Abstract

Background. Perioperative sleep disorders exert a severe adverse impact on postoperative recovery. Recently, some observational studies reported that acupuncture-point stimulation (APS) provided benefits for promoting perioperative sleep quality. However, the effects of APS on perioperative sleep disorders following general anesthesia have not been thoroughly assessed by any systematic study and meta-analysis. Therefore, we conducted this systematic review and meta-analysis to reveal the effects of APS on perioperative sleep disorders. Methods. Eight databases (Chinese: CNKI, VIP, CBM, and Wanfang; English: PubMed, Embase, Web of Science, and Cochrane Library) were thoroughly searched to find randomized controlled trials (RCTs) that indicated a link between APS and the occurrence of perioperative sleep disorders. We applied RevMan 5.4 (Cochrane Collaboration) and Stata 16.0 (Stata Corp) to conduct our meta-analysis. In addition, the trial sequential analysis (TSA) tool was utilized to estimate the validity and reliability of the data. Results. In this study, nine RCTs with 719 patients were conducted. Compared to the control group, APS significantly improved perioperative subjective sleep quality (SMD: −1.36; 95% CI: −1.71 to −1.01; ). Besides, it increased perioperative TST (preoperative period MD = 24.29, 95% CI: 6.4 to 42.18, ; postoperative period MD = 45.86, 95% CI: 30.00 to 61.71, ) and SE (preoperative MD = 3.62, 95% CI: 2.84 to 4.39, ; postoperative MD = 6.43, 95% CI: 0.95 to 11.73, ). The consequence of trial sequential analysis further confirmed the reliability of our meta-analysis results. Conclusion. According to the currently available evidence, APS could effectively improve perioperative sleep quality and play an essential role in decreasing the incidence of perioperative sleep disorders.

Introduction

Sleep is vital for overall health as it influences the quality of life and physical functioning [1]. Good sleep quality is a sign of wellbeing, whereas poor sleep quality raises comorbidity, mortality, and medical expenses [2]. Despite advances in operative and anesthetic procedures, sleep disorders continue to be a concern in the perioperative period. Perioperative sleep problems, as compared to household sleep disorders, have gained more attention recently, such as reduced nighttime sleep, fragmentation of sleep, and circadian rhythm disruptions at night after admission [3,C4]. The proportion of sleep disorders in hospitalized patients (64%–73.1%) is much higher than that in people at home (30.6%–41.2%) due to the surgical stress response, ward environment, and patients’ underlying diseases [5–7]. Perioperative sleep problems were noted in several observational studies to be important risk factors for poor recovery [8] and were associated with anxiety, altered pain perception, and postoperative cognitive dysfunction (such as delirium), further deteriorating the patient’s physical state [1, 6, 7, 9]. Moreover, it was suggested that perioperative sleep disorders might lead to postoperative fatigue, episodic hypoxemia, cardiovascular disorders, metabolic impairment, and immune disorders [10, 11]. Enhancing perioperative sleep quality is probably related to improved health, surgery effect, and patient safety [9]. Unfortunately, there are no specific therapeutic strategies for perioperative sleep disorders. Even though it was reported that pharmaceutical therapies, such as benzodiazepines and other sedating agents, might improve perioperative sleep quality, the potentially addictive nature and risk of tolerance restricted their clinical application [9].

In complementary and alternative medicine, acupuncture-point stimulation (APS) was employed to improve perioperative sleep quality [12]. According to the theory of traditional Chinese medicine (TCM), APS refers to stimulating the body’s acupoints with various methods, including acupressure (manual stimulation), acupuncture (sterile needle stimulation), laser acupuncture, electrical stimulation (transcutaneous electrical acupoint stimulation and electrical acupuncture), magnetic acupuncture (stimulation with special equipment), moxibustion therapy, and auricular point pressing, with the goal of achieving therapeutic effects such as sleep improvement and pain management [13–18]. APS has been an integral part of traditional Chinese medicine treatment. Based on the traditional Chinese medicine meridian theory, the body’s qi circulation is restored when target acupoints along meridians are stimulated [14]. It was reported that APS might lower the risk of poor sleep quality due to its considerable analgesic and sedative effects [19, 20]. Recently, a significant number of RCTs were carried out on the effects of APS on perioperative sleep disorders [8, 15]. Besides, a previous review concluded that EA might work to alter neurotransmitter concentrations and reduce the levels of norepinephrine and dopamine so as to enhance the quality of postoperative sleep quality after general anesthesia [12]. However, this study not only did not conduct a statistical assessment of the efficiency of EA on inpatients’ sleep quality during the postoperative period but also did not examine preoperative sleep quality thoroughly [12].

Conclusion

Taken together, our systematic review and meta-analysis suggested that APS could effectively improve perioperative sleep disorders, indicating that it was worthwhile to offer such therapies in perioperative healthcare institutions. Although our results had certain clinical implications of APS for the treatment of perioperative sleep disorders, more studies are needed to offer stronger evidence to further verify our conclusions in the future.

Abstract

Background. Rasagiline or pramipexole monotherapy has been suggested for the management of early Parkinson’s disease (PD). The aim of this research was to systematically review the clinical efficacy and safety of rasagiline or pramipexole in early PD (defined as disease duration ≤5 years and Hoehn and Yahr stage of ≤3). Methods. Randomized controlled trials (RCTs) of rasagiline or pramipexole for early PD published up to September 2021 were retrieved. Outcomes of interest included changes in the Unified Parkinson’s Disease Rating Scale (UPDRS) Parts II and III and the incidence of adverse events. Standardized mean difference (SMD), odds ratio (OR), and 95% confidence interval (CI) were calculated, and heterogeneity was measured with the I2 test. Results. Nine rasagiline and eleven pramipexole RCTs were included. One post hoc analysis of one rasagiline study was included. Five studies for each drug were included in meta-analyses of the UPDRS scores. The rasagiline meta-analysis focused on patients receiving 1 mg/day. Rasagiline and pramipexole significantly improved UPDRS Part II and III scores when compared to placebo. Significant heterogeneity among the studies was present (I2 > 70%). Neither rasagiline nor pramipexole increased the relative risk for any adverse events, serious adverse events, or adverse events leading to withdrawal when compared with placebo. Conclusion. Applying a Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach to summarize the evidence, we found moderate confidence in the body of evidence for the efficacy of rasagiline or pramipexole in early PD, suggesting further well-designed, multicenter comparative RCTs remain needed.

Introduction

Parkinson’s disease (PD) is a progressive neurodegenerative disorder characterized clinically by bradykinesia, tremor, rigidity, and postural instability and histologically by neuronal inclusions composed of α-synuclein. Nonmotor symptoms including olfactory dysfunction, rapid eye movement, sleep behavior disorder, mood disorders, and autonomic dysfunction often precede the appearance of motor symptoms by several months or years [1, 2]. These motor and nonmotor symptoms can adversely affect a patient’s quality of life [3]. The incidence of PD is rising and increases with age [4, 5].

