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Article ; Online: Analyzing the relationship between the vitamin D deficiency and COVID-19 mortality rate and modeling the time-delay interactions between body's immune healthy cells, infected cells, and virus particles with the effect of vitamin D levels.

Pham, Hoang

Mathematical biosciences and engineering : MBE

2022 Volume 19, Issue 9, Page(s) 8975–9004

Abstract: This paper presents some recent views on the aspects of vitamin D levels in relation to the COVID-19 infections and analyzes the relationship between the prevalence rates of vitamin D deficiency and COVID-19 death rates per million of various countries ... ...

Abstract	This paper presents some recent views on the aspects of vitamin D levels in relation to the COVID-19 infections and analyzes the relationship between the prevalence rates of vitamin D deficiency and COVID-19 death rates per million of various countries in Europe and Asia using the data from the PubMed database. The paper also discusses a new mathematical model of time-delay interactions between the body's immune healthy cells, infected cells, and virus particles with the effect of vitamin D levels. The model can be used to monitor the timely progression of healthy immune cells with the effects of the levels of vitamin D and probiotics supplement. It also can help to predict when the infected cells and virus particles free state can ever be reached as time progresses. The consideration of the time delay in the modeling due to effects of the infected cells or virus particles and the growth of healthy cells is also an important factor that can significantly change the outcomes of the body's immune cells as well as the infections.
MeSH term(s)	COVID-19 ; Dietary Supplements ; Humans ; Virion ; Vitamin D/pharmacology ; Vitamin D Deficiency/epidemiology
Chemical Substances	Vitamin D (1406-16-2)
Language	English
Publishing date	2022-08-09
Publishing country	United States
Document type	Journal Article
ZDB-ID	2265126-3
ISSN	1551-0018 ; 1551-0018
ISSN (online)	1551-0018
ISSN	1551-0018
DOI	10.3934/mbe.2022417
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: A Multifactorial Approach in Quality Improvement to Reduce Urinary Tract Infections in Pediatric Surgery.

Pham, Helen / Richardson, Arthur

Journal of the American College of Surgeons

2024

Language	English
Publishing date	2024-03-25
Publishing country	United States
Document type	Journal Article
ZDB-ID	1181115-8
ISSN	1879-1190 ; 1072-7515
ISSN (online)	1879-1190
ISSN	1072-7515
DOI	10.1097/XCS.0000000000001015
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Article ; Online: Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

Pham, H.

Abstract: COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and ... ...

Abstract	COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: * How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.
Keywords	covid19
Publisher	MedRxiv; WHO
Document type	Article ; Online
DOI	10.1101/2020.06.15.20132357
Database	COVID19

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Article ; Online: Integration of Next-Generation Sequencing in the Surgical Management of Colorectal Liver Metastasis.

Pham, Helen / Dixon, Elijah

Annals of surgical oncology

2023 Volume 30, Issue 11, Page(s) 6815–6823

Abstract: Hepatic resection remains the treatment of choice for colorectal liver metastases. The advancement of surgical technique and use of perioperative systemic therapy has expanded the number and complexity of patients eligible for surgical resection. In ... ...

Abstract	Hepatic resection remains the treatment of choice for colorectal liver metastases. The advancement of surgical technique and use of perioperative systemic therapy has expanded the number and complexity of patients eligible for surgical resection. In recent years, investigation into gene mutations, such as RAS/RAF pathway, have led to targeted therapies that have significantly improved outcomes. Next-generation sequencing allows analysis of large number of genes that may have potential prognostic relevance in the clinical setting. This review summarizes the current applications of next-generation sequencing technology in metastatic colorectal cancer, focusing on its prognostic implications on patient management.
MeSH term(s)	Humans ; Colorectal Neoplasms/genetics ; Colorectal Neoplasms/surgery ; Colorectal Neoplasms/pathology ; Prognosis ; Mutation ; Liver Neoplasms/genetics ; Liver Neoplasms/surgery ; Liver Neoplasms/secondary ; High-Throughput Nucleotide Sequencing
Language	English
Publishing date	2023-06-14
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	1200469-8
ISSN	1534-4681 ; 1068-9265
ISSN (online)	1534-4681
ISSN	1068-9265
DOI	10.1245/s10434-023-13750-7
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Article ; Online: Mean-field neural networks: Learning mappings on Wasserstein space.

