Size-Selective Capturing of Exosomes Using DNA Tripods.

Fractionating and characterizing target samples are fundamental to the analysis of biomolecules. Extracellular vesicles (EVs), containing information regarding the cellular birthplace, are promising targets for biology and medicine. However, the requirement for multiple-step purification in conventional methods hinders analysis of small samples. Here, we apply a DNA origami tripod with a defined aperture of binders (e.g., antibodies against EV biomarkers), which allows us to capture the target molecule. Using exosomes as a model, we show that our tripod nanodevice can capture a specific size range of EVs with cognate biomarkers from a broad distribution of crude EV mixtures. We further demonstrate that the size of captured EVs can be controlled by changing the aperture of the tripods. This simultaneous selection with the size and biomarker approach should simplify the EV purification process and contribute to the precise analysis of target biomolecules from small samples.

Significance of preoperative exercise oxygen desaturation in lung cancer with interstitial lung disease.

OBJECTIVES: Evaluating the diffusing capacity for carbon monoxide (DLco) is crucial for patients with lung cancer and interstitial lung disease. However, the clinical significance of assessing exercise oxygen desaturation (EOD) remains unclear. METHODS: We retrospectively analysed 186 consecutive patients with interstitial lung disease who underwent lobectomy for non-small-cell lung cancer. EOD was assessed using the two-flight test (TFT), with TFT positivity defined as ≥5% SpO2 reduction. We investigated the impact of EOD and predicted postoperative (ppo)%DLco on postoperative complications and prognosis. RESULTS: A total of 106 (57%) patients were identified as TFT-positive, and 58 (31%) patients had ppo% DLco < 30%. Pulmonary complications were significantly more prevalent in TFT-positive patients than in TFT-negative patients (52% vs 19%, P < 0.001), and multivariable analysis revealed that TFT-positivity was an independent risk factor (odds ratio 3.46, 95% confidence interval 1.70-7.07, P < 0.001), whereas ppo%DLco was not (P = 0.09). In terms of long-term outcomes, both TFT positivity and ppo%DLco < 30% independently predicted overall survival. We divided the patients into 4 groups based on TFT positivity and ppo%DLco status. TFT-positive patients with ppo%DLco < 30% exhibited the significantly lowest 5-year overall survival among the 4 groups: ppo%DLco ≥ 30% and TFT-negative, 54.2%; ppo%DLco < 30% and TFT-negative, 68.8%; ppo%DLco ≥ 30% and TFT-positive, 38.1%; and ppo%DLco < 30% and TFT-positive, 16.7% (P = 0.001). CONCLUSIONS: Incorporating EOD evaluation was useful for predicting postoperative complications and survival outcomes in patients with lung cancer and interstitial lung disease.

[A framework for promoting and evaluating "Kayoi-no-ba" initiatives according to the Plan-Do-Check-Action cycle: The ACT-RECIPE framework].

Objective　In 2019, the Ministry of Health, Labour and Welfare emphasized the importance of promoting "Kayoi-no-ba" (or "places to go") initiatives according to the Plan-Do-Check-Action (PDCA) cycle. However, it proposed no specific promotion measures or standardized evaluation frameworks. This study is intended to propose a framework for local government officials to promote and evaluate "Kayoi-no-ba" initiatives according to the PDCA cycle.Methods　The working group (WG) conducted a narrative review of research and extracted evaluation models and indicators that could be used to create the framework. The co-researcher review committee discussed a draft framework prepared by the WG, and the WG revised it based on the discussion; this process was repeated four times. Using the completed framework, we calculated the score of the "Kayoi-no-ba" initiatives in 50 Tokyo municipalities and conducted regional correlation analyses on the relationship between the score of the "Kayoi-no-ba" initiatives in 50 Tokyo municipalities and the number of "Kayoi-no-ba" per 1,000 older persons.Results　The completed framework (named "ACT-RECIPE" by rearranging the underlined characters below) comprised the following six evaluation phases: (1) Comprehension: understanding the key lessons around disability and frailty prevention, and the necessity for "Kayoi-no-ba"; (2) Research and Planning: clarifying the current status of "Kayoi-no-ba," the strengths of the community, and the issues to be addressed through a community assessment, and developing a plan to resolve the issues; (3) Team Building and Collaboration: building a team by collaborating with organizations to solve problems; (4) Implementation: implementing the initiatives necessary to solve problems; (5) Evaluation: verifying changes in output and outcome indicators from the initiatives; and (6) Adjustment and Improvement: re-examining plans, teams, content, and goals based on the evaluation results. In these six phases, we designated 10 core items and accompanying subitems. The median score rate of the ACT-RECIPE framework in 50 municipalities was 75% for "Comprehension," 61% for "Research and Planning," 69% for "Team Building and Collaboration," 64% for "Implementation," 31% for "Evaluation," and 56% for "Adjustment and Improvement," and the mean ACT-RECIPE score rate was 57%. A significant positive correlation (rs=0.43, P=0.002) was observed between the ACT-RECIPE mean score rate and the number of "Kayoi-no-ba" per 1,000 older persons.Conclusion　We proposed the ACT-RECIPE as a framework for promoting and evaluating "Kayoi-no-ba" initiatives according to the PDCA cycle. We hope that this framework will lead to further progress in "Kayoi-no-ba" initiatives and facilitate evaluation of their effectiveness according to the PDCA cycle.

