














































The Role of the Gut Microbiome in Neuromodulation Therapies as a Potential Treatment Adjunct for Multiple Sclerosis


Berkeley
Pharma Tech
Journal of Medicine

Correspondence: 
vukellin@gmail.com

Keywords:
Genetic Editing
CRISPR-Cas9
Ethical Considerations
Mental Health Treatments
Public Medical Influences

Submitted July 14, 2024 
Accepted August 19, 2024
Published December 27, 2024

Full Open Access

Creative Commons Attribution 
License 4.0

Abstract
Genetic editing has become a pivotal biotechnology tool in healthcare, enabling precise 
manipulation of DNA through insertions, deletions, modifications, or replacements. Its 
applications have expanded from treating monogenic diseases to addressing polygenic 
conditions, including potential mental health therapies. This study examines public 
sentiment and ethical considerations surrounding genetic editing systems like CRISPR-
Cas9, zinc-finger nucleases (ZFNs), and transcription activator-like effector nucleases 
(TALENs). Analyzing a decade of medical literature, the research highlights a growing 
public acceptance of these technologies despite ethical concerns, such as off-target effects 
and unintended consequences. Positive sentiment trends indicate that social acceptance 
may support the integration of genetic editing in mental health treatments, driving 
advancements in healthcare. Addressing risks, including long-term impacts and ethical 
dilemmas, remains critical for responsible implementation. As public attitudes continue 
to improve, genetic editing may become a widely accepted therapeutic approach, fostering 
innovation in healthcare. Future research should focus on mitigating risks and enhancing 
the safety and efficacy of genetic editing to ensure its sustainable development and 
adoption.

Exploring Ethical Frontiers: A Content 
Analysis of Perceptions Surrounding 
Genetic Editing in Mental Health 
Treatments Over Time 
By: Kellin Vu



1. Introduction

Genetic, or genome, editing is reshaping modern medicine as one of the
latest advancements in the field, where improvements in genetic
modification technology are now being applied to polygenic disorders
rather than just monogenic disorders. Genome editing uses genetic
engineering to manipulate DNA sequences from one nucleotide in length
to large fragments, allowing the modeling for individual cells to complete
organisms.1 Genome editing tools are molecular strategies for modeling and
correcting errors in mutated genes; thus, genome editing could change the
human genome to treat or develop gene-based diagnoses and therapeutics
for a number of diseases and disorders.2 Bioengineering is a constantly
progressing field, with genetic editing as a relatively new development in
healthcare and medicine as a tool for treatment. Nevertheless, genetic
editing has little insight into the progress of genome editing and its
application to the polygenic nature of psychiatric disorders is still in its
infancy.3 With this remarkable progress in biotechnology, genetic editing
presents promising applications for mental illness treatments in healthcare.

The rapid advancement of genetic editing technology engenders the
question: What are the potential ethical considerations and public
perceptions that arise from the implementation of genetic editing
technology in healthcare as a treatment for polygenic diseases and mental
health? Exploring the ethical frontiers of genetic editing is pivotal for
scientists and medical professionals to understand its transformative
potential and limits in medicine. The insights gained from examining this
field of inquiry will play a vital role in defining the ethical framework and
societal acceptance of this new advancing technology. In order to establish a
connection between the perceptions and patterns in attitudes supporting or
against the application of genetic editing technologies and the ethical
considerations of this form of healthcare treatment, it is appropriate to
hypothesize that the overall trend will exhibit a positive correlation in
sentiments of various polygenic diseases as time progresses over the last
decade.

