Diagnostic accuracy of CT-based radiomics and deep learning for predicting lymph node metastasis in esophageal cancer.

BACKGROUND: Esophageal cancer remains a global challenge due to late diagnoses and limited treatments. Lymph node metastasis (LNM) is crucial for prognosis, yet traditional diagnostics fall short. Integrating radiomics and deep learning (DL) with CT imaging for LNM diagnosis could revolutionize prognostic assessment and treatment planning. METHODS: A systematic review and meta-analysis were conducted by searching PubMed, Scopus, Web of Science, and Embase up to October 1, 2023. The focus was on studies developing CT-based radiomics and/or DL models for preoperative LNM detection in esophageal cancer. Methodological quality was assessed using the METhodological RadiomICs Score (METRICS). RESULTS: Twelve studies were reviewed, and seven were included in the meta-analysis, most showing excellent methodological quality. Training sets revealed a pooled AUC of 87 % (95 % CI: 78 %-90 %), and internal validation sets showed an AUC of 85 % (95 % CI: 76 %-89 %), with no significant difference (p = 0.39). Sensitivity and specificity for training sets were 78.7 % and 81.8 %, respectively, with validation sets at 81.2 % and 76.2 %. DL models in training sets showed better diagnostic accuracy than radiomics (p = 0.054), significant after removing outliers (p < 0.01). Incorporating clinical data improved sensitivity in validation sets (p = 0.029). No significant difference was found between models based on CE or non-CE imaging (p = 0.281) or arterial or venous phase imaging (p = 0.927). CONCLUSION: Integrating CT-based radiomics and DL improves LNM detection in esophageal cancer. Including clinical data could enhance model performance. Future research should focus on multicenter studies with independent validations to confirm these findings and promote broader clinical adoption.

Lifestyle and occupational risks assessment of bladder cancer using machine learning-based prediction models.

BACKGROUND: Bladder cancer, one of the most prevalent cancers globally, can be regarded as considerable morbidity and mortality for patients. The bladder is an organ that comes in constant exposure to the environment and other risk factors such as inflammation. AIMS: In the current study, we used machine learning (ML) methods and developed risk prediction models for bladder cancer. METHODS: This population-based case-control study is focused on 692 cases of bladder cancer and 692 healthy people. The ML, including Neural Network (NN), Random Forest (RF), Decision Tree (DT), Naive Bayes (NB), Gradient Boosting (GB), and Logistic Regression (LR), were applied, and the model performance was evaluated. RESULTS: The RF (AUC = .86, precision = 79%) had the best performance, and the RT (AUC = .78, precision = 73%) was in the next rank. Based on variable importance analysis in RF, recurrent infection, bladder stone history, neurogenic bladder, smoking and opium use, chronic renal failure, spinal cord paralysis, analgesic, family history of bladder cancer, diabetic mellitus, low dietary intake of fruit and vegetable, high dietary intake of ham, sausage, can and pickles were respectively the most important factors, which effect on the probability of bladder cancer. CONCLUSION: Machine learning approaches can predict the probability of bladder cancer according to medical history, occupational risk factors, and dietary and demographical characteristics.

Possible Interaction of Opioidergic and Nitrergic Pathways in the Anticonvulsant Effect of Ivermectin on Pentylenetetrazole-Induced Clonic Seizures in Mice.

