Future world cancer death rate prediction

Design, Synthesis, and Anticancer Activity of Novel Enmein-Type Diterpenoid Derivatives Targeting the PI3K/Akt/mTOR Signaling Pathway

The enmein-type diterpenoids are a class of anticancer ent-Kaurane diterpnoids that have received much attention in recent years. Herein, a novel 1,14-epoxy enmein-type diterpenoid 4, was reported in this project for the first time. A series of novel enmein-type diterpenoid derivatives were also synthesized and tested for anticancer activities. Among all the derivatives, compound 7h exhibited the most significant inhibitory effect against A549 cells (IC50 = 2.16 µM), being 11.03-folds better than its parental compound 4. Additionally, 7h exhibited relatively weak anti-proliferative activity (IC50 > 100 µM) against human normal L-02 cells, suggesting that it had excellent anti-proliferative selectivity for cancer cells. Mechanism studies suggested that 7h induced G0/G1 arrest and apoptosis in A549 cells by inhibiting the PI3K/AKT/mTOR pathway. This process was associated with elevated intracellular ROS levels and collapsed MMP. In summary, these data identified 7h as a promising lead compound that warrants further investigation of its anticancer properties.

Antitumoral melatonin-loaded nanostructured lipid carriers

Aim: Cancer constitutes the second leading cause of death worldwide, with conventional therapies limited by significant side effects. Melatonin (MEL), a natural compound with antitumoral properties, suffers from instability and low solubility. To overcome these issues, MEL was encapsulated into nanostructured lipid carriers (MEL-NLC) containing rosehip oil to enhance stability and boost its antitumoral activity.Methods: MEL-NLC were optimized by a design of experiments approach and characterized for their physicochemical properties. Stability and biopharmaceutical behavior were assessed, along with interaction studies and in vitro antitumoral efficacy against various cancer cell lines.Results: Optimized MEL-NLC exhibited desirable physicochemical characteristics, including small particle size and sustained MEL release, along with long-term stability. In vitro studies demonstrated that MEL-NLC selectively induced cytotoxicity in several cancer cell lines while sparing healthy cells.Conclusion: MEL-NLC represent a promising alternative for cancer, combining enhanced stability and targeted antitumoral activity, potentially overcoming the limitations of conventional treatments.

Future worldwide coronavirus disease 2019 epidemic predictions by Gaidai multivariate risk evaluation method

Accurate estimation of pandemic likelihood in every US state of interest and at any time. Coronavirus disease 2019 (COVID-19) is an infectious illness with a high potential for global dissemination and low rates of fatality and morbidity, placing some strains on national public health systems. This research intends to benchmark a novel technique, that enables hazard assessment, based on available clinical data, and dynamically observed patient numbers while taking into account pertinent territorial and temporal mapping. Multicentre, population-based, and biostatistical strategies have been utilized to process raw/unfiltered medical survey data. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. First, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case,-not to mention challenges with system dimensionality higher than 2D. Assessing outbreak risks of future outbreaks in any nation/region of interest. Existing bio-statistical approaches do not always have the benefits of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. Apply contemporary, novel statistical/reliability techniques directly to raw/unfiltered clinical data. The current study outlines a novel bio-system hazard assessment technique that is particularly suited for multi-regional environmental, bio, and public health systems, observed over a representative period. With the use of the Gaidai multivariate hazard assessment approach, epidemic outbreak spatiotemporal risks may be properly assessed. Based on raw/unfiltered clinical survey data, the Gaidai multivariate hazard assessment approach may be applied to a variety of public health applications. The study's primary finding was an assessment of the risks of epidemic outbreaks, along with a matching confidence range. Future global COVID-19/severe acute respiratory syndrome coronavirus 2 (SARS-COV2) epidemic risks have been examined in the current study; however, COVID-19/SARS-COV2 infection transmission mechanisms have not been discussed.

