Enhancing perianal disease management with integrated physical and psychological approaches

This article provides a comprehensive analysis of the study by Hou et al,focusing on the complex interplay between psychological and physical factors in the postoperative recovery(POR)of patients with perianal diseases.The study sheds light on how illness perception,anxiety,and depression significantly influence recovery outcomes.Hou et al developed a predictive model that demonstrated high accuracy in identifying patients at risk of poor recovery.The article explores the critical role of pre-operative psychological assessment,highlighting the need for mental health support and personalized recovery plans in enhancing POR quality.A multidisciplinary approach,integrating mental health professionals with surgeons,anesthesiologists,and other specialists,is emphasized to ensure comprehensive care for patients.The study’s findings serve as a call to integrate psychological care into surgical practice to optimize outcomes for patients with perianal diseases.

Elevated ETV4 expression in cholangiocarcinoma is linked to poor prognosis and may guide targeted therapies

Cholangiocarcinoma(CCA),a highly aggressive bile duct cancer,is associated with late-stage diagnosis and limited treatment options,leading to poor patient outcomes.Early detection and personalized treatment strategies are crucial.The study by Wang et al highlights the prognostic potential of the PEA3 subfamily genes(ETV1,ETV4,and ETV5)in CCA,identifying ETV4 as a particularly promising biomarker.Their bioinformatic analysis revealed that elevated ETV4 expression correlates with poorer survival,positioning it as a strong indicator of disease progression.These findings suggest that ETV4 could enhance prognostic precision and guide personalized therapies,although further validation through large-scale clinical trials is essential.Challenges in clinical application include the need for comprehensive experimental validation and addressing the tumor heterogeneity in CCA.Future research should focus on validating these biomarkers in diverse cohorts and developing targeted therapies,especially in regions where CCA is endemic.

Timely identification and treatment of uterine artery pseudoaneurysm after hysteroscopic procedures

Uterine artery pseudoaneurysm(UAP)is a rare but potentially life-threatening complication that can occur following hysteroscopic surgery for endometrial polyp resection.This article discusses the case study by Kakinuma et al,which highlights the successful diagnosis and treatment of UAP in a 48-year-old primiparous woman.Utilizing advanced imaging techniques such as ultrasound and computed tomography(CT),the medical team was able to promptly identify the UAP and subsequently perform a uterine artery embolization to treat the condition.The study underscores the critical need for rapid diagnosis and intervention to prevent severe outcomes and provides practical clinical recommendations for managing similar cases.This article aims to expand on the study’s findings,discuss the clinical implications,and suggest future research directions to optimize the management of UAP post-hysteroscopic surgery.

Importance of timely diagnosis and intervention in uterine artery pseudoaneurysm caused by hysteroscopic surgery

This manuscript explores the case on the occurrence of uterine artery pseudoaneurysm(UAP)during hysteroscopy endometrial polypectomy and the subsequent successful treatment via uterine artery embolization(UAE).Moreover,we focus on the management and treatment options for UAP in patients of advanced maternal age.A pseudoaneurysm is an extraluminal blood collection with a disrupted flow that communicates with the parent vessel via a defect in the arterial wall.The reported case involved a 48-year-old primiparous woman who developed a UAP after uterine polyp removal.The study enhances the understanding of UAP,a rare but potentially life-threatening condition,by providing a detailed and well-documented account of the comprehensive case presentation,effective use of medical imaging techniques for diagnosis,successful postoperative patient management following UAE,and practical clinical recommendations for clinicians managing similar cases.Overall,this study highlights the importance of considering UAP as a differential diagnosis in patients with abnormal vaginal bleeding following hysteroscopic surgery.Additionally,this manuscript recommends that clinicians with a high index of suspicion for UAP promptly request ultrasonography and computed tomography to facilitate early diagnosis.UAE is suggested as a primary treatment due to its effectiveness and safety,particularly in facilities capable of avoiding hysterectomy.

Glymphatic system function in diverse glucose metabolism states

The rising prevalence of diabetes and prediabetes globally necessitates a deeper understanding of associated complications,including glymphatic system dysfunction.The glymphatic system,crucial for brain waste clearance,is implicated in cognitive decline and neurodegenerative diseases like Alzheimer’s disease.This letter explores recent research on glymphatic function across different glucose metabolism states.Tian et al’s study reveals significant glymphatic dysfunction in type 2 diabetes mellitus patients,evidenced by lower diffusion tensor imaging analysis along perivascular space indices compared to those with normal glucose metabolism and prediabetes.The research also reveals a link between glymphatic dysfunction and cognitive impairment.Additional research underscores the role of glymphatic impairment in neurodegenerative diseases.These findings highlight the importance of integrating glymphatic health into diabetes management and suggest potential biomarkers for early diagnosis and targeted therapeutic interventions.

