Synthesized Anti-HER2 Trastuzumab-MCC-DM1 Conjugate: An Evaluation of Efficacy and Cytotoxicity

Background: Trastuzumab is a humanized monoclonal antibody that targets site-specifically human epidermal growth factor-2 receptor (HER2) cell surface antigen overexpressed in approximately 20% of human breast carcinomas. Despite its positive therapeutic outcomes, a large proportion of individuals are unresponsive to the treatment with trastuzumab or develop resistance to it.Objective: To evaluate a chemically synthesized trastuzumab-based antibody-drug conjugate (ADC) to improve the trastuzumab therapeutic index.Methods: The current study explored the physiochemical characteristics of the trastuzumab conjugated to a cytotoxic chemotherapy agent DM1 via Succinimidyl 4-(N-maleimidomethyl) cyclohexane-1-carboxylate (SMCC) linker, created in our earlier study, using SDS-PAGE, UV/VIS, and RP-HPLC analyses. The antitumor effects of the ADCs were analyzed using MDA-MB-231 (HER2-negative) and SK-BR-3 (HER2-positive) cell lines utilizing in vitro cytotoxicity, viability, and binding assays. Three different formats of a HER2-targeting agent: trastuzumab, synthesized trastuzumab-MCC-DM1, and commercially available drug T-DM1 (Kadcyla®) were compared.Results: UV-VIS spectroscopic analysis showed that the trastuzumab-MCC-DM1 conjugates, on average, entailed 2.9 DM1 payloads per trastuzumab. A free drug level of 2.5% was determined by RP-HPLC. The conjugate appeared as two bands on a reducing SDS-PAGE gel. MTT viability assay showed that conjugating trastuzumab with DM1 significantly improved the antiproliferative effects of this antibody in vitro. Importantly, the evaluations using LDH release and cell apoptosis assays confirmed that trastuzumab maintains its ability to induce cell death response while conjugating with the DM1. The binding efficiency of trastuzumab-MCC-DM1 was comparable to that of the naked trastuzumab.Conclusion: Trastuzumab-MCC-DM1 was found effective against HER2+ tumors. The potency of this synthesized conjugate brings it closer to the commercially available T-DM1

Molecular docking evaluation of celecoxib on the boron nitride nanostructures for alleviation of cardiovascular risk and inflammatory

Celecoxib (CXB) is a nonsteroidal anti-inflammatory drug (NSAID) that can be used to treat rheumatoid arthritis and ischemic heart disease. In this research, density functional theory (DFT) and molecular docking simulations were performed to study the interaction of boron nitride nanotube (BNNT) and boron nitride nanosheet (BNNS) with CXB and its inhibitor effect on pro-inflammatory cytokines. The calculated adsorption energies of CXB with the BNNT were determined in aqueous phase. The results revealed that adsorption of CXB molecule via its SO2 group on BNNT is thermodynamically favored than the NH2 and CF3 groups in the solvent environment. Adsorption of CXB on BN nanomaterials are weak physisorption in nature. This can be attributed to the fact that both phenyl groups in CXB are not on the same plane and require significant activation energies for conformational changes to obtain greater H-π interaction. Both BNNT and BNNS materials had huge sensitivity in electronic change and short recovery time during CXB interaction, thus having potential as molecular sensor and biomedical carrier for the delivery of CXB drug. IL-1A and TNF-α were implicated as vital cytokines in diverse diseases, and they have been a validated therapeutic target to manage cardiovascular risk in patients with inflammatory bowel disease. A molecular docking simulation confirms that the BNNT loaded CXB could inhibit more pro-inflammatory cytokines including IL-1A and TNF-α receptors as compared to BNNS loaded to CXB

Global, regional, and national cancer incidence, mortality, years of life lost, years lived with disability, and disability-Adjusted life-years for 29 cancer groups, 1990 to 2017 : A systematic analysis for the global burden of disease study