Virtual screening of FOXO3a activators from natural product-like compound library

FOXO3a is an inevitable transcription factor, which is involved in the regulation of biological processes such as proliferation, DNA damage repair, cell cycle arrest and cell death. Previous studies confirmed that FOXO3a is an excellent tumor suppressor and in cancer cells, it gets phosphorylated followed by proteasomal degradation. FOXO3a is found to be inactivated in cancer cells, whereas in normal cells it gets activated and upregulates its downstream targets, which induces apoptotic pathways. Hence, activation of FOXO3a can be implicated in cancer prevention and treatment. A variety of commercially available FOXO3a activators such as doxorubicin and metformin possess undesirable adverse effects to normal cells and tissues, which are their major limitations. Natural bioactive compounds, eliminating the limitations of such compounds, become an excellent choice for the treatment and prevention of cancer. In this study, a library of natural product-like compounds was screened for their FOXO3a activation potential through in silico approach, which included the use of several bioinformatics tools and processes. Other molecular interaction studies as well as binding and specificity studies were carried out with the help of molecular dynamics simulation. Virtual screening of 7700 small molecules from the Natural Products-like Compound Library revealed the top three FOXO3a activators F3385-6269, F2183-0033 and F3351-0330. Further validation studies are warranted to confirm these findings.

Public health system sustainability assessment by Gaidai hypersurface approach

BACKGROUND

to determine extreme cardiovascular and cancer diseases deathrate risks at any time in any region of interest.

DESIGN

Apply modern novel statistical methods to raw clinical surveillance data.

METHODS

multi-centre, population-based, medical survey data-based bio statistical approach. For this study, cardiovascular and cancer diseases annual recorded deaths numbers in all 195 world countries have been selected, constituting 390D (390-dimensional) biosystem. It is challenging to model such phenomena.

RESULTS

this paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient timelapse. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality. The suggested methodology coped with this challenge well.

CONCLUSIONS

the suggested methodology may be used in various public health applications, based on raw clinical survey data.

Exploring the Potential Role of Phytopharmaceuticals in Alleviating Toxicities of Chemotherapeutic Agents

BACKGROUND

Chemotherapy is the mainstay of cancer treatment, bringing patients optimism about recurrence and survival. However, the clinical effectiveness of chemotherapeutic drugs is frequently jeopardized by their intrinsic toxicity, resulting in side effects affecting the quality of life of cancer patients. This analysis explores the ethnopharmacological impact of phytopharmaceuticals, highlighting their traditional use in many cultures. The present study, which takes its cues from indigenous knowledge, aims to close the knowledge gap between traditional medicine and modern medicine in reducing the toxicities of chemotherapy treatments.

AIM

The present in-depth study aims to highlight the current research and upcoming developments in phytopharmaceuticals for reducing the toxicity of chemotherapeutic drugs. Further, we address the mechanisms through which phytopharmaceuticals may reduce chemotherapy-induced side effects that include nausea, vomiting, myelosuppression, nephropathy, neuropathy, and cardiotoxicity using data from a variety of preclinical and clinical investigations.

MATERIALS AND METHODS

The literature search was carried out by employing search engines such as PubMed and Google Scholar with keywords such as cancer, chemotherapy, CNS toxicity, hematopoietic toxicity, renal toxicity, GI toxicity, CNS toxicity, and phytopharmaceuticals.

RESULTS

Bioactive chemicals found in plants, such as fruits, vegetables, herbs, and spices, are being studied for their capacity to improve the safety and acceptability of chemotherapy regimens. The current review also dives into the investigation of phytopharmaceuticals as adjuvant medicines in cancer treatment, which is a viable path for addressing the pressing need to lessen chemotherapy-induced toxicities.

CONCLUSION

The present review revealed that the potential of phytopharmaceuticals in alleviating chemotherapeutic drug toxicities would pave the way for better cancer treatment and patient outcomes, harmonizing with the larger trend towards personalized and holistic approaches to chemotherapy.

Gaidai reliability method for high-dimensional spatio-temporal biosystems

This study presents novel methodology for pandemic risks assessment for a national health system of interest. The 2019 coronavirus disease (COVID-19) is a contagious disease with certain potential for worldwide spread and potentially significant effects on public health globally. Suggested methodology enables risks assessment of an epidemic, that may happen in the near future at any time, and in any national region of interest. Traditional spatio-temporal reliability methodologies do not have benefit of easily handling health system's high-dimensionality and complex cross-correlations between regional observations. Contrarily, advocated Gaidaireliability approach successfully addresses spatiotemporal clinical observations, as well as multi-regional epidemiological dynamics. This study aimed at benchmarking of a novel bio-statistical technique, enabling national health risk assessment, based on available clinical surveys with dynamically observed patient numbers, while accounting for relevant territorial mappings. The method developed in this study opens up the possibility of accurate epidemiological risk forecast for multi-regional biological and health systems. Suggested bioinformatical methodology may be used in a wide range of public health applications.