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2. Literature Review

2.1 Current Landscape of Mental Health Treatments

Mental health poses a significant challenge in contemporary society and
continues to steadily escalate as a prevalent issue. A study found that
“approximately half the population can expect to develop one or more
mental disorders by age 75.”4 According to the National Institutes of
Health, a mental illness is “a health condition that changes a person’s
thinking, feelings, or behavior (or all three) and that causes the person
distress and difficulty in functioning.”5 Presently, the main traditional forms
of treatments for mental health include psychotherapy, which encompasses
counseling and cognitive-behavioral therapy, and pharmacotherapy,
involving the administration of medications like antidepressants or
antipsychotics. Psychotherapy has been defined as an effective psychological
intervention for a multitude of psychological, behavioral, and somatic
problems, symptoms, and disorders and thus rightfully considered as a main
approach in mental and somatic health care management.6 Yet, a study by
Sarah Cook and her team challenge this idea, suggesting that psychotherapy
to be difficult to apply to individuals of complex multimorbidities, specific
socio- demographic groups, and minority populations.7 This study
highlights that psychotherapy often overlooks individual factors influencing
a patient’s health, potentially leading to reduced efficacy and negative
outcome.8 Another form of mental health treatment is pharmacotherapy,
the use of medicines for combating a disease or its symptoms that allow for
the alleviation of symptoms and improvements in the health and quality of
life of patients.9 Nonetheless, Witczak and her team reveal that the frequent
errors in prescription of medicine and medicine administration due to the
complex and multifaceted nature of the pharmacotherapy process present
alarming risks.10

Brucker and Faucher complement this finding, as they argue that
pharmacotherapy introduces several disadvantages, such as drug resistance
that makes pharmacotherapy inefficient as a long-term treatment option,
adverse drug reactions, and drug-to-drug interactions that can cause further
negative side effects that may be unpredictable and be more damaging than
beneficial.11 These studies ultimately imply that traditional approaches may

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present challenges and limitations such as partial efficacy, side effects, and
variability in individual responses. This growing understanding of the
biological basis of mental disorders emphasize the importance of
considering biological interventions in mental health treatments. For
instance, “scientists have identified genes as a factor that plays a role in
determining whether someone develops a mental illness, specifically illnesses
that are most likely to have a genetic component such as autism, bipolar
disorder, schizophrenia, and ADHD, where the interaction of several genes
may trigger mental illness in contrast to a change in a single gene”12 This
further highlights the concept of precision medicine in mental health, as
“precision medicine is an approach for clinical treatment that uses genomic,
lifestyle, and environmental information to provide targeted, individualized
care that is tailored to patients’ specific needs.”13 These studies insinuate and
introduce the need for new innovative forms of mental health treatment,
such as gene therapy. As a potential approach for more precise mental health
intervention, gene therapy addresses genetic components associated with
mental disorders and illnesses to affect the overall treatment efficacy for
individuals.

2.2 Genetic Editing Technology

Gene therapy, defined by Sherkow is “the intentional, expected permanent,
and specific alteration of the DNA sequence of the cellular genome, for a
clinical purpose.”14 This signifies the potential of gene therapy to cure
hereditary diseases like mental illnesses by manipulating gene sequences.
“This therapy became possible through the advances of genetics and
bioengineering that enabled manipulating vectors for delivery of
extrachromosomal material to target cells.”15 Within these advances include
some of the most prominent approaches to genetic editing, which include
the Clustered Regulatory Interspaced Short Palindromic Repeats
(CRISPR) / Cas9 (CRISPR-associated protein 9) system, zinc-finger
nucleases (ZFNs), and transcription activator-like effector nucleases
(TALENs).

The CRISPR/Cas9 system, one of the most significant gene editing
technologies in the development of gene therapies, consists of “two essential

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components: a guide RNA [ribonucleic acid] to match a desired target
gene, and Cas9—an endonuclease which causes a double-stranded DNA
[deoxyribonucleic acid] break, allowing modifications to the genome.”16

The CRISPR/Cas system “affords the requisite recognition selectivity
necessary to ensure single-site specificity in complex genomes, allowing the
CRISPR system to provide acquired immunity against invading foreign
DNA through RNA-guided DNA cleavage.17 This insight implies how this
form of technology holds the ability to address complexity in gene
sequences and proposes this genome editing tool as a treatment approach
for polygenic diseases. This development contrasts with earlier genetic
editing technologies, such as ZFNs and TALENs, which relied on specially
coded proteins to recognize key DNA sequences.18

