critical metric given the higher risk associated with misclassifying non-HCC malignancies. Ablation studies confirmed the contribution of each module, with the full model outperforming both standard C3D and 3D ResNet backbones integrated with attention mechanisms. A reader study involving junior and senior radiologists further validated the clinical utility of AI assistance, showing consistent improvements in specificity and inter-reader consistency, particularly for less experienced clinicians. ConclusionThese results surpass existing benchmark models and demonstrate the potential of our approach to enhance diagnostic precision in clinically specific cases. By intelligently fusing multi-modal ultrasound data with attention-guided mechanisms, TUS-Net offers a reliable and interpretable tool that holds promise for improving the non-invasive diagnosis of atypical HCC in challenging LR-M liver nodules.

OBJECTIVE: To explore and quantify the association of hot night exposure during the sperm development period (0-90 lag days) with semen quality. METHODS: A total of 6,640 male sperm donors from 6 human sperm banks in China during 2014-2020 were recruited in this multicenter study. Two indices (i.e., hot night excess [HNE] and hot night duration [HND]) were used to estimate the heat intensity and duration during nighttime. Linear mixed models were used to examine the association between hot nights and semen quality parameters. RESULTS: The exposure-response relationship revealed that HNE and HND during 0-90 days before semen collection had a significantly inverse association with sperm motility. Specifically, a 1 °C increase in HNE was associated with decreased sperm progressive motility of 0.0090 (95% confidence interval [ CI]: -0.0147, -0.0033) and decreased total motility of 0.0094 (95% CI: -0.0160, -0.0029). HND was significantly associated with reduced sperm progressive motility and total motility of 0.0021 (95% CI: -0.0040, -0.0003) and 0.0023 (95% CI: -0.0043, -0.0002), respectively. Consistent results were observed at different temperature thresholds on hot nights. CONCLUSION Our findings highlight the need to mitigate nocturnal heat exposure during spermatogenesis to maintain optimal semen quality.
Humans ; Male ; Semen Analysis ; Adult ; Sperm Motility ; Hot Temperature/adverse effects* ; China ; Middle Aged ; Spermatozoa/physiology* ; Young Adult

Objective To investigate the changes of brain structure and functional connectivity by multimodal magnetic resonance imaging(MRI)in patients with type 2 diabetes mellitus(T2DM)and mild cognitive impairment(MCI).Methods A total of 120 patients with T2DM who were treated in the our hospital were enrolled in this study from April 2023 to April 2024.All the patients were divided into simple T2DM group(n=62)and MCI group(n=58)according to whether they were complicated with MCI.The general data and cognitive function related indicators were compared between the two groups,and the volume of MRI subcortical structures were analyzed.The fractional amplitude of low-frequency fluctuation(fALFF)values were compared between the two groups,and the brain regions with different fALFF values were used as seed points for voxel-based functional connectivity analysis.Pearson correlation analysis was used to analyze the correlation between MRI subcortical structure volume and abnormal functional connectivity brain regions and cognitive function in T2DM patients with MCI.Results Connection test A(TMT-A),TMT-B scores and bilateral temporal angle width of ventricles were higher,while Dorsal number span test(FDST),dorsal number span test(BDST),Wechsler adult intelligence scale(WAIS-RC)arithmetic,WAIS-RC knowledge,Montreal cognitive assessment scale(MoCA)score,bilateral caudate nucleus,putamen,hippocampal volume and subcutaneous gray matter volume were lower in MCI group than in T2DM group(P<0.05).The fALFF values increased in the left pericallosal cortex,right lingual gyrus,corpus callosum and right medial and parascingulate gyrus,while fALFF values decreased in the right inferior temporal gyrus,fusiform gyrus,left suboccipital gyrus,left parietal gyrus,left anterior cuneiform lobe,left parietal lobule and left supplementary motor area.Whole brain voxel functional connectivity analysis showed that the functional connectivity of right inferior temporal gyrus and left inferior parietal gyrus was stronger,while the functional connectivity of right lingual gyrus was weaker in MCI group than in T2DM group.Pearson correlation analysis showed that FDST,BDST,WAIS-RC arithmetic,WAIS-RC knowledge,and MoCA score were positively correlated with bilateral caudate nucleus,putamen nucleus,hippocampus volume,subcortical gray matter volume,and right inferior temporal gyrus and left inferior parietal gyrus functions(P<0.05),and negatively correlated with temporal angle width of bilateral ventricles and right superior frontal gyrus function(P<0.05).TMT-a and TMT-B scores were positively correlated with bilateral temporal angle width and right superior frontal gyrus function(P<0.05),and negatively correlated with bilateral caudate nucleus,putamen nucleus,hippocampus volume,subcortical gray matter volume,right inferior temporal gyrus and left inferior parietal gyrus function(P<0.05).Conclusions The volume of subcortical structures is changed in patients with T2DM and MCI,and there are multiple brain regions with abnormal functional connectivity.These alterations demonstrate varying degrees of correlation with MCI,providing partial insight into its neuropathological basis.

