TAK-3 Inhibits Lipopolysaccharide-Induced Neuroinflammation in Traumatic Brain Injury Rats Through the TLR-4/NF-κB Pathway.

Purpose: The activation of the inflammatory response is regarded as a pivotal factor in the pathogenesis of TBI. Central nervous system infection often leads to the exacerbation of neuroinflammation following TBI, primarily caused by Gram-negative bacteria. This study aims to elucidate the effects of the novel anti-inflammatory drug TAK-3 on LPS-induced neuroinflammation in TBI rats. Methods: In conjunction with the rat controlled cortical impact model, we administered local injections of Lipopolysaccharide to the impact site. Subsequently, interventions were implemented through intraperitoneal injections of TAK-3 and NF-κB activitor2 to modulate the TLR4/NF-κB axis The impact of LPS on neurological function was assessed using mNSS, open field test, and brain water content measurement. Inflammatory markers, including TNF-α, IL-1ß, IL-6 and IL-10 were assessed to evaluate the condition of neuritis by Elisa. The activation of the TLR-4/NF-κB signaling pathway was detected by immunofluorescence staining and Western blot to assess the anti-inflammatory effects of TAK-3. Results: The administration of LPS exacerbated neurological damage in rats with TBI, as evidenced by a reduction in motor activity and an increase in anxiety-like behavior. Furthermore, LPS induced disruption of the blood-brain barrier integrity and facilitated the development of brain edema. The activation of microglia and astrocytes by LPS at the cellular and molecular levels has been demonstrated to induce a significant upregulation of neuroinflammatory factors. The injection of TAK-3 attenuated the neuroinflammatory response induced by LPS. Conclusion: The present study highlights the exacerbating effects of LPS on neuroinflammation in TBI through activation of the TLR-4/NF-κB signaling pathway. TAK-3 can modulate the activity of this signaling axis, thereby attenuating neuroinflammation and ultimately reducing brain tissue damage.

Identification of cholesterol metabolism-related subtypes in nonfunctioning pituitary neuroendocrine tumors and analysis of immune infiltration.

OBJECTIVE: This study aimed to investigate the role of cholesterol metabolism-related genes in nonfunctioning pituitary neuroendocrine tumors (NF-PitNETs) invading the cavernous sinus and analyze the differences in immune cell infiltration between invasive and noninvasive NF-PitNETs. METHODS: First, a retrospective analysis of single-center clinical data was performed. Second, the immune cell infiltration between invasive and noninvasive NF-PitNETs in the GSE169498 dataset was further analyzed, and statistically different cholesterol metabolism-related gene expression matrices were obtained from the dataset. The hub cholesterol metabolism-related genes in NF-PitNETs were screened by constructing machine learning models. In accordance with the hub gene, 73 cases of NF-PitNETs were clustered into two subtypes, and the functional differences and immune cell infiltration between the two subtypes were further analyzed. RESULTS: The clinical data of 146 NF-PitNETs were evaluated, and the results showed that the cholesterol (P = 0.034) between invasive and noninvasive NF-PitNETs significantly differed. After binary logistic analysis, cholesterol was found to be an independent risk factor for cavernous sinus invasion (CSI) in NF-PitNETs. Bioinformatics analysis found three immune cells between invasive and noninvasive NF-PitNETs were statistically significant in the GSE169498 dataset, and 34 cholesterol metabolism-related genes with differences between the two groups were obtained 12 hub genes were selected by crossing the two machine learning algorithm results. Subsequently, cholesterol metabolism-related subgroups, A and B, were obtained by unsupervised hierarchical clustering analysis. The results showed that 12 immune cells infiltrated differentially between the two subgroups. The chi-square test revealed that the two subgroups had statistically significance in the invasive and noninvasive samples (P = 0.001). KEGG enrichment analysis showed that the differentially expressed genes were mainly enriched in the neural ligand-receptor pathway. GSVA analysis showed that the mTORC signaling pathway was upregulated and played an important role in the two-cluster comparison. CONCLUSION: By clinical data and bioinformatics analysis, cholesterol metabolism-related genes may promote the infiltration abundance of immune cells in NF-PitNETs and the invasion of cavernous sinuses by NF-PitNETs through the mTOR signaling pathway. This study provides a new perspective to explore the pathogenesis of cavernous sinus invasion by NF-PitNETs and determine potential therapeutic targets for this disease.