This process required “complex, labor-intensive development processes that
limited their practicality and efficiency in terms of time, cost, and efficiency
(e.g., limited specificity and target recognition, off-target effects).”19 This
denotes how these developing technologies present several barriers in their
application to medicine, suggesting how these advanced tools still hold
disadvantages and restrictions that depict possible ethical considerations to
take notice of. TALENs is an earlier genetic editing technology that
comprises of a “non-specific DNA-cleaving nuclease fused to a
DNA-binding domain that can be easily engineered so that TALENs can
target essentially any sequence.”20 “TALENs are similar to ZFNs in that they
can generate DSBs [double-strand breaks] at a desired target site in the
genome and so can be used to knock out genes or knock in mutations in the
same way.”21 Zinc-finger nucleases are more specifically defined as targetable
DNA cleavage reagents, in which ZFN-induced double-strand breaks are
subject to cellular DNA repair processes that lead to both targeted
mutagenesis and targeted gene replacement at remarkably high
frequencies.”22 This further signifies the capabilities of these gene editing
tools, revealing the numerous methods and approaches available to
potentially act as an effective mental health treatment. Hence, these new
advancements in genetic editing technology found by recent medical
experiments and studies portray the possibilities of this biotechnology and
how it may revolutionize healthcare.

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2.3 Research Gap

As I examine the notable gene editing technologies and their applications in
healthcare, it becomes evident that the use of genome editing for mental
illnesses is still a treatment in its infancy. Despite the recent advancements in
genetic editing technologies, there is a notable lack of research that discusses
the ethical considerations and perceptions associated with the use of
genome editing for mental health treatments. Since the introduction of gene
editing in the 1970’s, much research has focused on the application of
genetic editing technologies toward preventing and treating certain
inherited disorders or monogenic diseases.23 However, as the capabilities of
these technologies advance and expand to the domain of polygenic diseases,
such as mental illnesses, it remains unclear what the ethical implications and
surrounding perceptions are in using genome editing technology as a form
of treatment for mental illnesses in healthcare. Therefore, the purpose of
this study was to predict the potential ethical considerations and public
perceptions towards the use of genetic editing for mental disorders through
analysis of public sentiment and add to the conversation of existing
literature on the function and perceptions of genetic editing in the medical
field.

3. Methods

3.1 Introduction

Over the course of 120 days, I conducted a study to construe trends of the
sentiment expressed from ethical considerations towards gene editing as a
medical treatment to interpret the possible implications and perceptions of
genetic editing utilized for a mental health treatment. To achieve this
objective, a content analysis was necessary to streamline the principal ethical
considerations associated with the implementation of genetic editing in
healthcare and facilitate the range of sentiments within significant academic,
medical publications. This was essential to create a generalized
comprehension of the ethical implications in contemporary society towards
genetic editing and clarify an existence of a trend within the attitudes
correlated with the implementation of this technology in healthcare,

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whether it is positive, negative or neutral, to predict the perceptions of
genetic editing technology for mental health treatments. A correlational
analysis was also conducted based on the time intervals and the sentiment
scores connected with the attitudes toward the implementation of genetic
editing as a medical treatment, as well as with the ethical implications and
perceptions towards the application of gene editing technology for mental
health treatments to ascertain the most prevalent ethical considerations.

3.2 Instrumentation

This study employed a content analysis to deduce the major considerations
of genetic editing in healthcare and utilized a sentiment analysis to discover
the perceptions that will develop from these implications. In order to
conduct this method of study, a semantic differential scale was used, shown
in Table 1. The semantic differential scale is a 7-point rating scale that
presents pairs of bipolar adjectives at the endpoints of the scale. This scale
was processed with opposite adjectives that indicate extremely strong
negative sentiment, very strong negative sentiment, moderate negative
sentiment, neutral sentiment, moderate positive sentiment, very strong
sentiment, and extremely strong positive sentiment. With a set of bipolar
adjectives on the endpoints, each of these codes were assigned a value to
measure the attitude toward a specific negative or positive sentiment,
ranging from the sentiments of extremely strong negative sentiment at -3,
very strong negative sentiment at -2, moderate negative sentiment at -1,
neutral sentiment at 0, moderate positive sentiment at +1, very strong
positive sentiment at +2, and extremely strong positive sentiment at +3.
This study underscores the notable distinction between negative and
positive sentiments to facilitate easier differentiation of the perceptions
towards genetic editing for healthcare treatments. A statement within
academic articles or journals will be categorized as moderate sentiment if
stance is primarily neutral with a slightly skewed viewpoint, regardless of
denunciation or commendation. For a classification of very strong on the
scale required such intense words like damaging, great, bad or beneficial. For
a classification of extremely strong on the scale required such extreme words
like extraordinary, catastrophic, remarkable or disastrous. These words
emphasize and reflect the pronounced opinions and attitudes of genetic

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editing in healthcare. For this study, statements or paragraph fragments
from medical papers were analyzed and applied to the semantic differential
scale. The primary data sources used for my method of study were medical
journals and studies retrieved from credible databases, such as PubMed or
ScienceDirect.

