Bayesian Case-deletion Model Complexity and Information Criterion

Impact of chemotherapy delay on long-term prognosis of laparoscopic radical surgery for locally advanced gastric cancer: a pooled analysis of four randomized controlled trials

BACKGROUND

Adjuvant chemotherapy following curative surgery for locally advanced gastric cancer (AGC) significantly improves long-term patient prognosis. However, delayed chemotherapy (DC), in which patients are unable to receive timely treatment, is a common phenomenon in clinical practice for various reasons. This study aimed to investigate the impact of DC on the prognosis of patients with stage II-III locally AGC and explore the associated risk factors.

METHODS

Data from four prospective studies were included in the pooled analysis. The planned chemotherapy (PC) group was defined as the time interval between surgery and the first chemotherapy ≤ 49 d, while the DC group was defined as the time interval between surgery and chemotherapy > 49 d. The prognosis, recurrence, and risk factors were compared, and a nomogram for predicting DC was established.

RESULTS

In total, 596 patients were included, of whom 531 (89.1%) had PC and 65 (10.9%) had DC. Survival analysis revealed that the 5-year overall survival (OS) and disease-free survival (DFS) were significantly lower in the DC group than those in the PC group (log-rank P < 0.001). Cox univariable and multivariable analyses showed that DC was an independent risk factor for OS and DFS in stage II-III patients (P < 0.05). Based on the significant factors for DC, a prediction model was established that had a good fit, high accuracy (AUC = 0.780), and clinical applicability in both the training and validation sets.

CONCLUSION

Delayed chemotherapy after gastrectomy is associated with poor long-term prognosis in patients with locally advanced stage II-III GC disease. But standardized, full-cycle adjuvant chemotherapy after surgery may play a remedial role, and can to a certain extent compensate the poor effects caused by delayed chemotherapy.

Efficacy of extracellular vesicles of different cell origins in traumatic brain injury: A systematic review and network meta-analysis

Background

There was still no effective treatment for traumatic brain injury (TBI). Recently, many preclinical studies had shown promising efficacy of extracellular vesicles (EVs) from various cell sources. Our aim was to compare which cell-derived EVs were most effective in treating TBI through a network meta-analysis.

Methods

We searched four databases and screened various cell-derived EVs for use in preclinical studies of TBI treatment. A systematic review and network meta-analysis were conducted for two outcome indicators, modified Neurological Severity Score (mNSS) and Morris Water Maze (MWM), and they were ranked by the surface under the cumulative ranking curves (SUCRA). Bias risk assessment was performed with SYRCLE. R software (version 4.1.3, Boston, MA, USA) was used for data analysis.

Results

A total of 20 studies were included in this study, involving 383 animals. Astrocyte-derived extracellular vesicles (AEVs) ranked first in response to mNSS at day 1 (SUCRA: 0.26%), day 3 (SUCRA: 16.32%), and day 7 (SUCRA: 9.64%) post-TBI. Extracellular vesicles derived from mesenchymal stem cells (MSCEVs) were most effective in mNSS assessment on day 14 (SUCRA: 21.94%) and day 28 (SUCRA: 6.26%), as well as MWM’s escape latency (SUCRA: 6.16%) and time spent in the target quadrant (SUCRA: 86.52%). The result of mNSS analysis on day 21 showed that neural stem cell-derived extracellular vesicles (NSCEVs) had the best curative effect (SUCRA: 6.76%).

Conclusion

AEVs may be the best choice to improve early mNSS recovery after TBI. The efficacy of MSCEVs may be the best in the late mNSS and MWM after TBI.

Systematic review registration

https://www.crd.york.ac.uk/prospero/, identifier CRD42023377350.

Spatial-Temporal Epidemiology of Tuberculosis in Mainland China: An Analysis Based on Bayesian Theory

OBJECTIVE

To explore the spatial-temporal interaction effect within a Bayesian framework and to probe the ecological influential factors for tuberculosis.

METHODS

Six different statistical models containing parameters of time, space, spatial-temporal interaction and their combination were constructed based on a Bayesian framework. The optimum model was selected according to the deviance information criterion (DIC) value. Coefficients of climate variables were then estimated using the best fitting model.

RESULTS

The model containing spatial-temporal interaction parameter was the best fitting one, with the smallest DIC value (-4,508,660). Ecological analysis results showed the relative risks (RRs) of average temperature, rainfall, wind speed, humidity, and air pressure were 1.00324 (95% CI, 1.00150-1.00550), 1.01010 (95% CI, 1.01007-1.01013), 0.83518 (95% CI, 0.93732-0.96138), 0.97496 (95% CI, 0.97181-1.01386), and 1.01007 (95% CI, 1.01003-1.01011), respectively.

CONCLUSIONS

The spatial-temporal interaction was statistically meaningful and the prevalence of tuberculosis was influenced by the time and space interaction effect. Average temperature, rainfall, wind speed, and air pressure influenced tuberculosis. Average humidity had no influence on tuberculosis.