Early management of PD could prolong the ability of a patient to stay in working life and improve their overall quality of life [6, 7]. Levodopa is widely considered one of the most effective treatments for PD, but its use is often delayed because of drug-induced dyskinesias and wearing-off and on-off fluctuations [8, 9]. Alternatives to levodopa in early pharmacologic treatment of PD are dopamine agonists and monoamine oxidase B (MAO-B) inhibitors [10, 11]. A meta-analysis by Chang and coworkers (2017) [12] showed the irreversible inhibitor of MAO-B inhibitor rasagiline to have benefits both as a monotherapy and in combination with another intervention. The safety profile of rasagiline was similar to that of placebo in a systematic review of its use in PD patients [13]. Pramipexole, a nonergoline, D3-preferring dopamine agonist, is another treatment option for the management of motor symptoms associated with PD [14]. A recent systematic review showed that combined pramipexole and levodopa therapy was superior to levodopa monotherapy for the improvement of clinical symptoms in PD patients [15].

Prior systematic reviews evaluating the efficacy and safety of either rasagiline or pramipexole have evaluated patient populations at all stages of the disease. The goal of this study was to focus on the safety and efficacy of these drugs in early PD. Defining early PD in our review was based on the duration of PD in affected patients (five years or less) as well as the presence of mild to moderate symptoms based on the Hoehn and Yahr scale. The Hoehn and Yahr scale is commonly used in clinical studies to stage the level of functional disability and impairment seen in PD patients [16].

Results
4.1. Description of the Included Rasagiline Studies
The key characteristics of the nine rasagiline studies are reported in Table 1. One study [21] compared pramipexole and rasagiline as interventions and is included in this portion of the analysis. One post hoc analysis of a randomized controlled trial [22] met the inclusion criteria and was included in the systematic review. There were a total of 2121 patients in the studies, and 1059 patients were on rasagiline: 738 patients had a dose of 1 mg/day, 307 had a dose of 2 mg/day, and 14 had a dose of 4 mg/day. The mean age of study populations ranged from 59.3 to 70.2 years. The mean duration of PD ranged from 2.5 months to 4.8 years. The proportion of the study population who were males in the intervention groups ranged from 42.9 to 76.9%. There were four studies where other PD medications were either discontinued before the study or patients were PD-drug naïve [21, 23–25]. The rest of the studies allowed other PD medications with stable dosage for more than 4 weeks before the start of the study [26–29]. Rescue LD was accepted in one rasagiline study if PD symptoms worsened, and the current therapy did not relieve the symptoms [28]. Hoehn and Yahr stage was ≤3 in every study. The overall discontinuation rates were 7.3% and 10.7% in the rasagiline and placebo groups, respectively. One study reported seven discontinued patients without indicating which intervention the patients were using [27].

Abstract

Background. The latest Movement Disorder Society (MDS) diagnostic criteria require a good and sustained response to medication to get a diagnosis of Parkinson’s disease, PD. Objective. The aim of this study was to evaluate levodopa response in a group of patients with probable PD, diagnosed by movement disorder specialists. Methods. An acute levodopa challenge test (LDCT) was performed after pausing the dopaminergic medication for 6 half-times. The motor part of the Unified Parkinson’s Disease Rating Scale was performed in the OFF-state and after LDCT (ON). A good effect was defined as >30% improvement. A video-protocol was used to secure standardized motor examination with blinded assessments of the UPDRS-III OFF and ON. An age-matched group of control subjects (CS) was included but did not go through LDCT. All participants were evaluated with Montreal Cognitive Assessment (MoCA) and Beck’s Depression Inventory (BDI). Results. In the statistical analysis, 37 patients were included. Twenty-one patients showed an improvement ≤30%, while 16 patients showed an improvement >30%. LDCT showed an overall mean improvement of 27.3% of motor UPDRS. In 43.2%, there was a discrepancy between the effect seen with the LDCT and the patients’ self-perceived medicine evaluation. Patients with PD had a significantly lower MoCA score and more depressive symptoms compared to CS. Conclusions. We showed an acute effect of levodopa using LDCT that was around 30% improvement. While it lends support to the use of this limit in the MDS diagnostic criteria, an acute effect of less than 30% should be considered acceptable in some patients. Our study highlights a discrepancy in the objective measure of medicine effect on motor symptoms and the patient’s subjective evaluation.

Introduction

Parkinson’s disease (PD) is the second most common neurodegenerative disease, causing severe disability with increasing prevalence. A diagnosis of PD is based on the motor manifestations: bradykinesia, rigidity, and tremor, and clinical symptoms related to the loss of dopamine. In PD, these symptoms of parkinsonism are related to a neurodegenerative pathological process involving misfolding and aggregation of α-synuclein and formation of inclusion bodies, Lewy bodies, primarily in dopaminergic neurons. A diagnosis of PD is clinicopathological and requires both this pathological hallmark as well as the clinical phenomenology. In 2015, the Movement Disorder Society (MDS) put forward updated clinical diagnostic criteria for PD to increase early diagnostic accuracy in patients with Lewy body pathology Parkinson’s disease [1]. Dopaminergic neuronal loss in addition to Lewy body formation is central in the pathology of PD. In the diagnostic criteria from 2015, the importance of this pathological hallmark is emphasized even more than in earlier diagnostic criteria [2], through suggestion of operational criteria including (i) support by objective measurement of effect on motor symptoms showing a >30% improvement with dopaminergic medication in the motor part of the Unified Parkinson’s Disease Rating Scale (UPDRS-III), (ii) a clearly documented history of marked changes with medication from the patient or the caregiver, and also that (iii) lack of sufficient motor improvement should be an exclusion criterion [3].

While the diagnostic criteria of PD from MDS have been validated against expert clinical diagnosis and the United Kingdom Brain Bank criteria from 1988 [4], the clinical importance of having this definition of the effect of medication in a movement disorder clinic may still be more thoroughly evaluated.

Treatment with levodopa (L-dopa) is the most efficient dopaminergic therapy and the golden standard treatment for PD [1]. An evaluation of dopaminergic effect is therefore necessarily based on treatment with L-dopa. To evaluate the treatment effect in an objective manner, the standardized acute levodopa challenge test (LDCT) can be used to assess motor symptoms both OFF and ON medication to evaluate the L-dopa response. Besides using LDCT in a diagnostic approach, the acute LDCT protocol has been described and used in the measurement of disease progression [5]. In addition, it has been widely accepted as an evaluation of levodopa effect as a preoperative evaluation before deep brain stimulation of the subthalamic nucleus (STN-DBS) [6].

Conclusion

We have performed a clinically relevant evaluation of levodopa response in a group of patients with PD with both a self-reported good and poor L-dopa response. An acute LDCT was performed as an objective measure, but a surprisingly large discrepancy was found between the subjectively reported usual response and the acute effect. As patients all otherwise had a diagnosis of PD, a good effect should be found for both the subjective and objective evaluation in accordance with diagnostic criteria. Our study sheds light on the importance of questioning the reported effect when making a diagnosis. The mean LCDT was close to 30% in most cases, but our study indicates that an acute effect of less than 30% should sometimes be expected.

