Phase retrieval based on deep learning with bandpass filtering in holographic data storage.

A phase retrieval method based on deep learning with bandpass filtering in holographic data storage is proposed. The relationship between the known encoded data pages and their near-field diffraction intensity patterns is established by an end-to-end convolutional neural network, which is used to predict the unknown phase data page. We found the training efficiency of phase retrieval by deep learning is mainly determined by the edge details of the adjacent phase codes, which are the high-frequency components of the phase code. Therefore, we can attenuate the low-frequency components to reduce material consumption. Besides, we also filter out the high-order frequency over twice Nyquist size, which is redundant information with poor anti-noise performance. Compared with full-frequency recording, the consumption of storage media is reduced by 2.94 times, thus improving the storage density.

Phase retrieval in holographic data storage by expanded spectrum combined with dynamic sampling method.

Phase retrieval in holographic data storage by expanded spectrum combined with dynamic sampling method is proposed, which serves to both reduce media consumption and to shorten the iterative number of phase code retrieval. Generally, high-fidelity phase retrieval requires twice Nyquist frequency in phase-modulated holographic data storage. To increase storage density, we only recorded and captured the signal with Nyquist size and used the frequency expanded method to realize high-fidelity phase retrieval. In the decoding process, the iterative Fourier transform algorithm is used to retrieve the phase information of the reconstructed beam. The expanded spectrum is dynamically sampled, which can provide a faster convergence path for the phase retrieval. We aimed to demonstrate the possibility of integrating various methods on the Fourier domain and providing a potential way to improve the performance of holographic data storage systems. The simulation and experimental results proved the combination of processing methods in frequency spectrum was benefit.

A nomogram for predicting breast cancer specific survival in elderly patients with breast cancer: a SEER population-based analysis.

BACKGROUND: The number of elderly patients diagnosed with breast cancer is increasing worldwide. However, treatment decisions for these patients are highly variable. Although researchers have identified the effects of surgery, radiotherapy, endocrine therapy, and chemotherapy in elderly patients with breast cancer, clinicians still struggle to make appropriate decisions for these patients. METHODS: We identified 75,525 female breast cancer patients aged ≥ 70 years in the Surveillance, Epidemiology, and End Results (SEER) database treated between January 1, 2010, and December 31, 2016. The patients were further divided into training and testing cohorts. The cumulative occurrence of breast cancer-specific deaths (BCSDs) and other cause-specific deaths (OCSD) was calculated using the cumulative incidence function. In the univariate analysis, risk factors were screened using the Fine-Gray model. In the multivariate analysis for competing risks, the sub-distribution hazard ratio with a 95% confidence interval for each independent predictor associated with BCSD was calculated for the construction of nomograms. Based on the above analyses, a competing risk nomogram was constructed to predict the probability of BCSD in the 1st, 3rd, and 5th years after treatment. During validation, the concordance index (C-index) was selected to quantify the predictive ability of the competing risk model. RESULTS: A total of 33,118 patients were included in this study, with 24,838 in the training group and 8,280 in the testing group. Age, race, marital status, cancer grade, tumor stage, node stage, estrogen receptor status, progesterone receptor status, human epidermal growth factor receptor--2 status, and treatment including surgery, radiation, and chemotherapy were used to establish a nomogram. The C-index of 0.852 (0.842-0.862) in the training cohort and 0.876 (0.868-0.892) in the testing cohort indicated satisfactory discriminative ability of the nomogram. Calibration plots showed favorable consistency between the nomogram predictions and actual observations in both the training and validation cohorts. CONCLUSIONS: Our study identified independent predictors of BCSD in elderly patients with breast cancer. A prognostic nomogram was developed and validated to aid clinical decision-making.

Diagnostic value of core needle biopsy for determining HER2 status in breast cancer, especially in the HER2-low population.

PURPOSE: The status of human epidermal growth factor receptor 2 (HER2) is important for treatment decision-making of breast cancer and was commonly determined by core needle biopsy (CNB). The concordance of CNB with surgical excision biopsy (SEB) has been verified, but remain unclear according to the newly developed classification of HER2 status. Our study aimed to re-evaluate the diagnostic value of CNB for determining HER2 status in breast cancer, especially in the HER2-low population. METHODS: Eligible breast cancer patients in West China Hospital between January 1, 2007 and December 31, 2021 were enrolled consecutively and data were extracted from the Hospital Information System. The agreement of HER2 status between CNB and SEB was calculated by concordance rate and κ statistics, as well as the sensitivity, specificity, positive, and negative predictive values (PPV & NPV). Logistic models were used to explore potential factors associated with the discordance between both tests. RESULTS: Of 1829 eligible patients, 1097 (60.0%) and 1358 (74.2%) were consistent between CNB and SEB by pathological and clinical classifications, respectively, with κ value being 0.46 (0.43-0.49) and 0.57 (0.53-0.60). The sensitivity (50.9%-52.7%) and PPV (50.5%-55.2%) of CNB were especially low among IHC 1+ and 2+/ISH - subgroups by pathological classifications; however, it showed the highest sensitivity (77.5%) and the lowest specificity (73.9%) in HER2-low population by clinical classifications. Advanced N stages might be a stable indicator for the discordance between both tests. CONCLUSION: The diagnostic value of CNB was limited for determining HER2 status in breast cancer, especially in HER2-low population.

