Digital assessment of nonverbal behaviors forecasts first onset of depression.

BACKGROUND: Adolescence is marked by a sharp increase in the incidence of depression, especially in females. Identification of risk for depressive disorders (DD) in this key developmental stage can help prevention efforts, mitigating the clinical and public burden of DD. While frequently used in diagnosis, nonverbal behaviors are relatively understudied as risk markers for DD. Digital technology, such as facial recognition, may provide objective, fast, efficient, and cost-effective means of measuring nonverbal behavior. METHOD: Here, we analyzed video-recorded clinical interviews of 359 never-depressed adolescents females via commercially available facial emotion recognition software. RESULTS: We found that average head and facial movements forecast future first onset of depression (AUC = 0.70) beyond the effects of other established self-report and physiological markers of DD risk. CONCLUSIONS: Overall, these findings suggest that digital assessment of nonverbal behaviors may provide a promising risk marker for DD, which could aid in early identification and intervention efforts.

Impact of Demographics on Early-Stage vs. Stage IV Diagnosis in Amelanotic Melanoma: An Analysis of the National Cancer Database.

Introduction Amelanotic melanoma (AM) is a rare form of melanoma that lacks pigment. Although curable when diagnosed early, it is often missed or mistaken for other benign conditions. There has not been a study investigating the impact of demographic features on the diagnosis of stage 0-I (early-stage) versus stage IV AM. Objective This study addresses a gap in knowledge regarding demographic factors that influence the odds of early-stage vs. stage IV diagnosis of AM. Methods This study identified 684 patients from the National Cancer Database who were diagnosed with early-stage AM or stage IV AM from 2004 to 2020 and compared them based on age, sex, race, insurance, income, education, insurance status, rurality, facility type, geographic region, and Charleson-Deyo score. Socioeconomic and demographic features of patients with early-stage and stage IV were compared using the chi-squared test, the independent t-test, and multivariate logistic regression. Statistical significance was set at α = 0.05. Results Most cases analyzed were White (98.5%), male (57.7%), and lived in a metropolitan setting (86.7%). Males made up most of the early-stage and stage IV groups (55.0% vs. 45% and 66.5% vs. 33.5%, respectively, p < 0.05). Younger age is associated with decreased odds of stage IV disease (OR = 0.973, 95% CI = 0.952-0.993, p < 0.05). In addition, the female sex is associated with decreased odds of stage IV disease (OR = 0.584, 95% CI = 0.381-0.897, p < 0.05). Conclusions Age and sex are two variables that influence the odds of stage IV diagnosis in patients with AM, which is strongly associated with worse survival outcomes.

An Artificial Intelligence (AI)-Integrated Approach to Enhance Early Detection and Personalized Treatment Strategies in Lung Cancer Among Smokers: A Literature Review.

Lung cancer (LC) is a significant global health issue, particularly among smokers, and is characterized by high rates of incidence and mortality. This comprehensive review offers detailed insights into the potential of artificial intelligence (AI) to revolutionize early detection and personalized treatment strategies for LC. By critically evaluating the limitations of conventional methodologies, we emphasize the innovative potential of AI-driven risk prediction models and imaging analyses to enhance diagnostic precision and improve patient outcomes. Our in-depth analysis of the current state of AI integration in LC care highlights the achievements and challenges encountered in real-world applications, thereby shedding light on practical implementation. Furthermore, we examined the profound implications of AI on treatment response, survival rates, and patient satisfaction, addressing ethical considerations to ensure responsible deployment. In the future, we will outline emerging technologies, anticipate potential barriers to their adoption, and identify areas for further research, emphasizing the importance of collaborative efforts to fully harness the transformative potential of AI in reshaping LC therapy. Ultimately, this review underscores the transformative impact of AI on LC care and advocates for a collective commitment to innovation, collaboration, and ethical stewardship in healthcare.

A simplified approach to discriminate between healthy subjects and patients with heart failure using cardiac magnetic resonance myocardial deformation imaging.