In conclusion, this study showed that the LCDT can be used as a diagnostic support and that it is a valuable tool in addition to the self-reported effect from the patient. But also, that conclusions based on an acute effect of less than 30% should be evaluated with caution, as this was the case in 56.8% of the patients with PD in our study.

Abstract

Cancer vaccines based on tumor cell components have shown promising results in animal and clinical studies. The vaccine system contains abundant tumor antigen components, which can activate the immune system by antigens. However, their efficacy has been limited by the inability of antigens delivery, which are the core components of vaccines, further fail to be presented and activation of effective cells. Nanotechnology offers a novel platform to enhance the immunogenicity of tumor-associated antigens and deliver them to antigen-presenting cells (APCs) more efficiently. In addition, nanotreatment of tumor cells derivate active ingredients could also help improve the effectiveness of cancer vaccines. In this review, we summarize recent advances in the development of cancer vaccines by the combination of nanotechnology and tumor-based ingredients, including liposomes, polymeric nanoparticles, metallic nanoparticles, virus-like particles and tumor cells membrane, tumor lysate, and specific tumor antigens. These nanovaccines have been designed to increase antigen uptake, prolong antigen presentation, and modulate immune responses through codelivery of immunostimulatory agents. We also further discuss challenges and opportunities in the clinical translation of these nanovaccines.

Introduction

Cancer immunotherapy has revolutionized cancer treatment by harnessing the immune system to recognize and attack cancer cells [1–3]. Among various immunotherapeutic approaches, cancer vaccines have attracted significant attention due to their potential to induce long-lasting and specific immune responses against tumor cells [4–7]. By comparison with surgery, it hardly produce large wounds, nor does it cause higher toxicity in normal tissues and drug tolerance like chemotherapy in the treatment of tumor. However, the clinical efficacy of cancer vaccines based on tumor cell components, such as peptides and proteins, is limited by their poor immunogenicity and inefficient delivery to antigen presentation cells (APCs). Although, many cancer vaccines were applied with the immune-stimulatory agents, such as the programmed cell death protein 1 (PD-1), CpG oligonucleotide, etc. The clinical response rates remain low, especially in the hypoimmunogenic tumors. As we all know, pancreatic cancer is a more difficult type for immunotherapy as a low immunogenic tumor. Therefore, there are few effective cancer vaccines that can actively promote the pancreatic cancer treatment. Meanwhile, limited by the specific tumor antigens, the development of cancer vaccines was extremely difficult.

The field of cancer vaccines has seen significant advancements in recent years, with numerous studies focusing on different aspects of vaccine development and application [8–12]. These advances have been facilitated by the exploration of various types of cancer vaccines, including those targeting viral antigens, neoantigens, and other tumor-specific antigens [13–16]. One approach in cancer vaccine research involves targeting shared antigens from viral infections linked to certain cancer types, such as Epstein–Barr virus and HPV. Studies have shown promising results with vaccines targeting viral antigens like Epstein–Barr nuclear antigen 1 and latent membrane proteins, as well as HPV-related antigens, demonstrating favorable antitumor efficacy and the potential to establish durable T cell memory in rapidly progressing HPV-positive tumors [17]. Another significant area of research is neoantigen cancer vaccines. Neoantigens, derived from nonsynonymous cell variants, are only expressed in tumor cells and can bypass thymus-negative selection, leading to robust neoantigen-specific T cell responses [14]. Advances in genomic and transcriptional profiling have made identifying putative neoantigens possible, although challenges remain in predicting which neoantigens can effectively induce neoantigen-specific T cell responses [18–20]. Cancer vaccines are being developed to stimulate antitumor immunity with tumor antigens delivered in various forms, such as cells, peptides, viruses, and nucleic acids [21–24]. These vaccines aim to overcome tumor heterogeneity and reverse the immunosuppressive microenvironment. Challenges in the field include single antigen targeting, weak immunogenicity, off-target effects, and impaired immune response [25–27]. Current research and reviews provide insights into the principle of action, components, classification, delivery methods, and progress of cancer vaccines, offering a broader perspective for future vaccine design.

Recent studies have shown that tumor antigens can be used for the design of cancer vaccines without the necessity to identify, parse, and reconstruct certain antigens [28–31]. A novel concept has been proposed that the whole antigen vaccine systems derived from tumor cells could be designed to conquer cancer treatments [32–34]. The tumor cells themselves are a huge antigen pool with countless tumor antigen epitopes, which can be used to activate antitumor immune response. Among them, more studies have focused on the application of tumor cell membrane as tumor antigens to stimulate APCs and further activate the antitumor immune response of T cells. In addition, the activation of T cells in immune response requires the involvement of costimulatory molecules. Some studies are based on this theoretical foundation, using genetic engineering to modify tumor cell membranes. By retaining antigen on the cell membrane and inducing the expression of costimulatory molecule CD80, T cells can be directly activated, thereby achieving antitumor immune response [35] (Figure 1). Of course, many studies combine tumor cell membranes that retain tumor antigens with other functional molecules to directly activate dendritic cells (DCs) for antigen presentation, leading to the activation of T cell immune response. These are two different approaches aimed at achieving the same goal [36–38]. A series of nanovaccines developed on this basis have been widely used in the treatment of different tumor cells and preclinical experiments.

Abstract

Background. Psychological distress is a progressive health problem that has been linked to decreased quality of life among university students. This meta-analysis reviews existing randomized controlled trials (RCTs) that have examined the effects of mindfulness-based stress reduction (MBSR) on the relief of psychosomatic stress-related outcomes and quality of life among university students. Methods. The PubMed, EMBASE, Web of Science, PsycINFO (formerly PsychLit), Ovid MEDLINE, ERIC, Scopus, Google Scholar, ProQuest, and Cochrane Library databases were searched in November 2023 to identify the RCTs for analysis. Data on pathology (anxiety, depression, and perceived stress), physical capacity (sleep quality and physical health), and well-being (mindfulness, self-kindness, social function, and subjective well-being) were analyzed. Results. Of the 276 articles retrieved, 29 met the inclusion criteria. Compared with control therapies, the pooled results suggested that MBSR had significant effects, reducing anxiety (SMD = −0.29; 95% CI: −0.49 to −0.09), depression (SMD = −0.32; 95% CI: −0.62 to −0.02), and perceived stress (SMD = −0.41; 95% CI: −0.60 to −0.29) and improving mindfulness (SMD = 0.34; 95% CI: 0.08 to 0.59), self-kindness (SMD = 0.57; 95% CI: 0.30 to 1.12), and physical health (SMD = −0.59; 95% CI: −1.14 to −0.04). No significant differences were observed in sleep quality (SMD = −0.20; 95% CI: −0.06 to 0.20), social function (SMD = −0.71; 95% CI: −2.40 to 0.97), or subjective well-being (SMD = 0.07; 95% CI: −0.18 to 0.32). The quality of the evidence regarding sleep quality and physical health outcomes was low. Conclusions. MBSR therapy appears to be potentially useful in relieving functional emotional disorders. However, additional evidence-based large-sample trials are required to definitively determine the forms of mindfulness-based therapy that may be effective in this context and ensure that the benefits obtained are ongoing. Future studies should investigate more personalized approaches involving interventions that are tailored to various barriers and students’ clinical characteristics. To optimize the effects of such interventions, they should be developed and evaluated using various designs such as the multiphase optimization strategy, which allows for the identification and tailoring of the most valuable intervention components.