Ivermectin (IVM) is an antiparasitic drug that primarily works by the activation of GABAA receptors. The potential pharmacological pathways behind the anti-convulsant effect of IVM haven't yet been identified. In this study, intravenous injection of pentylenetetrazole (PTZ)-induced clonic seizure in mice was investigated in order to assess the possible influence of IVM on clonic seizure threshold (CST). We also look at the function of the Opioidergic and nitrergic pathways in IVM anticonvulsant action on clonic seizure threshold. IVM (0.5, 1, 5, and 10 mg/kg, i.p.) raised the PTZ-induced CST, according to our findings. Furthermore, the ineffective dose of nitric oxide synthase inhibitors (L-NAME 10 mg/kg, i.p.), and (7-NI 30 mg/kg, i.p.) or opioidergic system agonist (morphine 0.25 mg/kg, i.p.) were able to amplify the anticonvulsive action of IVM (0.2 mg/kg, i.p.). Moreover, the anticonvulsant effect of IVM was reversed by an opioid receptor antagonist (naltrexone 1 mg/kg, i.p.). Furthermore, the combination of the ineffective dose of morphine as an opioid receptor agonist with either L-NAME (2 mg/kg, i.p.) or 7-NI (10 mg/kg, i.p.) and with an ineffective dose of IVM (0.2 mg/kg, i.p.) had a significant anticonvulsant effect. Taken together, IVM has anticonvulsant activity against PTZ-induced clonic seizures in mice, which may be mediated at least in part through the interaction of the opioidergic system and the nitric oxide pathway.

Cytokines and chemokines profile in encephalitis patients: A meta-analysis.

BACKGROUND: Encephalitis is caused by autoimmune or infectious agents marked by brain inflammation. Investigations have reported altered concentrations of the cytokines in encephalitis. This study was conducted to determine the relationship between encephalitis and alterations of cytokine levels in cerebrospinal fluid (CSF) and serum. METHODS: We found possibly suitable studies by searching PubMed, Embase, Scopus, and Web of Science, systematically from inception to August 2021. 23 articles were included in the meta-analysis. To investigate sources of heterogeneity, subgroup analysis and sensitivity analysis were conducted. The protocol of the study has been registered in PROSPERO with a registration ID of CRD42021289298. RESULTS: A total of 23 met our eligibility criteria to be included in the meta-analysis. A total of 12 cytokines were included in the meta-analysis of CSF concentration. Moreover, 5 cytokines were also included in the serum/plasma concentration meta-analysis. According to the analyses, patients with encephalitis had higher CSF amounts of IL-6, IL-8, IL-10, CXCL10, and TNF-α than healthy controls. The alteration in the concentration of IL-2, IL-4, IL-17, CCL2, CXCL9, CXCL13, and IFN-γ was not significant. In addition, the serum/plasma levels of the TNF-α were increased in encephalitis patients, but serum/plasma concentration of the IL-6, IL-10, CXCL10, and CXCL13 remained unchanged. CONCLUSIONS: This meta-analysis provides evidence for higher CSF concentrations of IL-6, IL-8, IL-10, CXCL10, and TNF-α in encephalitis patients compared to controls. The diagnostic and prognostic value of these cytokines and chemokines should be investigated in future studies.

Indirect effects of COVID-19 in referring women to gynecologic oncology, perinatology and gynecology clinics in Iran.

PURPOSE: COVID-19 has captured the world. We hypothesized that this pandemic reduced referral of other non-COVID-19 patients to the hospitals or clinics, including gynecological and perinatological referrals. Women can be at risk in limited use of health services. METHODS: In this retrospective study, referrals from gynecologic oncology, perinatology, and gynecology clinics in a large teaching hospital of Tehran University of Medical Sciences (TUMS) were compared from February 20 to May 20, 2020, with the same period in 2019. Finally, referral trends in 2020 were compared with the COVID-19 admission pattern. RESULTS: Total admissions to all three clinics declined 63% in 2020 compared to 2019. There was a significant relationship between the number of visits to three clinics during these2  years (p < 0.001). The reduction in referrals to the gynecology clinic was more than gynecologic oncology and perinatology. The COVID-19 referral pattern was conversely linked to gynecology-related admissions. CONCLUSION: As the pandemic situation makes patients hesitant to go to the hospitals or not, health policymakers should consider other non-COVID issues, including maternal and fetal concerns. Providing safe places for other patients to visit is a goal that can be achieved through developing guidelines for nosocomial hygiene and training informed healthcare staff. Moreover, non-urgent visits should be avoided or postponed. This issue calls for new strategies, including telemedicine in situations similar to the current pandemic to both identify and manage such conditions.