Thyroxine changes in COVID-19 pandemic: A systematic review and meta-analysis.

Objective: COVID-19 infection may affect thyroid function. However, changes in thyroid function in COVID-19 patients have not been well described. This systematic review and meta-analysis assess thyroxine levels in COVID-19 patients, compared with non-COVID-19 pneumonia and healthy cohorts during the COVID-19 epidemic. Methods: A search was performed in English and Chinese databases from inception to August 1, 2022. The primary analysis assessed thyroid function in COVID-19 patients, comparing non-COVID-19 pneumonia and healthy cohorts. Secondary outcomes included different severity and prognoses of COVID-19 patients. Results: A total of 5873 patients were enrolled in the study. The pooled estimates of TSH and FT3 were significantly lower in patients with COVID-19 and non-COVID-19 pneumonia than in the healthy cohort (P < 0.001), whereas FT4 were significantly higher (P < 0.001). Patients with the non-severe COVID-19 showed significant higher in TSH levels than the severe (I2 = 89.9%, P = 0.002) and FT3 (I2 = 91.9%, P < 0.001). Standard mean differences (SMD) of TSH, FT3, and FT4 levels of survivors and non-survivors were 0.29 (P= 0.006), 1.11 (P < 0.001), and 0.22 (P < 0.001). For ICU patients, the survivors had significantly higher FT4 (SMD=0.47, P=0.003) and FT3 (SMD=0.51, P=0.001) than non-survivors. Conclusions: Compared with the healthy cohort, COVID-19 patients showed decreased TSH and FT3 and increased FT4, similar to non-COVID-19 pneumonia. Thyroid function changes were related to the severity of COVID-19. Thyroxine levels have clinical significance for prognosis evaluation, especially FT3.

Perioperative fluctuation and overall evaluation of adenohypophyseal hormone secretion in patients with nonfunctioning pituitary adenoma.

OBJECTIVE: Perioperative adenohypophyseal hormone assessment can improve therapeutic strategies and be used to evaluate the prognosis of pituitary adenomas. An individual hormone level does not entirely reflect the pituitary gland. Thus, this study aimed to analyze perioperative hormonal changes and propose a normalized method to facilitate overall assessment of the adenohypophysis. METHODS: The authors retrospectively analyzed 89 male patients with nonfunctioning pituitary adenoma (NFPA) who underwent transsphenoidal surgery. Preoperative clinical data, imaging data, and perioperative hormone levels of the anterior pituitary gland were evaluated. Hormone values were rescaled using minimum-maximum normalization. The sum of the normalized hormone levels was defined as the total hormonal rate (THR). RESULTS: Preoperative findings indicated correlations among different adenohypophyseal hormones. Luteinizing hormone (p = 0.62) and adrenocorticotropic hormone (p = 0.89) showed no significant changes after surgery, but growth hormone levels increased (p < 0.001). On the contrary, the levels of thyroid-stimulating hormone (p < 0.001), follicle-stimulating hormone (p = 0.02), and prolactin (p < 0.001) decreased. THR indicated a significant postoperative reduction in adenohypophyseal function (p = 0.04). Patients with postoperative hypopituitarism had significantly lower THR than those without (p = 0.003), with an area under the curve of 0.66. For NFPAs that presented with normal preoperative hormone levels, THR was a good clinical predictor of immediate postoperative hypopituitarism, with an area under the curve of 0.74. CONCLUSIONS: The normalized synthesis index of hormones is a novel and clinically valuable method used to reflect adenohypophyseal secretion. Compared with individual hormones, these results indicated that THR can facilitate the analysis of general hormone levels despite various fluctuations in adenohypophyseal hormones. THR may also contribute to the effective prediction of short-term surgery-induced hypopituitarism.

