Efficacy and Safety of Gabapentinoids for Acute Herpes Zoster Neuralgia: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

OBJECTIVE: his study aimed to systematically evaluate the clinical efficacy of gabapentin and pregabalin in the treatment of acute herpes zoster neuralgia, including pain control and the occurrence of adverse effects. METHOD: A systematic computerized search was conducted in October 2023 in PubMed, Embase, Web of Science, Cochrane Library, VIP, CNKI, and Wanfang databases. Data from randomized controlled trials comparing gabapentin analogs for the treatment of acute herpes zoster neuralgia were searched. Endpoints were visual analog scores (VAS) and adverse effects at 1, 2, and 4 weeks. Data from studies that met the inclusion criteria were extracted for meta-analysis and sensitivity analysis using Revman 5.4 and Stata16. RESULTS: The study included 292 patients from 6 RCTs. Of these, 118 were in the gabapentin-treated group, 37 were in the pregabalin-treated group, and 137 were in the placebo-controlled group. The gabapentin group showed superior pain reduction compared to the placebo group (P<0.05), but adverse events were more frequent. CONCLUSION: Gabapentin can effectively reduce acute herpes zoster neuralgia in patients. Pregabalin requires additional randomized controlled trials to supplement the analysis. PROSPERO REGISTRATION: CRD42023446643.

Sintilimab-induced erythema multiforme drug eruption in the treatment of sigmoid colon cancer: A case report and literature review.

RATIONALE: Dermatologic toxicity has been reported as the most common immune-related side effect of programmed cell death 1 inhibitors. Previous reports related to Sintilimab include rash, pruritus, vitiligo, Stevens-Johnson syndrome, toxic epidermal necrolysis, and so on. PATIENT CONCERNS: A 66-year-old man was treated with Sintilimab as monotherapy for sigmoid colon cancer. After the second prescription, he developed a more severe and widespread rash. DIAGNOSES: The diagnose of erythema multiforme drug eruption induced by Sintilimab was considered. INTERVENTIONS: The patient received intravenous and oral methylprednisolone, routine antihistamines and topical gluccorticoids. OUTCOMES: The patient's symptoms were gradually relieved during hospitalization and was discharged following resolution of symptoms. He refused to continue using Sintilimab. LESSONS: This is the first reported case of Sintilimab-induced erythema multiforme drug eruption. It is advisable to inform patients of potential dermatologic toxicity that may occur after using immune checkpoint inhibitors, so that we may prevent the further development of it and avoid the discontinuation of immune checkpoint inhibitors.

Predictive value of radiomics-based machine learning for the disease-free survival in breast cancer: a systematic review and meta-analysis.

Purpose: This study summarized the previously-published studies regarding the use of radiomics-based predictive models for the identification of breast cancer-associated prognostic factors, which can help clinical decision-making and follow-up strategy. Materials and methods: This study has been pre-registered on PROSPERO. PubMed, Embase, Cochrane Library, and Web of Science were searched, from inception to April 23, 2022, for studies that used radiomics for prognostic prediction of breast cancer patients. Then the search was updated on July 18, 2023. Quality assessment was conducted using the Radiomics Quality Score, and meta-analysis was performed using R software. Results: A total of 975 articles were retrieved, and 13 studies were included, involving 5014 participants and 35 prognostic models. Among the models, 20 models were radiomics-based and the other 15 were based on clinical or pathological information. The primary outcome was Disease-free Survival (DFS). The retrieved studies were screened using LASSO, and Cox Regression was applied for modeling. The mean RQS was 18. The c-index of radiomics-based models for DFS prediction was 0.763 (95%CI 0.718-0.810) in the training set and 0.702 (95%CI 0.637-0.774) in the validation set. The c-index of combination models was 0.807 (95%CI0.736-0.885) in the training set and 0.840 (95%CI 0.794-0.888) in the validation set. There was no significant change in the c-index of DFS at 1, 2, 3, and over 5 years of follow-up. Conclusion: This study has proved that radiomics-based prognostic models are of great predictive performance for the prognosis of breast cancer patients. combination model shows significantly enhanced predictive performance. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42022332392.

