Objective: To analyze the causes and distribution characteristics of leukopenia in apheresis platelet donors, and to formulate effective pre-donation intervention measures. Methods: The data of apheresis platelet donors with leukopenia in Dongguan Central Blood Station during the entire year of 2021 were collected. Combined with the results of peripheral blood smear examination, the related factors that may cause leukopenia were analyzed. Results: There were 57 apheresis platelet donors with leukopenia in peripheral blood, with an incidence of 1.53% (57/3 726). The rate of leukopenia showed no significant difference between male and female apheresis platelet donors (χ =0.627, P>0.05), and was not related to the frequency of platelet donation (χ =1.48, P>0.05). However, there were statistically significant differences in the rate of leukopenia across seasons (χ =10.13, P<0.05), highly significant differences among different age groups (χ =22.98, P<0.001), and a significant association with the number of apheresis platelet donations (χ =7.80, P<0.05). Multivariate logistic regression analysis showed that age (36-55 years old), number of donations (≥26 times), and season (first and fourth quarters) were independent risk factors for leukopenia in apheresis platelet donors, while gender had no significant independent effect on leukopenia. Peripheral blood smear examination was performed on all apheresis platelet donors with leukopenia, and primary malignant hematological diseases infiltrated into peripheral blood were excluded. Among them, two cases of peripheral blood smear showed left shift of granulocyte nucleus with increased and thickened granules, whereas the other 55 cases only showed decreased peripheral blood nucleated cell counts without obvious morphological abnormalities. Conclusion: Leukopenia in apheresis platelet donors mainly occurred in young and middle-aged people and those with ≥26 donations, with high incidence in winter and spring, and more common in males. Blood routine examination combined with blood smear examination can facilitate the detection of conditions that are not suitable for blood donation, including hematological malignant diseases and infection-related leukopenia. Strengthening health consultation before blood donation is an important measure to identify blood donors with leukopenia.

Prescription optimization is a crucial aspect in the study of traditional Chinese medicine （TCM） compounds. In recent years， the introduction of mathematical methods， data mining techniques， and artificial neural networks has provided new tools for elucidating the compatibility rules of TCM compounds. The study of TCM compounds involves numerous variables， including the proportions of different herbs， the specific extraction parts of each ingredient， and the interactions among multiple components. These factors together create a complex nonlinear dose-effect relationship. In this context， it is essential to identify methods that suit the characteristics of TCM compounds and can leverage their advantages for effective application in new drug development. This paper provided a comprehensive review of the cutting-edge optimization experimental design methods applied in recent studies of TCM compound compatibilities. The key technical issues， such as the optimization of source material selection， dosage optimization of compatible herbs， and multi-objective optimization indicators， were discussed. Furthermore， the evaluation methods for component effects were summarized during the optimization process， so as to provide scientific and practical foundations for innovative research in TCM and the development of new drugs based on TCM compounds.

This paper discussed the nature of ulcerative colitis， that is deficiency of the root and excess of the branch， from the theory of sweat pore， and to explore the theoretical basis and experience of treating this disease with retention enema of traditional Chinese medicine （TCM）. The main location of this disease is in the intestine. As a part of sweat pore， the intestinal sweat pore serves as the gateway for the ascending， descending， exiting and entering of qi movement in the zang fu （脏腑） organs， meridians and collaterals， as well as the channel for the transportation of qi， blood and body fluids. The constraint and closure of the intestinal sweat pore are the main pathological basis of ulcerative colitis. According to the manifestations of colonoscopy， and the different etiological factors and pathogenesis that lead to the constraint and closure of sweat pore， there should be different treatment focuses such as expelling wind to open sweat pore， clearing fire to open sweat pore， promoting blood circulation to open sweat pore， for which wind-dispersing herbs， heat-clearing herbs， and blood-activating herbs are used accordingly. The method of retention enema can directly induce Chinese medicinal herbs to the affected part， so as to diffuse and unblock the sweat pore， regulate qi and blood， and thus restore the normal function of the intestinal sweat pore.

