Table_1_Epidemiology and survival of cervical cancer in Iran based on national cancer registry data (2008-2014).docx

BackgroundCervical cancer (CC) is the third most commonly diagnosed cancer and the fourth leading cause of cancer death in females worldwide, associated with the incidence of human papillomavirus (HPV) infection. The CC incidence is low in Iran, ranking 11th among cancers. This study aimed to estimate the survival rate of CC and the reasons for its low survival rate based on the data retrieved from the Iranian National Cancer Registry System.MethodsIn this retrospective cohort study, data for patients diagnosed with CC from 2008 to 2014 were collected and analyzed. The Kaplan-Meier method was used for survival analysis based on epidemiological and clinical factors.ResultsA total of 5,304 women were diagnosed from March 10, 2008 to March 9, 2014 and 2,423 patients were followed. The mean age of the cases was 51.91 years, and 65.91% were alive. The 5- and 10-year survival rates were 58% and 50%, respectively, with no difference between younger cases with SCC or AC but better survival rates for older patients with SCC.ConclusionsAs a preventable disease, CC is related to biological factors and geographical and sociodemographic indices. Geographical, cultural, and religious behaviors affect the CC incidence and survival. In Iran, the 5-year survival rate ranges from 34% to 70% among different geographic regions. Hence, effective screening based on cultural and sociodemographic issues is recommended.</p

Table_2_Epidemiology and survival of cervical cancer in Iran based on national cancer registry data (2008-2014).docx

BackgroundCervical cancer (CC) is the third most commonly diagnosed cancer and the fourth leading cause of cancer death in females worldwide, associated with the incidence of human papillomavirus (HPV) infection. The CC incidence is low in Iran, ranking 11th among cancers. This study aimed to estimate the survival rate of CC and the reasons for its low survival rate based on the data retrieved from the Iranian National Cancer Registry System.MethodsIn this retrospective cohort study, data for patients diagnosed with CC from 2008 to 2014 were collected and analyzed. The Kaplan-Meier method was used for survival analysis based on epidemiological and clinical factors.ResultsA total of 5,304 women were diagnosed from March 10, 2008 to March 9, 2014 and 2,423 patients were followed. The mean age of the cases was 51.91 years, and 65.91% were alive. The 5- and 10-year survival rates were 58% and 50%, respectively, with no difference between younger cases with SCC or AC but better survival rates for older patients with SCC.ConclusionsAs a preventable disease, CC is related to biological factors and geographical and sociodemographic indices. Geographical, cultural, and religious behaviors affect the CC incidence and survival. In Iran, the 5-year survival rate ranges from 34% to 70% among different geographic regions. Hence, effective screening based on cultural and sociodemographic issues is recommended.</p

Image_2_Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.jpeg

BackgroundBreast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.Materials and methodsThis study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype.ResultsThe random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals.ConclusionThis study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.</p

Image_3_Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.jpeg

BackgroundBreast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.Materials and methodsThis study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype.ResultsThe random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals.ConclusionThis study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.</p

Image_1_Time-related survival prediction in molecular subtypes of breast cancer using time-to-event deep-learning-based models.jpeg

BackgroundBreast cancer (BC) survival prediction can be a helpful tool for identifying important factors selecting the effective treatment reducing mortality rates. This study aims to predict the time-related survival probability of BC patients in different molecular subtypes over 30 years of follow-up.Materials and methodsThis study retrospectively analyzed 3580 patients diagnosed with invasive breast cancer (BC) from 1991 to 2021 in the Cancer Research Center of Shahid Beheshti University of Medical Science. The dataset contained 18 predictor variables and two dependent variables, which referred to the survival status of patients and the time patients survived from diagnosis. Feature importance was performed using the random forest algorithm to identify significant prognostic factors. Time-to-event deep-learning-based models, including Nnet-survival, DeepHit, DeepSurve, NMLTR and Cox-time, were developed using a grid search approach with all variables initially and then with only the most important variables selected from feature importance. The performance metrics used to determine the best-performing model were C-index and IBS. Additionally, the dataset was clustered based on molecular receptor status (i.e., luminal A, luminal B, HER2-enriched, and triple-negative), and the best-performing prediction model was used to estimate survival probability for each molecular subtype.ResultsThe random forest method identified tumor state, age at diagnosis, and lymph node status as the best subset of variables for predicting breast cancer (BC) survival probabilities. All models yielded very close performance, with Nnet-survival (C-index=0.77, IBS=0.13) slightly higher using all 18 variables or the three most important variables. The results showed that the Luminal A had the highest predicted BC survival probabilities, while triple-negative and HER2-enriched had the lowest predicted survival probabilities over time. Additionally, the luminal B subtype followed a similar trend as luminal A for the first five years, after which the predicted survival probability decreased steadily in 10- and 15-year intervals.ConclusionThis study provides valuable insight into the survival probability of patients based on their molecular receptor status, particularly for HER2-positive patients. This information can be used by healthcare providers to make informed decisions regarding the appropriateness of medical interventions for high-risk patients. Future clinical trials should further explore the response of different molecular subtypes to treatment in order to optimize the efficacy of breast cancer treatments.</p

sj-docx-1-opp-10.1177_10781552231189864 - Supplemental material for The effect of anamorelin (ONO-7643) on cachexia in cancer patients: Systematic review and meta-analysis of randomized controlled trials

Supplemental material, sj-docx-1-opp-10.1177_10781552231189864 for The effect of anamorelin (ONO-7643) on cachexia in cancer patients: Systematic review and meta-analysis of randomized controlled trials by Shahla Rezaei, Livia Costa de Oliveira, Matin Ghanavati, Mahdi Shadnoush, Mohammad Esmaeil Akbari, Atieh Akbari, Mohammad Hadizadeh, Seyed Hossein Ardehali, Hidetaka Wakabayashi, Ala Elhelali and Jamal Rahmani in Journal of Oncology Pharmacy Practice</p

