Platelet-related gene risk score: a predictor for pancreatic cancer microenvironmental signature, chemosensitivity and prognosis.

Recent studies have indicated that platelets may play a role in the advancement of pancreatic cancer by supporting tumor growth and increasing resistance to chemotherapy. This study aims to develop a prognostic model for pancreatic cancer using a platelet-related gene risk score. Prognostic platelet-related genes (PRGs) were identified from public databases and analyzed using cluster analysis. We investigated the microenvironment signatures and gene mutation patterns across different PRG-based molecular subtypes of pancreatic cancer. A prognostic model based on PRGs was developed using LASSO-Cox Regression Analysis. Additionally, we examined the correlation between the risk score and tumor clinical characteristics, as well as drug sensitivity. Two molecular subtypes, cluster C1 and C2, were identified. Cluster C2 was associated with a poorer prognosis compared to Cluster C1. The C1 group exhibited higher scores for activated CD8+ T cells, central memory CD4+ T cells, and natural killer T cells. The C2 group demonstrated a higher frequency of gene mutations. We established and validated a novel prognostic prediction model and platelet-related gene risk score for pancreatic cancer. The risk score was positively correlated with T stage, N stage, and tumor grade, and it presented a significant prognostic value compared to other clinical factors. In conclusion, a novel prognostic prediction model focusing on platelet involvement in pancreatic cancer has been developed, offering potential benefits for future drug therapies and clinical prognostic assessments.

3- to 24-month Follow-up on COVID-19 with Pulmonary Tuberculosis Survivors after Discharge: Results from a Prospective, Multicenter Study.

Objective: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are major public health and social issues worldwide. The long-term follow-up of COVID-19 with pulmonary TB (PTB) survivors after discharge is unclear. This study aimed to comprehensively describe clinical outcomes, including sequela and recurrence at 3, 12, and 24 months after discharge, among COVID-19 with PTB survivors. Methods: From January 22, 2020 to May 6, 2022, with a follow-up by August 26, 2022, a prospective, multicenter follow-up study was conducted on COVID-19 with PTB survivors after discharge in 13 hospitals from four provinces in China. Clinical outcomes, including sequela, recurrence of COVID-19, and PTB survivors, were collected via telephone and face-to-face interviews at 3, 12, and 24 months after discharge. Results: Thirty-two COVID-19 with PTB survivors were included. The median age was 52 (45, 59) years, and 23 (71.9%) were men. Among them, nearly two-thirds (62.5%) of the survivors were moderate, three (9.4%) were severe, and more than half (59.4%) had at least one comorbidity (PTB excluded). The proportion of COVID-19 survivors with at least one sequela symptom decreased from 40.6% at 3 months to 15.8% at 24 months, with anxiety having a higher proportion over a follow-up. Cough and amnesia recovered at the 12-month follow-up, while anxiety, fatigue, and trouble sleeping remained after 24 months. Additionally, one (3.1%) case presented two recurrences of PTB and no re-positive COVID-19 during the follow-up period. Conclusion: The proportion of long symptoms in COVID-19 with PTB survivors decreased over time, while nearly one in six still experience persistent symptoms with a higher proportion of anxiety. The recurrence of PTB and the psychological support of COVID-19 with PTB after discharge require more attention.

Association of Overlapped and Un-overlapped Comorbidities with COVID-19 Severity and Treatment Outcomes: A Retrospective Cohort Study from Nine Provinces in China.

OBJECTIVE: Several COVID-19 patients have overlapping comorbidities. The independent role of each component contributing to the risk of COVID-19 is unknown, and how some non-cardiometabolic comorbidities affect the risk of COVID-19 remains unclear. METHODS: A retrospective follow-up design was adopted. A total of 1,160 laboratory-confirmed patients were enrolled from nine provinces in China. Data on comorbidities were obtained from the patients' medical records. Multivariable logistic regression models were used to estimate the odds ratio ( OR) and 95% confidence interval (95% CI) of the associations between comorbidities (cardiometabolic or non-cardiometabolic diseases), clinical severity, and treatment outcomes of COVID-19. RESULTS: Overall, 158 (13.6%) patients were diagnosed with severe illness and 32 (2.7%) had unfavorable outcomes. Hypertension (2.87, 1.30-6.32), type 2 diabetes (T2DM) (3.57, 2.32-5.49), cardiovascular disease (CVD) (3.78, 1.81-7.89), fatty liver disease (7.53, 1.96-28.96), hyperlipidemia (2.15, 1.26-3.67), other lung diseases (6.00, 3.01-11.96), and electrolyte imbalance (10.40, 3.00-26.10) were independently linked to increased odds of being severely ill. T2DM (6.07, 2.89-12.75), CVD (8.47, 6.03-11.89), and electrolyte imbalance (19.44, 11.47-32.96) were also strong predictors of unfavorable outcomes. Women with comorbidities were more likely to have severe disease on admission (5.46, 3.25-9.19), while men with comorbidities were more likely to have unfavorable treatment outcomes (6.58, 1.46-29.64) within two weeks. CONCLUSION: Besides hypertension, diabetes, and CVD, fatty liver disease, hyperlipidemia, other lung diseases, and electrolyte imbalance were independent risk factors for COVID-19 severity and poor treatment outcome. Women with comorbidities were more likely to have severe disease, while men with comorbidities were more likely to have unfavorable treatment outcomes.

Some limit theorems for ratios of order statistics from uniform random variables.

In this paper, we study the ratios of order statistics based on samples drawn from uniform distribution and establish some limit properties such as the almost sure central limit theorem, the large deviation principle, the Marcinkiewicz-Zygmund law of large numbers and complete convergence.