Objective To understand the current situation of healthcare-associated infection(HAI)in China,pro-vide data support and decision-making basis for formulating scientific and effective strategies for HAI prevention and control.Methods A nationwide cross-sectional survey on HAI was conducted among various types and levels of medical institutions in China according to a unified protocol of bedside surveys and case investigations.Results In 2024,a total of 5 736 medical institutions and 2 751 765 patients were surveyed.Among them,34 889 HAI cases were identified,with a prevalence rate of 1.27％.The number of HAI episodes was 38 032,and case prevalence rate was 1.38％.The prevalence rate of HAI in medical institutions in different regions of China ranged from 0.66％to 2.35％.Among medical institutions of different scales,those with a bed capacity of ≥900 had the high-est incidence of HAI,reaching 1.65％.The most common infection site was the lower respiratory tract(44.66％),followed by the urinary tract(12.94％),surgical site(9.32％),upper respiratory tract(7.02％),and bloodstream infection(5.78％).The top 3 departments with the highest HAI rates were the general intensive care unit(10.02％),department of neurosurgery(5.51％),and department(group)of hematology(5.34％).A total of 23 238 strains of HAI pathogens were detected,with 10 714 strains(46.10％)from lower respiratory tract speci-mens.The top 5 detected strains were Klebsiella pneumoniae(14.76％),Pseudomonas aeruginosa(13.33％),Escherichia coli(12.79％),Acinetobacter baumannii(9.23％),and Staphylococcus aureus(7.88％).231 944 pa-tients underwent class Ⅰ incision surgery were monitored,with 1 647 cases experienced surgical site infection,and the prevalence rate of surgical site infection was 0.71％.The number of patients who should undergo pathogen de-tection(patients receiving therapeutic and therapeutic combined prophylactic antimicrobial agents)was 715 179,while the actual number was 480 492,with a pathogen detection rate of 67.18％.425 225 patients received patho-genic detection before treatment,with a detection rate of 59.46％.Conclusion The overall HAI prevalence in Chi-na is lower,showing disparities among medical institutions of different regions and scales.Therefore,precise imple-mentation of measures is necessary for HAI prevention and control,with a focus on high-risk institutions and high-risk departments,key areas,and critical procedures.All levels of medical institutions should continuously reduce the incidence of HAI by strengthening monitoring,standardizing the use of antimicrobial agents,and reinforcing basic HAI prevention and control measures.