Aims: Left ventricular global longitudinal strain (LV-GLS) shows promise as a marker to detect early heart failure (HF). This study sought to (i) establish cardiac magnetic resonance imaging (CMR)-derived LV-GLS cut-offs to differentiate healthy from HF for both acquisition-based and post-processing techniques, (ii) assess agreement, and (iii) provide a method to convert LV-GLS between both techniques. Methods and results: A secondary analysis of a prospective study enrolling healthy subjects (n = 19) and HF patients (n = 56) was conducted. LV-GLS was measured using fast strain-encoded imaging (fSENC) and feature tracking (FT). Receiver operating characteristic (ROC) analyses were performed to derive and evaluate LV-GLS cut-offs discriminating between healthy, HF with mild deformation impairment (DI), and HF with severe DI. Linear regression and Bland-Altman analyses assessed agreement. Cut-offs discriminating between healthy and HF were identified at -19.3% and -15.1% for fSENC and FT, respectively. Cut-offs of -15.8% (fSENC) and -10.8% (FT) further distinguished mild from severe DI. No significant differences in area under ROC curve were identified between fSENC and FT. Bland-Altman analysis revealed a bias of -4.01%, 95% CI -4.42, -3.50 for FT, considering fSENC as reference. Linear regression suggested a factor of 0.76 to rescale fSENC-derived LV-GLS to FT. Using this factor on fSENC-derived cut-offs yielded rescaled FT LV-GLS cut-offs of -14.7% (healthy vs. HF) and -12% (mild vs. severe DI). Conclusion: LV-GLS distinguishes healthy from HF with high accuracy. Each measurement technique requires distinct cut-offs, but rescaling factors facilitate conversion. An FT-based LV-GLS ≥ -15% simplifies HF detection in clinical routine.

The effects of APOEe4 allele on cerebral structure, function, and related interactions with cognition in young adults.

In the last decade, extensive research has emerged into understanding the impact of risk factors for Alzheimer's Disease (AD) on brain in pre-symptomatic stages. We investigated the neuroimaging correlates of the APOEe4 genetic risk factor for AD in young adulthood, its relationship with cognition, and potential effects of other variables on the findings. While conventional volumetric analyses revealed no consistent differences, more sophisticated analyses identified subtle structural differences between APOEe4 carriers and non-carriers. Findings from diffusion studies were limited, but functional studies demonstrated consistent alterations in connectivity and activity. The complex relationship between APOE genotype, neuroimaging variables, and cognition revealed no consensus on the directionality of findings. Methodological choices, including analytical approaches, sample size, and the influence of other genes, gender, and ethnicity, varied across studies, impacting comparability and generalizability. Recommendations for future research include multimodal and longitudinal imaging, standardisation of pipelines, advanced analytical techniques, and collaborative data pooling.

Multi-site EEG studies in early infancy: Methods to enhance data quality.

Brain differences linked to autism spectrum disorder (ASD) can manifest before observable symptoms. Studying these early neural precursors in larger and more diverse cohorts is crucial for advancing our understanding of developmental pathways and potentially facilitating earlier identification. EEG is an ideal tool for investigating early neural differences in ASD, given its scalability and high tolerability in infant populations. In this context, we integrated EEG into an existing multi-site MRI study of infants with a higher familial likelihood of developing ASD. This paper describes the comprehensive protocol established to collect longitudinal, high-density EEG data from infants across five sites as part of the Infant Brain Imaging Study (IBIS) Network and reports interim feasibility and data quality results. We evaluated feasibility by measuring the percentage of infants from whom we successfully collected each EEG paradigm. The quality of task-free data was assessed based on the duration of EEG recordings remaining after artifact removal. Preliminary analyses revealed low data loss, with average in-session loss rates at 4.16 % and quality control loss rates at 11.66 %. Overall, the task-free data retention rate, accounting for both in-session issues and quality control, was 84.16 %, with high consistency across sites. The insights gained from this preliminary analysis highlight key sources of data attrition and provide practical considerations to guide similar research endeavors.

Assessment of early factors for identification or prediction severe acute pancreatitis in pregnancy.