This study utilized the Delve Tool website to administer this process for
qualitative analysis and coding these medical journals. This website contains
coding software that generates transcripts of the medical journals and the
code of sentiment applied.

Consequently, the paper is averaged across the various sentiment scores,
charted in Table 2 below. Table 2 reflects the averages of the sentiment
analysis, employing a given range of values. The table comprises of a 7-point
scale, where within each is a range of assigned values with an extremely
strong negative at the spectrum of any value less than -2.0, to very strong
negative at a range of -2.0 to -1.0, to moderate negative at a range of -1.0 to
less than 0, neutral at 0, to moderate positive being between 0 and 1.0, to
very strong positive being between 1.0 and 2.0, and extremely strong
positive at any value more than 2.0. As qualitative research, this study
utilized the collected data along with a TI-84 calculator to compute the
correlation coefficient and values.

3.3 Content Analysis

I performed a content analysis, a detailed and systematic examination of the
contents of a particular body of material for the purpose of identifying
patterns, themes, and biases.24 The content analysis classified thirty medical
journals and studies, concluding the paper’s data to express the article’s
general sentiment. Using medical journals and studies, I conducted a
comprehensive analysis of the perceptions concerning the implementation
of genome editing in healthcare, followed by an examination of the attitudes
towards various polygenic diseases, to determine the potential ethical
considerations and perceptions regarding the use of genetic editing
technology as a viable mental health treatment.

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In order to ensure that the medical journals and studies would be relevant
and representative of how gene editing technology is presently used as a
treatment for polygenic disorders, criteria were established for the medical
journals and studies analyzed. The medical journals and studies were chosen
due to their relevance in healthcare as each indicate the use of one of the
foundational gene editing technologies, including the CRISPR/Cas9
system, TALENs, or ZFNs.25 Furthermore, due to the rapidly evolving
aspect of the medical field, all medical studies utilized have been conducted
in the past decade. I analyzed medical journals across various polygenic
diseases in order to gain a deeper comprehension on the extensive
perceptions that exist with gene editing as a healthcare treatment overall,
especially for more complex diseases. To analyze sentiment, this study used a
consistent search engine within medical journal databases PubMed and
ScienceDirect.

Once the study found all suitable medical journals and studies, the study
applied the qualitative code to words, adjectives, and paragraph fragments in
the medical journals that correlate to positive, neutral, or negative
sentiment. These sentiments were evaluated on the 7-point Semantic
Differential Scale to deduce a more comprehensive range of sentiment. The
thirty medical journals this study analyzed and fit the criteria were
transcribed into the Delve Tool website, where a trend became prevalent.
After all the papers were coded through Delve Tool, the study calculated the
sentiment score and how the sentiment varied across different types of
polygenic diseases. The polygenic diseases were separated into three
categories: neurological diseases, cancer, and other polygenic diseases. These
categories will help highlight and clarify any significant variance in attitudes
towards a specific type of polygenic disease to address if the perceptions
towards all types of polygenic diseases can be used to predict the attitudes of
genetic editing for mental health treatments.

Since this study is only utilizing medical journals and studies published in
the last decade due the rapid nature of the medical field and its
advancements, separate sentiment scores were also averaged for articles
published in the first five years compared to the last five years in the last

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decade to denote if attitudes have increasingly become more positive,
negative, or neutral towards genetic editing technology.