This paper aims to study the effect of Ziziphi Spinosae Semen extracts on chronic unpredictable mild stress(CUMS)-induced depression-like and insomnia behavior models of mice. The CUMS-induced depression-like and insomnia behavior model of mice was established by CUMS treatment for three weeks. The mice were randomly divided into control group, model group, positive drug diazepam group(2 mg·kg~(-1)), as well as low-dose group(1.95 g·kg~(-1)), medium-dose group(3.9 g·kg~(-1)), and high-dose group(7.8 g·kg~(-1)) of Ziziphi Spinosae Semen extracts, with 18 mice in each group. On the 15th day of modeling, the drug was administered intragastrically once a day for one week. Then, the pentobarbital sodium cooperative righting experiment, open field experiment, and elevated plus maze experiment were carried out, respectively. The contents of neurotransmitters 5-hydroxytryptamine(5-HT) and 5-hydroxyindoleacetic acid(5-HIAA) in serum and thalamus of mice, as well as the levels of corticotropin releasing hormone(CRH), adrenocorticotropic hormone(ACTH), and corticosterone(CORT) in serum, were determined by enzyme-linked immunosorbent assay(ELISA). The neuron damage in the hippocampus of mice was observed by hematoxylin-eosin(HE) staining and Nissl staining. Western blot was used to detect the expressions of tryptophan hydroxylase 2(TPH2), serotonin transporter(SERT), monoamine oxidase A(MAOA), five prime repressors under dual repression binding protein 1(Freud1), synaptic plasticity-related proteins [cellular gene FOS(C-FOS), postsynaptic density protein 95(PSD95), synapsin 1(SYN1), and activity-regulated cytoskeleton-associated gene(ARC)], blood-brain barrier(BBB) permeability-related proteins [zonula occludens 1(ZO-1), occludin, and claudin 1], inflammatory factors [NOD-, LRR-and pyrin domain-containing protein 3(NLRP3), apoptosis-associated speck-like protein(ASC), gasdermin D(GSDMD), caspase-3, and caspase-8], and antioxidant factors [nuclear factor erythroid 2-related factor 2(NRF2) and heme oxygenase 1(HO1)] in thalamic tissue of mice. The results indicated that compared with that in the model group, the sleep latency was significantly shortened, and the sleep duration was significantly prolonged in each dose group of Ziziphi Spinosae Semen extracts. The number of visits to the central area of the open field and the distance and time of visits were significantly increased in each dose group of Ziziphi Spinosae Semen extracts. In addition, the proportion of distance and time of entering the open arm area of the elevated plus maze was significantly increased in each dose group of Ziziphi Spinosae Semen extracts. The contents of 5-HT and 5-HIAA in serum and thalamus of mice increased to varying degrees in each dose group of Ziziphi Spinosae Semen extracts; the contents of CRH, ACTH, and CORT in serum of mice were significantly decreased. The protein expression of TPH2 was significantly increased. The protein expression of MAOA, SERT, and Freud1 was significantly decreased. Ziziphi Spinosae Semen extracts could also significantly reduce the protein expression of C-FOS but significantly increase the protein expression of PSD95, ARC, and SYN1. They could reduce the pathological damage of the hippocampus in mice and significantly increase the protein expression of ZO-1, occluding, and claudin 1. The protein expression of NLRP3, GSDMD, ASC, caspase-3, and caspase-8 in the thalamic tissue of mice was significantly decreased, and the protein expression of HO1 and NRF2 was significantly increased. In conclusion, Ziziphi Spinosae Semen extracts could effectively improve sleep disorders and depression-like behaviors in CUMS-induced model mice, which may be related to regulating the 5-HT anabolism process and hypothalamic-pituitary-adrenal(HPA) axis-related hormone levels, reducing pathological damage in the hippocampus, improving synaptic plasticity, repairing BBB integrity, and alleviating inflammatory response and oxidative stress damage.