Importance: Cancer and other noncommunicable diseases (NCDs) are now widely recognized as a threat to global development. The latest United Nations high-level meeting on NCDs reaffirmed this observation and also highlighted the slow progress in meeting the 2011 Political Declaration on the Prevention and Control of Noncommunicable Diseases and the third Sustainable Development Goal. Lack of situational analyses, priority setting, and budgeting have been identified as major obstacles in achieving these goals. All of these have in common that they require information on the local cancer epidemiology. The Global Burden of Disease (GBD) study is uniquely poised to provide these crucial data. Objective: To describe cancer burden for 29 cancer groups in 195 countries from 1990 through 2017 to provide data needed for cancer control planning. Evidence Review: We used the GBD study estimation methods to describe cancer incidence, mortality, years lived with disability, years of life lost, and disability-Adjusted life-years (DALYs). Results are presented at the national level as well as by Socio-demographic Index (SDI), a composite indicator of income, educational attainment, and total fertility rate. We also analyzed the influence of the epidemiological vs the demographic transition on cancer incidence. Findings: In 2017, there were 24.5 million incident cancer cases worldwide (16.8 million without nonmelanoma skin cancer [NMSC]) and 9.6 million cancer deaths. The majority of cancer DALYs came from years of life lost (97%), and only 3% came from years lived with disability. The odds of developing cancer were the lowest in the low SDI quintile (1 in 7) and the highest in the high SDI quintile (1 in 2) for both sexes. In 2017, the most common incident cancers in men were NMSC (4.3 million incident cases); tracheal, bronchus, and lung (TBL) cancer (1.5 million incident cases); and prostate cancer (1.3 million incident cases). The most common causes of cancer deaths and DALYs for men were TBL cancer (1.3 million deaths and 28.4 million DALYs), liver cancer (572000 deaths and 15.2 million DALYs), and stomach cancer (542000 deaths and 12.2 million DALYs). For women in 2017, the most common incident cancers were NMSC (3.3 million incident cases), breast cancer (1.9 million incident cases), and colorectal cancer (819000 incident cases). The leading causes of cancer deaths and DALYs for women were breast cancer (601000 deaths and 17.4 million DALYs), TBL cancer (596000 deaths and 12.6 million DALYs), and colorectal cancer (414000 deaths and 8.3 million DALYs). Conclusions and Relevance: The national epidemiological profiles of cancer burden in the GBD study show large heterogeneities, which are a reflection of different exposures to risk factors, economic settings, lifestyles, and access to care and screening. The GBD study can be used by policy makers and other stakeholders to develop and improve national and local cancer control in order to achieve the global targets and improve equity in cancer care. © 2019 American Medical Association. All rights reserved.Peer reviewe

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

Background: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. Methods: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. Findings: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. Interpretation: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic

Global age-sex-specific mortality, life expectancy, and population estimates in 204 countries and territories and 811 subnational locations, 1950–2021, and the impact of the COVID-19 pandemic: a comprehensive demographic analysis for the Global Burden of Disease Study 2021