3.4 Correlational Analysis

Using a sentiment analysis to discover the current attitudes towards genetic
editing, I used a correlational analysis in order to determine which
considerations were most prevalent for medical professionals in their
studies. Therefore, this study used the Pearson correlation coefficient, or
“r,” to establish the existence of an association between the number of
ethical considerations and the sentiment score of genetic editing technology
as a healthcare treatment. The Pearson correlation coefficient was used to
determine the strength of the correlation and to accept or reject the null
hypothesis that a relationship does not exist between sentiment towards
genetic editing and polygenic diseases.

4. Findings

After the study averaged the sentiment scores, general sentiment scores were
evaluated for the different types of polygenic diseases that have been tested
with genetic editing technology as a healthcare treatment. For neurological
diseases, the attitudes toward genetic editing technology treatment were
characterized with a positive sentiment at 0.295 for the first five years of the
last decade and a positive sentiment of 0.507 for the last five years. This
revealed an increase in positive sentiment over time, with a change of 0.212
in positive sentiment, and a total sentiment score resulting in a positive
sentiment of 0.4234. For types of cancer, the attitudes toward genetic
editing technology treatment were also characterized with a positive
sentiment starting at 0.350, compared to the concluding sentiment of 0.950
with a change of 0.60, and total sentiment score of 0.900, which can be seen
on Table 2. For other types of polygenic diseases, the perceptions also
started with a moderately positive sentiment of 0.518, however experienced
a slight decrease of 0.09 in positive sentiment, producing a moderately
positive final sentiment score of 0.428 and total sentiment of 0.4574. This
trend displays how the average sentiments over time had an increase in
positive sentiment for cancer and neurological diseases, as genetic editing

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technology strengthened as viable treatments for these polygenic diseases
over time. In contrast, for medical journals concerning other polygenic
diseases, the average sentiment score had encountered a small decline in
positive sentiment over time, yet the total average sentiment still remained
moderately positive which portrays that there was an increase of negative
sentiment. This implied that more concerns emerged as perceptions toward
the application of gene editing treatment became more negative.
After this study marked a trend of increased positive sentiment in the
attitudes towards genetic editing technology, the study utilized the
sentiment score and the number of ethical implications that were
considered and reflected in the perceptions of the medical journals to
establish if there is a relationship between the ethical implications most
prevalent and what implications signify advantages or disadvantages of
genetic editing. Although the study could not establish an accurate
correlation between all ethical implications across all polygenic diseases that
have a form of genetic editing treatment, the two quantitative variables of
sentiment score and number of ethical considerations could be utilized to
represent a correlation for each type of polygenic disease. For this study, the
Pearson correlation coefficient used the number of ethical considerations as
the X values, or independent variable, and the sentiment score as the Y
values, or dependent variable, in order to convey if the prevalence of ethical
implications has a true influence on the sentiment of attitudes towards
genetic editing technology as a healthcare treatment for polygenic diseases.

The study analyzed the perceptions across the past ten years to indicate if
the sentiment and number of ethical implications affected any correlation or
trend of positive, negative, or neutral sentiment. The Pearson correlation
coefficient or “r” had resulted with r=0.4673, which expresses that there is a
moderately strong relationship between the number of ethical implications
and the sentiment score reflecting the public’s attitudes, as seen on Table 3.

Table 1 depicts the 7-point semantic differential scale that this study utilized
to calculate the sentiment scores, in which each scale value number is
assigned to how strong a statement or paragraph fragment found in the
medical paper is, in order to average the sentiment score on a scale of
negative to positive.

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Table 1. Semantic Differential Scale for Sentiment Analysis. This table defines the
7-point semantic differential scale used in the study. It outlines how statements from
medical journals were rated based on sentiment polarity, ranging from extremely negative to
extremely positive, for precise sentiment classification.

Figures 1, 2, and 3 represent the number of phrases or paragraph fragments
observed for each level of sentiment for genetic editing technology in
healthcare treatments across each category of polygenic diseases in the past
decade. These polygenic diseases, labeled Figures 1, 2, and 3, were
respectively categorized as neurological diseases, cancer, and other polygenic
diseases to highlight the three main categories of polygenic diseases that have
used genetic editing technology as a form of treatment. Each of these three
figures are also separated into two 5-year time periods, 2016-2020 and
2021-2024, to differentiate changes in the observed sentiment polarity over
time, as healthcare treatments experience rapid changes in its evolving field
with new advancements daily.

