[The clinical features, survival analysis, and geriatric assessment of 85 patients with follicular lymphoma: a single-center study].

Objective: To retrospectively analyze the clinical characteristics and prognosis of 85 newly diagnosed patients with follicular lymphoma (FL), as well as the prognostic value of comprehensive geriatric assessment (CGA) in patients with FL aged ≥ 60 years old. Methods: The clinical data and prognosis of 85 newly diagnosed FL patients admitted from August 2011 to June 2022 were collected. The clinical features, laboratory indicators, therapeutic efficacy, survival and prognostic factors of patients were statistically analyzed, and the prognosis of patients was stratified using various geriatric assessment tools. Results: ① The patients with FL were mostly middle-aged and older, with a median age of 59 (20-87) years, including 41 patients (48.2%) aged ≥60 years. The ratio of male to female was 1∶1.36. Overall, 77.6% of the patients were diagnosed with Ann Arbor stage Ⅲ-Ⅳ, and 17 cases (20.0%) were accompanied by B symptoms. Bone marrow involvement was the most common (34.1%). ②Overall, 71 patients received immunochemotherapy. The overall response rate was 86.6%, and the complete recovery rate was 47.1% of 68 evaluated patients. Disease progression or relapse in the first 2 years was observed in 23.9% of the patient. Overall, 14.1% of the patients died during follow-up. ③Of the 56 patients receiving R-CHOP-like therapies, the 3-year and 5-year progression-free survival (PFS) rates were 85.2% and 72.8%, respectively, and the 3-year and 5-year overall survival (OS) rates were 95.9% and 88.8%, respectively. The univariate analysis showed that age ≥60 years old (HR=3.430, 95% CI 1.256-9.371, P=0.016), B symptoms (HR=5.030, 95% CI 1.903-13.294, P=0.016), Prognostic Nutritional Index (PNI) <45.25 (HR=3.478, 95% CI 1.299-9.310, P=0.013), Follicular Lymphoma International Prognostic Index (FLIPI) high-risk (HR=2.918, 95% CI 1.074-7.928, P=0.036), and PRIMA-prognostic index (PRIMA-PI) high-risk (HR=2.745, 95% CI 1.057-7.129, P=0.038) significantly predicted PFS. Moreover, age ≥60 years old and B symptoms were independent risk factors for PFS. Progression of disease within 24 months (POD24) significantly predicted OS in the univariate analysis. Conclusions: FL is more common among middle-aged and older women. Age, B symptoms, PNI score, FLIPI high-risk, PRIMA-PI high-risk, and POD24 influenced PFS and OS. The CGA can be used for treatment selection and risk prognostication in older patients with FL.

Sex identification of ducklings based on acoustic signals.

Sex identification of ducklings is a critical step in the poultry farming industry, and accurate sex identification is beneficial for precise breeding and cost savings. In this study, a method for identifying the sex of ducklings based on acoustic signals was proposed. In the first step, duckling vocalizations were collected and an improved spectral subtraction method and high-pass filtering were applied to reduce the influence of noise. Then, duckling vocalizations were automatically detected by using a double-threshold endpoint detection method with 3 parameters: short-time energy (STE), short-time zero-crossing rate (ZCR), and duration (D). Following the extraction of Mel-Spectrogram features from duckling vocalizations, an improved Res2Net deep learning algorithm was used for sex classification. This algorithm was introduced with the Squeeze-and-Excitation (SE) attention mechanism and Ghost module to improve the bottleneck of Res2Net, thereby improving the model accuracy and reducing the number of parameters. The ablative experimental results showed that the introduction of the SE attention mechanism improved the model accuracy by 2.01%, while the Ghost module reduced the number of model parameters by 7.26M and the FLOPs by 0.85G. Moreover, this algorithm was compared with 5 state-of-the-art (SOTA) algorithms, and the results showed that the proposed algorithm has the best cost-effectiveness, with accuracy, recall, specificity, number of parameters, and FLOPs of 94.80, 94.92, 94.69, 18.91M, and 3.46G, respectively. After that, the vocalization detection score and the average confidence strategy were used to predict the sex of individual ducklings, and the accuracy of the proposed model reached 96.67%. In conclusion, the method proposed in this study can effectively detect the sex of ducklings and serve as a reference for automated sex identification of ducklings.