What is the role of cognitive-behavioral career coaching in mitigating work anxiety and depression among public employees?

This editorial explores the study by Mkpoikanke Sunday Otu and Maximus Monaheng Sefotho on the use of cognitive-behavioral career coaching(CBCC)to reduce work anxiety and depression among public employees.Public sector workers often face significant psychological stressors,leading to mental health issues that impair well-being and job performance.The study employed a grouprandomized trial design,involving 120 public employees diagnosed with severe anxiety and depression.Participants were divided into a treatment group,receiving CBCC,and a control group with no intervention.Results showed a significant reduction in anxiety and depression levels in the treatment group,sustained through follow-up assessments.The findings underscore the clinical relevance of CBCC as an effective intervention for improving mental health among public employees.Future research should explore the scalability of CBCC across different sectors and cultural contexts,as well as its integration with other mental health interventions.This editorial advocates for the broader implementation of CBCC practices in public service to enhance employee productivity and psychological well-being.By addressing cognitive distortions and fostering adaptive coping mechanisms,CBCC can help public employees navigate complex professional environments,ultimately contributing to a healthier and more efficient workforce.

Challenges and prospects in bridging precision medicine and artificial intelligence in genomic psychiatric treatment

Precision medicine is transforming psychiatric treatment by tailoring personalized healthcare interventions based on clinical,genetic,environmental,and lifestyle factors to optimize medication management.This study investigates how artificial intelligence(AI)and machine learning(ML)can address key challenges in integrating pharmacogenomics(PGx)into psychiatric care.In this integration,AI analyzes vast genomic datasets to identify genetic markers linked to psychiatric conditions.AI-driven models integrating genomic,clinical,and demographic data demonstrated high accuracy in predicting treatment outcomes for major depressive disorder and bipolar disorder.This study also examines the pressing challenges and provides strategic directions for integrating AI and ML in genomic psychiatry,highlighting the importance of ethical considerations and the need for personalized treatment.Effective implementation of AI-driven clinical decision support systems within electronic health records is crucial for translating PGx into routine psychiatric care.Future research should focus on developing enhanced AI-driven predictive models,privacy-preserving data exchange,and robust informatics systems to optimize patient outcomes and advance precision medicine in psychiatry.

Enhancing adolescent mental health through cognitive and social support:Insights from study on depression in Chinese adolescents

Adolescent depression is a growing global health concern,affecting 14%of adolescents and leading to severe consequences such as academic failure,substance abuse,and suicidal ideation.The study by Yu et al,investigates the cognitive and social factors influencing depression in 795 Chinese adolescents.Findings reveal that negative life events(NLEs)and dysfunctional attitudes are strongly associated with depressive symptoms,while social support moderates the impact of NLEs but not dysfunctional attitudes.The study highlights the need for cognitivebehavioural interventions targeting perfectionism and autonomy,and the importance of strengthening social support systems in schools and communities.Culturally sensitive,holistic approaches to adolescent mental health are crucial for addressing both the internal vulnerabilities and external pressures contributing to depression.Further research is needed to explore the roles of peer and parental support and the long-term effects of these factors across diverse cultural contexts.

Holistic approaches to mitigating psychological distress in gynecological cancer patients

This article delves into the psychological impact of gynecological malignancies and suggests pathways to improve the quality of life(QoL)for affected patients.Building on Shang et al's comprehensive analysis,this piece integrates insights from various studies to highlight the profound influence of psychological and physical symptoms on patients undergoing treatment for gynecological cancers.The study underscores that anxiety and depression significantly exacerbate the disease's toll.Factors such as physical exercise and digital and interactive health interventions show promise in mitigating these adverse effects.The article emphasizes the necessity for a holistic care approach that addresses both physical and emotional needs.Recommendations include enhanced training for healthcare providers,public awareness campaigns,streamlined diagnostic pathways,and improved access to specialist care.These integrated strategies aim to ensure that women facing gynecological cancers can maintain an optimal QoL through comprehensive and multidisciplinary care models.