The Effect of an Educational Intervention on Breast Cancer Screening of Rural Women: Application of the Theory of Planned Behavior

BACKGROUND

Early diagnosis of breast cancer is a key factor affecting patient survival, so screening can reduce the burden of this disease. The present study aimed to investigate the effect of education based on the theory of planned behavior (TPB) on breast cancer screening in rural women.

METHODS

In this quasi-experimental study, 480 women referring to the health care centers in the cities of Fasa and Shiraz were divided into two groups, intervention (n = 240) and control (n = 240), using cluster random sampling method, in 2021-2022. We randomly selected two health care centers in Shiraz and Fasa and invited them to participate in the study. The demographic information questionnaire and a researcher-made questionnaire based on the TPB were used to collect the data. The intervention included 50-minute sessions on topics such as breast cancer basics, screening methods, barriers to mammography, and the role of peer groups. Data were collected before and 4 months after the intervention.

RESULTS

The results showed no difference between the two groups in awareness, attitude, perceived behavioral control, subjective norms, behavioral intention, and breast cancer screening performance before the intervention. Four months after the intervention, a significant increase was found in the intervention group in awareness (7.46 ± 1.50 to 18.54 ± 1.20), attitude (28.55 ± 4.62 to 58.69 ± 4.35), perceived behavioral control (22.52 ± 3.32 to 40.88 ± 3.84), and subjective norms (20.37 ± 3.34 to 21.99 ± 3.38). Instead, no significant difference in the mentioned constructs (P < 0.05) was observed in the control group.

CONCLUSION

This study demonstrated that TPB-based education enhanced awareness, attitude, perceived behavioral control, subjective norms, and behavioral intention towards breast cancer screening. The TPB empowers women in rural communities to prioritize their health and seek timely breast cancer screening. Continued efforts and improved access to screening services are crucial for improved outcomes.

COVID-19 spatio-temporal forecast in England

The 2019 novel coronavirus disease (COVID-19, SARS-CoV-2) being contagious illness with allegedly high potential for global transmission, low potential for morbidity and fatality, and certain impact on global public health. This study describes a novel bio-system reliability spatio-temporal approach, that is especially appropriate for multi-regional environmental, biological and health systems and that, when observed for a sufficient amount of time, produces a reliable long-term forecast of the likelihood of an outbreak of a highly pathogenic virus. Conventional statistical approaches do not have the benefit of effectively handling large regional dimensionality and cross-correlation between various regional observations. These methods deal with temporal observations of multi-regional phenomena. The most afflicted districts of England's COVID-19 daily counts of reported patients were used for this investigation. In order to extract the essential data from dynamically observed patient numbers while taking into consideration pertinent geographical mapping, this study utilized recently developed bio-reliability methodology. With the use of the spatio-temporal approach described in this study, future epidemic outbreak risks for multi-regional public health systems may be predicted with sufficient accuracy.

Dementia death rates prediction

BACKGROUND

Prevalence of dementia illness, causing certain morbidity and mortality globally, places burden on global public health. This study primary goal was to assess future risks of dying from severe dementia, given specific return period, within selected group of regions or nations.

METHODS

Traditional statistical approaches do not have benefits of effectively handling large regional dimensionality, along with nonlinear cross-correlations between various regional observations. In order to produce reliable long-term projections of excessive dementia death rate risks, this study advocates novel bio-system reliability technique, that being particularly suited for multi-regional environmental, biological, and health systems.

DATA

Raw clinical data has been used as an input to the suggested population-based, bio-statistical technique using data from medical surveys and several centers.