Deep learning model to predict Ki-67 expression of breast cancer using digital breast tomosynthesis.

BACKGROUND: Developing a deep learning (DL) model for digital breast tomosynthesis (DBT) images to predict Ki-67 expression. METHODS: The institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age: 50.5 years, range: 29-90 years) referred to our hospital for breast cancer were participated, 126 patients with pathologically confirmed breast cancer were selected and their Ki-67 expression measured. The Xception architecture was used in the DL model to predict Ki-67 expression levels. The high Ki-67 vs low Ki-67 expression diagnostic performance of our DL model was assessed by accuracy, sensitivity, specificity, areas under the receiver operating characteristic curve (AUC), and by using sub-datasets divided by the radiological characteristics of breast cancer. RESULTS: The average accuracy, sensitivity, specificity, and AUC were 0.912, 0.629, 0.985, and 0.883, respectively. The AUC of the four subgroups separated by radiological findings for the mass, calcification, distortion, and focal asymmetric density sub-datasets were 0.890, 0.750, 0.870, and 0.660, respectively. CONCLUSIONS: Our results suggest the potential application of our DL model to predict the expression of Ki-67 using DBT, which may be useful for preoperatively determining the treatment strategy for breast cancer.

Effects of Radio-Taiso on Health-Related Quality of Life in Older Adults with Frailty: A Randomized Controlled Trial.

BACKGROUND: Radio-Taiso could be a sustainable public health strategy for maintaining quality of life (QoL) in older adults with frailty. This study aimed to investigate whether Radio-Taiso provided greater benefits for health-related quality of life (HR-QoL) and to identify the mechanisms underlying the effectiveness in this population. METHODS: A 12-week randomized controlled trial enrolled 226 older Japanese adults with pre-frailty or frailty, assessed using the modified frailty phenotype. Participants were randomly allocated to the intervention (Radio-Taiso + nutrition program) or control (nutrition program) groups. The Radio-Taiso program comprised five 60-min group sessions and daily practice at the participants' homes. The primary outcome was the change in the mental domain of HR-QoL, assessed using the SF-36®. The secondary outcomes included six physical fitness items and exercise self-efficacy. RESULTS: Overall, 104 and 105 participants in the intervention and control groups, respectively, were analyzed based on the intention-to-treat principle. The median (interquartile range) daily practice rate of Radio-Taiso was 94.1% (73.2-98.8%). Although general linear models adjusted for baseline values and allocation stratification factors showed that the intervention group obtained greater benefits (adjusted mean differences [95% confidence intervals]) in the up-and-go (0.3 [0.1, 0.6] s), 2-min step-in-place (-3.2 [-6.2, -0.2] steps) tests, and exercise self-efficacy scale (-1.4 [-2.6, -0.1] points) than the control group, there were no group differences in changes in the mental domain score of HR-QoL. CONCLUSIONS: Radio-Taiso provided greater benefits for agility/dynamic balance, aerobic endurance, and exercise self-efficacy in older adults with frailty; however, these changes do not improve HR-QoL.

Postoperative management using a digital drainage system for massive air leakage after pulmonary resection.