Development of a depression in Parkinson's disease prediction model using machine learning

BACKGROUND It is important to diagnose depression in Parkinson’s disease(DPD)as soon as possible and identify the predictors of depression to improve quality of life in Parkinson’s disease(PD)patients.AIM To develop a model for predicting DPD based on the support vector machine,while considering sociodemographic factors,health habits,Parkinson's symptoms,sleep behavior disorders,and neuropsychiatric indicators as predictors and provide baseline data for identifying DPD.METHODS This study analyzed 223 of 335 patients who were 60 years or older with PD.Depression was measured using the 30 items of the Geriatric Depression Scale,and the explanatory variables included PD-related motor signs,rapid eye movement sleep behavior disorders,and neuropsychological tests.The support vector machine was used to develop a DPD prediction model.RESULTS When the effects of PD motor symptoms were compared using“functional weight”,late motor complications(occurrence of levodopa-induced dyskinesia)were the most influential risk factors for Parkinson's symptoms.CONCLUSION It is necessary to develop customized screening tests that can detect DPD in the early stage and continuously monitor high-risk groups based on the factors related to DPD derived from this predictive model in order to maintain the emotional health of PD patients.

Predicting South Korea adolescents vulnerable to depressive disorder using Bayesian nomogram:A community-based crosssectional study

BACKGROUND Although South Korea has developed and carried out evidence-based interventions and prevention programs to prevent depressive disorder in adolescents,the number of adolescents with depressive disorder has increased every year for the past 10 years.AIM To develop a nomogram based on a naïve Bayesian algorithm by using epidemiological data on adolescents in South Korea and present baseline data for screening depressive disorder in adolescents.METHODS Epidemiological data from 2438 subjects who completed a brief symptom inventory questionnaire were used to develop a model based on a Bayesian nomogram for predicting depressive disorder in adolescents.RESULTS Physical symptoms,aggression,social withdrawal,attention,satisfaction with school life,mean sleeping hours,and conversation time with parents were influential factors on depressive disorder in adolescents.Among them,physical symptoms were the most influential.CONCLUSION Active intervention by periodically checking the emotional state of adolescents and offering individual counseling and in-depth psychological examinations when necessary are required to mitigate depressive disorder in adolescents.

Screening dementia and predicting high dementia risk groups using machine learning

New technologies such as artificial intelligence,the internet of things,big data,and cloud computing have changed the overall society and economy,and the medical field particularly has tried to combine traditional examination methods and new technologies.The most remarkable field in medical research is the technology of predicting high dementia risk group using big data and artificial intelligence.This review introduces:(1)the definition,main concepts,and classification of machine learning and overall distinction of it from traditional statistical analysis models;and(2)the latest studies in mental science to detect dementia and predict high-risk groups in order to help competent researchers who are challenging medical artificial intelligence in the field of psychiatry.As a result of reviewing 4 studies that used machine learning to discriminate high-risk groups of dementia,various machine learning algorithms such as boosting model,artificial neural network,and random forest were used for predicting dementia.The development of machine learning algorithms will change primary care by applying advanced machine learning algorithms to detect high dementia risk groups in the future.

Developing a nomogram for predicting the depression of senior citizens living alone while focusing on perceived social support

BACKGROUND Although the number of senior citizens living alone is increasing,only a few studies have identified factors related to the depression characteristics of senior citizens living alone by using epidemiological survey data that can represent a population group.AIM To evaluate prediction performance by building models for predicting the depression of senior citizens living alone that included subjective social isolation and perceived social support as well as personal characteristics such as age and drinking.METHODS This study analyzed 1558 senior citizens(695 males and 863 females)who were 60 years or older and completed an epidemiological survey representing the South Korean population.Depression,an outcome variable,was measured using the short form of the Korean version CES-D(short form of CES-D).RESULTS The prevalence of depression among the senior citizens living alone was 7.7%.The results of multiple logistic regression analysis showed that the experience of suicidal urge over the past year,subjective satisfaction with help from neighbors,subjective loneliness,age,and self-esteem were significantly related to the depression of senior citizens living alone(P<0.05).The results of 10-fold cross validation showed that the area under the curve of the nomogram was 0.96,and the F1 score of it was 0.97.CONCLUSION It is necessary to strengthen the social network of senior citizens living alone with friends and neighbors based on the results of this study to protect them from depression.

