Cardiovascular Complications in Patients with Prostate Cancer: Potential Molecular Connections

Kakkat, Sooraj; Pramanik, Paramahansa; Singh, Seema; Singh, Ajay P.; Sarkar, Chandrani; Chakroborty, Debanjan

doi:10.3390/ijms24086984

Cited by 6 publications

(5 citation statements)

References 107 publications

(135 reference statements)

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“…We did not subdivide this category under two types: natural immunity, received from the maternal side, and artificial immunity, received from medicine [34,39] as it is beyond the scope of this paper. In Figure 1 we have created an Erdos-Renyi random network of 30 agents where deep magenta, light magenta, white, lighter green, and deep green represent an agent with very low, somewhat low, medium, somewhat high and very high respectively [40,41].…”

Section: Assumption 1 the Following Set Of Assumptions Regarding The ...mentioning

confidence: 99%

Estimation of Optimal Lock-Down and Vaccination Rate of a Stochastic SIR Model: A Mathematical Approach

Pramanik

2024

Eur. J. Stat.

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This paper utilizes a stochastic Susceptible-Infected-recovered (SIR) model with a nonlinear incidence rate to estimate the optimal lock-down intensity and vaccination rate under the COVID-19 pandemic environment. We use a Feynman-type path integral control approach to determine a Fokker-Plank type equation of this system. Since we assume the availability of information on the COVID-19 pandemic is complete and perfect, we show the existence of a unique fixed point. A non-linear incidence rate is used because, it can be raised from saturation effects that if the proportion of infected agents is very high so that exposure to the pandemic is inevitable, then the transmission rate responds slower than linearity to the increase in the number of infections. The simulation study shows that with higher diffusion coefficients susceptible and recovery curves keep the downward trends while the infection curve becomes ergodic. Finally, we perform a data analysis using UK data at the beginning of 2021 and compare it with our theoretical results.

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Section: Assumption 1 the Following Set Of Assumptions Regarding The ...mentioning

confidence: 99%

Estimation of Optimal Lock-Down and Vaccination Rate of a Stochastic SIR Model: A Mathematical Approach

Pramanik

2024

Eur. J. Stat.

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“…Reducing the cardiovascular toxicity of ADT is generally approached by controlling modifiable risk factors such as blood pressure and lipid profile while encouraging a healthy diet, physical activity, and the avoidance of tobacco and excess alcohol consumption [63]. The etiology of cardiovascular disease in men with prostate cancer continues to be a subject of ongoing research [64,65].…”

Section: Cardiovascular Effects Of Adtmentioning

confidence: 99%

Androgen Deprivation Therapy for Prostate Cancer: Focus on Cognitive Function and Mood

Reiss,

Gulkarov,

Pinkhasov

et al. 2023

Medicina

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Prostate cancer is the second leading cause of cancer death in men in the United States. Androgen deprivation therapy (ADT) is currently the primary treatment for metastatic prostate cancer, and some studies have shown that the use of anti-androgen drugs is related to a reduction in cognitive function, mood changes, diminished quality of life, dementia, and possibly Alzheimer’s disease. ADT has potential physiological effects such as a reduction in white matter integrity and a negative impact on hypothalamic functions due to the lowering of testosterone levels or the blockade of downstream androgen receptor signaling by first- and second-generation anti-androgen drugs. A comparative analysis of prostate cancer patients undergoing ADT and Alzheimer patients identified over 30 shared genes, illustrating common ground for the mechanistic underpinning of the symptomatology. The purpose of this review was to investigate the effects of ADT on cognitive function, mood, and quality of life, as well as to analyze the relationship between ADT and Alzheimer’s disease. The evaluation of prostate cancer patient cognitive ability via neurocognitive testing is described. Future studies should further explore the connection among cognitive deficits, mood disturbances, and the physiological changes that occur when hormonal balance is altered.