Introduction

Mindfulness therapy is a standardized psychological intervention that aims to reduce stress, encourage mindful thinking habits, and allow recipients to manage difficult emotional processing. It focuses on the concentration of one’s attention in the moment, nonsubjective judgment, and openness to accepting personal experience and involves corresponding behavioral training, such as attention training, body scanning, and sitting meditation [1]. Previous research on psychological interventions has found that mindfulness therapy can help reduce stress among university students and affects their overall quality of life and the psychological functions to which they adapt [2].

The theoretical support and the understanding of the operating mode are reasonable but still have differences. Thus, the mental health benefits, potential effects, and limitations of mindfulness therapy for university students warrant further examination. Although education and social support can be effective in preventing and treating the underlying psychological problems among university students with poor mental health, such interventions do not always affect or improve psychological well-being [3, 4].

The most prevalent psychological symptoms among university students are anxiety, mental stress, and emotional distress, which can result in decreased functioning in the context of academic and interpersonal stress. Global mental health surveys conducted by the World Health Organization (WHO) indicate that mental disorders are highly prevalent among university students, with 12-month prevalence rates ranging from 20.3% to 45% of university students over the age of 18 [5–7]. The prevalence rates of insomnia, depressive symptoms, and anxiety symptoms were 37.80%, 48.20%, and 36.70%, respectively, among Chinese university students during the COVID-19 pandemic [8]. In a randomized controlled trial with 11,169 university students conducted by the WHO, students had increasing intentions to use mental health services: The results indicated lifetime use rates of 12.6% and 7.3% for psychotherapy and medication for emotional problems, respectively. Suicidal thoughts and behaviors and nonsuicidal self-injury within the preceding 12 months were also common, with 21.1% of students in the sample reporting suicidal ideation, 10.6% reporting suicide plans, 0.4% reporting suicide attempts, and 6.8% reporting nonsuicidal self-injury [5]. Furthermore, one previous study found that high-compliance mental health services had a significant effect on students’ intention to seek mental health services in the next semester [9–11].

Results

3.1. Description of Studies
A total of 276 studies items were initially obtained through the database search. Using NoteExpress, 193 deduplicated studies were obtained, and the remaining 220 items were obtained. After reviewing their titles and abstracts, 135 studies that were of incompatible research types were excluded. A total of 13 articles were further excluded after reading the full text, and the remaining 29 studies [24–53] were retained for data extraction. The baselines of the trials were comparable. The literature search process and results are shown in Figure 1.

Abstract

Fintech revolutionized the traditional banking business models in emerging countries. The effect of fintech on banks’ operating efficiency and risk-taking behavior is still inconclusive. The study is aimed at exploring the effect of fintech products on banks’ operating efficiency and risk-taking behavior. The study used a quantitative research approach by collecting secondary data from annual reports of 50 commercial banks from emerging countries, namely, China, India, Pakistan, and Bangladesh, for the period 2014 to 2021. The study used panel data for path analysis and structural equation modeling (SEM) to test the theoretical mediation model by using STATA. The results show that the fintech product reduces the bank’s risk-taking behavior by enhancing the bank’s operating efficiency. The path analysis results show that operating efficiency mediates the relationship between fintech products and bank risk-taking behavior in emerging countries. The paper offers useful recommendations for central bank and commercial bank policymakers. The study is also beneficial for commercial banks that use fintech solutions to increase operational effectiveness and reduce risk. The study is the first empirical investigation into the connection between the growth of fintech products, bank operational effectiveness, and risk-taking behavior in developing nations.

Introduction

The global financial industry has been rapidly changing with the advancement of financial technology over the last few years. New emerging technologies such as Internet finance, blockchain, and mobile banking are penetrating, forming the fintech that revolutionized the whole financial industry. It offered lower-cost services with more choices for consumers to buy financial services that shift their financial behavior to digitalization. The rise of financial technology (fintech) has brought about significant changes in the financial industry, including the way banks manage risk. Fintech has introduced new tools and technologies that enable banks to more accurately assess and manage risk, as well as new business models that challenge traditional approaches to risk-taking.

The pace of fintech development in developed and emerging economies countries is different. In developed countries such as the UK, USA, and Europe, fintech extended the financing transaction methods through algorithmic trading, helping the manager to build better compliance systems to manage risk, and it also improves the electronic payment systems for both domestic and cross-border payments [1]. However, in developing countries, fintech adoption has been slow despite having growth and expansion opportunities in the digital economy (Zhang et al., 2020). Fintech has expanded financial services digitally across countries, particularly during the COVID-19 pandemic. During the pandemic, the usage of fintech services and products has increased tremendously as identified in research [2].

Conclusion and Recommendations/Implications of the Study

The evolution of digital technology has changed the world drastically. The COVID-19 pandemic changed the whole business structure. The customers’ increasing demand for fintech products increases the competition in the financial industry. To retain their customers and remain competitive in the industry, traditional banks adopted fintech products. So that they can deliver efficient, accessible, and low-cost financial services to their customers with the help of technology. Fintech has facilitated the banking industry with better user experience and lower costs by using technological advancement.

The primary objective of the study is to explore the relationship between fintech products and bank risk-taking behavior with mediating role of a bank’s operating efficiency using a sample of commercial banks from China, India, Pakistan, and Bangladesh. The empirical evidence shows that the bank’s risk-taking behavior decreases, and bank stability is improved with the development of fintech products. Fintech products improve the bank’s operating efficiency. The effect of fintech products on bank risk-taking is more pronounced in countries such as China and India because they are extensively using fintech products in their operations. Banks are adopting fintech products such as artificial intelligence blockchain, mobile banking, and Internet banking. The results show that the banks adopting fintech products in their initial phase increase the bank risk-taking behavior, and then later on, it decreases the bank risktaking behavior by improving their operating efficiency.

Abstract

Aims. To explore university students’ levels of stress, stressors, and their coping style. Methods. A cross-sectional correlational design with a convenience sample (n = 676) of university students who completed the Student-Life Stress Inventory (SSI) and Coping Strategies Indicator (CSI) was used. Findings. Overall, two-thirds of the participant reported moderate levels of stress. Students with chronic illness, living alone, low CGPA, and having exams today experienced a statistically higher mean level of stress. Students who are living alone used the “avoidance” method more significantly and the “social support” method significantly less compared with students who are living with their families and friends. Conclusion. This study concurs with others that university students are prone to distress. To our knowledge, this is the first study in the region to explore the students’ coping skills. Some of the employed coping and associated factors could be used to lay the groundwork for evidence-based prevention and mitigation.

