Effect of fire and post-fire management on soil microbial communities in a lower subtropical forest ecosystem after a mountain fire.

Wildfires and post-fire management exert profound effects on soil properties and microbial communities in forest ecosystems. Understanding microbial community recovery from fire and what the best post-fire management should be is very important but needs to be sufficiently studied. In light of these gaps in our understanding, this study aimed to assess the short-term effects of wildfire and post-fire management on both bacteria and fungi community composition, diversity, structure, and co-occurrence networks, and to identify the principal determinants of soil processes influencing the restoration of these communities. Specifically, we investigated soil bacterial and fungal community composition, diversity, structure, and co-occurrence networks in lower subtropical forests during a short-term (<3 years) post-fire recovery period at four main sites in Guangdong Province, southern China. Our results revealed significant effects of wildfires on fungal community composition, diversity, and co-occurrence patterns. Network analysis indicated reduced bacterial network complexity and connectivity post-fire, while the same features were enhanced in fungal networks. However, post-fire management effects on microbial communities were negligible. Bacterial diversity correlated positively with soil microbial biomass nitrogen, soil organic carbon, and soil total nitrogen. Conversely, based on the best random forest model, fungal community dynamics were negatively linked to nitrate-nitrogen and soil water content. Spearman's correlation analysis suggested positive associations between bacterial networks and soil factors, whereas fungal networks exhibited predominantly negative associations. This study elucidates the pivotal role of post-fire management in shaping ecological outcomes. Additionally, it accentuates the discernible distinctions between bacterial and fungal responses to fire throughout a short-term recovery period. These findings contribute novel insights that bear significance in evaluating the efficacy of environmental management strategies.

Construction and validation of a prediction model for inguinal lymph node metastasis of penile malignancy.

Background: Penile squamous cell carcinoma is a relatively rare malignancy among male malignancies, there are more than 30,000 new cases and more than 10,000 deaths of penile cancer annually. In patients with penile malignancy, inguinal lymph node metastasis (ILNM) significantly reduces patient survival. Thus, we identified the risk factors for ILNM in penile malignancies, aiming to develop a precise prediction model. Methods: We retrospectively analyzed 112 male patients with penile cancer. All subjects underwent penile surgery and inguinal lymphadenectomy at the same time, and postoperative pathology confirmed ILNM. Fisher's exact test, t-test, and Wilcoxon rank sum test were used to assess differences in demographic information and clinical features between the two groups, followed by logical least absolute shrinkage and selection operator (LASSO) regression analysis to determine risk factors of ILNM. The prediction model was constructed using nomogram. Results: LASSO regression revealed that age [ß=-0.005, odds ratio (OR) =0.995], smoking history (ß=-0.006, OR =0.994) and interleukin 2 (IL-2) level (ß=-0.0112, OR =0.989) were protective against ILNM. However, lymph node diameter (ß=0.3117, OR =1.366), T-stage (ß=0.1254, OR =1.134), fibrinogen (ß=0.0377, OR =1.038), IL-4 level (ß=0.004, OR =1.001), and neutrophil-to-lymphocyte ratio (ß=0.0355, OR =1.034) were risk factors for developing ILNM. When assessing the risk of metastasis, it is crucial to balance these factors. The aforementioned characteristics were utilized to establish the predictive model, which demonstrated a good predictive ability with an area under the curve (AUC) value of 0.81. Moreover, internal leave-one-way cross-validation was used to construct a nomogram showing consistency, with an AUC of 0.75. Conclusions: The diagnosis of ILNM in penile malignant tumors can be predicted through clinicopathological features, biochemical tests, and prediction models based on tumor markers.