RESULTS

Novel spatiotemporal health system reliability methodology has been developed and applied to dementia death rates raw clinical data. Suggested methodology shown to be capable of dealing efficiently with spatiotemporal clinical observations of multi-regional nature. Accurate disease risks multi-regional spatiotemporal prediction being done, relevant confidence intervals have been presented as well.

CONCLUSIONS

Based on available clinical survey dataset, the proposed approach may be applied in a variety of clinical public health applications. Confidence bands, given for predicted dementia-associated death rate levels with return periods of interest, have been reasonably narrow, indicating practical values of advocated prognostics.

Carbon Storage Tanker Lifetime Assessment

CO₂ capture and storage (CCS) is an important strategy to reduce global CO₂ emissions. This work presents both cutting-edge carbon storage tanker design, as well as novel reliability method making possible to extract useful information about the lifespan distribution of carbon capture systems from their recorded time history. The method outlined may be applied on more complex sustainable systems that are exposed to environmental stresses throughout the whole period of their planned service life. The latter is of paramount importance at the design stage for complex engineering systems. Novel design for CCS system is discussed and accurate numerical simulation results are used to apply suggested novel reliability methodology. Furthermore, traditional reliability approaches that deal with complex energy systems are not well suited for handling high dimensionality and cross-correlation between various system components of innovative dynamic CO₂ storage subsea shuttle tanker. This study has two distinctive key features: the state of art CCS design concept, and the novel general purpose reliability method, recently developed by authors, and particularly suitable for operational safety study of complex energy systems.

Oil tanker under ice loadings

As a result of global warming, the area of the polar pack ice is diminishing, making merchant travel more practical. Even if Arctic ice thickness reduced in the summer, fractured ice is still presenting operational risks to the future navigation. The intricate process of ship-ice interaction includes stochastic ice loading on the vessel hull. In order to properly construct a vessel, the severe bow forces that arise must be accurately anticipated using statistical extrapolation techniques. This study examines the severe bow forces that an oil tanker encounters when sailing in the Arctic Ocean. Two stages are taken in the analysis. Then, using the FEM program ANSYS/LS-DYNA, the oil tanker bow force distribution is estimated. Second, in order to estimate the bow force levels connected with extended return periods, the average conditional exceedance rate approach is used to anticipate severe bow forces. The vessel's itinerary was planned to take advantage of the weaker ice. As a result, the Arctic Ocean passage took a meandering route rather than a linear one. As a result, the ship route data that was investigated was inaccurate with regard to the ice thickness data encountered by a vessel yet skewed with regard to the ice thickness distribution in the region. This research intends to demonstrate the effective application of an exact reliability approach to an oil tanker with severe bow forces on a particular route.

Gaidai-Xing reliability method validation for 10-MW floating wind turbines

In contrast to well-known bivariate statistical approach, which is known to properly forecast extreme response levels for two-dimensional systems, the research validates innovative structural reliability method, which is particularly appropriate for multi-dimensional structural responses. The disadvantage of dealing with large system dimensionality and cross-correlation across multiple dimensions is not a benefit of traditional dependability approaches that deal with time series. Since offshore constructions are built to handle extremely high wind and wave loads, understanding these severe stresses is essential, e.g. wind turbines should be built and operated with the least amount of inconvenience. In the first scenario, the blade root flapwise bending moment is examined, whereas in the second, the tower bottom fore-aft bending moment is examined. The FAST simulation program was utilized to generate the empirical bending moments for this investigation with the load instances activated at under-rated, rated, and above-rated speeds. The novel reliability approach, in contrast to conventional reliability methods, does not call for the study of a multi-dimensional reliability function in the case of numerical simulation. As demonstrated in this work, it is now possible to assess multi-degree-of-freedom nonlinear system failure probability, in the case when only limited system measurements are available.

COVID-19 Epidemic Forecast in Brazil

This study advocates a novel spatio-temporal method for accurate prediction of COVID-19 epidemic occurrence probability at any time in any Brazil state of interest, and raw clinical observational data have been used. This article describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient time period, resulting in robust long-term forecast of the virus outbreak probability. COVID-19 daily numbers of recorded patients in all affected Brazil states were taken into account. This work aimed to benchmark novel state-of-the-art methods, making it possible to analyse dynamically observed patient numbers while taking into account relevant regional mapping. Advocated approach may help to monitor and predict possible future epidemic outbreaks within a large variety of multi-regional biological systems. Suggested methodology may be used in various modern public health applications, efficiently using their clinical survey data.