Predictive factors for delayed hyponatremia after transsphenoidal surgery in patients with Rathke's cleft cysts.

Purpose: We aimed to assess factors influencing the occurrence of delayed hyponatremia after transsphenoidal surgery in patients with Rathke's cleft cysts (RCCs). Methods: We retrospectively collected the clinical data of patients who underwent transsphenoidal surgery for RCCs from January 2014 to January 2022. Univariate and multivariate analyses were used to determine the factors influencing the occurrence of postoperative delayed hyponatremia. Results: Of the 78 microscopic transsphenoidal surgery recipients with RCCs, 15 experienced postoperative delayed hyponatremia. There were 35 men and 43 women, and mean age was 43.75 ± 14.95 years. The clinical manifestations of RCCs were headache (62 cases, 79.5%), visual dysfunction (35 cases, 44.9%), endocrine dysfunction symptoms (12 cases, 15.4%). After transsphenoidal surgery, 93.5% (58/62) had improvements in headache, and 97.1% (34/35) had improved or resolved compressive visual symptoms. Delayed hyponatremia occurred on average on day 6.46 and lasted on average for 4.40 days. Logistic regression analysis showed that the independent influencing factor of delayed hyponatremia after transsphenoidal surgery in patients with RCCs was postoperative diaphragma sellae height. Conclusion: Postoperative diaphragma sellae height was identified as an independent influencing factor for delayed hyponatremia after transsphenoidal surgery in patients with RCCs.

A Convolutional Neural Network Model for Detecting Sellar Floor Destruction of Pituitary Adenoma on Magnetic Resonance Imaging Scans.

Objective: Convolutional neural network (CNN) is designed for image classification and recognition with a multi-layer neural network. This study aimed to accurately assess sellar floor invasion (SFI) of pituitary adenoma (PA) using CNN. Methods: A total of 1413 coronal and sagittal magnetic resonance images were collected from 695 patients with PAs. The enrolled images were divided into the invasive group (n = 530) and the non-invasive group (n = 883) according to the surgical observation of SFI. Before model training, 100 images were randomly selected for the external testing set. The remaining 1313 cases were randomly divided into the training and validation sets at a ratio of 80:20 for model training. Finally, the testing set was imported to evaluate the model performance. Results: A CNN model with a 10-layer structure (6-layer convolution and 4-layer fully connected neural network) was constructed. After 1000 epoch of training, the model achieved high accuracy in identifying SFI (97.0 and 94.6% in the training and testing sets, respectively). The testing set presented excellent performance, with a model prediction accuracy of 96%, a sensitivity of 0.964, a specificity of 0.958, and an area under the receptor operator curve (AUC-ROC) value of 0.98. Four images in the testing set were misdiagnosed. Three images were misread with SFI (one with conchal type sphenoid sinus), and one image with a relatively intact sellar floor was not identified with SFI. Conclusion: This study highlights the potential of the CNN model for the efficient assessment of PA invasion.

Microscopic Transsphenoidal Resection of Giant Pituitary Adenomas: Analysis of the Factors Limiting the Degree of Resection in 73 Cases.