Introduction

Mounting evidence supports the belief that university students experience moderate to high levels of stress [1–5]. The interplay between biology and the environment renders vulnerable adolescents and young adults to experience stress and distress. Researchers have identified various predictors of stress among university students, including individual, environmental, and coping factors. Personality traits are an example of individual factors. A study by Rettew et al. [6] found that students with high levels of neuroticism, a personality trait characterized by anxiety and negative thinking, reported higher levels of stress. In addition, students who are introverted and have a tendency to withdraw from social situations may be more susceptible to stress than those who are extroverted and enjoy social interaction [7]. There is evidence to suggest that other personality traits, such as conscientiousness and agreeableness, may also be positively associated with stress levels among university students [6].

Environmental factors such as specialisation, living situation, social support, and campus resources are also important explanatory variables of stress among university students. For example, a study by McLean et al. [8] found that students who reported lower levels of social support had higher levels of stress. In addition, students who have supportive peers and family members are more likely to have positive attitudes toward university and lower levels of stress [9]. Similarly, a meta-analysis found that students who had access to mental health services and other campus resources reported lower levels of stress than those who lacked such support [10]. Additionally, students who have access to other campus resources, such as academic support services, may be better equipped to manage stress and succeed academically [10]. Other factors which were significantly associated with a higher level of stress were smart phone use and sleeping hours [11].

Discussion

The current study explored university students’ levels of stress, stressors, and their coping styles. The results revealed that the majority of students have a moderate level of stress (75.1%), followed by those with a severe level of stress (13.5%) and a mild level of stress (11.4%). Stress and burnout syndrome have emerged in the Arabian Gulf countries from research using different measures such as Influence of Studying on Student Health (ISSH), Perceived Stress Scale (PSS), Stress subscale of Depression Anxiety Stress Scales (DASS-21), Copenhagen Burnout Inventory, Cohen’s Perceived Stress Scale, Common Stressor Inventory, Maslach Burnout Inventory, and Maslach Burnout Inventory (Student Survey). In Oman, in studies of medical students, various researchers including Aboalshamat et al. [32], Al-Alawi et al. [33], Al-Dabal et al. [34], Al-Khani et al. [35], Al Rasheed et al. [36], and Mahfouz et al. [37] have reported the rate of stress and distress as ranging from 7.4% to 96.3%. In Bahrain, Al Ubaidi et al. [38] and Sanad [39] found 47% and 92% to be subject to stress and distress, respectively; in Qatar, Fadhel and Adawi [40] reported 89.2% stress and distress. In Kuwait, the prevalence of 43% and 43.8% was reported by Ahmed et al. [41] and Badr et al. [42], respectively. The rate in the present study appears to tend towards a higher figure, even though such comparison may not be valid because of the use of different assessment measures and catchment areas.

Abstract

The behaviors of children with autism spectrum disorder (ASD) are often erratic and difficult to predict. Most of the time, they are unable to communicate effectively in their own language. Instead, they communicate using hand gestures and pointing phrases. Because of this, it can be difficult for caregivers to grasp their patients’ requirements, although early detection of the condition can make this much simpler. Assistive technology and the Internet of Things (IoT) can alleviate the absence of verbal and nonverbal communication in the community. The IoT-based solutions use machine Learning (ML) and deep learning (DL) algorithms to diagnose and enhance the lives of patients. A thorough review of ASD techniques in the setting of IoT devices is presented in this research. Identifying important trends in IoT-based health care research is the primary objective of this review. There is also a technical taxonomy for organizing the current articles on ASD algorithms and methodologies based on different factors such as AI, SS network, ML, and IoT. On the basis of criteria such as accuracy and sensitivity, the statistical and operational analyses of the examined ASD techniques are presented.

Introduction

Disabilities in social behavior and interaction are characteristics of ASD. According to Jon Baio [1], an estimated 1 in every 59 children is diagnosed with ASD. Special care and welfare facilities are needed by all impaired children more than by healthy youngsters [2]. In addition to limiting the lives of the sufferers, this long-term condition has a detrimental impact on their caretakers’ quality of life (QoL). Patients can be monitored remotely using systems based on IoT devices, which have numerous beneficial characteristics. There have so been a number of healthcare applications leveraging IoT devices in recent years. GPS, heart rate, microphone, and ear clips [2] are some of the most common IoT sensors used in wearable devices like smartwatches and smartphones. Sensors and devices are used to identify autistic youngsters, rather than traditional techniques of diagnosis [3, 4].

To help protect youngsters from developing life-threatening disorders, several studies have been conducted during the last decade. However, there were no major breakthroughs. Hence, the most important components of assisting the patient are early diagnosis and improving the QoL of the patients. Autistic children are frequently misdiagnosed until they are two years old [4]. As a result, they are still unable to carry out their daily routines. Consequently, this article examines several IoT device techniques for children with ASD to evaluate and contrast novel ways of detecting the disorder or enhancing quality of life for individuals already diagnosed [4, 5]. The Internet of Things is using artificial intelligence, machine learning, SS network, and deep learning to identify and protect patients from physical and emotional problems [5]. Patients’ vital signs are gathered by these systems, which then use various machine learning and deep learning algorithms to select the most appropriate responses. They may even be able to assist in the early detection of ASD. There are risky behaviors that autistic children perform when they are irritated, which can impair their physical health. An alarm is sent to caretakers and doctors, informing them of the condition and requesting assistance. Every one of these IoT-based devices monitors the body’s vital signs and records any changes depending on a variety of criteria (e.g., sensitivity, specificity, time, and accuracy) [6].

Conclusion

Patients with ASD benefit greatly from the adoption of IoT devices with SS network consist. One of the most difficult aspects of treating autism in children is finding the correct IoT solutions. Sensors, platforms, and methodologies in ASD can have a significant influence on the children, and this is often the case. There were 28 articles included in this review that looked at various methods to ASD published between 2014 and 2020. In both 2016 and 2018, the number of articles published was close to the previous year’s total. The most papers are published in the IEEE journal, with a percentage of 51%. Selected 28 studies were divided into two groups: those that focused on diagnosing patients and those that supported efforts to enhance the QoL of such patients. Nearly 43% of respondents thought of studies examining new methods for diagnosing and assessing the severity of ASD in children, while 57% thought about ways to enhance the quality of life for such youngsters. Additionally, all of the selected methodologies were evaluated in terms of accuracy, sensitivity, specificity, and time, among other variables. There has been a comparative examination of ASD and IoT-based devices based on the case studies offered. Most research studies are aimed at enhancing the QoL of autistic children, according to the findings. New ASD techniques and gadgets for autistic persons will benefit from research into IoT-based variations on traditional ASD approaches such as AL, ML, and SS network.

To keep classic ASD techniques safe, IoT-based devices are becoming more and more common. Also, several studies have shown that children with ASD prefer robots over people for assistance. NAO’s eye contact attention is steadier than in a traditional classroom for youngsters with ASD [47]. In addition, IoT solutions cut caregiving and support expenses and are simple to use, in contrast to traditional methods of protecting patients.

