Neutrophil to lymphocyte ratio is a transdiagnostic biomarker of depression and structural and functional brain alterations in older adults

Lymphocyte-related ratios, systemic immune-inflammatory and systemic inflammatory response index in alcohol use disorder

Addictive disorders are associated with systemic and central nervous system inflammation, which may be important for the onset and development of these diseases. Although lymphocyte-related parameters have recently been studied in alcohol use disorder (AUD), systemic immune-inflammation index (SII) and systemic inflammatory response index (SIRI) haven't. Lymphocyte-related ratios, SII and SIRI levels were evaluated between AUD and healthy controls (HC) in this study. It was a retrospective and cross-sectional study. This study included 72 patients with AUD and 184 individuals in the HC group. Lymphocyte related ratios such as neutrophil to lymphocyte ratio (NLR), monocyte to lymphocyte ratio (MLR) and platelet to lymphocyte ratio (PLR), SII and SIRI values were compared. Compared to HC group, NLR (p < 0.001), MLR (p < 0.001), and SIRI (p < 0.001) levels were significantly higher in AUD group. There was also a significant relationship between NLR and AST/ALT ratio in the AUD group (p = 0.022). The results of this study support that AUD is a chronic inflammatory psychiatric disorder. In addition, it may be useful to evaluate these markers in relation to liver enzymes in patients with AUD, as alcohol consumption causes liver damage. These markers may also be used in future studies to assess treatment response and disease severity.

The potential of high-order features of routine blood test in predicting the prognosis of non-small cell lung cancer

BACKGROUND

Numerous studies have demonstrated that the high-order features (HOFs) of blood test data can be used to predict the prognosis of patients with different types of cancer. Although the majority of blood HOFs can be divided into inflammatory or nutritional markers, there are still numerous that have not been classified correctly, with the same feature being named differently. It is an urgent need to reclassify the blood HOFs and comprehensively assess their potential for cancer prognosis.

METHODS

Initially, a review of existing literature was conducted to identify the high-order features (HOFs) and classify them based on their calculation method. Subsequently, a cohort of patients diagnosed with non-small cell lung cancer (NSCLC) was established, and their clinical information prior to treatment was collected, including low-order features (LOFs) obtained from routine blood tests. The HOFs were then computed and their associations with clinical features were examined. Using the LOF and HOF data sets, a deep learning algorithm called DeepSurv was utilized to predict the prognostic risk values. The effectiveness of each data set's prediction was evaluated using the decision curve analysis (DCA). Finally, a prognostic model in the form of a nomogram was developed, and its accuracy was assessed using the calibration curve.

RESULTS

From 1210 documents, over 160 blood HOFs were obtained, arranged into 110, and divided into three distinct categories: 76 proportional features, 6 composition features, and 28 scoring features. Correlation analysis did not reveal a strong association between blood features and clinical features; however, the risk value predicted by the DeepSurv LOF- and HOF-models is significantly linked to the stage. Results from DCA showed that the HOF model was superior to the LOF model in terms of prediction, and that the risk value predicted by the blood data model could be employed as a complementary factor to enhance the prognosis of patients. A nomograph was created with a C-index value of 0.74, which is capable of providing a reasonably accurate prediction of 1-year and 3-year overall survival for patients.

CONCLUSIONS

This research initially explored the categorization and nomenclature of blood HOF, and proved its potential in lung cancer prognosis.

Inflammation-Related Functional and Structural Dysconnectivity as a Pathway to Psychopathology

Findings from numerous laboratories and across neuroimaging modalities have consistently shown that exogenous administration of cytokines or inflammatory stimuli that induce cytokines disrupts circuits and networks involved in motivation and motor activity, threat detection, anxiety, and interoceptive and emotional processing. While inflammatory effects on neural circuits and relevant behaviors may represent adaptive responses promoting conservation of energy and heightened vigilance during immune activation, chronically elevated inflammation may contribute to symptoms of psychiatric illnesses. Indeed, biomarkers of inflammation such as cytokines and acute phase reactants are reliably elevated in a subset of patients with unipolar or bipolar depression, anxiety-related disorders, and schizophrenia and have been associated with differential treatment responses and poor clinical outcomes. A growing body of literature also describes higher levels of endogenous inflammatory markers and altered, typically lower functional or structural connectivity within these circuits in association with transdiagnostic symptoms such as anhedonia and anxiety in psychiatric and at-risk populations. This review presents recent evidence that inflammation and its effects on the brain may serve as one molecular and cellular mechanism of dysconnectivity within anatomically and/or functionally connected cortical and subcortical regions in association with transdiagnostic symptoms. We also discuss the need to establish reproducible methods to assess inflammation-associated dysconnectivity in relation to behavior for use in translational studies or biomarker-driven clinical trials for novel pharmacological or behavioral interventions targeting inflammation or its effects on the brain.