Pham, Huyên / Warin, Xavier

Neural networks : the official journal of the International Neural Network Society

2023 Volume 168, Page(s) 380–393

Abstract: We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e.g. in mean-field games/control problems. Two classes of neural networks based on bin density and ... ...

Abstract	We study the machine learning task for models with operators mapping between the Wasserstein space of probability measures and a space of functions, like e.g. in mean-field games/control problems. Two classes of neural networks based on bin density and on cylindrical approximation, are proposed to learn these so-called mean-field functions, and are theoretically supported by universal approximation theorems. We perform several numerical experiments for training these two mean-field neural networks, and show their accuracy and efficiency in the generalization error with various test distributions. Finally, we present different algorithms relying on mean-field neural networks for solving time-dependent mean-field problems, and illustrate our results with numerical tests for the example of a semi-linear partial differential equation in the Wasserstein space of probability measures.
MeSH term(s)	Neural Networks, Computer ; Algorithms ; Machine Learning ; Probability
Language	English
Publishing date	2023-09-16
Publishing country	United States
Document type	Journal Article
ZDB-ID	740542-x
ISSN	1879-2782 ; 0893-6080
ISSN (online)	1879-2782
ISSN	0893-6080
DOI	10.1016/j.neunet.2023.09.015
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Article: The Continuing Problem of Expert Evidence in Medical Litigation - A Surgical Perspective with Reference to Daubert.

Richardson, Arthur / Pham, Helen / Hollands, Michael

Journal of law and medicine

2024 Volume 30, Issue 2, Page(s) 472–487

Abstract: The tension that exists between the medical and legal professions regarding expert evidence is longstanding. In this article, we will examine some of the issues regarding expert evidence particularly as it relates to matters involving surgeons. Many of ... ...

Abstract	The tension that exists between the medical and legal professions regarding expert evidence is longstanding. In this article, we will examine some of the issues regarding expert evidence particularly as it relates to matters involving surgeons. Many of the current aspects of the Australian uniform evidence law in relation to expert testimony were based on the Federal Rules of Evidence promulgated in the United States in 1975. We will discuss some of the problems of expert evidence in surgical matters, particularly in New South Wales, and offer some thoughts on how the so-called Daubert trilogy could form a basis on which to re-examine the concept of an "expert". Our analysis offers suggestions for further improvements to the process of adducing expert evidence in claims involving surgical matters.
MeSH term(s)	United States ; Australia ; Expert Testimony ; New South Wales
Language	English
Publishing date	2024-02-01
Publishing country	Australia
Document type	Journal Article
ZDB-ID	1236328-5
ISSN	1320-159X
ISSN	1320-159X
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Conference proceedings ; Online: Dynamics and impact of diurnal warm layers in the ocean

Schmitt, M. / Sarkar, S. / Pham, H. / Umlauf, L.

XXVIII General Assembly of the International Union of Geodesy and Geophysics (IUGG)

2023

Abstract: Thin Diurnal Warm Layers (DWLs) form near the surface of the ocean on days of large solar radiation, weak to moderate winds, and small surface waves. DWLs are characterized by complex dynamics, and are relevant to the ocean especially by modifying ... ...

Abstract	Thin Diurnal Warm Layers (DWLs) form near the surface of the ocean on days of large solar radiation, weak to moderate winds, and small surface waves. DWLs are characterized by complex dynamics, and are relevant to the ocean especially by modifying surface-layer mixing and atmosphere-ocean fluxes. Here, we use idealized Large Eddy Simulations (LES) and second-moment turbulence modelling, both including the effects of Langmuir turbulence, to identify the key non-dimensional parameters of the problem, and explore DWL properties and dynamics across a wide parameter space. Comparison of LES and the second-moment turbulence models shows that the latter provide an accurate representation of the DWL structure and dynamics. We find that, for equilibrium wave conditions, Langmuir effects are significant only in the Stokes layer very close to the surface. While we see pulses in the turbulent stresses and shear in the LES, there are no relevant effects of Langmuir turbulence on DWL bulk properties and total entrainment. Results of the parameter space analysis agree with the midday scaling by Pollard et al. (1986), however, with modified model coefficients and deviations of up to 30 percent especially at high-latitudes. We develop non-dimensional expressions for the strength and timing of the DWL temperature peak in the afternoon, and discuss the mixing efficiency and energetics of DWLs in the presence of Langmuir turbulence.
Subject code	551
Language	English
Publishing country	de
Document type	Conference proceedings ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Predictive Modeling on the Number of Covid-19 Death Toll in the United States Considering the Effects of Coronavirus-Related Changes and Covid-19 Recovered Cases