PURPOSE: To elucidate clinical outcomes using a digital drainage system (DDS) for massive air leakage (MAL) after pulmonary resection. METHODS: A total of 135 consecutive patients with pulmonary resection air leakage of > 100 ml/min on the DDS were evaluated retrospectively. In this study, MAL was defined as ≥ 1000 ml/min on the DDS. We analyzed the clinical characteristics and surgical outcomes of patients with MAL compared with non-MAL (101-999 ml/min). Using the DDS data, the duration of the air leak was plotted with the Kaplan‒Meier method and compared using the log-rank test. RESULTS: MAL was detected in 19 (14%) patients. The proportions of heavy smokers (P = 0.04) and patients with emphysematous lung (P = 0.03) and interstitial lung disease (P < 0.01) were higher in the MAL group than in the non-MAL group. The MAL group had a higher persistence rate of air leakage at 120 h after surgery than the non-MAL group (P < 0.01) and required significantly more frequent pleurodesis (P < 0.01). Drainage failure occurred in 2 (11%) and 5 (4%) patients from the MAL and non-MAL groups, respectively. Neither reoperation nor 30-day surgical mortality was observed in patients with MAL. CONCLUSIONS: MAL was able to be treated conservatively without surgery using the DDS.

Intensive care unit mortality and cost-effectiveness associated with intensivist staffing: a Japanese nationwide observational study.

BACKGROUND: Japan has four types of intensive care units (ICUs) that are divided into two categories according to the management fee charged per day: ICU management fees 1 and 2 (ICU1/2) (equivalent to high-intensity staffing) and 3 and 4 (ICU3/4) (equivalent to low-intensity staffing). Although ICU1/2 charges a higher rate than ICU3/4, no cost-effectiveness analysis has been performed for ICU1/2. This study evaluated the clinical outcomes and cost-effectiveness of ICU1/2 compared with those of ICU3/4. METHODS: This retrospective observational study used a nationwide Japanese administrative database to identify patients admitted to ICUs between April 2020 and March 2021 and divided them into the ICU1/2 and ICU3/4 groups. The ICU mortality rates and in-hospital mortality rates were determined, and the incremental cost-effectiveness ratio (ICER) (Japanese Yen (JPY)/QALY), defined as the difference between quality-adjusted life year (QALY) and medical costs, was compared between ICU1/2 and ICU3/4. Data analysis was performed using the Chi-squared test; an ICER of < 5 million JPY/QALY was considered cost-effective. RESULTS: The ICU1/2 group (n = 71,412; 60.7%) had lower ICU mortality rates (ICU 1/2: 2.6% vs. ICU 3/4: 4.3%, p < 0.001) and lower in-hospital mortality rates (ICU 1/2: 6.1% vs. ICU 3/4: 8.9%, p < 0.001) than the ICU3/4 group (n = 46,330; 39.3%). The average cost per patient of ICU1/2 and ICU3/4 was 2,249,270 ± 1,955,953 JPY and 1,682,546 ± 1,588,928 JPY, respectively, with a difference of 566,724. The ICER was 718,659 JPY/QALY, which was below the cost-effectiveness threshold. CONCLUSIONS: ICU1/2 is associated with lower ICU patient mortality than ICU3/4. Treatments under ICU1/2 are more cost-effective than those under ICU3/4, with an ICER of < 5 million JPY/QALY.

Comparison of neurological manifestation in children with and without coronavirus 2019 experiencing seizures with fever.

Whether neurologic symptoms due to SARS-CoV-2 differ from those of non-SARS-CoV-2 viral infection is unclear. We aimed to describe these neurological manifestations and compare the clinical characteristics and treatments in children with seizures and fever with or without COVID-19. We retrospectively analyzed data from 105 hospitalized children (<18 years) with clinical seizures and fever between September 2021 and August 2022. We compared the clinical characteristics and treatments between the COVID-19 (n = 20) and non-COVID-19 (n = 85) groups. Patients with COVID-19 were older than those without (32.5 [20-86] months vs. 20 [16-32] months, p = 0.029). Seizure type and duration and impaired consciousness duration did not differ between groups. Six and 32 patients experienced status epilepticus lasting 30 min in the COVID-19 and non-COVID-19 groups, respectively. Most treatments did not differ between groups; however, electroencephalography was used less frequently for COVID-19. Neurological sequelae occurred in one and four patients in the COVID-19 and non-COVID-19 groups, respectively. In conclusion, seizures with fever due to SARS-CoV-2 were more common in older children. Seizure characteristics and neurologic sequelae did not differ in children with and those without COVID-19. In general, electroencephalography was used less during COVID-19 for infection control measures.

