Demystifying COVID-19 mortality causes with interpretable data mining.

While COVID-19 becomes periodical, old individuals remain vulnerable to severe disease with high mortality. Although there have been some studies on revealing different risk factors affecting the death of COVID-19 patients, researchers rarely provide a comprehensive analysis to reveal the relationships and interactive effects of the risk factors of COVID-19 mortality, especially in the elderly. Through retrospectively including 1917 COVID-19 patients (102 were dead) admitted to Xiangya Hospital from December 2022 to March 2023, we used the association rule mining method to identify the risk factors leading causes of death among the elderly. Firstly, we used the Affinity Propagation clustering to extract key features from the dataset. Then, we applied the Apriori Algorithm to obtain 6 groups of abnormal feature combinations with significant increments in mortality rate. The results showed a relationship between the number of abnormal feature combinations and mortality rates within different groups. Patients with "C-reactive protein > 8 mg/L", "neutrophils percentage > 75.0 %", "lymphocytes percentage < 20%", and "albumin < 40 g/L" have a 2 × mortality rate than the basic one. When the characteristics of "D-dimer > 0.5 mg/L" and "WBC > 9.5 × 10 9 /L" are continuously included in this foundation, the mortality rate can be increased to 3 × or 4 × . In addition, we also found that liver and kidney diseases significantly affect patient mortality, and the mortality rate can be as high as 100%. These findings can support auxiliary diagnosis and treatment to facilitate early intervention in patients, thereby reducing patient mortality.

Evaluation of inpatient services of tertiary comprehensive hospitals based on DRG payment.

Objective: This study aims to evaluate inpatient services in 49 tertiary comprehensive hospitals using indicators from the diagnosis related groups (DRG) payment system. Method: DRG data from 49 tertiary comprehensive hospitals were obtained from the quality monitoring platform for provincial hospitals, and relevant indicators were identified. The analytic hierarchy process (AHP) was used to compute the weight of each indicator. The rank sum ratio method was used to calculate the weight rank sum ratio (WRSR) value and the corresponding probit value of each hospital. The hospitals were divided into four grades based on the threshold value: excellent, good, fair, and poor. Results: Eight indicators of the 49 hospitals were scored, and the hospital rankings of indicators varied. The No. 1 hospital ranked first in the indicators of "total number of DRG", "number of groups", and "proportion of relative weights (RW) ≥ 2". The WRSR value of the No.1 hospital was the largest (0.574), and the WRSR value of the No. 44 hospital was the smallest (0.139). The linear regression equation was established: WRSRpredicted =-0.141+0.088*Probit, and the regression model was well-fitted (F = 2066.672, p < 0.001). The cut-off values of the three WRSRspredicted by the four levels were 0.167, 0.299, and 0.431, respectively. The 49 hospitals were divided into four groups: excellent (4), good (21), average (21), and poor (3). There were significant differences in the average WRSR values of four categories of hospitals (p < 0.05). Conclusion: There were notable variances in the levels of inpatient services among 49 tertiary comprehensive hospitals, and hospitals of the same category also showed different service levels. The evaluation results contribute to the health administrative department and the hospital to optimize the allocation of resources, improve the DRG payment system, and enhance the quality and efficiency of inpatient services.

HDAC inhibitors target IRS4 to enhance anti­AR therapy in AR­positive triple­negative breast cancer.

Triple­negative breast cancer (TNBC) is the most malignant subtype of breast cancer. Androgen receptor (AR) has been identified as a potential therapeutic target for AR­positive TNBC; however, clinical trials have not yet produced an effective treatment. The present study aimed to identify a novel treatment regimen to improve the prognosis of AR­positive TNBC. First, a combination of an AR inhibitor (enzalutamide, Enz) and a selective histone deacetylase inhibitor (chidamide, Chid) was used to treat AR­positive TNBC cell lines, and a synergistic effect of these drugs was observed. The combination treatment inhibited cell proliferation and migration by arresting the cell cycle at the G2/M phase. Subsequently, next­generation sequencing was performed to detect changes in gene regulation. The results showed that the PI3K/Akt signalling pathway was significantly inhibited by the combination treatment of Enz and Chid. Gene Set Enrichment Analysis revealed that the combination group was significantly enriched in KRAS signalling. Analysis of the associated genes revealed that insulin receptor substrate 4 (IRS4) may have a critical role in blocking the activation of KRAS signalling. In a mouse xenograft model, combination treatment also inhibited the PI3K/Akt signalling pathway by upregulating the expression of IRS4 and thereby suppressing tumour growth. In conclusion, the results of the present study revealed that combination treatment with Enz and Chid can upregulate IRS4, which results in the blocking of KRAS signalling and suppression of tumour growth. It may be hypothesised that the expression levels of IRS4 could be used as a biomarker for screening patients with AR­positive TNBC using Enz and Chid combination therapy.