Additional file 1 of Beneficial effects of the combination of BCc1 and Hep-S nanochelating-based medicines on IL-6 in hospitalized moderate COVID-19 adult patients: a randomized, double-blind, placebo-controlled clinical trial

Additional file 1: Table S A) Descriptive Statistics of cell blood count by Group (nanomedicines vs. Placebo). B) Tests of Within-Subjects Effects. The blood samples were taken and analyzed on day zero, at discharge, and at the end of the treatment (on day 28). The results indicated that all the measured parameters were at normal range on day 28, and there was no significant difference between the treatment and placebo groups

Video_1_A human pilot study on positive electrostatic charge effects in solid tumors of the late-stage metastatic patients.MP4

BackgroundCorrelative interactions between electrical charges and cancer cells involve important unknown factors in cancer diagnosis and treatment. We previously reported the intrinsic suppressive effects of pure positive electrostatic charges (PEC) on the proliferation and metabolism of invasive cancer cells without any effect on normal cells in cell lines and animal models. The proposed mechanism was the suppression of pro-caspases 3 and 9 with an increase in Bax/Bcl2 ratio in exposed malignant cells and perturbation induced in the KRAS pathway of malignant cells by electrostatic charges due to the phosphate molecule electrostatic charge as the trigger of the pathway. This study aimed to examine PECs as a complementary treatment for patients with different types of solid metastatic tumors, who showed resistance to chemotherapy and radiotherapy.MethodsIn this study, solid metastatic tumors of the end-stage patients (n = 41) with various types of cancers were locally exposed to PEC for at least one course of 12 days. The patient’s signs and symptoms, the changes in their tumor size, and serum markers were followed up from 30 days before positive electrostatic charge treating (PECT) until 6 months after the study.ResultsEntirely, 36 patients completed the related follow-ups. Significant reduction in tumor sizes and cancer-associated enzymes as well as improvement in cancer-related signs and symptoms and patients’ lifestyles, without any side effects on other tissues or metabolisms of the body, were observed in more than 80% of the candidates.ConclusionPECT induced significant cancer remission in combination with other therapies. Therefore, this non-ionizing radiation would be a beneficial complementary therapy, with no observable side effects of ionizing radiotherapy, such as post-radiation inflammation.</p

Video_2_A human pilot study on positive electrostatic charge effects in solid tumors of the late-stage metastatic patients.MP4

BackgroundCorrelative interactions between electrical charges and cancer cells involve important unknown factors in cancer diagnosis and treatment. We previously reported the intrinsic suppressive effects of pure positive electrostatic charges (PEC) on the proliferation and metabolism of invasive cancer cells without any effect on normal cells in cell lines and animal models. The proposed mechanism was the suppression of pro-caspases 3 and 9 with an increase in Bax/Bcl2 ratio in exposed malignant cells and perturbation induced in the KRAS pathway of malignant cells by electrostatic charges due to the phosphate molecule electrostatic charge as the trigger of the pathway. This study aimed to examine PECs as a complementary treatment for patients with different types of solid metastatic tumors, who showed resistance to chemotherapy and radiotherapy.MethodsIn this study, solid metastatic tumors of the end-stage patients (n = 41) with various types of cancers were locally exposed to PEC for at least one course of 12 days. The patient’s signs and symptoms, the changes in their tumor size, and serum markers were followed up from 30 days before positive electrostatic charge treating (PECT) until 6 months after the study.ResultsEntirely, 36 patients completed the related follow-ups. Significant reduction in tumor sizes and cancer-associated enzymes as well as improvement in cancer-related signs and symptoms and patients’ lifestyles, without any side effects on other tissues or metabolisms of the body, were observed in more than 80% of the candidates.ConclusionPECT induced significant cancer remission in combination with other therapies. Therefore, this non-ionizing radiation would be a beneficial complementary therapy, with no observable side effects of ionizing radiotherapy, such as post-radiation inflammation.</p

Table_1_A human pilot study on positive electrostatic charge effects in solid tumors of the late-stage metastatic patients.docx

BackgroundCorrelative interactions between electrical charges and cancer cells involve important unknown factors in cancer diagnosis and treatment. We previously reported the intrinsic suppressive effects of pure positive electrostatic charges (PEC) on the proliferation and metabolism of invasive cancer cells without any effect on normal cells in cell lines and animal models. The proposed mechanism was the suppression of pro-caspases 3 and 9 with an increase in Bax/Bcl2 ratio in exposed malignant cells and perturbation induced in the KRAS pathway of malignant cells by electrostatic charges due to the phosphate molecule electrostatic charge as the trigger of the pathway. This study aimed to examine PECs as a complementary treatment for patients with different types of solid metastatic tumors, who showed resistance to chemotherapy and radiotherapy.MethodsIn this study, solid metastatic tumors of the end-stage patients (n = 41) with various types of cancers were locally exposed to PEC for at least one course of 12 days. The patient’s signs and symptoms, the changes in their tumor size, and serum markers were followed up from 30 days before positive electrostatic charge treating (PECT) until 6 months after the study.ResultsEntirely, 36 patients completed the related follow-ups. Significant reduction in tumor sizes and cancer-associated enzymes as well as improvement in cancer-related signs and symptoms and patients’ lifestyles, without any side effects on other tissues or metabolisms of the body, were observed in more than 80% of the candidates.ConclusionPECT induced significant cancer remission in combination with other therapies. Therefore, this non-ionizing radiation would be a beneficial complementary therapy, with no observable side effects of ionizing radiotherapy, such as post-radiation inflammation.</p