Stepwise progression of invasive mucinous adenocarcinoma based on radiological and biological characteristics.

INTRODUCTION: Invasive mucinous lung adenocarcinoma (IMA) has unique radiological findings and pathological characteristics. IMA is classified into solitary and pneumonic types; however, it is unclear whether these are biologically identical. METHODS: A single-center retrospective analysis was performed for 70 IMA patients (solitary type [n = 38] and pneumonic type [n = 32]) who underwent pulmonary resection between January 2010 and December 2018. We compared clinical and biological characteristics between the two types. RESULTS: The frequencies of genetic alternations such as EGFR, KRAS, BRAF, GNAS, ERBB2, TP53, NRG1, and MET were not different. Immunohistochemically, expression of MUC1 was significantly more common in the pneumonic type (5.0% versus 20.0%, p = 0.01) and diffuse MUC6 positive in the solitary type (39.0% versus 13.0%, p = 0.02). We further classified solitary types into those with or without ground-glass opacity (GGO) and pneumonic types into those with or without crazy-paving appearance (CPA), and evaluated their surgical outcomes. Five-year overall survival and relapse free survival rates were 95.8%/86.6%, 64.3%/70.7%, 74.6%/68.9%, and 50.0%/28.6% in patients with solitary type with GGO, solitary type without GGO, pneumonic type without CPA, and pneumonic type with CPA, respectively. CONCLUSIONS: There were no differences in genetic alternations; however, mucin expression pattern was different. Surgical outcomes were different according to the presence of GGO in the solitary type and the presence of CPA in the pneumonic type. These findings suggested a stepwise progression from solitary to pneumonic IMA.

Extracellular aaRSs drive autoimmune and inflammatory responses in rheumatoid arthritis via the release of cytokines and PAD4.

OBJECTIVES: Recent studies demonstrate that extracellular-released aminoacyl-tRNA synthetases (aaRSs) play unique roles in immune responses and diseases. This study aimed to understand the role of extracellular aaRSs in the pathogenesis of rheumatoid arthritis (RA). METHODS: Primary macrophages and fibroblast-like synoviocytes were cultured with aaRSs. aaRS-induced cytokine production including IL-6 and TNF-α was detected by ELISA. Transcriptomic features of aaRS-stimulated macrophages were examined using RNA-sequencing. Serum and synovial fluid (SF) aaRS levels in patients with RA were assessed using ELISA. Peptidyl arginine deiminase (PAD) 4 release from macrophages stimulated with aaRSs was detected by ELISA. Citrullination of aaRSs by themselves was examined by immunoprecipitation and western blotting. Furthermore, aaRS inhibitory peptides were used for inhibition of arthritis in two mouse RA models, collagen-induced arthritis and collagen antibody-induced arthritis. RESULTS: All 20 aaRSs functioned as alarmin; they induced pro-inflammatory cytokines through the CD14-MD2-TLR4 axis. Stimulation of macrophages with aaRSs displayed persistent innate inflammatory responses. Serum and SF levels of many aaRSs increased in patients with RA compared with control subjects. Furthermore, aaRSs released PAD4 from living macrophages, leading to their citrullination. We demonstrate that aaRS inhibitory peptides suppress cytokine production and PAD4 release by aaRSs and alleviate arthritic symptoms in a mouse RA model. CONCLUSIONS: Our findings uncovered the significant role of aaRSs as a novel alarmin in RA pathogenesis, indicating that their blocking agents are potent antirheumatic drugs.

Deep learning model for predicting the presence of stromal invasion of breast cancer on digital breast tomosynthesis.

