Natural STAT3 inhibitors for cancer treatment: A Comprehensive Literature Review.

Cancer is one of the leading causes of mortality and morbidity worldwide, affecting millions of people physically and financially every year. Over time, many anticancer treatments have been proposed and studied, including synthetic compound consumption, surgical procedures, or grueling chemotherapy. Although these treatments have improved the daily life quality of patients and increased their survival rate and life expectancy, they have also shown significant drawbacks, including staggering costs, multiple side effects, and difficulty in compliance and adherence to treatment. Therefore, natural compounds have been considered a possible key to overcoming these problems in recent years, and thorough research has been done to assess their effectiveness. In these studies, scientists have discovered a meaningful interaction between several natural materials and signal transducer and activator of transcription 3 molecules. STAT3 is a transcriptional protein that is vital for cell growth and survival. Mechanistic studies have established that activated STAT3 can increase cancer cell proliferation and invasion while reducing anticancer immunity. Thus, inhibiting STAT3 signaling by natural compounds has become one of the favorite research topics and an attractive target for developing novel cancer treatments. In the present article, we intend to comprehensively review the latest knowledge about the effects of various organic compounds on inhibiting the STAT3 signaling pathway to cure different cancer diseases.

Diagnostic performance of artificial intelligence in multiple sclerosis: a systematic review and meta-analysis.

BACKGROUND: The expansion of the availability of advanced imaging methods needs more time, expertise, and resources which is in contrast to the primary goal of the imaging techniques. To overcome most of these difficulties, artificial intelligence (AI) can be used. A number of studies used AI models for multiple sclerosis (MS) diagnosis and reported diverse results. Therefore, we aim to perform a comprehensive systematic review and meta-analysis study on the role of AI in the diagnosis of MS. METHODS: We performed a systematic search using four databases including PubMed, Scopus, Web of Science, and IEEE. Studies that applied deep learning or AI to the diagnosis of MS based on any modalities were considered eligible in our study. The accuracy, sensitivity, specificity, precision, and area under curve (AUC) were pooled with a random-effects model and 95% confidence interval (CI). RESULTS: After the screening, 41 articles with 5989 individuals met the inclusion criteria and were included in our qualitative and quantitative synthesis. Our analysis showed that the overall accuracy among studies was 94% (95%CI: 93%, 96%). The pooled sensitivity and specificity were 92% (95%CI: 90%, 95%) and 93% (95%CI: 90%, 96%), respectively. Furthermore, our analysis showed 92% precision in MS diagnosis for AI studies (95%CI: 88%, 97%). Also, the overall pooled AUC was 93% (95%CI: 89%, 96%). CONCLUSION: Overall, AI models can further improve our diagnostic practice in MS patients. Our results indicate that the use of AI can aid the clinicians in accurate diagnosis of MS and improve current diagnostic approaches as most of the parameters including accuracy, sensitivity, specificity, precision, and AUC were considerably high, especially when using MRI data.

Artificial intelligence in the diagnosis of multiple sclerosis: A systematic review.

BACKGROUND: In recent years Artificial intelligence (AI) techniques are rapidly evolving into clinical practices such as diagnosis and prognosis processes, assess treatment effectiveness, and monitoring of diseases. The previous studies showed interesting results regarding the diagnostic efficiency of AI methods in differentiating Multiple sclerosis (MS) patients from healthy controls or other demyelinating diseases. There is a great lack of a comprehensive systematic review study on the role of AI in the diagnosis of MS. We aimed to perform a systematic review to document the performance of AI in MS diagnosis. METHODS: A systematic search was performed using four databases including PubMed, Scopus, Web of Science, and IEEE on August 2021. All original studies which focused on deep learning or AI to analyze any modalities with the purpose of diagnosing MS were included in our study. RESULTS: Finally, 38 studies were included in our systematic review after the abstract and full-text screening. A total of 5433 individuals were included, including 2924 cases of MS and 2509 healthy controls. Sensitivity and specificity were reported in 29 studies which ranged from 76.92 to 100 for sensitivity and 74 to 100 for specificity. Furthermore, 34 studies reported accuracy ranged 81 to 100. Among included studies, Magnetic Resonance Imaging (MRI) (20 studies), OCT (six studies), serum and cerebrospinal fluid markers (six studies), movement function (three studies), and other modalities such as breathing and evoked potential was used for detecting MS via AI. CONCLUSION: In conclusion, diagnosis of MS based on new markers and AI is a growing field of research with MRI images, followed by images obtained from OCT, serum and CSF biomarkers, and motor associated markers. All of these results show that with advances made in AI, the way we monitor and diagnose our MS patients can change drastically.

Statins and risk of amyotrophic lateral sclerosis: a systematic review and meta-analysis.

Amyotrophic lateral sclerosis (ALS) is a paralytic, heterogeneous and progressive disease characterized by the degeneration of both upper and lower motor neurons. Several studies about the effects of statins drug on the risk of ALS showed contradictory results and evidence for this is inconclusive. So we aimed to perform a meta-analysis on previous studies to clarify the association between statin use and risk of ALS. The databases including PubMed, Scopus, and Web of science were searched in February 2021 for studies that reported the association between statin use and risk of ALS. The eligible studies had to provide a report on the effect of statin and the incidence of ALS while comparing it to the control group. Articles that had low statin exposure time, the absence of a control group and an unknown number of ALS patients were excluded. The rate ratio and 95% confidence interval (CI) were used for association measures in case-control and cohort studies. After full-text and abstract review, data from 8 studies with a total of 547,622 participants and 13,890 cases of ALS were entered in the present meta-analysis. We combined eight studies using a random-effect model and the RR for statin users among groups was 0.98 (95% CI 0.80-1.20) which indicates no association between statin and incidence of ALS. Also high heterogeneity was detected across the studies (Q value = 26.62, P = .00; I2 = 72.71%). In our meta-analysis study, we found no association between statin use and an increase in ALS incidence. This result is in line with some previous studies and provides strong evidence that denies the possible association between statin uptake and disease induction.