Best early-onset Parkinson dementia predictor using ensemble learning among Parkinson's symptoms,rapid eye movement sleep disorder,and neuropsychological profile

BACKGROUND Despite the frequent progression from Parkinson’s disease(PD)to Parkinson’s disease dementia(PDD),the basis to diagnose early-onset Parkinson dementia(EOPD)in the early stage is still insufficient.AIM To explore the prediction accuracy of sociodemographic factors,Parkinson's motor symptoms,Parkinson’s non-motor symptoms,and rapid eye movement sleep disorder for diagnosing EOPD using PD multicenter registry data.METHODS This study analyzed 342 Parkinson patients(66 EOPD patients and 276 PD patients with normal cognition),younger than 65 years.An EOPD prediction model was developed using a random forest algorithm and the accuracy of the developed model was compared with the naive Bayesian model and discriminant analysis.RESULTS The overall accuracy of the random forest was 89.5%,and was higher than that of discriminant analysis(78.3%)and that of the naive Bayesian model(85.8%).In the random forest model,the Korean Mini Mental State Examination(K-MMSE)score,Korean Montreal Cognitive Assessment(K-MoCA),sum of boxes in Clinical Dementia Rating(CDR),global score of CDR,motor score of Untitled Parkinson’s Disease Rating(UPDRS),and Korean Instrumental Activities of Daily Living(KIADL)score were confirmed as the major variables with high weight for EOPD prediction.Among them,the K-MMSE score was the most important factor in the final model.CONCLUSION It was found that Parkinson-related motor symptoms(e.g.,motor score of UPDRS)and instrumental daily performance(e.g.,K-IADL score)in addition to cognitive screening indicators(e.g.,K-MMSE score and K-MoCA score)were predictors with high accuracy in EOPD prediction.

Can the prediction model using regression with optimal scale improve the power to predict the Parkinson's dementia?

BACKGROUND Efficiently detecting Parkinson's disease(PD)with dementia(PDD)as soon as possible is an important issue in geriatric medicine.AIM To develop a model for predicting PDD based on various neuropsychological tests using data from a nationwide survey conducted by the Korean Centers for Disease Control and Prevention and to present baseline data for the early detection of PDD.METHODS This study comprised 289 patients who were 60 years or older with PD[110 with PDD and 179 Parkinson's Disease-Mild Cognitive Impairment(PD-MCI)].Regression with optimal scaling(ROS)was used to identify independent relationships between the neuropsychological test results and PDD.RESULTS In the ROS analysis,Korean version of mini mental state ex-amination(MMSE)(KOREAN version of MMSE)(b=-0.52,SE=0.16)and Hoehn and Yahr staging(b=0.44,SE=0.19)were significantly effective models for distinguishing PDD from PD-MCI(P<0.05),even after adjusting for all of the Parkinson's motor symptom and neuropsychological test results.The optimal number of categories(scaling factors)for KOREAN version of MMSE and Hoehn and Yahr Scale was 10 and 7,respectively.CONCLUSION The results of this study suggest that among the various neuropsychological tests conducted,the optimal classification scores for KOREAN version of MMSE and Hoehn and Yahr Scale could be utilized as an effective screening test for the early discrimination of PDD from PD-MCI.

Insights from exploring the interrelated dynamics of gallstone disease,non-alcoholic fatty liver disease,and kidney stone disease

This article delved into the comprehensive study by Jiang et al,which meticulously examined the bidirectional relationships among gallstone disease,nonalcoholic fatty liver disease,and kidney stone disease through a multicenter study,systematic review,and meta-analysis.The study provides significant evidence supporting these associations,offering valuable insights into the etiology and potential prevention strategies for these interconnected conditions.The clinical significance of these bidirectional relationships is profound,as they underscore the importance of recognizing these conditions not only as isolated diseases but as part of a complex network that can influence each other.These results highlight the critical need for thorough screening and personalized prevention strategies for individuals with these interconnected conditions.Explicit implications for prevention strategies and early screening practices are crucial,as they can lead to early detection and intervention,significantly altering disease progression and outcomes.Furthermore,identifying potential therapeutic targets within these shared pathways may enhance treatment efficacy and patient outcomes,making this research highly relevant to clinical practice.By comprehending the common pathophysiological mechanisms and applying specific interventions,healthcare professionals can greatly enhance patient care and lessen the impact of these widespread diseases on global health.