Objective: To investigate the epidemiological characteristics of human brucellosis in Jiande City, Zhejiang Province from 2005 to 2024, so as to provide the evidence for strengthening the prevention and control of brucellosis. Methods: Data on brucellosis cases and surveillance in Jiande City from 2005 to 2024 were collected through the Chinese Disease Prevention and Control Information System, the annual brucellosis surveillance reports from the Jiande Center for Disease Control and Prevention, and the annual summaries of brucellosis prevention and control efforts. The epidemiological characteristics of human brucellosis were analyzed using a descriptive epidemiological method. Results: A total of 1 125 individuals were monitored in Jiande City from 2005 to 2024, with 18 seropositive cases identified and the seropositivity rate of 1.60%. The average annual seropositivity rate from 2015 to 2024 was 3.35%, which was significantly higher than that of 0.57% from 2005 to 2014 (P<0.05). There were 10 confirmed brucellosis cases and 8 asymptomatic infections, with no reported deaths. The peak incidence occurred between March and August. Among the 16 towns (streets) in Jiande City, 8 reported brucellosis cases. Of the brucellosis cases, 14 were male and 4 were female, with a male-to-female ratio of 3.5∶1. The majority of cases (13 cases) were aged between 40 and 60 years. Occupational exposure was identified in 16 cases, all of whom were infected through direct hand contact with the excreta, secretions, or animal products of infected sheep or cattle. The primary source of infection was sheep, followed by cattle. Five strains of Brucella were isolated and cultured, all identified as Brucella melitensis biovar 3. Conclusions The brucellosis epidemic in Jiande City remained at a sporadic and low prevalence level from 2005 to 2024, with an increasing trend observed from 2015 to 2024. Male occupational groups aged 40 to 60 years were the key population for brucellosis prevention and control, and sheep were the primary source of infection.

Twelve compounds were isolated from the rice fermentation extracts of Penicillium expansum GY618 by silica column chromatography, Sephadex gel column chromatography, ODS column chromatography and semi preparative HPLC methods. They were determined as 11-hydroxyl-penicitrinone F (1), penicitrinone F (2), betulin (3), erythrodiol (4), ergosterol (5), ergost-5α,8α-epidioxy-6,22-dien-3β-ol (6), (5α,8α-epidioxy-(22E,24R)-ergosta-6,9(11),22-trien-3β-ol) (7), 5α,8α-epidioxy-(22E,24R)-23-methylergosta-6,22-dien-3β-ol (8), 5α,8α-epidioxy- 23,24(R)-dimethylcholesta-6,9(11),22-trien-3β-ol (9), dankasterone A (10), (17R)-4-hydroxy-17-methylincisterol (11) and ergosta-4,6,8(14),22-tetraen-3-one (12), through mass spectrometer, nuclear magnetic resonance (NMR) and comparison with the literature. Compound 1 was a new compound and compounds 2-4, 6-12 were isolated from Penicillium expansum fungus for the first time. The tyrosinase inhibitory activity experiment showed that compounds 1, 3 and 12 showed certain inhibitory activity against tyrosinase with IC₅₀ values of (75 ± 9), (69 ± 8) and (64 ± 2) μmol·L^-1, respectively. The IC₅₀ of other compounds were all greater than 100 μmol·L^-1, while IC₅₀ of the positive control kojic acid was (46 ± 4) μmol·L^-1.

Objective To explore the clinical value of artificial intelligence (AI) quantitative parameters in distinguishing pathological grades of stageⅠ invasive adenocarcinoma (IAC). Methods Clinical data of patients with clinical stageⅠ IAC admitted to Yantaishan Hospital Affiliated to Binzhou Medical University from October 2018 to May 2023 were retrospectively analyzed. Based on the 2021 WHO pathological grading criteria for lung adenocarcinoma, IAC was divided into gradeⅠ, grade Ⅱ, and grade Ⅲ. The differences in parameters among the groups were compared, and logistic regression analysis was used to evaluate the predictive efficacy of AI quantitative parameters for grade Ⅲ IAC patients. Parameters were screened using least absolute shrinkage and selection operator (LASSO) regression analysis. Three machine learning models were constructed based on these parameters to predict grade Ⅲ IAC and were internally validated to assess their efficacy. Nomograms were used for visualization. Results A total of 261 IAC patients were included, including 101 males and 160 females, with an average age of 27-88 (61.96±9.17) years. Six patients had dual primary lesions, and different lesions from the same patient were analyzed as independent samples. There were 48 patients of gradeⅠ IAC, 89 patients of grade Ⅱ IAC, and 130 patients of grade Ⅲ IAC. There were statitical differences in the AI quantitive parameters such as consolidation/tumor ratio (CTR), ect among the three goups. (P<0.05). Univariate analysis showed that the differences in all variables except age were statistically significant (P<0.05) between the group gradeⅠ+grade Ⅱand the group grade Ⅲ . Multivariate analysis suggested that CTR and CT standard deviation were independent risk factors for identifying grade Ⅲ IAC, and the two were negatively correlated. Grade Ⅲ IAC exhibited advanced TNM staging, more pathological high-risk factors, higher lymph node metastasis rate, and higher proportion of advanced structure. CTR was positively correlated with the proportion of advanced structures in all patients. This correlation was also observed in grade Ⅲ but not in gradeⅠand grade ⅡIAC. CTR and CT median value were selected by using LASSO regression. Logistic regression, random forest, and XGBoost models were constructed and validated, among which, the XGBoost model demonstrated the best predictive performance. Conclusion Cautious consideration should be given to grade Ⅲ IAC when CTR is higher than 39.48% and CT standard deviation is less than 122.75 HU. The XGBoost model based on combined CTR and CT median value has good predictive efficacy for grade Ⅲ IAC, aiding clinicians in making personalized clinical decisions.