Effects of High-Pressure Homogenization Treatment on the Development of Antioxidant Zanthoxylum bungeanum Leaf Powder Films for Preservation of Fresh-Cut Apple.

This study determined that Zanthoxylum bungeanum leaves (ZBLs) are rich in functional components such as cellulose, protein, flavone, and polyphenols. Therefore, they were used as the main raw material, with sodium alginate as a thickener and glycerol as a plasticizer, to investigate the preparation of active films from ZBL powder through high-pressure homogenization (HPH). The physical, optical, mechanical, and antioxidant properties of the films were evaluated, and their application in preserving fresh-cut apples was examined. The results showed that the optimal concentration of ZBL powder was 1.5% under a 30 MPa HPH treatment. The resulting HPH-treated films exhibited a denser microstructure and improved water vapor barrier properties and mechanical strength. Compared to the films without HPH treatment, the tensile strength increased from 4.61 MPa to 12.13 MPa, the elongation at break increased from 21.25% to 42.86%, the water vapor permeability decreased from 9.9 × 10-9 g/m·s·Pa to 8.0 × 10-9 g/m·s·Pa, and the transparency increased from 25.36% to 38.5%. Compared to the control group, the fresh-cut apples packaged with the HPH-treated ZBL active films exhibited effective preservation of apple quality during a five-day period at 4 °C and 70% humidity, showing better preservation effects than the other groups. In conclusion, the use of HPH treatment in developing novel biopolymer active films from ZBL powders with enhanced properties holds potential for various applications.

Effect of adjuvant chemotherapy on the survival outcomes of elderly breast cancer: A retrospective cohort study based on SEER database.

BACKGROUND: Currently, the proportion of standard chemotherapy for elderly patients is much lower than that for young patients, with little evidence from clinical trials supporting the use of chemotherapy for elderly patients. The effectiveness of chemotherapy for the elderly suffering from breast cancer remains to be further verified. METHODS: A total of 75,525 female breast cancer patients aged 70 years or older were hereby identified, all from the Surveillance, Epidemiology, and End Results (SEER) database from January 1, 2010 to December 31, 2016. Kaplan-Meier analysis and multivariable Cox proportional model were performed to evaluate the effectiveness of chemotherapy on overall survival (OS) and breast cancer-specific survival (BCSS). Propensity score matching (PSM) (PSM ratio: 1:1, caliper: 0.2 standard deviation of propensity score) was applied to construct balanced cohorts with or without chemotherapy based on demographic and pathophysiological characteristics. RESULTS: A total of 33,177 eligible patients were included, with 5273 (15.89%) receiving chemotherapy. Through PSM, 8360 patients were successfully matched, and balances between groups were almost reached. In the matched data set, multivariable Cox analysis reveals that chemotherapy was associated with a 36% and 21% risk reduction on OS (HR = 0.64, 95% CI 0.58 to 0.71) and BCSS (HR = 0.79, 95% CI 0.69 to 0.91), respectively. Furthermore, subgroups with more adjacent lymph nodes involved by tumor, or nonluminal A, were inclined to benefit more from chemotherapy. Moreover, chemotherapy did not increase the chances of dying from heart disease. CONCLUSIONS: The present study provided evidence that chemotherapy may improve the prognosis of elderly breast cancer, especially for those subpopulations that benefit more from chemotherapy treatment.

COVID-19 Vaccination Status and Hesitancy among Breast Cancer Patients after Two Years of Pandemic: A Cross-Sectional Survey.