Pham, Hoang

medRxiv

Abstract	COVID-19 is caused by a coronavirus called SARS-CoV-2. Many countries around the world implemented their own policies and restrictions designed to limit the spread of Covid-19 in recent months. Businesses and schools transitioned into working and learning remotely. In the United States, many states were under strict orders to stay home at least in the month of April. In recent weeks, there are some significant changes related restrictions include social-distancing, reopening states, and staying-at-home orders. The United States surpassed 2 million coronavirus cases on Monday, June 15, 2020 less than five months after the first case was confirmed in the country. The virus has killed at least 115,000 people in the United States as of Monday, June 15, 2020, according to data from Johns Hopkins University. With the recent easing of coronavirus-related restrictions and changes on business and social activity such as stay-at-home, social distancing since late May 2020 hoping to restore economic and business activities, new Covid-19 outbreaks are on the rise in many states across the country. Some researchers expressed concern that the process of easing restrictions and relaxing stay-at-home orders too soon could quickly surge the number of infected Covid-19 cases as well as the death toll in the United States. Some of these increases, however, could be due to more testing sites in the communities while others may be are the results of easing restrictions due to recent reopening and changed policies, though the number of daily death toll does not appear to be going down in recent days due to Covid-19 in the U.S. This raises the challenging question: * How can policy decision-makers and community leaders make the decision to implement public policies and restrictions and keep or lift staying-at-home orders of ongoing Covid-19 pandemic for their communities in a scientific way? In this study, we aim to develop models addressing the effects of recent Covid-19 related changes in the communities such as reopening states, practicing social-distancing, and staying-at-home orders. Our models account for the fact that changes to these policies which can lead to a surge of coronavius cases and deaths, especially in the United States. Specifically, in this paper we develop a novel generalized mathematical model and several explicit models considering the effects of recent reopening states, staying-at-home orders and social-distancing practice of different communities along with a set of selected indicators such as the total number of coronavirus recovered and new cases that can estimate the daily death toll and total number of deaths in the United States related to Covid-19 virus. We compare the modeling results among the developed models based on several existing criteria. The model also can be used to predict the number of death toll in Italy and the United Kingdom (UK). The results show very encouraging predictability for the proposed models in this study. The model predicts that 128,500 to 140,100 people in the United States will have died of Covid-19 by July 4, 2020. The model also predicts that between 137,900 and 154,000 people will have died of Covid-19 by July 31, and 148,500 to 169,700 will have died by the end of August 2020, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the Covid-19 death data available on June 13, 2020. The model also predicts that 34,900 to 37,200 people in Italy will have died of Covid-19 by July 4, and 36,900 to 40,400 people will have died by the end of August based on the data available on June 13, 2020. The model also predicts that between 43,500 and 46,700 people in the United Kingdom will have died of Covid-19 by July 4, and 48,700 to 51,900 people will have died by the end of August, as a result of the SARS-CoV-2 coronavirus that causes COVID-19 based on the data available on June 13, 2020. The model can serve as a framework to help policy makers a scientific approach in quantifying decision-makings related to Covid-19 affairs.
Keywords	covid19
Language	English
Publishing date	2020-06-17
Publisher	Cold Spring Harbor Laboratory Press
Document type	Article ; Online
DOI	10.1101/2020.06.15.20132357
Database	COVID19

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Article: On estimating the number of deaths related to Covid-19

Pham, Hoang

Mathematics

Abstract: In this paper, we discuss an explicit model function that can estimate the total number of deaths in the population, and particularly, estimate the cumulative number of deaths in the United States due to the current Covid-19 virus. We compare the ... ...