To develop a deep learning (DL)-based algorithm to predict the presence of stromal invasion in breast cancer using digital breast tomosynthesis (DBT). Our institutional review board approved this retrospective study and waived the requirement for informed consent from the patients. Initially, 499 patients (mean age 50.5 years, age range, 29-90 years) who were referred to our hospital under the suspicion of breast cancer and who underwent DBT between March 1 and August 31, 2019, were enrolled in this study. Among the 499 patients, 140 who underwent surgery after being diagnosed with breast cancer were selected for the analysis. Based on the pathological reports, the 140 patients were classified into two groups: those with non-invasive cancer (n = 20) and those with invasive cancer (n = 120). VGG16, Resnet50, DenseNet121, and Xception architectures were used as DL models to differentiate non-invasive from invasive cancer. The diagnostic performance of the DL models was assessed based on the area under the receiver operating characteristic curve (AUC). The AUC for the four models were 0.56 [95% confidence intervals (95% CI) 0.49-0.62], 0.67 (95% CI 0.62-0.74), 0.71 (95% CI 0.65-0.75), and 0.75 (95% CI 0.69-0.81), respectively. Our proposed DL model trained on DBT images is useful for predicting the presence of stromal invasion in breast cancer.

Evaluating the effect of two-dimensional molecular layout on DNA origami-based transporters.

Cellular transport systems are sophisticated and efficient. Hence, one of the ultimate goals of nanotechnology is to design artificial transport systems rationally. However, the design principle has been elusive, because how motor layout affects motile activity has not been established, partially owing to the difficulty in achieving a precise layout of the motile elements. Here, we employed a DNA origami platform to evaluate the two-dimensional (2D) layout effect of kinesin motor proteins on transporter motility. We succeeded in accelerating the integration speed of the protein of interest (POI) to the DNA origami transporter by up to 700 times by introducing a positively charged poly-lysine tag (Lys-tag) into the POI (kinesin motor protein). This Lys-tag approach allowed us to construct and purify a transporter with high motor density, allowing a precise evaluation on the 2D layout effect. Our single-molecule imaging showed that the densely packed layout of kinesin decreased the run length of the transporter, although its velocity was moderately affected. These results indicate that steric hindrance is a critical parameter to be considered in the design of transport systems.

Relationship between Cognitive Decline and Daily Life Gait among Elderly People Living in the Community: A Preliminary Report.

Introduction: Early detection and intervention are important to prevent dementia. Gait parameters have been recognized as a potentially easy screening tool for mild cognitive impairment (MCI); however, differences in gait parameters between cognitive healthy individuals (CHI) and MCI are small. Daily life gait change may be used to detect cognitive decline earlier. In the present study, we aimed to clarify the relationship between cognitive decline and daily life gait. Methods: We performed 5-Cog function tests and daily life and laboratory-based gait assessments on 155 community-dwelling elderly people (75.5 ± 5.4 years old). Daily life gait was measured for 6 days using an iPod-touch with an accelerometer. Laboratory-based 10-m gait (fast pace) was measured using an electronic portable walkway. Results: The subjects consisted of 98 CHI (63.2%) and 57 cognitive decline individuals (CDI; 36.8%). Daily life maximum gait velocity in the CDI group (113.7 [97.0-128.5] cm/s) was significantly slower than that in the CHI group (121.2 [105.8-134.3] cm/s) (p = 0.032). In the laboratory-based gait, the stride length variability in the CDI group (2.6 [1.8-4.1]) was significantly higher than that in the CHI group (1.8 [1.2-2.7]) (p < 0.001). The maximum gait velocity in daily life gait was weakly but significantly correlated with stride length variability in laboratory-based gait (ρ = -0.260, p = 0.001). Conclusion: We found an association between cognitive decline and slower daily life gait velocity among community-dwelling elderly people.

Machine learning approach to stratify complex heterogeneity of chronic heart failure: A report from the CHART-2 study.

