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The nervous system plays critical roles in the stress response. Animals can survive and function under harsh conditions, and resist and recover from injuries because neurons perceive and respond to various stressors through specific regulatory mechanisms. Caenorhabditis elegans has served as an excellent model to discover fundamental mechanisms underlying the neuronal response to stress. The basic physiological processes that C. elegans exhibits under stress conditions are similar to those observed in higher organisms. Many molecular pathways activated by environmental and cellular stresses are also conserved. In this review, we summarize major findings in examining neuronal responses to hypoxia, oxidative stress, osmotic stress, and traumatic injury. These studies from C. elegans have provided novel insights into our understanding of neuronal responses to stress at the molecular, cellular, and circuit levels.

Introduction: Lymph node metastasis of nonfunctioning pancreatic neuroendocrine neoplasms (pNENs) potentially leads to poor survival. Given the contradictory results in the literature regarding factors associated with lymph node metastasis of nonfunctioning pNENs, we conducted a systematic review and meta-analysis to determine the preoperative predictors of lymph node metastasis. Methods: Original studies reporting factors associated with lymph node metastasis in patients with nonfunctioning pNENs were identified in PubMed, EMBASE, and Cochrane Library databases, and data from eligible studies were analyzed using random-effects meta-analysis to obtain pooled estimates of odds ratios (ORs) and their 95% confidence intervals (CIs). Results: Eleven studies were included. Tumor size (>2 cm or >2.5 cm; OR, 5.80 [95% CI, 4.07–8.25]) and pancreatic head location (OR, 1.75 [95% CI, 1.05–2.94]) were significant preoperative predictors of lymph node metastasis. Old age (OR, 1.07 [95% CI, 0.68–1.68]) and male sex (OR, 1.12 [95% CI, 0.74–1.70]) were not significantly associated with lymph node metastasis. Conclusions: A large tumor size and pancreatic head location can be useful for planning optimal treatment in patients with nonfunctioning pNENs. [ABSTRACT FROM AUTHOR]

We propose a self-supervised machine learning (ML) algorithm for sequence-type classification of brain MRI using a supervisory signal from DICOM metadata (i.e., a rule-based virtual label). A total of 1787 brain MRI datasets were constructed, including 1531 from hospitals and 256 from multi-center trial datasets. The ground truth (GT) was generated by two experienced image analysts and checked by a radiologist. An ML framework called ImageSort-net was developed using various features related to MRI acquisition parameters and used for training virtual labels and ML algorithms derived from rule-based labeling systems that act as labels for supervised learning. For the performance evaluation of ImageSort-net (MLvirtual), we compare and analyze the performances of models trained with human expert labels (MLhumans), using as a test set blank data that the rule-based labeling system failed to infer from each dataset. The performance of ImageSort-net (MLvirtual) was comparable to that of MLhuman (98.5% and 99%, respectively) in terms of overall accuracy when trained with hospital datasets. When trained with a relatively small multi-center trial dataset, the overall accuracy was relatively lower than that of MLhuman (95.6% and 99.4%, respectively). After integrating the two datasets and re-training them, MLvirtual showed higher accuracy than MLvirtual trained only on multi-center datasets (95.6% and 99.7%, respectively). Additionally, the multi-center dataset inference performances after the re-training of MLvirtual and MLhumans were identical (99.7%). Training of ML algorithms based on rule-based virtual labels achieved high accuracy for sequence-type classification of brain MRI and enabled us to build a sustainable self-learning system. [ABSTRACT FROM AUTHOR]

Vitamin D insufficiency has been linked to unfavourable outcomes in diverse malignancies. However, the prognostic significance of vitamin D levels in peripheral T-cell lymphoma (PTCL) remains unclear. In this study, we thus aimed to assess the prognostic relevance of 25-hydroxyvitamin D [25(OH)D] levels in patients newly diagnosed with PTCL. The analysis included 144 patients with PTCL treated from March 2015 to May 2020. The median 25(OH)D level was 12.2 (1.7–48.8) ng/mL, and 59 (41%) patients had vitamin D deficiency. Patients with vitamin D deficiency demonstrated significantly worse event-free survival (EFS) and overall survival (OS). In the multivariate analysis, vitamin D was independently associated with OS, with a hazard ratio of 1.66 (95% confidence interval, 1.05–2.63, p = 0.030). These findings suggest that vitamin D deficiency significantly correlates with poor survival outcomes in patients with PTCL. [ABSTRACT FROM AUTHOR]