BACKGROUND: Acute pancreatitis in pregnancy (APIP) is a rare and serious condition, and severe APIP (SAPIP) can lead to pancreatic necrosis, abscess, multiple organ dysfunction, and other adverse maternal and infant outcomes. Therefore, early identification or prediction of SAPIP is important. AIM: To assess factors for early identification or prediction of SAPIP. METHODS: The clinical data of patients with APIP were retrospectively analyzed. Patients were classified with mild acute pancreatitis or severe acute pancreatitis, and the clinical characteristics and laboratory biochemical indexes were compared between the two groups. Logical regression and receiver operating characteristic curve analyses were performed to assess the efficacy of the factors for identification or prediction of SAPIP. RESULTS: A total of 45 APIP patients were enrolled. Compared with the mild acute pancreatitis group, the severe acute pancreatitis group had significantly increased (P < 0.01) heart rate (HR), hemoglobin, neutrophil ratio (NEUT%), and neutrophil-lymphocyte ratio (NLR), while lymphocytes were significantly decreased (P < 0.01). Logical regression analysis showed that HR, NEUT%, NLR, and lymphocyte count differed significantly (P < 0.01) between the groups. These may be factors for early identification or prediction of SAPIP. The area under the curve of HR, NEUT%, NLR, and lymphocyte count in the receiver operating characteristic curve analysis was 0.748, 0.732, 0.821, and 0.774, respectively. The combined analysis showed that the area under the curve, sensitivity, and specificity were 0.869, 90.5%, and 70.8%, respectively. CONCLUSION: HR, NEUT%, NLR, and lymphocyte count can be used for early identification or prediction of SAPIP, and the combination of the four factors is expected to improve identification or prediction of SAPIP.

Neuroanatomical and clinical factors predicting future cognitive impairment.

Identifying cognitively normal (CN) older adults who will convert to cognitive impairment (CI) due to Alzheimer's disease is crucial for early intervention. Clinical and neuroimaging measures were acquired from 301 CN adults who converted to CI within 15 years of baseline, and 294 who did not. Regional volumes and brain age measures were extracted from T1-weighted magnetic resonance images. Linear discriminant analysis compared non-converters' characteristics against those of short-, mid-, and long-term converters. Conversion was associated with clinical measures such as hearing impairment and self-reported memory decline. Converters' brain volumes were smaller than non-converters' across 48 frontal, temporal, and subcortical structures. Brain age measures of 12 structures were correlated with shorter times to conversion. Conversion prediction accuracy increased from 81.5% to 90.5% as time to conversion decreased. Proximity to CI conversion is foreshadowed by anatomic features of brain aging that enhance the accuracy of predicting conversion.

Caregivers' perspectives of early developmental tele-assessments in challenging circumstances.

BACKGROUND:  Outbreaks, such as the COVID-19 pandemic in 2020, exacerbate barriers to accessing early childhood developmental care. Tele-assessment may serve as an innovative approach to developmental monitoring to overcome service delivery amidst challenging circumstances. It is vital to collect caregivers' perspectives of this potential service delivery method to inform clinical decision making. OBJECTIVES:  This study aimed to determine caregivers' perspectives of interview-based early developmental tele-assessment in a South African context. METHOD:  Thirty caregivers of children (aged birth - 36 months) completed a caregiver-report developmental assessment via a telecommunications platform, as well as an online questionnaire probing their perspectives on the tele-assessment. RESULTS:  Most participants (96.7%, n = 29 out of 30) rated their overall experience of the tele-assessment as positive; however, 53.8% (n = 14 out of 26 that answered the question) indicated that they would additionally still prefer in-person assessment. CONCLUSION:  Tele-assessment appears to be a viable approach for caregivers to access developmental care during circumstances such as COVID-19.Contribution: This study provided valuable insight into a novel approach using interview-based early developmental tele-assessment and the perspectives of caregivers thereof.

Risk Factors and Clinical Features of Peripartum Cardiomyopathy in a Chinese Population.