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Figure 1. Sentiment of Genetic Editing Treatments in Healthcare across
Neurological Diseases. This graph illustrates the changes in sentiment regarding genetic
editing treatments for neurological diseases over the past decade. The data shows a notable
increase in positive sentiment from 2016 to 2024, reflecting growing acceptance of this
technology in treating neurological conditions.

Figure 2. Sentiment of Genetic Editing Treatments in Healthcare across Cancer.
This graph highlights the increasing positive sentiment toward genetic editing technologies

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in cancer treatment from 2016 to 2024. The sharp rise in sentiment demonstrates growing
confidence and optimism in using genetic editing as a therapeutic approach for oncology.

Figure 3. Sentiment of Genetic Editing Treatments in Healthcare across Other
Polygenic Diseases. This graph shows sentiment trends for genetic editing technologies in
polygenic diseases other than cancer and neurological conditions. While sentiment remains
moderately positive overall, the slight decline in positivity over time suggests increasing
ethical and practical concerns.

Table 2 portrays the average sentiment scores and the various interval scales
that classifies a score as extremely negative, very strong negative, or
moderately negative, neutral, moderately positive, very strong positive, or
extremely strong positive. These scores are calculated using the number of
phrases or paragraph fragments sorted as negative, neutral, or positive with
the scale value numbers outlined in the semantic differential scale, shown by
Table 1. The table conveys the average sentiment score for each category
and each 5-year time period to express any relevant changes in public
sentiment observed over time. A total sentiment score is also calculated for
each category of neurological, cancer, or other polygenic diseases, so the
average value of sentiment overall can compare against the different
categories.

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Table 2. Average Sentiment Score. This table presents the calculated average sentiment
scores for each polygenic disease category over two time periods, 2016–2020 and
2021–2024. The data highlights an overall positive trend in sentiment, with significant
growth in cancer and neurological disease treatments. Note: values are averages.

Table 3 illustrates the correlational data between the number of ethical
considerations and sentiment score. The number of ethical considerations is
the ethical implications observed when coding the medical journal’s words
and phrases with negative sentiment. This is compared to the sentiment
score of each category of polygenic diseases, neurological, cancer, and other,
to establish how strong the existing relationship between the number of
ethical considerations and the sentiment score is.

Table 3. Ethical Considerations Correlation Data. This table shows the correlation
between the number of ethical considerations and sentiment scores for genetic editing
technologies. It provides insights into how ethical implications influence public attitudes
across different categories of polygenic diseases.

Figure 4 illustrates how the average sentiment of genetic editing technology
changes over the past decade. The sentiment score was calculated for 5-year
periods, between 2016-2020 and between 2021-2024, to determine the
difference of sentiment score over the past decade. Figure 4 illustrates the
increase in positive sentiment, indicating how attitudes towards the

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implementation of genetic editing technology significantly becomes more
positive as the technology continues to advance over the years.

Figure 4. Average Sentiment over the Past Decade. This chart compares the average
sentiment scores for genetic editing technologies across different categories—neurological
diseases, cancer, and other polygenic diseases—over two five-year intervals. It underscores a
consistent rise in positive sentiment, particularly in cancer and neurological applications,
reflecting advancements in genetic editing technology.

4. Analysis

Thirty medical journals extending across the last decade were annotated and
analyzed for their sentiment, exemplifying the existence of a positive trend
of sentiment over time for the application of genetic editing technologies for
polygenic diseases in healthcare. Since the correlation coefficient between
sentiment scores and ethical implications was r=0.4673, it concluded that a
moderately strong relationship was present between sentiment and the
number of ethical implications presented. In similar regards with other
polygenic diseases, the results demonstrated a moderately positive
correlation with the total sentiment of genetic editing technology as a
healthcare treatment overall, yet a minor negative trend did occur over time
on contrary to the positive trend that arose for cancer and neurological
diseases. Therefore, the polygenic diseases reject the null hypothesis because
there is an existence of a trend between time and sentiment across all three
categories of polygenic diseases. This study can conclude that there is a