BACKGROUND: Estimates of demographic metrics are crucial to assess levels and trends of population health outcomes. The profound impact of the COVID-19 pandemic on populations worldwide has underscored the need for timely estimates to understand this unprecedented event within the context of long-term population health trends. The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 provides new demographic estimates for 204 countries and territories and 811 additional subnational locations from 1950 to 2021, with a particular emphasis on changes in mortality and life expectancy that occurred during the 2020–21 COVID-19 pandemic period. METHODS: 22 223 data sources from vital registration, sample registration, surveys, censuses, and other sources were used to estimate mortality, with a subset of these sources used exclusively to estimate excess mortality due to the COVID-19 pandemic. 2026 data sources were used for population estimation. Additional sources were used to estimate migration; the effects of the HIV epidemic; and demographic discontinuities due to conflicts, famines, natural disasters, and pandemics, which are used as inputs for estimating mortality and population. Spatiotemporal Gaussian process regression (ST-GPR) was used to generate under-5 mortality rates, which synthesised 30 763 location-years of vital registration and sample registration data, 1365 surveys and censuses, and 80 other sources. ST-GPR was also used to estimate adult mortality (between ages 15 and 59 years) based on information from 31 642 location-years of vital registration and sample registration data, 355 surveys and censuses, and 24 other sources. Estimates of child and adult mortality rates were then used to generate life tables with a relational model life table system. For countries with large HIV epidemics, life tables were adjusted using independent estimates of HIV-specific mortality generated via an epidemiological analysis of HIV prevalence surveys, antenatal clinic serosurveillance, and other data sources. Excess mortality due to the COVID-19 pandemic in 2020 and 2021 was determined by subtracting observed all-cause mortality (adjusted for late registration and mortality anomalies) from the mortality expected in the absence of the pandemic. Expected mortality was calculated based on historical trends using an ensemble of models. In location-years where all-cause mortality data were unavailable, we estimated excess mortality rates using a regression model with covariates pertaining to the pandemic. Population size was computed using a Bayesian hierarchical cohort component model. Life expectancy was calculated using age-specific mortality rates and standard demographic methods. Uncertainty intervals (UIs) were calculated for every metric using the 25th and 975th ordered values from a 1000-draw posterior distribution. FINDINGS: Global all-cause mortality followed two distinct patterns over the study period: age-standardised mortality rates declined between 1950 and 2019 (a 62·8% [95% UI 60·5–65·1] decline), and increased during the COVID-19 pandemic period (2020–21; 5·1% [0·9–9·6] increase). In contrast with the overall reverse in mortality trends during the pandemic period, child mortality continued to decline, with 4·66 million (3·98–5·50) global deaths in children younger than 5 years in 2021 compared with 5·21 million (4·50–6·01) in 2019. An estimated 131 million (126–137) people died globally from all causes in 2020 and 2021 combined, of which 15·9 million (14·7–17·2) were due to the COVID-19 pandemic (measured by excess mortality, which includes deaths directly due to SARS-CoV-2 infection and those indirectly due to other social, economic, or behavioural changes associated with the pandemic). Excess mortality rates exceeded 150 deaths per 100 000 population during at least one year of the pandemic in 80 countries and territories, whereas 20 nations had a negative excess mortality rate in 2020 or 2021, indicating that all-cause mortality in these countries was lower during the pandemic than expected based on historical trends. Between 1950 and 2021, global life expectancy at birth increased by 22·7 years (20·8–24·8), from 49·0 years (46·7–51·3) to 71·7 years (70·9–72·5). Global life expectancy at birth declined by 1·6 years (1·0–2·2) between 2019 and 2021, reversing historical trends. An increase in life expectancy was only observed in 32 (15·7%) of 204 countries and territories between 2019 and 2021. The global population reached 7·89 billion (7·67–8·13) people in 2021, by which time 56 of 204 countries and territories had peaked and subsequently populations have declined. The largest proportion of population growth between 2020 and 2021 was in sub-Saharan Africa (39·5% [28·4–52·7]) and south Asia (26·3% [9·0–44·7]). From 2000 to 2021, the ratio of the population aged 65 years and older to the population aged younger than 15 years increased in 188 (92·2%) of 204 nations. INTERPRETATION: Global adult mortality rates markedly increased during the COVID-19 pandemic in 2020 and 2021, reversing past decreasing trends, while child mortality rates continued to decline, albeit more slowly than in earlier years. Although COVID-19 had a substantial impact on many demographic indicators during the first 2 years of the pandemic, overall global health progress over the 72 years evaluated has been profound, with considerable improvements in mortality and life expectancy. Additionally, we observed a deceleration of global population growth since 2017, despite steady or increasing growth in lower-income countries, combined with a continued global shift of population age structures towards older ages. These demographic changes will likely present future challenges to health systems, economies, and societies. The comprehensive demographic estimates reported here will enable researchers, policy makers, health practitioners, and other key stakeholders to better understand and address the profound changes that have occurred in the global health landscape following the first 2 years of the COVID-19 pandemic, and longer-term trends beyond the pandemic. FUNDING: Bill & Melinda Gates Foundation

Use of Complementary Alternative Medicine and the Associated Factors among Patients with Depression

Complementary Alternative Medicine (CAM) has been widely used in the world, but limited data are available on the use of CAM in depression. This study aimed to evaluate the use of CAM and its associated factors in depression. This cross-sectional study was conducted on 300 depressed patients referred to the Yasuj Neurology and Psychiatric Clinic, southern Iran, between 2019 and 2020. A valid semistructured international questionnaire was used; amongst the participants, 230 (77%) were female. The mean age of the patients was 41.47 ± 12.2 years and the mean duration of the disease was 4.49 ± 4.88 years. The prevalence of CAM use was 37.6% among the patients. The results showed a significant difference between the CAM users and nonusers regarding the disease duration (p=0.045) and body mass index (p=0.007). Moreover, the results of logistic regression analysis revealed a significant relationship between CAM use and female gender, disease duration, overweight, obesity, and self-employment (p=0.039, p=0.028, p=0.029, p=0.048, and p=0.044, resp.). The most frequently used type of CAM was herbal medicine (97.35%) followed by pray therapy (23.89%). Additionally, the most widely used herbs were borage (77%), chamomile (46.9%), and lavender (21.2%). Furthermore, 62.8% of the patients reported that their main reason for using CAM was its effectiveness. The majority of the patients (77%) had not consulted their physicians prior to utilization of CAM therapies. Herbal medicine was the most common form of CAM in depression, with a high satisfaction level. Thus, it is necessary to increase physicians’ awareness in different fields of CAM

Use of Complementary Alternative Medicine and the Associated Factors among Patients with Depression