Purpose: We investigated the risk factors and characteristic clinical features of peripartum cardiomyopathy (PPCM) to lay the groundwork for early identification, screening, diagnosis, and intervention in high-risk pregnant women. Patients and methods: A retrospective case-control study was conducted to analyze data from 44 patients with PPCM and 226 normal pregnant women from a Chinese population. Results: Significant differences were found between the groups in terms of various factors such as age, body mass index (BMI), heart rate, and medical history. Logistic regression models identified abnormal electrocardiography (OR=18.852), upper respiratory tract infection (OR=41.822), gestational hypertension (OR=18.188), and cesarean section (OR=8.394) as risk factors for PPCM. Common clinical features observed in patients with PPCM included cough, wheezing, and chest tightness (68.18%), left heart enlargement (56.82%) and valvular insufficiency (81.82%). Additionally, cardiotropic virus was detected in a subset of patients (43.18%) and NT-proBNP was elevated ≥ 400 pg/mL (81.82%). Conclusion: In the Chinese population, the presence of abnormal electrocardiograms during pregnancy, history of upper respiratory tract infection, gestational hypertension, and maternal choice of cesarean section suggest the possibility of PPCM development. Factors such as advanced age, family history of cardiovascular disease, gestational diabetes mellitus, eclampsia, anemia, and hypoproteinemia should be considered. Clinically, patients present with cough, wheezing, chest tightness, enlarged left heart, valvular insufficiency and NT-proBNP elevated ≥ 400 pg/mL. This study could serve as a valuable reference for medical practitioners for the early identification and screening of patients with PPCM.

An advanced Artificial Intelligence platform for a personalised treatment of Eating Disorders.

Introduction: Eating Disorders (EDs) affect individuals globally and are associated with significant physical and mental health challenges. However, access to adequate treatment is often hindered by societal stigma, limited awareness, and resource constraints. Methods: The project aims to utilize the power of Artificial Intelligence (AI), particularly Machine Learning (ML) and Deep Learning (DL), to improve EDs diagnosis and treatment. The Master Data Plan (MDP) will collect and analyze data from diverse sources, utilize AI algorithms for risk factor identificat io n, treatment planning, and relapse prediction, and provide a patient-facing chatbot for information and support. This platform will integrate patient data, support healthcare professionals, and empower patients, thereby enhancing care accessibility, personalizing treatment plans, and optimizing care pathways. Robust data governance measures will ensure ethical and secure data management. Results: Anticipated outcomes include enhanced care accessibility and efficiency, personalized treatment plans leading to improved patient outcomes, reduced waiting lists, heightened patient engagement, and increased awareness of EDs with improved resource allocation. Discussion: This project signifies a pivotal shift towards data-driven, patient-centered ED care in Italy. By integrat ing AI and promoting collaboration, it seeks to redefine mental healthcare standards and foster better well- being among individuals with EDs.

Prevalence of Common Child Mental Health Disorders Using Administrative Health Data and Parent Report in a Prospective Community-Based Cohort from Alberta, Canada: Prévalence des troubles communs de santé mentale de l'enfant à l'aide des données de santé administratives et des rapports des parents dans une cohorte prospective communautaire d'Alberta, Canada.

OBJECTIVE: Knowing the prevalence of mental health difficulties in young children is critical for early identification and intervention. In the current study, we examine the agreement among three different data sources estimating the prevalence of diagnoses for attention deficit hyperactivity disorder (ADHD) and emotional disorders (i.e., anxiety or mood disorder) for children between birth and 9 years of age. METHODS: Data from a prospective pregnancy cohort was linked with provincial administrative health data for children in Alberta, Canada. We report the positive agreement, negative agreement, and Cohen's Kappa of parent-reported child diagnoses provided by a health professional ("parent report"), exceeding a clinical cut-off on a standardized questionnaire completed by parents (the Behavior Assessment System for Children, 3rd edition ["BASC-3"]), and cumulative inpatient, outpatient, or physician claims diagnoses ("administrative data"). RESULTS: Positive and negative agreement for administrative data and parent-reported ADHD diagnoses were 70.8% and 95.6%, respectively, and 30.5% and 94.9% for administrative data and the BASC-3, respectively. For emotional disorders, administrative data and parent-reported diagnoses had a positive agreement of 35.7% and negative agreement of 96.30%. Positive and negative agreement for emotional disorders using administrative data and the BASC-3 were 20.0% and 87.4%, respectively. Kappa coefficients were generally low, indicating poor chance-corrected agreement between these data sources. CONCLUSIONS: The data sources highlighted in this study provide disparate agreement for the prevalence of ADHD and emotional disorder diagnoses in young children. Low Kappa coefficients suggest that parent-reported diagnoses, clinically elevated symptoms using a standardized questionnaire, and diagnoses from administrative data serve different purposes and provide discrete estimates of mental health difficulties in early childhood.Plain Language Title: Prevalence of child mental health disorders according to different data sources in Canada.