Animals ; Ziziphus/chemistry* ; Mice ; Male ; Depression/psychology* ; Drugs, Chinese Herbal/administration & dosage* ; Sleep Initiation and Maintenance Disorders/psychology* ; Stress, Psychological/complications* ; Behavior, Animal/drug effects* ; Humans ; Disease Models, Animal

Objective To investigate the changes of brain structure and functional connectivity by multimodal magnetic resonance imaging(MRI)in patients with type 2 diabetes mellitus(T2DM)and mild cognitive impairment(MCI).Methods A total of 120 patients with T2DM who were treated in the our hospital were enrolled in this study from April 2023 to April 2024.All the patients were divided into simple T2DM group(n=62)and MCI group(n=58)according to whether they were complicated with MCI.The general data and cognitive function related indicators were compared between the two groups,and the volume of MRI subcortical structures were analyzed.The fractional amplitude of low-frequency fluctuation(fALFF)values were compared between the two groups,and the brain regions with different fALFF values were used as seed points for voxel-based functional connectivity analysis.Pearson correlation analysis was used to analyze the correlation between MRI subcortical structure volume and abnormal functional connectivity brain regions and cognitive function in T2DM patients with MCI.Results Connection test A(TMT-A),TMT-B scores and bilateral temporal angle width of ventricles were higher,while Dorsal number span test(FDST),dorsal number span test(BDST),Wechsler adult intelligence scale(WAIS-RC)arithmetic,WAIS-RC knowledge,Montreal cognitive assessment scale(MoCA)score,bilateral caudate nucleus,putamen,hippocampal volume and subcutaneous gray matter volume were lower in MCI group than in T2DM group(P<0.05).The fALFF values increased in the left pericallosal cortex,right lingual gyrus,corpus callosum and right medial and parascingulate gyrus,while fALFF values decreased in the right inferior temporal gyrus,fusiform gyrus,left suboccipital gyrus,left parietal gyrus,left anterior cuneiform lobe,left parietal lobule and left supplementary motor area.Whole brain voxel functional connectivity analysis showed that the functional connectivity of right inferior temporal gyrus and left inferior parietal gyrus was stronger,while the functional connectivity of right lingual gyrus was weaker in MCI group than in T2DM group.Pearson correlation analysis showed that FDST,BDST,WAIS-RC arithmetic,WAIS-RC knowledge,and MoCA score were positively correlated with bilateral caudate nucleus,putamen nucleus,hippocampus volume,subcortical gray matter volume,and right inferior temporal gyrus and left inferior parietal gyrus functions(P<0.05),and negatively correlated with temporal angle width of bilateral ventricles and right superior frontal gyrus function(P<0.05).TMT-a and TMT-B scores were positively correlated with bilateral temporal angle width and right superior frontal gyrus function(P<0.05),and negatively correlated with bilateral caudate nucleus,putamen nucleus,hippocampus volume,subcortical gray matter volume,right inferior temporal gyrus and left inferior parietal gyrus function(P<0.05).Conclusions The volume of subcortical structures is changed in patients with T2DM and MCI,and there are multiple brain regions with abnormal functional connectivity.These alterations demonstrate varying degrees of correlation with MCI,providing partial insight into its neuropathological basis.

ObjectivePrimary liver cancer, predominantly hepatocellular carcinoma (HCC), is a significant global health issue, ranking as the sixth most diagnosed cancer and the third leading cause of cancer-related mortality. Accurate and early diagnosis of HCC is crucial for effective treatment, as HCC and non-HCC malignancies like intrahepatic cholangiocarcinoma (ICC) exhibit different prognoses and treatment responses. Traditional diagnostic methods, including liver biopsy and contrast-enhanced ultrasound (CEUS), face limitations in applicability and objectivity. The primary objective of this study was to develop an advanced, light-weighted classification network capable of distinguishing HCC from other non-HCC malignancies by leveraging the automatic analysis of brightness changes in CEUS images. The ultimate goal was to create a user-friendly and cost-efficient computer-aided diagnostic tool that could assist radiologists in making more accurate and efficient clinical decisions. MethodsThis retrospective study encompassed a total of 161 patients, comprising 131 diagnosed with HCC and 30 with non-HCC malignancies. To achieve accurate tumor detection, the YOLOX network was employed to identify the region of interest (ROI) on both B-mode ultrasound and CEUS images. A custom-developed algorithm was then utilized to extract brightness change curves from the tumor and adjacent liver parenchyma regions within the CEUS images. These curves provided critical data for the subsequent analysis and classification process. To analyze the extracted brightness change curves and classify the malignancies, we developed and compared several models. These included one-dimensional convolutional neural networks (1D-ResNet, 1D-ConvNeXt, and 1D-CNN), as well as traditional machine-learning methods such as support vector machine (SVM), ensemble learning (EL), k-nearest neighbor (KNN), and decision tree (DT). The diagnostic performance of each method in distinguishing HCC from non-HCC malignancies was rigorously evaluated using four key metrics: area under the receiver operating characteristic (AUC), accuracy (ACC), sensitivity (SE), and specificity (SP). ResultsThe evaluation of the machine-learning methods revealed AUC values of 0.70 for SVM, 0.56 for ensemble learning, 0.63 for KNN, and 0.72 for the decision tree. These results indicated moderate to fair performance in classifying the malignancies based on the brightness change curves. In contrast, the deep learning models demonstrated significantly higher AUCs, with 1D-ResNet achieving an AUC of 0.72, 1D-ConvNeXt reaching 0.82, and 1D-CNN obtaining the highest AUC of 0.84. Moreover, under the five-fold cross-validation scheme, the 1D-CNN model outperformed other models in both accuracy and specificity. Specifically, it achieved accuracy improvements of 3.8% to 10.0% and specificity enhancements of 6.6% to 43.3% over competing approaches. The superior performance of the 1D-CNN model highlighted its potential as a powerful tool for accurate classification. ConclusionThe 1D-CNN model proved to be the most effective in differentiating HCC from non-HCC malignancies, surpassing both traditional machine-learning methods and other deep learning models. This study successfully developed a user-friendly and cost-efficient computer-aided diagnostic solution that would significantly enhances radiologists’ diagnostic capabilities. By improving the accuracy and efficiency of clinical decision-making, this tool has the potential to positively impact patient care and outcomes. Future work may focus on further refining the model and exploring its integration with multimodal ultrasound data to maximize its accuracy and applicability.