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positive correlation between the implementation of genetic editing
technology as a treatment for polygenic diseases within the domain of
cancer and neurological diseases and their sentiment over time in the past
decade but a slight negative correlation under other polygenic diseases.
In addition, this study identified a pattern of which positive sentiment was
often associated, behind context, that the specific benefits of genetic editing
technology include its efficiency and effectiveness in modifying specific
genes. 38 percent of the coding in positive sentiment was correlated with
how effective and precise genetic editing technology is for polygenic
diseases. In contrast, this study observed a pattern of negative sentiment that
was frequently connected to the ethical consideration that genetic editing
technology has the significant risk of negative unpredictable effects and
off-target effects, which entails genome editing affecting other parts of the
target DNA that were unintentional. 36 percent of the coding in negative
sentiment was related to this risk, and thus indicates the distinct danger and
ethical consideration that genetic editing presents.

This overall portrays how the attitudes towards the use of genetic editing
technology as a form of treatment for polygenic diseases has increasingly
become more positive over time as more advancements are being made in
this type of treatment. This indicates that as genetic editing is presently used
for mental health disease and psychiatric treatment, it will be met with a
more positive response according to the trend of positive sentiment
reflecting public attitudes and perceptions of the use of genetic editing
technology as a healthcare treatment. This study found a consistent positive
correlation of positive sentiment over time in polygenic diseases such as
cancer and neurological diseases, reflecting how attitudes and perceptions
towards the use of this technology in healthcare will likely follow this
positive trend. Although other polygenic diseases experienced a slight
decrease in positive sentiment within its perceptions, it concluded with a
moderately positive sentiment, indicating that genetic editing for mental
health diseases will be received with positive attitudes. This is significant
because the public acceptance of the new medical technology can
substantially affect the ability of genetic editing technology developing
further. Opposition against the development of genetic editing technology
to be implemented into healthcare as a treatment would reflect that the

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perceptions of gene editing focus on negative ethical considerations and
concerns. If negative sentiment prevailed to be most prominent, it would
conclude that relevant perceptions reject the proposition of gene editing
based treatment for polygenic diseases in healthcare. Nevertheless, across the
various polygenic diseases, the total sentiment concluded as moderately
positive, suggesting acceptance of the application of genetic editing
technology in healthcare.

Hence, this reinforces my initial assumptions that attitudes toward genetic
editing technology in polygenic diseases will increasingly become positive, as
a positive correlation indicates a growth in positive perceptions and
establishes a total moderately positive sentiment across all polygenic diseases
in the past decade. This shows how polygenic diseases are met with
moderately positive attitudes and regarded with positive perceptions,
further supporting the advancement of genetic editing technology to be
utilized in polygenic diseases’ treatments. Although a moderately positive
sentiment is reflected throughout the last decade, the correlation between
the sentiment over time and relevant ethical considerations cannot be
concretely proven, even though a general trend of sentiment is apparent.
There is no definite methodology to confidently prove the correlation of
data across time, as the patterns of sentiment are not a clear catalyst due to
existing external variables. Therefore, this study can only absolutely establish
that there is a possible larger trend that exists.

4. Conclusion

4.1 Implications

The trend of total sentiment resulting in moderately positive across the
polygenic diseases disclose how genetic editing technology-based treatments
are often met with positive perceptions and attitudes. This implies that the
public will hold similar sentiments toward the application of genetic editing
for other complex polygenic diseases, specifically mental health diseases. “In
psychiatry, GWAS [Genome-wide association studies] have uncovered a
high degree of polygenicity underlying mental illnesses and related complex
phenotypes.”26 This indicates how mental health diseases are very complex

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polygenic diseases, as multiple genes must be considered in the treatment of
mental health illnesses. Hence, the positive sentiment observed over time
towards the application of genetic editing technology for the treatment of
various polygenic diseases signifies how public perceptions and attitudes will
likely follow a similar trend of positive sentiment. With a trend of positive
sentiment, it insinuates that public attitudes and perceptions will likely be
supportive of the implementation of genetic editing in mental health
treatments over time, and this social acceptance will empower the
advancement of genetic editing technology in healthcare treatments.