Knowing the prevalence of mental health difficulties in young children is critical for informing mental health policy and decision-making. Yet, different sources yield different estimates and we do not know how these estimates compare. In the current study, we examine the agreement among three different information sources estimating the prevalence of diagnoses for attention deficit hyperactivity disorder (ADHD) and emotional disorders (i.e., anxiety or mood disorder) for children between birth and 9 years of age. To estimate the prevalence of mental disorders, we asked parents if their child had ever been diagnosed, we asked parents to complete a questionnaire using clinical symptom cut-offs for diagnosis, and we looked at data collected in the health care system to see if a child was ever diagnosed by a healthcare provider. We found that for ADHD, parent report that their child had received a diagnosis and their child having received a diagnosis in the healthcare system were similar. There were larger differences between a parent report of elevated symptoms on a questionnaire and whether they had been diagnosed by a healthcare provider. For emotion disorders, there were larger differences between parent report that their child had received a diagnosis and whether one was documented in the health record. Overall, there was somewhat low agreement between these three sources of data. We conclude that the different sources of data used in this study provide different estimates of ADHD and emotional disorder diagnoses in children. Therefore, when trying to understand the burden of child mental health disorders in young children, it is important to consider multiple sources to obtain a comprehensive picture of the issue.

Pseudonectria keratitis-emerging pathogenic fungi in the eye.

BACKGROUND: Infectious keratitis, a significant contributor to blindness, with fungal keratitis accounting for nearly half of cases, poses a formidable diagnostic and therapeutic challenge due to its delayed clinical presentation, prolonged culture times, and the limited availability of effective antifungal medications. Furthermore, infections caused by rare fungal strains warrant equal attention in the management of this condition. CASE PRESENTATION: A case of fungal keratitis was presented, where corneal scraping material culture yielded pink colonies. Lactophenol cotton blue staining revealed distinctive spore formation consistent with the Fusarium species. Further analysis using Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) identified the causative agent as Fusarium proliferatum. However, definitive diagnosis of Pseudonectria foliicola infection was confirmed through ITS sequencing. The patient's recovery was achieved with a combination therapy of voriconazole eye drops and itraconazole systemic treatment. CONCLUSION: Pseudonectria foliicola is a plant pathogenic bacterium that has never been reported in human infections before. Therefore, ophthalmologists should consider Pseudonectria foliicola as a possible cause of fungal keratitis, as early identification and timely treatment can help improve vision in most eyes.

A Machine Learning Approach for Early Identification of Prodromal Parkinson's Disease.

Parkinson's disease (PD) affects approximately 6 million people worldwide. Data analysis of early PD symptoms using machine learning (ML) models may provide an inexpensive, non-invasive, and simple method for the remote diagnosis of early PD. The aim of this project was to analyze voice, computer keystrokes, spiral drawings, and gait data involving PD patients and controls available in public databases using ML models and identify early PD characteristics that are more pronounced than others. An ML model was developed using Random Forest to analyze existing clinical data for PD patients, prodromal PD patients with REM (rapid eye movement) sleep behavior disorder (RBD) symptoms, and non-PD healthy controls. We reviewed and collected data from the UCI (University of California Irvine) Machine Learning Repository, PPMI (Parkinson's Progression Markers Initiative), and Kaggle databases. ML analysis was carried out on voice samples in PD and RBD patients, computer keystroke data, spiral drawings, and gait datasets. The ML prediction model developed may be helpful in improving risk prediction for PD, enabling early intervention and resource prioritization. The ML study suggests that voice analysis is the most robust test, followed by computer keystroke data, spiral drawings, and gait analysis, in that order. Voice is affected even in RBD patients, revealing that it is a sensitive and early measure of prodromal PD. The low accuracy of the analysis indicates that several PD-positive samples may remain undetected and unclassified. Combining all four features, that is, voice analysis, computer keystroke data, spiral drawings, and gait analysis, may improve the overall accuracy.