BACKGROUND: The purpose of this study was to evaluate the safety and efficacy of subsequent radiotherapy (RT) following first-line treatment with durvalumab plus chemotherapy in patients with extensive-stage small cell lung cancer (ES-SCLC). METHODS: A total of 122 patients with ES-SCLC from three hospitals during July 2019 to December 2021 were retrospectively analyzed. Inverse probability of treatment weighting (IPTW) analysis was performed to address potential confounding factors. The primary focus of our evaluation was to assess the impact of RT on progression-free survival (PFS) and overall survival (OS). RESULTS: After IPTW analysis, 49 patients received durvalumab plus platinum-etoposide (EP) chemotherapy followed by RT (Durva + EP + RT) and 72 patients received immunochemotherapy (Durva + EP). The median OS was 17.2 months vs . 12.3 months (hazard ratio [HR]: 0.38, 95% confidence interval [CI]: 0.17-0.85, P = 0.020), and the median PFS was 8.9 months vs . 5.9 months (HR: 0.56, 95% CI: 0.32-0.97, P = 0.030) in Durva + EP + RT and Durva + EP groups, respectively. Thoracic radiation therapy (TRT) resulted in longer OS (17.2 months vs . 14.7 months) and PFS (9.1 months vs . 7.2 months) compared to RT directed to other metastatic sites. Among patients with oligo-metastasis, RT also showed significant benefits, with a median OS of 17.4 months vs . 13.7 months and median PFS of 9.8 months vs . 5.9 months compared to no RT. Continuous durvalumab treatment beyond progression (TBP) prolonged OS compared to patients without TBP, in both the Durva + EP + RT (NA vs . 15.8 months, HR: 0.48, 95% CI: 0.14-1.63, P = 0.238) and Durva + EP groups (12.3 months vs . 4.3 months, HR: 0.29, 95% CI: 0.10-0.81, P = 0.018). Grade 3 or 4 adverse events occurred in 13 (26.5%) and 13 (18.1%) patients, respectively, in the two groups; pneumonitis was mostly low-grade. CONCLUSION Addition of RT after first-line immunochemotherapy significantly improved survival outcomes with manageable toxicity in ES-SCLC.
Humans ; Small Cell Lung Carcinoma/therapy* ; Retrospective Studies ; Male ; Female ; Middle Aged ; Lung Neoplasms/therapy* ; Aged ; Antibodies, Monoclonal/therapeutic use* ; Adult ; Immunotherapy/methods* ; Aged, 80 and over

Prostate cancer (PCa) ranks as the second most prevalent malignancy among men worldwide. Early diagnosis, personalized treatment, and prognosis prediction of PCa play a crucial role in improving patients' survival rates. The advancement of artificial intelligence (AI), particularly the utilization of deep learning (DL) algorithms, has brought about substantial progress in assisting the diagnosis, treatment, and prognosis prediction of PCa. The introduction of the foundation model has revolutionized the application of AI in medical treatment and facilitated its integration into clinical practice. This review emphasizes the clinical application of AI in PCa by discussing recent advancements from both pathological and imaging perspectives. Furthermore, it explores the current challenges faced by AI in clinical applications while also considering future developments, aiming to provide a valuable point of reference for the integration of AI and clinical applications.
Humans ; Prostatic Neoplasms/diagnosis* ; Male ; Artificial Intelligence ; Deep Learning ; Prognosis