Optimizing anesthesia depth to enhance seizure quality during electroconvulsive therapy in major depressive disorder

This editorial evaluated the findings of a comprehensive study focused on the effects of anesthesia depth on seizure parameters during electroconvulsive therapy(ECT)in patients with major depressive disorder.The study utilized quantitative consciousness and quantitative nociceptive indices for monitoring sedation,hypnosis,and nociceptive responses.The analysis included 193 ECT sessions across 24 patients,revealing significant impacts of anesthesia depth on electroencephalography(EEG)seizure parameters.Key findings include that lighter anesthesia resulted in longer EEG seizure duration and higher post-ictal suppression index,without increasing complications.These insights emphasize the importance of optimal anesthesia management to improve therapeutic outcomes in ECT.

Brain-derived neurotrophic factor alterations and cognitive decline in schizophrenia:Implications for early intervention

This manuscript explores the recent study by Cui et al which assessed the interplay between inflammatory cytokines and brain-derived neurotrophic factor(BDNF)levels in first-episode schizophrenia patients.The study revealed that higher levels of interleukin-6 and tumor necrosis factor-αcorrelated with reduced BDNF levels and poorer cognitive performance.Schizophrenia is a severe psy-chiatric disorder impacting approximately 1%of the global population,charac-terized by positive symptoms(hallucinations and delusions),negative symptoms(diminished motivation and cognitive impairments)and disorganized thoughts and behaviors.Emerging research highlights the role of BDNF as a potential biomarker for early diagnosis and therapeutic targeting.The findings from Cui et al’s study suggest that targeting neuroinflammation and enhancing BDNF levels may improve cognitive outcomes.Effective treatment approaches involve a com-bination of pharmacological and non-pharmacological interventions tailored to individual patient needs.Hence,monitoring cognitive and neuroinflammatory markers is essential for improving patient outcomes and quality of life.Conse-quently,this manuscript highlights the need for an integrated approach to schizo-phrenia management,considering both clinical symptoms and underlying neuro-biological changes.

Neutrophil gelatinase-associated lipocalin as a biomarker for neuropsychiatric complications in acute ischemic stroke

This study evaluates the findings of Gu et al,who investigated the role of neutrophil gelatinase-associated lipocalin(NGAL)as a biomarker for predicting neuropsychiatric complications in acute ischemic stroke(AIS)patients.The results revealed that elevated serum NGAL levels at admission are associated with a higher risk of cognitive impairment,anxiety,and depressive symptoms at discharge.The study analyzed 150 AIS patients(mean age 65.4 years,58%male)using the Mini-Mental State Examination and the Hospital Anxiety and Depression Scale to assess neuropsychiatric outcomes.Multivariate analysis demonstrated that higher NGAL levels were independent predictors of cognitive impairment[odds ratio(OR)=1.42],anxiety(OR=1.28),and depression(OR=1.39).Notably,NGAL exhibited strong predictive power for cognitive impairment,with an area under the curve of 0.78.Despite these promising findings,NGAL’s clinical utility is limited by its non-specificity across various conditions.Nevertheless,NGAL levels could help identify AIS patients at risk for neuropsychiatric complications,enabling timely intervention and comprehensive neuropsychiatric evaluation.The study emphasizes the need for further research to validate NGAL’s predictive accuracy and specificity in diverse AIS populations and advocates for its integration with other diagnostic modalities to enhance clinical decision-making.

Evaluating the impact of virtual reality on reducing restrictive practices in psychiatric wards

This manuscript critically evaluates the randomized controlled trial(RCT)conducted by Phiri et al,which assessed the effectiveness of virtual reality(VR)training for psychiatric staff in reducing restrictive practices(RPs).Specifically,this RCT investigated the impact of VR on the handling of aggressive patients by psychiatric staff compared to traditional training methods.Despite significant reductions in perceived discrimination in the VR group,there were no major improvements in self-efficacy or anxiety levels.The system usability scale rated the VR platform highly,but it did not consistently outperform traditional training methods.Indeed,the study shows the potential for VR to reduce RPs,although fluctuations in RP rates suggest that external factors,such as staff turnover,influenced the outcomes.This manuscript evaluates the study’s methodology,results,and broader implications for mental health training.Additionally,it highlights the need for more comprehensive research to establish VR as a standard tool for psychiatric staff education,focusing on patient care outcomes and real-world applicability.Finally,this study explores future research di-rections,technological improvements,and the potential impact of policies that could enhance the integration of VR in clinical training.