Background: Patients with cancer show greater susceptibility and vulnerability to severe acute respiratory syndrome coronavirus 2 infection. However, data on the vaccination status among patients with breast cancer and any structured analysis of the factors influencing patients' decisions regarding vaccines are lacking. Methods: This cross-sectional study on patients with breast cancer in China was conducted from 1 June 2022, to 17 June 2022. Every participant completed an online questionnaire about their vaccination status and any adverse reactions, and a scale based on the Health Belief Model (HBM) to assess the vaccination status of respondents and their willingness to receive following doses or boosters. Results: Among the 1132 participants, 55.2% had received a COVID-19 vaccine. The incidence of adverse events per dose was around 40%. Vaccine hesitancy of 61.9% was observed among patients who had not fully received three doses of vaccine or boosters. The only variable found to be associated with vaccine hesitancy was time since diagnosis (p < 0.05). In the HBM scale, vaccine hesitancy was closely related to a low level of perceived susceptibility, a low level of perceived benefit, a high level of perceived barriers and a low level of agreement with doctors' advice. Conclusions: For patients with breast cancer, perceived susceptibility, benefits and barriers should be prioritized, and the advice from authoritative doctors is a vital cue to action.

Lensless phase retrieval based on deep learning used in holographic data storage.

This paper proposes a lensless phase retrieval method based on deep learning (DL) used in holographic data storage. By training an end-to-end convolutional neural network between the phase-encoded data pages and the corresponding near-field diffraction intensity images, the new unknown phase data page can be predicted directly from the intensity image by the network model without any iterations. The DL-based phase retrieval method has a higher storage density, lower bit-error-rate (BER), and higher data transfer rate compared to traditional iterative methods. The retrieval optical system is simple, stable, and robust to environment fluctuations which is suitable for holographic data storage. Besides, we studied and demonstrated that the DL method has a good suppression effect on the dynamic noise of the holographic data storage system.

Dynamic sampling iterative phase retrieval for holographic data storage.

A dynamic sampling iterative phase retrieval method, which dynamically samples the Fourier intensity distribution of the reconstruction beam captured by the detector, is proposed to shorten the iterative number and decrease the phase error rate of phase retrieval in the phase-modulated holographic data storage. By the dynamic sampling method, that keeping relatively low frequency component of Fourier intensity spectrum at the beginning of iteration and gradually releasing more high frequency component at the subsequent iterations, we shortened the iterative number by 2 times and decreased the phase error rate to some extent because our method provided a better convergent path to the phase retrieval. We also believe the thought of our method can be used in more image retrieval fields.

Pattern of Time-to-Surgery in Patients With Breast Cancer at Different Stages of the COVID-19 Pandemic.

BACKGROUND: The management of cancer surgeries is under unprecedented challenges during the COVID-19 pandemic, and the breast cancer patients may face a time-delay in the treatment. This retrospective study aimed to present the pattern of time-to-surgery (TTS) and analyze the features of breast cancer patients under the different stages of the COVID-19 pandemic. METHODS: Patients who received surgeries for breast cancers at West China Hospital between February 15, 2020 and April 30, 2020 (the outbreak and post-peak stages), and between March 10, 2021 and May 25, 2021 (the normalization stage) were included. TTS was calculated as the time interval between the pathological diagnosis and surgical treatment of breast cancer patients. And the pandemic was divided into three stages based on the time when the patients were pathologically diagnosed and the severity of pandemic at that time point. TTS, demographic and clinicopathological features were collected from medical records. RESULTS: A total of 367 patients were included. As for demographic features, it demonstrated statistically significant differences in insurance type (p<0.001) and regular screening (p<0.001), as well as age (p=0.013) and menstrual status (p=0.004). As for clinicopathological features, axillary involvement (p=0.019) was a factor that differed among three stages. The overall TTS was 23.56 ± 21.39 days. TTS for patients who were diagnosed during the outbreak of COVID-19 were longer than those diagnosed during pandemic post-peak and normalization stage (p<0.001). Pandemic stage (p<0.001) and excision biopsy before surgery (OR, 6.459; 95% CI, 2.225-18.755; p=0.001) were markedly correlated with the TTS of patients. CONCLUSIONS: TTS of breast cancer patients significantly varied in different stages of the COVID-19 pandemic. And breast cancer patients' daily lives and disease treatments were affected by the pandemic in many aspects, such as health insurance access, physical screening and change of therapeutic schedules. As the time-delay may cause negative influences on patients' disease, we should minimize the occurrence of such time-delay. It is vital to come up with comprehensive measures to deal with unexpected situations in case the pandemic occurs.

Improved phase retrieval in holographic data storage based on a designed iterative embedded data.

Embedded data are used to retrieve phases quicker with high accuracy in phase-modulated holographic data storage (HDS). We propose a method to design an embedded data distribution using iterations to enhance the intensity of the high-frequency signal in the Fourier spectrum. The proposed method increases the antinoise performance and signal-to-noise ratio (SNR) of the Fourier spectrum distribution, realizing a more efficient phase retrieval. Experiments indicate that the bit error rate (BER) of this method can be reduced by a factor of one after 10 iterations.