Objective:To investigate the clinical efficacy and factors influencing treatment of pediatric noninfectious uveitis with Adalimumab (ADA).Methods:A retrospective clinical study. A total of 86 pediatric patients with non-infectious uveitis, diagnosed and treated with ADA at Department of Uveitis Specialist of Xi'an People's Hospital (Xi' an Fourth Hospital) from January 1, 2021 to December 31, 2023, were included in this study. The age of all patients was ≤16 years. Among them, 55 (63.95%, 55/86) patients received ADA combined with one immunosuppressive agent, 28 (32.56%, 28/86) patients received ADA combined with ≥2 immunosuppressive agents, and 3 (3.49%, 3/86) patients received ADA alone without any immunosuppressive agents. All patients underwent best-corrected visual acuity (BCVA) and optical coherence tomography (OCT) examinations. The thickness of the retinal nerve fiber layer (RNFL) in the macular region was measured using an OCT device. The cumulative treatment effectiveness rate at 12 months post-treatment was evaluated using the Kaplan-Meier survival analysis. Multivariate analysis was performed using the Cox proportional hazards regression model, and the optimal predictive model was selected based on the Bayesian information criterion. The association between different treatment regimens and various clinical outcomes was assessed.Results:Among the 86 pediatric patients, 42 were male and 44 were female, with a mean age of (10.47±3.23) years. The distribution of uveitis types was as follows: anterior uveitis in 37 cases, intermediate uveitis in 15 cases, posterior uveitis in 10 cases, and panuveitis in 24 cases. Anterior chamber cells (ACC), keratic precipitates, and synechiae were present in 66, 55, and 38 cases, respectively. The cumulative treatment effectiveness at 12 months was 85.1% [95% confidence interval ( CI) 71.9-92.2], with a median time to treatment effectiveness of 3 months. Compared with baseline, after 6 months of treatment, the BCVA, RNFL thickness ( Z=?6.323, ?8.017), and the grading of ACC and vitreous haze ( χ2= ?6.917, ?5.027) showed significant improvement, with statistically significant differences ( P<0.05). Multivariate analysis revealed that ACC (hazard ratio=22.31, 95% CI 2.43-204.68) and anterior uveitis (hazard ratio=3.88, 95% CI 2.03-7.42) were significantly associated with treatment effectiveness ( P<0.05). Patients with ACC had a median time to treatment effectiveness of 2 months, with a 12-month cumulative treatment effectiveness of 95.5% (95% CI 86.3-98.5). Patients with anterior uveitis had a median time to treatment effectiveness of 2 months, with a 12-month cumulative treatment effectiveness of 97.3% (95% CI 81.3-99.6). Patients without anterior uveitis had a median time to treatment effectiveness of 5 months, with a 12-month cumulative treatment effectiveness of 76.7% (95% CI 54.1-88.2). The cumulative recurrence risk at 12 months was 15.6% (95% CI 6.2-24.1). Conclusion:ADA is safe and effective in treating pediatric non-infectious uveitis, and ACC and anterior uveitis are associated with response rate.