4.2 Limitations

This study utilized a coding process in order to rate the sentiment of words,
phrases, and paragraph fragments in the medical journals analyzed;
nonetheless, the subjective aspect of coding the sentiment is a crucial
consideration as a limitation. In this particular study, the content analysis
consisted of identifying words that are associated with a positive or negative
connotation and the extremity of the connotation, where the scale values
were rated from extremely strong, very strong, moderately strong, and
neutral. This can cause varying results of the content analysis in replicability
because it is subjective in what the coder defines a word or phrase with
regards to how extreme of a positive or negative connotation exists. To
maintain as much objectivity as possible, the study ensured to emphasize the
English language’s definitions and connotations. Additionally, this study
analyzes and codes thirty medical journals, which is a small sample in total;
therefore, this limited sample size can restrict the study’s total reflection on
the perceptions towards genetic editing technology for polygenic diseases.
Due to the small sample size, this study will conclude general data for the
trend of sentiment that is insufficient to be utilized as a measure for how
other trends will appear for genetic editing technology in healthcare.

Moreover, it is necessary to acknowledge that since genetic editing
technology is encompassed within the rapidly evolving medical field, time
brings forth change in how people perceive the information presented in
medical journals and studies. Significant events that have occurred in the
past decade, such as the coronavirus pandemic, can incite bias towards how

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the medical journals’ information and studies are received or accepted by the
public. The lack of focus on analyzing the trend of sentiment for only one
specific polygenic disease also presents as a limitation since it is difficult to
accurately identify if a certain sentiment is only present for a specific disease.
However, this study examines various polygenic diseases, categorized as
neurological, cancer, and other, in order to efficiently conclude a general
trend of data for the sentiment that reflects the public’s attitudes and
perceptions towards the implementation of this technology in healthcare
overall.

In addition, the 7-point semantic scale is a limitation, as the scale restricts
the ability to rate the public perceptions present in the medical journals past
the sentiment extremity of positive, neutral, and negative. Yet, the scale is
optimized as an efficient method to classify the general data of public
attitudes and perceptions towards genetic editing. This is a prime research
method to identify any existing trends of sentiment towards genetic editing
over time due to the lack of direct public surveys that analyze the
perceptions and ethical considerations of the public. This is the best
methodology to predict the sentiments of the public’s perceptions towards
the implementation of genetic editing technology in mental health diseases
and treatments due to the absence of medical studies with a focus on genetic
editing technologies utilized as a mental health treatment.

4.3 Future Directions

The genetic editing technologies of ZFNs, TALENs, and the
CRISPR/Cas9 system present a new revolution in the healthcare field as it
reflects the significant advancements developed in technology and medical
devices. Nonetheless, this widely-used genetic editing tool still has rising
concerns associated with its ethical implications, demonstrated by the slight
decrease in positive sentiment analyzed under other polygenic diseases, but
specifically shown by this correlational analysis that negative off-target
effects are the most relevant ethical implication. Hence, future research
should draw a focus on how to prevent these negative off-target effects
associated with the application of genetic editing technology. The accuracy
and precision of this technology was notably recognized throughout the

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medical journals, contributing to a significant percentage of the positive
sentiment coded; therefore, it is imperative that additional research
addressing this concern is investigated in order to maintain the observed
trend of positive sentiment which insinuates that public sentiment and
perceptions support the implementation of genetic editing technology in
mental health illness treatments and other healthcare applications.

Another concern that arises when analyzing the negative sentiment of
medical journals is the challenge of unpredictable long-term effects and
consequences caused by genetic editing technology. To address this issue,
future research should analyze the long-term effects of genetic editing
technology for polygenic diseases, as this study only examined medical
journals regarding the last decade. Thus, these guiding topics can help
further the discourse about the implementation and advancements of
genetic editing technology as viable treatments in healthcare for various
diseases, with the consideration of the ethical implications and public
perceptions surrounding the subject, and this research can serve to enable
and revolutionize the efficiency of mental health treatments through this
biotechnology.

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5. Appendix A: Sentiment Code Ratings

Each number indicates the number of words or phrases that were coded and
correlated with that sentiment extremity of positive, neutral, or negative
within the medical journal.

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Each number indicates the number of words or phrases that were coded and
correlated with that sentiment extremity of positive, neutral, or negative

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5. Appendix B: Representative Quotations of Sentiment
Score

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