Abstract

Lymphoma and leukemia are fatal syndromes of cancer that cause other diseases and affect all types of age groups including male and female, and disastrous and fatal blood cancer causes an increased savvier death ratio. Both lymphoma and leukemia are associated with the damage and rise of immature lymphocytes, monocytes, neutrophils, and eosinophil cells. So, in the health sector, the early prediction and treatment of blood cancer is a major issue for survival rates. Nowadays, there are various manual techniques to analyze and predict blood cancer using the microscopic medical reports of white blood cell images, which is very steady for prediction and causes a major ratio of deaths. Manual prediction and analysis of eosinophils, lymphocytes, monocytes, and neutrophils are very difficult and time-consuming. In previous studies, they used numerous deep learning and machine learning techniques to predict blood cancer, but there are still some limitations in these studies. So, in this article, we propose a model of deep learning empowered with transfer learning and indulge in image processing techniques to improve the prediction results. The proposed transfer learning model empowered with image processing incorporates different levels of prediction, analysis, and learning procedures and employs different learning criteria like learning rate and epochs. The proposed model used numerous transfer learning models with varying parameters for each model and cloud techniques to choose the best prediction model, and the proposed model used an extensive set of performance techniques and procedures to predict the white blood cells which cause cancer to incorporate image processing techniques. So, after extensive procedures of AlexNet, MobileNet, and ResNet with both image processing and without image processing techniques with numerous learning criteria, the stochastic gradient descent momentum incorporated with AlexNet is outperformed with the highest prediction accuracy of 97.3% and the misclassification rate is 2.7% with image processing technique. The proposed model gives good results and can be applied for smart diagnosing of blood cancer using eosinophils, lymphocytes, monocytes, and neutrophils.

Introduction

Leukemia and lymphoma are the most frequent kinds of blood cancer in people of all ages, particularly young people. This abnormal situation is induced by red blood cell proliferation and immature growth, which can harm red blood cells, bone marrow, and the immune system [1]. Leukemia accounts for more than 3.5% of new cancer cases in the United States, with over 50,000 new cases diagnosed in 2018 [2]. Cancerous lymphoblasts in the blood travel to other organs, including the heart, brain, lungs, and arteries, before spreading to important tissues throughout the body. Red blood cells are normally in charge of transporting oxygen from the heart to all organs. They make up half of the total blood volume. White blood cells, on the other hand, serve an important role in the human immune system, serving as the first line of protection against a variety of diseases and disorders [3]. As a result, accurately identifying these white blood cells is crucial in understanding the symptoms of the problem. Their categorization is determined by their cytoplasmic composition. Changes in lymphocytes, a kind of white blood cell, cause acute lymphoblastic leukemia [4]. Acute or chronic leukemia are the two types of leukemia. Without treatment, the typical recovery period for acute myeloid leukemia is roughly three months; however, the time of appearance of chronic leukemia is longer than that of acute leukemia. Chronic lymphoblastic leukemia is the most common kind of acute leukemia, accounting for around 25% of all juvenile malignancies [5, 6]. Early detection of leukemia and lymphoma has always been difficult for researchers, clinicians, and hematologists. Leukemia symptoms include enlarged lymph nodes, paleness, fever, and weight loss, although these symptoms can also be caused by other diseases [7]. Because of the moderate nature of the symptoms, diagnosing leukemia and lymphoma in its early stages is challenging. PBS microscopic assessment is the most often used leukemia and lymphoma diagnostic approach, while the gold standard for leukemia and lymphoma diagnosis only entails obtaining and analyzing white blood cell samples [8]. Several research have utilized machine learning and deep learning and machine diagnostics approaches to laboratory image processing during the last two decades in the hopes of pushing the boundaries of late diagnosis of leukemia and lymphoma and establishing their subtypes [9]. In this research, blood smear pictures were evaluated to diagnose, distinguish, and count cells in distinct kinds of leukemia and lymphoma [10].

Conclusion

The early detection of blood cancer using white blood cells can help meritoriously in its cure. The study’s proposed framework consists of three transfer learning models AlexNet, MobileNet, and ResNet empowered with SGDM, ADAM, and RMSPROP. The proposed framework applies all transfer learning models with varying learning rates effectively to white blood cancerous cells for early classification. To enhance the results, the proposed study used image processing techniques incorporating transfer learning and achieved the highest CA of 97.3% and 2.7% MCR. All experiments in the proposed study are comprehensively explained with respect to every model training and testing phase. This study helps health 5.0 to predict blood cancer in its early stages for early treatment. Furthermore, in the future, federated machine learning using the fed average technique will play a major role in better early prediction of blood cancer empowered with fuzzed machine learning and deep learning techniques will also apply more statistical techniques such as ANOVA and Chi-Square.

Abstract

Convolutional neural networks (CNNs) are often used in tasks involving vision processing, and unclear images can hinder the performance of convolutional neural networks and increase its computational time. Furthermore, artificial intelligence (AI) and machine learning (ML) are related technologies, which are considered a branch of computer science, which are used to simulate and enhance human intelligence. In e-healthcare, AI and ML can be used to optimize the workflow, automatically process large amounts of medical data, and provide effective medical decision support. In this paper, the authors take several mainstream artificial intelligence models currently open on the market for reference. In this paper, the optimized model (AL-CNN) is tested for noise image recognition, and the AL-CNN model is established by using activation functions, matrix operations, and feature recognition methods, and the noisy images are processed after custom configuration. Not only does this model require no prior preparation when processing images, but it also improves the accuracy of dealing with noise in convolutional neural networks. In the AL-CNN in this paper, the architecture of the convolutional neural network includes a noise layer and a layer that can be automatically resized. After the comparison of the recognition experiments, the accuracy rate of AL-CNN is 20% higher than that of MatConvNet-moderate, and the accuracy rate is 40% higher than that of MatConvNet-chronic. In the second set of experiments, the accuracy exceeds MXNet and TensorFlow by 50% and 70%, respectively. In addition, the authors optimized the convolutional layer, pooling layer, and loss function of AL-CNN in different parameters, which improved the stability of noise processing, respectively. After customizing the two configuration optimizations, the authors found that the second optimized AL-CNN has higher recognition accuracy, and after the optimization test, the error rate can be continuously decreased as the number of recognition increases in a very short number of times.

Introduction

Convolutional neural network (CNN) is a kind of artificial neural network, unlike recurrent neural, Boltzmann machine, etc., because the visual system is stimulated by neural mechanisms, so the biological model of convolutional neural network can recognize two-dimensional shapes. Convolutional neural network systems can perform convolution operations. Because of its uniqueness, it has excellent performance in many aspects of life, such as image classification, retrieval, and computer-related visual tasks. With more and more research on convolutional neural networks today, this technology will be applied to road engineering, medical imaging, and artificial intelligence involving vision in the future. Also, it achieves better performance than traditional technology.