Purpose: To analyze the risk factors affecting the gross-total resection of giant pituitary adenomas using a transsphenoidal approach under a microscope to provide a reference basis for formulating an appropriate surgical strategy. Methods: The clinical data of patients who underwent microscopic transsphenoidal resection of giant pituitary adenomas in a single center from January 2011 to December 2020 were retrospectively analyzed. Based on magnetic resonance imaging and surgical records, the predictive factors affecting the gross-total resection of giant pituitary adenomas under microscopy were determined through univariate and multivariate analyses. Results: A total of 73 patients with giant pituitary adenomas underwent transsphenoidal microsurgery. Gross-total resection was performed in 19 cases (26%), subtotal resection in 31 cases (42%), partial resection in 21 cases (29%), and the degree of resection was <50% in only two cases (3%). After binary logistic analysis, it was found that it was more difficult to completely remove giant pituitary adenomas with a Knosp grade 3-4 [odds ratio (OR) = 0.214, 95% confidence interval (CI): 0.05-0.917; P = 0.038], greater proportion of tumor suprasellar volume (odds ratio = 0.937, 95% confidence interval: 0.898-0.978; P = 0.003), and intraoperative evidence of invasion of the cavernous sinus (odds ratio = 0.187, 95% CI: 0.039-0.898; P = 0.036). Conclusion: It is difficult to remove a giant pituitary adenoma invading the cavernous sinus completely with a higher degree of invasion of the suprasellar region using microscopic transsphenoidal surgery. The combined application of multiple surgical methods can help to improve the degree of resection during a single operation.

Systemic immune inflammation index and peripheral blood carbon dioxide concentration at admission predict poor prognosis in patients with severe traumatic brain injury.

Background: Recent studies have shown that systemic inflammation responses and hyperventilation are associated with poor outcomes in patients with severe traumatic brain injury (TBI). The aim of this retrospective study was to investigate the relationships between the systemic immune inflammation index (SII = platelet × neutrophil/lymphocyte) and peripheral blood CO2 concentration at admission with the Glasgow Outcome Score (GOS) at 6 months after discharge in patients with severe TBI. Methods: We retrospectively analyzed the clinical data for 1266 patients with severe TBI at three large medical centers from January 2016 to December 2021, and recorded the GOS 6 months after discharge. The receiver operating characteristic (ROC) curve was used to determine the best cutoff values for SII, CO2, neutrophil to lymphocyte ratio (NLR), platelet to lymphocyte ratio (PLR), and lymphocyte to monocyte ratio (LMR), and chi-square tests were used to evaluate the relationships among SII, CO2 and the basic clinical characteristics of patients with TBI. Multivariate logistic regression analysis was used to determine the independent prognostic factors for GOS in patients with severe TBI. Finally, ROC curve, nomogram, calibration curve and decision curve analyses were used to evaluate the value of SII and coSII-CO2 in predicting the prognosis of patients with severe TBI. And we used the multifactor regression analysis method to build the CRASH model and the IMPACT model. The CRASH model included age, GCS score (GCS, Glasgow Coma Scale) and Pupillary reflex to light: one, both, none. The IMPACT model includes age, motor score and Pupillary reflex to light: one, both, none. Results: The ROC curves indicated that the best cutoff values of SII, CO2, PLR, NLR and LMR were 2651.43×109, 22.15mmol/L, 190.98×109, 9.66×109 and 1.5×109, respectively. The GOS at 6 months after discharge of patients with high SII and low CO2 were significantly poorer than those with low SII and high CO2. Multivariate logistic regression analysis revealed that age, systolic blood pressure (SBP), pupil size, subarachnoid hemorrhage (SAH), SII, PLR, serum potassium concentration [K+], serum calcium concentration [Ca2+], international normalized ratio (INR), C-reactive protein (CRP) and co-systemic immune inflammation index combined with carbon dioxide (coSII-CO2) (P < 0.001) were independent prognostic factors for GOS in patients with severe TBI. In the training group, the C-index was 0.837 with SII and 0.860 with coSII-CO2. In the external validation group, the C-index was 0.907 with SII and 0.916 with coSII-CO2. Decision curve analysis confirmed a superior net clinical benefit with coSII-CO2 rather than SII in most cases. Furthermore, the calibration curve for the probability of GOS 6 months after discharge showed better agreement with the observed results when based on the coSII-CO2 rather than the SII nomogram. According to machine learning, coSII-CO2 ranked first in importance and was followed by pupil size, then SII. Conclusions: SII and CO2 have better predictive performance than NLR, PLR and LMR. SII and CO2 can be used as new, accurate and objective clinical predictors, and coSII-CO2, based on combining SII with CO2, can be used to improve the accuracy of GOS prediction in patients with TBI 6 months after discharge.