Experiences of identifying pre-school children with disabilities in resource limited settings - an account from Malawi, Pakistan and Uganda.

Although access to effective medical care for acutely sick children has improved globally, the number of children surviving but who may not be thriving due to disability, is increasing. This study aimed to understand the views of health professionals, educators and caregivers of pre-school children with disabilities in Malawi, Pakistan and Uganda regarding early identification, referral and support. Using applied thematic analysis, we identified themes relating to; limited 'demand' by caregivers for services; different local beliefs and community perceptions regarding the causes of childhood disability. Themes relating to 'supply' of services included inability to respond to community needs, and inadequate training among professionals for identification and referral. Stepwise, approaches provided to the families, community health worker and higher-level services could include training for community and primary care health workers on basic identification techniques and enhanced awareness for families and communities on the importance of early identification of children with disabilities.

Lack of collaboration between the community and health services may lead to entrenched pessimistic views of what can be done to support children with disabilities - generating a greater mistrust and low parental take-up of vital health services.If parents do not receive help at the community and clinic level, then, there is a need to move away from trying to provide a 'specific diagnosis' to working more on a level of assessing the child's functioning in terms of what their limitations are and how they can be addressed.Any identification and referral programme needs to consider the varying local beliefs, the stigma of having a child with a disability and feelings of blame, right from the start.A stepwise, incremental approaches, ranging from the provision of basic information, such as using brief materials highlighting 'red flag' milestones and conditions which are linked to guidance for support to families, community health workers, as well as higher levels of medical services, are likely to work best.

Prediction of disease progression in individuals with subjective cognitive decline using brain network analysis.

OBJECTIVE: The objective of this study is to explore potential differences in brain functional networks at baseline between individuals with progressive subjective cognitive decline (P-SCD) and stable subjective cognitive decline (S-SCD), as well as to identify potential indicators that can effectively distinguish between P-SCD and S-SCD. METHODS: Alzheimer's Disease Neuroimaging Initiative (ADNI) database was utilized to enroll SCD individuals with a follow-up period of over 3 years. This study included 39 individuals with S-SCD, 15 individuals with P-SCD, and 45 cognitively normal (CN) individuals. Brain functional networks were constructed based on the AAL template, and graph theory analysis was performed to determine the topological properties. RESULTS: For global metric, the S-SCD group exhibited stronger small-worldness with reduced connectivity among nearby nodes and accelerated compensatory information transfer capacity. For nodal efficiency, the S-SCD group showed increased connectivity in bilateral posterior cingulate gyri (PCG). However, for nodal local efficiency, the P-SCD group exhibited significantly reduced connectivity in the right cerebellar Crus I compared with the S-SCD group. CONCLUSION: There are differences in brain functional networks at baseline between P-SCD and S-SCD groups. Furthermore, the right cerebellar Crus I region may be a potentially useful brain area to distinguish between P-SCD and S-SCD.

Pharmacist-led screening for mental illness: A systematic review.

BACKGROUND: Early identification and treatment of mental illnesses is imperative for optimal patient outcomes. Pharmacists may play an important role in mental healthcare through the provision of screening services for mental illnesses. OBJECTIVE: (s): To systematically review the impact of pharmacist-led mental illness screening on clinical or patient-reported outcomes and identify and report any follow-up or referral systems used in pharmacist-led screening interventions for mental illnesses. METHODS: A systematic review was conducted by searching MEDLINE, CINAHL, Embase and APA PsycInfo via EBSCOhost from inception to 9 March 2023 to identify studies involving pharmacist-led screening interventions for mental illnesses. Data was collected on the mental illness in question, setting and population characteristics, screening tools used, clinical or patient-reported outcomes, and follow-up and referral systems reported. RESULTS: Twenty six studies were identified that related to screening for mental illnesses, such as depressive disorders and substance use disorders. There were a variety of study designs, including uncontrolled studies (n = 23), pre-post studies (n = 2) and randomised controlled trials (n = 1). Screening was conducted in different settings, with most studies conducted in community pharmacies (n = 21/26, 87.8 %) and focusing on depression screening (n = 12/26, 46.1 %). A range of follow-up and referral methods to other healthcare professionals were reported, including verbal (n = 3/26, 11.5 %), both written and verbal (n = 3/26, 11.5 %), communications via electronic health record (n = 2/26, 7.7 %) and written (n = 1/26, 3.8 %). CONCLUSIONS: Pharmacists provide screening for a variety of mental illnesses in different settings. Various referral methods and follow-up pathways may be utilised for post-screening patient care. However, current evidence is insufficient to establish improvements in early detection, treatment, or outcomes. Further large, well-designed studies are required to support the role of pharmacists in mental illness screening, provide evidence on the impact of pharmacist-led mental illness screening services and inform the most effective follow up and referral methods.