In the 21st century, artificial intelligence is constantly developing, but at present there is no intelligent model in the true sense that can possess the computing and recognition ability of the traditional neural network system of animals. The concept of deep learning is proposed in this context. Unlike traditional machine learning models, deep learning will complete learning tasks through feature learning and the participation of feature abstraction. The working principle behind the convolutional neural network is deep learning, and because of its excellent processing effect and accuracy, it has played an unprecedented role in many fields of production and life. It mainly includes printing and publishing, logistics and transportation, medical management, and other fields. In the case that there are so many precedents for convolutional neural networks to create excellent results in many fields, this article will demonstrate an intelligent model building and optimization process based on convolutional neural networks. This process has practical experience for practical operators or field beginners to construct their own program models. Moreover, in the field of e-healthcare, applications of AI and machine learning can be widely used in a variety of different scenarios. For example, they can be used to identify a patient’s condition and provide personalized treatment advice. In addition, AI and ML can be used to track a patient’s condition and to remind doctors to examine or adjust treatment options.

Conclusion

In this paper, the authors established an artificial intelligence model AL-CNN based on convolutional neural network and tested its computational efficiency when processing images with different noise points. When AL-CNN processes low-quality images, it does not need any external tools, does not occupy memory, and has fast computing speed. The authors designed a variety of different types of noise to simulate the processing effect of the experiment and compared the model with MatConvNet-moderate, MatConvNet-chronic, MXNet, and TensorFlow. The results show that the model is more efficient than other types in processing images. In order to make this model more stable, the authors made a custom optimization, adding a noise layer and an automatic adjustment tool to the convolutional neural network. At the same time, considering the operational efficiency of different components in AL-CNN, this paper also performs corresponding optimizations for different noise problems. In order to improve the processing performance of CNN, this paper also designs an automatic adjustment noise reduction algorithm based on convolutional neural network in response to different situations. Experimental results show that the optimized AL-CNN has better efficiency in noisy image processing compared with MatConvNet-moderate, MatConvNet-chronic, MXNet, and TensorFlow. Also, because the model does not require prior preparation or other tools, it is faster to process. Finally, AI and ML can be used to automatically identify medical images to improve diagnostic accuracy. They can also be used to analyze patient medical records and data, predict future disease trends, and provide more accurate treatment advice to physicians. Overall, AI and ML in e-healthcare contribute to improve efficiency, provide better care, and lower healthcare costs.

Abstract

Depression is a disorder that if not treated can hamper the quality of life. EEG has shown great promise in detecting depressed individuals from depression control individuals. It overcomes the limitations of traditional questionnaire-based methods. In this study, a machine learning-based method for detecting depression among young adults using EEG data recorded by the wireless headset is proposed. For this reason, EEG data has been recorded using an Emotiv Epoc+ headset. A total of 32 young adults participated and the PHQ9 screening tool was used to identify depressed participants. Features such as skewness, kurtosis, variance, Hjorth parameters, Shannon entropy, and Log energy entropy from 1 to 5 sec data filtered at different band frequencies were applied to KNN and SVM classifiers with different kernels. At AB band (8–30 Hz) frequency, 98.43 ± 0.15% accuracy was achieved by extracting Hjorth parameters, Shannon entropy, and Log energy entropy from 5 sec samples with a 5-fold CV using a KNN classifier. And with the same features and classifier overall accuracy = 98.10 ± 0.11, NPV = 0.977, precision = 0.984, sensitivity = 0.984, specificity = 0.976, and F1 score = 0.984 was achieved after splitting the data to 70/30 ratio for training and testing with 5-fold CV. From the findings, it can be concluded that EEG data from an Emotiv headset can be used to detect depression with the proposed method.

Introduction

Depressive disorder is a highly prevalent mental illness. Sadness, loss of interest or enjoyment, feelings of guilt or low self-worth, interrupted sleep or food, fatigue, and difficulty concentrating are some characteristics of depression. It may affect a person’s capacity to operate in daily life or at work or school. According to the World Health Organization (WHO) back in 2015, almost 4.4% of the world’s population was suffering from depression [1]. Because of the COVID-19 pandemic, many people suffered from depression due to job loss, study hampering, losing close relatives, staying indoors, etc. A study showed 19.3% increase in depression symptoms among people after COVID-19 in the United States [2]. A study has shown the changes in obsession, depression, and quality of life in schizophrenia patients before and after COVID-19 [3]. When depression is severe it can lead to suicide. Every year around 800 thousand people die because of suicide [1]. In 2017, 13.2% of young adults (aged 18–25) in the U.S. suffered from depression which was 5.1% less in the year 2009 [4]. Of the deaths of young people, around 9.1% are due to suicide [5]. In most suicide cases, people had psychiatric disorders where depression is the most common disorder among others [6]. According to a recent study, insecure attachment styles are linked to greater problems such as depression, social anxiety, and suicidal thoughts [7]. So, depression is a major issue that should be diagnosed and treated at an early age to prevent suicide and for the betterment of the quality of life.

Conclusion

In this work, we have recorded EEG data of young adults (19 depressed and 13 Control) evaluated by the PHQ9 screening tool and proposed a machine learning approach to learn about the EEG properties for depression detection.

We conducted multiple experiments with the reported machine learning (SVM and KNN) classifiers with our recorded data. The first experiment we conducted was on segmentation to find the better sample length suitable for ML. From our experiments, we have identified that 5-second segments are suitable for our work. Then, we have identified a suitable frequency range from various experiments that improve performance using features that are related to depression detection. We have found out that Hjorth parameters along with Shannon entropy and long energy entropy provide better results among other reported features and the beta band (12–30 Hz) gives the highest accuracy of 97.21 ± 0.21% with 25 iterations and 5-fold CV using weighted KNN compared to the other sub-bands. By combining the sub-bands, we have also investigated some other frequency ranges. We have found out that by taking the range from alpha to beta 8–30 Hz (AB), we can improve ML performance and achieve 98.43 ± 0.15% accuracy with 25 iterations and a 5-fold CV with fine KNN classifier. Using AB (8–30 Hz), we can see a significant improvement of 1.22% accuracy and slandered deviation. To further investigate the reliability, we divided the dataset 70/30 for training and testing with 5-fold CV and 10 iterations. In this experiment, we have found out that the ML performance is better by choosing the AB (8–30 Hz) band with fine KNN classifier with an accuracy of 98.10 ± 0.11%, precision of 0.984 ± 0.003, NPV of 0.977 ± 0.002, sensitivity of 0.984 ± 0.002, specificity of 0.976 ± 0.005, and F1 score of 0.984 ± 0.001. Then, we analyzed the ML performance in different regions of the brain and concluded that using the whole brain for depression detection will give the highest accuracy. The proposed method can detect depression among young adults with minimum requirements compared to other related works.

Abstract

Aim. This study investigated the effect of tumor size and other factors on the survival and prognosis of hepatocellular carcinoma (HCC). Methods. All HCC populations based on the National Cancer Institute’s SEER database to receive from 2010 to 2016 were employed in the study. Results. This study enrolled a total of 14,634 HCC. Among them, 1,686 patients had tumors ≤ 2 cm, 6,169 patients had tumors 2–5 cm, and 6,779 patients had tumors > 5 cm. The results using univariate analysis showed that all factors were significant prognostic factors for overall survival and specific survival. Patients with tumor size ≤ 2 cm were more likely to survive, while patients with tumor size > 5 cm had a lower survival rate. Patients who had surgery or surgery plus chemotherapy had a higher chance of survival in stages I-II, and the survival rate declined smoothly during the 80 months. The change rate of the mortality rate increased rapidly during the period of 1–12 cm; afterwards, the mortality rate’s HR was basically and smoothly maintained at a high level. Conclusions. Tumor size was positively correlated with the mortality rate of HCC. Survival rates were greater in patients with tumors ≤ 2 cm who underwent surgery or surgery plus chemotherapy. Patients with HCC in the early stage had a higher survival probability particularly when they had experienced surgery or surgery plus chemotherapy.