Abstract	In this paper, we discuss an explicit model function that can estimate the total number of deaths in the population, and particularly, estimate the cumulative number of deaths in the United States due to the current Covid-19 virus. We compare the modeling results to two related existing models based on a new criteria and several existing criteria for model selection. The results show the proposed model fits significantly better than the other two related models based on the U.S. Covid-19 death data. We observe that the errors of the fitted data and the predicted data points on the total number of deaths in the U.S. on the last available data point and the next coming day are less than 0.5% and 2.0%, respectively. The results show very encouraging predictability for the model. The new model predicts that the maximum total number of deaths will be approximately 62,100 across the United States due to the Covid-19 virus, and with a 95% confidence that the expected total death toll will be between 60,951 and 63,249 deaths based on the data until 22 April, 2020. If there is a significant change in the coming days due to various testing strategies, social-distancing policies, the reopening of community strategies, or a stay-home policy, the predicted death tolls will definitely change. Future work can be explored further to apply the proposed model to global Covid-19 death data and to other applications, including human population mortality, the spread of disease, and different topics such as movie reviews in recommender systems.
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #116885
Database	COVID19

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Article: Estimating the COVID-19 Death Toll by Considering the Time-Dependent Effects of Various Pandemic Restrictions

Pham, Hoang

Mathematics

Abstract: COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2 As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social ... ...

Abstract	COVID-19, known as Coronavirus disease 2019, is caused by a coronavirus called SARS-CoV-2 As coronavirus restrictions ease and cause changes to social and business activities around the world, and in the United States in particular, including social distancing, reopening states, reopening schools, and the face mask mandates, COVID-19 outbreaks are on the rise in many states across the United States and several other countries around the world The United States recorded more than 1 9 million new infections in July, which is nearly 36 percent of the more than 5 4 million cases reported nationwide since the pandemic began, including more than 170,000 deaths from the disease, according to data from Johns Hopkins University as of 16 August 2020 In April 2020, the author of this paper presented a model to estimate the number of deaths related to COVID-19, which assumed that there would be no significant change in the COVID-19 restrictions and guidelines in the coming days This paper, which presents the evolved version of the previous model published in April, discusses a new explicit mathematical model that considers the time-dependent effects of various pandemic restrictions and changes related to COVID-19, such as reopening states, social distancing, reopening schools, and face mask mandates in communities, along with a set of selected indicators, including the COVID-19 recovered cases and daily new cases We analyzed and compared the modeling results to two recent models based on several model selection criteria The model could predict the death toll related to the COVID-19 virus in the United States and worldwide based on the data available from Worldometer The results show the proposed model fit the data significantly better for the United States and worldwide COVID-19 data that were available on 16 August 2020 The results show very encouraging predictability that reflected the time-dependent effects of various pandemic restrictions for the proposed model The proposed model predicted that the total number of U S deaths could reach 208,375 by 1 October 2020, with a possible range of approximately 199,265 to 217,480 deaths based on data available on 16 August 2020 The model also projected that the death toll could reach 233,840 by 1 November 2020, with a possible range of 220,170 to 247,500 American deaths The modeling result could serve as a baseline to help decision-makers to create a scientific framework to quantify their guidelines related to COVID-19 affairs The model predicted that the death toll worldwide related to COVID-19 virus could reach 977,625 by 1 October 2020, with a possible range of approximately 910,820 to 1,044,430 deaths worldwide based on data available on 16 August 2020 It also predicted that the global death toll would reach nearly 1,131,000 by 1 November 2020, with a possible range of 1,030,765 to 1,231,175 deaths The proposed model also predicted that the global death toll could reach 1 47 million deaths worldwide as a result of the SARS CoV-2 coronavirus that causes COVID-19 We plan to apply or refine the proposed model in the near future to further study the COVID-19 death tolls for India and Brazil, where the two countries currently have the second and third highest total COVID-19 cases after the United States
Keywords	covid19
Publisher	WHO
Document type	Article
Note	WHO #Covidence: #783979
Database	COVID19

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