Complementary Alternative Medicine (CAM) has been widely used in the world, but limited data are available on the use of CAM in depression. This study aimed to evaluate the use of CAM and its associated factors in depression. This cross-sectional study was conducted on 300 depressed patients referred to the Yasuj Neurology and Psychiatric Clinic, southern Iran, between 2019 and 2020. A valid semistructured international questionnaire was used; amongst the participants, 230 (77%) were female. The mean age of the patients was 41.47 ± 12.2 years and the mean duration of the disease was 4.49 ± 4.88 years. The prevalence of CAM use was 37.6% among the patients. The results showed a significant difference between the CAM users and nonusers regarding the disease duration (p=0.045) and body mass index (p=0.007). Moreover, the results of logistic regression analysis revealed a significant relationship between CAM use and female gender, disease duration, overweight, obesity, and self-employment (p=0.039, p=0.028, p=0.029, p=0.048, and p=0.044, resp.). The most frequently used type of CAM was herbal medicine (97.35%) followed by pray therapy (23.89%). Additionally, the most widely used herbs were borage (77%), chamomile (46.9%), and lavender (21.2%). Furthermore, 62.8% of the patients reported that their main reason for using CAM was its effectiveness. The majority of the patients (77%) had not consulted their physicians prior to utilization of CAM therapies. Herbal medicine was the most common form of CAM in depression, with a high satisfaction level. Thus, it is necessary to increase physicians’ awareness in different fields of CAM

UBE2C: A pan‐cancer diagnostic and prognostic biomarker revealed through bioinformatics analysis

Abstract Background The diverse and complex attributes of cancer have made it a daunting challenge to overcome globally and remains to endanger human life. Detection of critical cancer‐related gene alterations in solid tumor samples better defines patient diagnosis and prognosis, and indicates what targeted therapies must be administered to improve cancer patients' outcome. Materials and methods To identify genes that have aberrant expression across different cancer types, differential expressed genes were detected within the TCGA datasets. Subsequently, the DEGs common to all pan cancers were determined. Furthermore, various methods were employed to gain genetic alterations, co‐expression genes network and protein–protein interaction (PPI) network, pathway enrichment analysis of common genes. Finally, the gene regulatory network was constructed. Results Intersectional analysis identified UBE2C as a common DEG between all 28 types of studied cancers. Upregulated UBE2C expression was significantly correlated with OS and DFS of 10 and 9 types of cancer patients. Also, UBE2C can be a diagnostic factor in CESC, CHOL, GBM, and UCS with AUC = 100% and diagnose 19 cancer types with AUC ≥90%. A ceRNA network constructed including UBE2C, 41 TFs, 10 shared miRNAs, and 21 circRNAs and 128 lncRNAs. Conclusion In summary, UBE2C can be a theranostic gene, which may serve as a reliable biomarker in diagnosing cancers, improving treatment responses and increasing the overall survival of cancer patients and can be a promising gene to be target by cancer drugs in the future

The A-kinase anchoring proteins correlation with disease free survival in breast cancer

Background: Researchers are always trying to find specific markers which express specifically in cancer. These specific markers help to diagnose and treat cancer without affecting normal tissues. Cancer-testis antigens are among the new promising biomarkers, especially for targeted therapy. These markers are specially expressed in testis. Various studies have been reported individual expression of these proteins in some tumor tissues. Since testis is an immune privilege organ, abnormal expression of the above mentioned genes raises immune response and the serum antibody against them (CT antigene) can be detected as a marker of cancer. However, understanding their differential role in normal and cancer tissues may introduce them as new candidates of cancer biomarkers. The aim of this study was to evaluate AKAP3 gene expression in breast cancer and its correlation with clinicopathologic features of the disease. Methods: This study is a case-control study conducted at the Brest Cancer Research Center (BCRC)- Iran, between October 2014 to May 2016. AKAP3 gene expression was investigated with real-time PCR in breast samples including: 74 tumors, 73 normal adjacents and 15 normal tissues. On the other hand the correlation between gene expression, clinicopathologic features of the tumors and treatment regimen were evaluated. Results: Statistical analysis showed a significant correlation between lack of AKAP3 expression, tumor size (P=0.01) and stage (P=0.04). The association between poor prognosis and the absence of AKAP3 expression in normal adjacent tissues were observed. Kaplan Meier plot showed a significant better disease free survival in the normal adjacent patients group that are expressed AKAP3. Conclusion: It was observed that the better free survival in the normal adjacent group is because of the different AKAP3 expression, not treatment variations between two patient groups. As a result, AKAP3 can be a suitable candidate biomarker for breast cancer patients. Also, the study of gene expression in normal tissue of patients may be used to predict response to therapy