Introduction

Hepatocellular carcinoma (HCC) with an extremely high mortality rate is one of the most common malignant tumors. Previous study [1] showed the HCC has the poor of prognosis, and its survival rate of 5-year was under 20%. HCC has a special feature: growth with metastasis to distant organs, which accounts for poor survival. The HCC with distant metastasis is commonly found in the lungs, bones, and brain [2]. Moreover, the most common risk factor for HCC globally is the hepatitis B virus. In a previous study, the most common causative agent was the hepatitis C virus [3]. More than 90% of primary liver cancers are HCC [4]. In recent years, relevant research studies confirmed that specific therapeutic regimens, including surgical resection, chemotherapy, and radiotherapy, provided HCC patients favorable prognosis and long-term survival. The current meta-analysis from Francesca’s study [5] found that regorafenib was a safe and effective treatment option for patients with advanced HCC. In addition, the efficacy and safety of microwave ablation and radiofrequency ablation were similar, but microwave ablation reduced the long-term recurrence rate [6]. A review summarized the trajectory of various aspects of HCC management over the last 15 years, providing additional information for clinicians to aggregate [7]. Previous research studies [8] found that HCC patients were more likely to feel hopeless, depressed, and even have serious suicidal thoughts. HCC monitoring for national minority and individuals of lower socioeconomic status was poorly performed, further exacerbating the difference in HCC prognosis among these disadvantaged populations [9]. Current therapeutic regimens, including surgery of liver resection, transplantation, radiotherapy, percutaneous ethanol injection, transarterial chemoembolization, microwave ablation, radiofrequency ablation, cryoablation, irreversible electroporation, combination therapies (such as postoperative adjuvant chemoradiotherapy and nutrition supportive therapy) and palliative care, will create high costs for HCC [10].

Conclusions

This study revealed that the age of majority in HCC was over 50 years old, and the majority of HCC had solid tumor sizes of 2–5 cm and not otherwise specified histology. The incidence of HCC in males was much higher than that in females, and whites also proved to be more than half the population at risk for HCC. Based on different stages, the majority of patients were in the early stages of HCC. Furthermore, HCC patients were distributed in above 2 cm, and the distribution of basic information for different tumor sizes was consistent with the overall populations. Single patients had more challenges than married patients. Otherwise, survival rates were greater in patients with tumor size ≤2 cm who underwent surgery or surgery with chemotherapy. Patients with HCC in the early stage had a higher survival probability, particularly when they had undergone surgery or surgery plus chemotherapy. The most important thing was that we further determined that the larger the tumor, the greater the risk, and that surgical treatment was the major and critical of the three methods.

Abstract

Background. Because nightmares seem to be associated with depression in schizophrenia, detecting them early in therapeutic practice might be critical to ensuring effective avoidance of the development of depressive symptomatology. This helps promote well-being and improve the patient’s quality of life and illness prognosis. Therefore, the aim of this study was to examine the indirect effect of depression between nightmares and well-being in a Lebanese sample of patients with schizophrenia. Method. This monocentric cross-sectional study, conducted in July 2022, enrolled patients with chronic schizophrenia admitted to the Psychiatric Hospital of the Cross. Data were collected from a total of 148 participants through face-to-face interviews. The questionnaire included a nightmares measure, PSYRATS, Calgary depression scale for schizophrenia, PTSD checklist for DSM-5, the digit span subset, and WHO-5Well-Being Index. Results. The presence of nightmares was significantly associated with more depression, whereas higher depression was significantly associated with lower well-being. It is noteworthy that the presence of nightmares was not directly associated with well-being. Conclusion. Nightmares lead indirectly to lower well-being in schizophrenia patients, with depression serving as a mediating factor in this association. This suggests that interventions aiming at improving dream content may also have a beneficial effect in reducing depression in schizophrenia leading therefore to better well-being of the patients.

Background

Schizophrenia is one of the most severe mental disorders when it comes to its detrimental effects on various psychosocial areas and the overall quality of life of the patient [1]. Because schizophrenia causes misery and incapacity, the majority of attention has been focused on understanding and mitigating its negative effects on daily life functioning, with a rising focus on broad positive variables such as life satisfaction and subjective well-being [2]. Well-being has been widely acknowledged to entail both functioning effectively and feeling well [3]. Therefore, it is important to note that suffering from mental disorders does not imply poor well-being, and the lack of psychopathology does not guarantee a productive lifestyle [4]. Promoting well-being, on the other hand, can greatly aid recovery and improve the patient’s quality of life and illness prognosis [5]. Therefore, it is important to study factors that may affect the well-being of patients diagnosed with schizophrenia for better functioning and recovery. A variety of psychological and biological factors, such as long-term environmental influences and ongoing stressors, general cognitive abilities and personality traits, exposure to transiently traumatic life events, mental health interventions, and coping, affect the functioning and well-being of schizophrenia patients [6]. Trauma and posttraumatic stress disorder (PTSD), which are commonly prevalent among individuals with schizophrenia [7], are linked to personal well-being, which can be optimized following PTSD treatment [8]. However, the diagnosis of depression is likely the one most closely associated with a lack of well-being [4]. Patients with mental disorders, particularly those with schizophrenia, commonly experience depression [9]. Comorbid depressive symptoms, estimated in 30–75% of schizophrenia patients [10], have been linked to worse long-term functioning [11] and a lower level of life quality and well-being [12]. The World Health Organization has reported that schizophrenia is the fourth most prominent cause of disability globally [13]. Individuals with schizophrenia who also suffer from depression experience a substantial level of disability [14], which impairs their ability to manage their mental and physical health, leading to a vicious cycle of deteriorating disability [15]. Moreover, it has been discovered that depression plays a partial mediator role in the connection between the severity of schizophrenia and disability [16]. Given the magnitude of negative symptoms, the impairments in functioning and well-being, and the risk of suicidality, schizophrenia patients with depressive symptoms should be monitored closely [17]. This occurrence of depressive symptoms in schizophrenia may be caused by overlapping risk factors, thereby having an impact on these patients’ general well-being.

Conclusion

Our findings demonstrate that nightmares lead to lower well-being in schizophrenia patients, with depression serving as a mediating factor in this association. As a result, the outcomes of this research may represent a first step towards emphasizing the importance of addressing patients’ distressing dreams during therapy in order to alleviate their depressive symptoms, which in turn promotes improved well-being. We support further investigation into nightmares as a potential indirect causal component of decreased well-being in schizophrenia to improve the quality of life and general subjective well-being among schizophrenia patients.