Predictive value of machine learning for breast cancer recurrence: a systematic review and meta-analysis.

PURPOSE: Recurrence of breast cancer leads to a high lifetime risk and a low 5 year survival rate. Researchers have used machine learning to predict the risk of recurrence in patients with breast cancer, but the predictive performance of machine learning remains controversial. Hence, this study aimed to explore the accuracy of machine learning in predicting breast cancer recurrence risk and aggregate predictive variables to provide guidance for the development of subsequent risk scoring systems. METHODS: We searched Pubmed, EMBASE, Cochrane, and Web of Science. The risk of bias in the included studies was evaluated using prediction model risk of bias assessment tool (PROBAST). Meta-regression was adopted to explore whether there was a significant difference in the recurrence time by machine learning. RESULTS: Thirty-four studies involving 67,560 subjects were included, among whom 8695 experienced breast cancer recurrence. The c-index of prediction models was 0.814 (95%CI 0.802-0.826) and 0.770 (95%CI 0.737-0.803) in the training and validation sets, respectively; the sensitivity and specificity were 0.69 (95% CI 0.64-0.74), 0.89 (95% CI 0.86-0.92) in the training, and 0.64 (95% CI 0.58-0.70), 0.88 (95% CI 0.82-0.92) in the validation, respectively. Age, histological grading, and lymph node status are the most commonly used variables in model construction. Attention should be paid to unhealthy lifestyles such as drinking, smoking and BMI as modeling variables. Risk prediction models based on machine learning have long-term monitoring value for breast cancer population, and subsequent studies should consider using large-sample and multi-center data to establish risk equations for verification. CONCLUSION: Machine learning may be used as a predictive tool for breast cancer recurrence. Currently, there is a lack of effective and universally applicable machine learning models in clinical practice. We expect to incorporate multi-center studies in the future and attempt to develop tools for predicting breast cancer recurrence risk, so as to effectively identify populations at high risk of recurrence and develop personalized follow-up strategies and prognostic interventions to reduce the risk of recurrence.

Alkannin exerts antitumor properties in cutaneous squamous cell carcinoma by inducing apoptosis and shifting the M1/M2 polarization of tumor-associated macrophages by upregulating PTEN.

Cutaneous squamous cell carcinoma (CSCC) is a common cancer in humans and is the second major type of skin cancer that causes death in humans. In this article, we investigated the effects of alkannin on CSCC progression. We revealed that alkannin curbed CSCC cell viability in a dose-dependent manner and accelerated CSCC cell apoptosis. In addition, alkannin expedited macrophage M1 polarization while curbing M2 polarization. Moreover, alkannin elevated phosphatase and tensin homolog (PTEN) abundance in CSCC cells. The results of bioinformatics analysis revealed that alkannin might modulate CSCC via PTEN. Downregulation of PTEN reversed the effects of alkannin on apoptosis of CSCC cells and M1/M2 polarization of macrophages. Alkannin reduced CSCC tumor growth in a mouse xenograft model. In conclusion, alkannin curbed the advancement of CSCC by expediting apoptosis and facilitating M1 polarization of macrophages by upregulating PTEN. These data may offer a therapeutic approach against CSCC.

Microglial repopulation alleviates age-related decline of stable wakefulness in mice.

Changes in wake/sleep architecture have been observed in both aged human and animal models, presumably due to various functional decay throughout the aging body particularly in the brain. Microglia have emerged as a modulator for wake/sleep architecture in the adult brain, and displayed distinct morphology and activity in the aging brain. However, the link between microglia and age-related wake/sleep changes remains elusive. In this study, we systematically examined the brain vigilance and microglia morphology in aging mice (3, 6, 12, and 18 months old), and determined how microglia affect the aging-related wake/sleep alterations in mice. We found that from young adult to aged mice there was a clear decline in stable wakefulness at nighttime, and a decrease of microglial processes length in various brain regions involved in wake/sleep regulation. The decreased stable wakefulness can be restored following the time course of microglia depletion and repopulation in the adult brain. Microglia repopulation in the aging brain restored age-related decline in stable wakefulness. Taken together, our findings suggest a link between aged microglia and deteriorated stable wakefulness in aged brains.