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“…Therefore, they are more vulnerable to occupational stress compared to their female counterparts. CVDs are more prone to when associated with other diseases [43]. Moreover, men are more likely to engage in unhealthy lifestyle behaviours such as tobacco use, heavy alcohol consumption, and physical inactivity, which are major risk factors for CVD.…”

Section: Fig 7 Blood Pressure Sample Mean Vs Population Meanmentioning

confidence: 99%

“…We can say that thyroid disorder increases the probability of developing CVDs. Chronic lung diseases, including chronic obstructive pulmonary disease (COPD) and asthma, are one of the top 10 NCDs responsible for 3.9 million deaths each year [43,44]. Air pollution is a significant contributor to the high rates of chronic respiratory disease in Bangladesh.…”

Section: Fig 7 Blood Pressure Sample Mean Vs Population Meanmentioning

confidence: 99%

Prevalence and major risk factors of non-communicable diseases: a machine learning based cross-sectional study

Roy,

Protity,

Das

et al. 2023

Eureka: HS

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The aim: The study aimed to determine the prevalence of several non-communicable diseases (NCD) and analyze risk factors among adult patients seeking nutritional guidance in Dhaka, Bangladesh. Participants: 146 hospitalized adults of both genders aged 18-93 participated in this cross-sectional research. Methods: We collected the demographic and vital information from 146 hospitalized patients in Dhaka, Bangladesh. We checked the physical and vital parameters, including blood sugar, serum creatinine, blood pressure, and the presence or absence of major non-communicable diseases. Then we used descriptive statistical approaches to explore the NCDs prevalence based on gender and age group. Afterwards, the relationship between different NCD pairs with their combined effects was analyzed using different hypothesis testing at a 95 % confidence level. Finally, the random forest and XGBoost machine learning algorithms are used to predict the comorbidity among the patients with the underlying responsible factors. Result: Our study observed the relationships between gender, age groups, obesity, and NCDs (DM, CKD, IBS, CVD, CRD, thyroid). The most frequently reported NCD was cardiovascular issues (CVD), which was present in 83.56 % of all participants. CVD was more common in male participants. Consequently, male participants had a higher blood pressure distribution than females. Diabetes mellitus (DM), on the other hand, did not have a gender-based inclination. Both CVD and DM had an age-based progression. Our study showed that chronic respiratory illness was more frequent in middle-aged participants than in younger or elderly individuals. Based on the data, every one in five hospitalized patients was obese. We analyzed the comorbidities and found that 31.5 % of the population has only one NCD, 30.1 % has two NCDs, and 38.3 % has more than two NCDs. Besides, 86.25 % of all diabetic patients had cardiovascular issues. All thyroid patients in our study had CVD. Using a t-test, we found a relationship between CKD and thyroid (p-value 0.061). Males under 35 years have a statistically significant relationship between thyroid and chronic respiratory diseases (p-value 0.018). We also found an association between DM and CKD among patients over 65 (p-value 0.038). Moreover, there has been a statistically significant relationship between CKD and Thyroid (P<0.05) for those below 35 and 35-65. We used a two-way ANOVA test to find the statistically significant interaction of heart issues and chronic respiratory illness in combination with diabetes. The combination of DM and RTI also affected CKD in male patients over 65 years old. Among machine learning algorithms, XGBoost produced the highest accuracy, 69.7 %, in comorbidity detection. Random forest feature importance detected age, weight and waist-hip ratio as the major risk factors behind the comorbidity. Conclusion: The prevalence study helps to identify the future risks and most vulnerable groups. By initiating and implementing control plans based on the prevalence study, it is possible to reduce the burden of NCDs on the elderly and middle-aged population of Bangladesh.

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Cardiovascular Complications in Patients with Prostate Cancer: Potential Molecular Connections

Cited by 6 publications

References 107 publications

Estimation of Optimal Lock-Down and Vaccination Rate of a Stochastic SIR Model: A Mathematical Approach

Estimation of Optimal Lock-Down and Vaccination Rate of a Stochastic SIR Model: A Mathematical Approach

Androgen Deprivation Therapy for Prostate Cancer: Focus on Cognitive Function and Mood

Prevalence and major risk factors of non-communicable diseases: a machine learning based cross-sectional study

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