Operational reliability study of ice loads acting on oil tanker bow

As a result of climate change, the Arctic glaciers start to melt, and the summer season arrives, making it acceptable for trade ships. There is still shattered ice in the saltwater even though the Arctic glaciers melt in the summer. The stochastic ice loading on the ship's hull is a complex ship-ice interaction. In order to properly build a vessel, it is necessary to reliably estimate the consequent high bow stresses using statistical extrapolation techniques. The bivariate reliability approach is used in this study to compute the excessive bow forces that an oil tanker encounters while sailing in the Arctic Ocean. Two stages are taken in the analysis. First, ANSYS/LS-DYNA is used to compute the oil tanker's bow stress distribution. Second, high bow stresses are projected utilizing a unique dependability methodology to evaluate return levels associated with extended return times. This research focuses on bow loads of an oil tanker travelling in the Artic Ocean using the recorded ice thickness distribution. To take advantage of weaker ice, the vessel's itinerary across the Arctic Ocean was windy (not the shortest straight path). This results in the ship route data used being inaccurate concerning the ice thickness statistics for the area yet skewed concerning the ice thickness data that was particular to a vessel's path. Therefore, this work aims to present a quick and precise approach for estimating the high bow stresses experienced by oil tankers along a given path. Most designs incorporate univariate characteristic values, while this study advocates a bivariate reliability approach for a safer and better design.

The Promise of Nanoparticles-Based Radiotherapy in Cancer Treatment

Radiation has been utilized for a long time for the treatment of cancer patients. However, radiotherapy (RT) has many constraints, among which non-selectivity is the primary one. The implementation of nanoparticles (NPs) with RT not only localizes radiation in targeted tissue but also provides significant tumoricidal effect(s) compared to radiation alone. NPs can be functionalized with both biomolecules and therapeutic agents, and their combination significantly reduces the side effects of RT. NP-based RT destroys cancer cells through multiple mechanisms, including ROS generation, which in turn damages DNA and other cellular organelles, inhibiting of the DNA double-strand damage-repair system, obstructing of the cell cycle, regulating of the tumor microenvironment, and killing of cancer stem cells. Furthermore, such combined treatments overcome radioresistance and drug resistance to chemotherapy. Additionally, NP-based RT in combined treatments have shown synergistic therapeutic benefit(s) and enhanced the therapeutic window. Furthermore, a combination of phototherapy, i.e., photodynamic therapy and photothermal therapy with NP-based RT, not only reduces phototoxicity but also offers excellent therapeutic benefits. Moreover, using NPs with RT has shown promise in cancer treatment and shown excellent therapeutic outcomes in clinical trials. Therefore, extensive research in this field will pave the way toward improved RT in cancer treatment.

Offloading operation bivariate extreme response statistics for FPSO vessel

The Floating Production Storage and Offloading unit (FPSO) is an offshore unit producing and storing crude oil prior to tanker transport. An important design concern is an accurate prediction of risky dynamic hawser tensions during FPSO offloading operations. Bivariate extreme hawser tension contours are important for selecting proper design values. This paper employed the AQWA hydrodynamic software to analyze vessel hydrodynamic wave loads dynamic response, acting on FPSO vessels under realistic sea state conditions. This paper presents an efficient method for estimating FPSO bivariate response statistics based on numerical simulations validated by various experiments. The bivariate Average Conditional Exceedance Rate (ACER2D) method offers an accurate bivariate extreme value probability distribution and return period contours estimation, utilizing available data efficiently. The two-dimensional probability contours, corresponding to low probability return periods, are easily obtained by the ACER2D method. The performance of the presented method has shown that the ACER2D method provides an efficient and accurate prediction of extreme return period contours. The suggested approach may be used for FPSO vessel design, minimizing potential FPSO hawser damage. Bivariate contours yield bivariate design points, as opposed to a pair of uncoupled univariate design points with the same return period as currently adopted in the industry.

Improving extreme offshore wind speed prediction by using deconvolution

This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently constructed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.