AIMS: Current approaches to classify chronic heart failure (HF) subpopulations may be limited due to the diversity of pathophysiology and co-morbidities in chronic HF. We aimed to elucidate the clusters of chronic patients with HF by data-driven approaches with machine learning in a hospital-based registry. METHODS AND RESULTS: A total of 4649 patients with a broad spectrum of left ventricular ejection fraction (LVEF) in the CHART-2 (Chronic Heart Failure Analysis and Registry in the Tohoku District-2) study were enrolled to this study. Chronic HF patients were classified using random forest clustering with 56 multiscale clinical parameters. We assessed the influence of the clusters on cardiovascular death, non-cardiovascular death, all-cause death, and free from hospitalization by HF. Latent class analysis using random forest clustering identified 10 clusters with four primary components: cardiac function (LVEF, left atrial and ventricular diameters, diastolic blood pressure, and brain natriuretic peptide), renal function (glomerular filtration rate and blood urea nitrogen), anaemia (red blood cell, haematocrit, haemoglobin, and platelet count), and nutrition (albumin and body mass index). All 11 significant clinical parameters in the four primary components and two disease aetiologies (ischaemic heart disease and valvular heart disease) showed statistically significant differences among the 10 clusters (P < 0.01). Cluster 1 (26.7% of patients), which is characterized by preserved LVEF (<59%, 37% of the total) with lowest brain natriuretic peptide (>111.3 pg/mL, 0.9%) and lowest left atrial diameter (>42 mm, 37.4%), showed the best 5 year survival rate of 98.1% for cardiovascular death, 95.9% for non-cardiovascular death, 92.9% for all-cause death, and 91.7% for free from hospitalization by HF. Cluster 10 (6.0% of the total), which is co-morbid disorders of all four primary components, showed the worst survival rate of 39.1% for cardiovascular death, 68.9% for non-cardiovascular death, 23.9% for all-cause death, and 28.1% for free from hospitalization by HF. CONCLUSIONS: These results suggest the potential applicability of the machine leaning approach, providing useful clinical prognostic information to stratify complex heterogeneity in patients with HF.

Statistical Analysis of Mortality Rates of Coronavirus Disease 2019 (COVID-19) Patients in Japan Across the 4C Mortality Score Risk Groups, Age Groups, and Epidemiological Waves: A Report From the Nationwide COVID-19 Cohort.

Background: The mortality rates of coronavirus disease 2019 (COVID-19) have been changed across the epidemiological waves. The aim was to investigate the differences in mortality rates of COVID-19 patients in Japan across the 6 epidemiological waves stratified by age group and Coronavirus Clinical Characterisation Consortium (4C) mortality score risk group. Methods: A total of 56 986 COVID-19 patients in the COVID-19 Registry Japan from 2 March 2020 to 1 February 2022 were enrolled. These patients were categorized into 4 risk groups based on their 4C mortality score. Mortality rates of each risk group were calculated separately for different age groups: 18-64, 65-74, 75-89, and ≥90 years. In addition, mortality rates across the wave periods were calculated separately in 2 age groups: <75 and ≥75 years. All calculated mortality rates were compared with reported data from the United Kingdom (UK) during the early epidemic. Results: The mortality rates of patients in Japan were significantly lower than in the UK across the board, with the exception of patients aged ≥90 years at very high risk. The mortality rates of patients aged ≥75 years at very high risk in the fourth and fifth wave periods showed no significant differences from those in the UK, whereas those in the sixth wave period were significantly lower in all age groups and in all risk groups. Conclusions: The present analysis showed that COVID-19 patients had a lower mortality rate in the most recent sixth wave period, even among patients ≥75 years old at very high risk.

Deep learning model for breast cancer diagnosis based on bilateral asymmetrical detection (BilAD) in digital breast tomosynthesis images.

The purpose of this study was to develop a deep learning model to diagnose breast cancer by embedding a diagnostic algorithm that examines the asymmetry of bilateral breast tissue. This retrospective study was approved by the institutional review board. A total of 115 patients who underwent breast surgery and had pathologically confirmed breast cancer were enrolled in this study. Two image pairs [230 pairs of bilateral breast digital breast tomosynthesis (DBT) images with 115 malignant tumors and contralateral tissue (M/N), and 115 bilateral normal areas (N/N)] were generated from each patient enrolled in this study. The proposed deep learning model is called bilateral asymmetrical detection (BilAD), which is a modified convolutional neural network (CNN) model of Xception with two-dimensional tensors for bilateral breast images. BilAD was trained to classify the differences between pairs of M/N and N/N datasets. The results of the BilAD model were compared to those of the unilateral control CNN model (uCNN). The results of BilAD and the uCNN were as follows: accuracy, 0.84 and 0.75; sensitivity, 0.73 and 0.58; and specificity, 0.93 and 0.92, respectively. The mean area under the receiver operating characteristic curve of BilAD was significantly higher than that of the uCNN (p = 0.02): 0.90 and 0.84, respectively. The proposed deep learning model trained by embedding a diagnostic algorithm to examine the asymmetry of bilateral breast tissue improves the diagnostic accuracy for breast cancer.