Predicting Kawasaki disease shock syndrome in children.

Background: Kawasaki disease shock syndrome (KDSS) is a critical manifestation of Kawasaki disease (KD). In recent years, a logistic regression prediction model has been widely used to predict the occurrence probability of various diseases. This study aimed to investigate the clinical characteristics of children with KD and develop and validate an individualized logistic regression model for predicting KDSS among children with KD. Methods: The clinical data of children diagnosed with KDSS and hospitalized between January 2021 and December 2023 were retrospectively analyzed. The best predictors were selected by logistic regression and lasso regression analyses. A logistic regression model was built of the training set (n = 162) to predict the occurrence of KDSS. The model prediction was further performed by logistic regression. A receiver operating characteristic curve was used to evaluate the performance of the logistic regression model. We built a nomogram model by visualizing the calibration curve using a 1000 bootstrap resampling program. The model was validated using an independent validation set (n = 68). Results: In the univariate analysis, among the 24 variables that differed significantly between the KDSS and KD groups, further logistic and Lasso regression analyses found that five variables were independently related to KDSS: rash, brain natriuretic peptide, serum Na, serum P, and aspartate aminotransferase. A logistic regression model was established of the training set (area under the receiver operating characteristic curve, 0.979; sensitivity=96.2%; specificity=97.2%). The calibration curve showed good consistency between the predicted values of the logistic regression model and the actual observed values in the training and validation sets. Conclusion: Here we established a feasible and highly accurate logistic regression model to predict the occurrence of KDSS, which will enable its early identification.

High-yield hybrid breeding of Camellia oleifolia based on ISSR molecular markers.

BACKGROUND: C. Oleifera is among the world's largest four woody plants known for their edible oil production, yet the contribution rate of improved varieties is less than 20%. The species traditional breeding is lengthy cycle (20-30 years), occupation of land resources, high labor cost, and low accuracy and efficiency, which can be enhanced by molecular marker-assisted selection. However, the lack of high-quality molecular markers hinders the species genetic analysis and molecular breeding. RESULTS: Through quantitative traits characterization, genetic diversity assessment, and association studies, we generated a selection population with wide genetic diversity, and identified five excellent high-yield parental combinations associated with four reliable high-yield ISSR markers. Early selection criteria were determined based on kernel fresh weight and cultivated 1-year seedling height, aided by the identification of these 4 ISSR markers. Specific assignment of selected individuals as paternal and maternal parents was made to capitalize on their unique attributes. CONCLUSIONS: Our results indicated that molecular markers-assisted breeding can effectively shorten, enhance selection accuracy and efficiency and facilitate the development of a new breeding system for C. oleifera.

Clinician and patient experiences of managing and living with oral and dental manifestations of scleroderma: A scoping review.

Oral and dental manifestations of scleroderma are extremely common, yet they are often overlooked within rheumatology and poorly understood within dentistry. Previous research has indicated the need to understand the oral and dental experiences of people living with scleroderma and those involved in their care. This scoping review aims, for the first time, to comprehensively map what is known regarding the identification and management of oral and dental manifestations of scleroderma, how these are experienced by people living with scleroderma, and to explore key characteristics of barriers and enablers to good oral and dental care in scleroderma. A scoping review was conducted using six databases (Embase, PubMed, PsychINFO, ASSIA, Scopus and SSCI), according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses - extension for Scoping Review. Grey literature was also included. Studies were eligible for inclusion if the full text and abstract were available in English, published between 2002 and 2022, and focused on the concept of oral and dental care in adults with scleroderma, either relating to identification and management, enablers and barriers to best practice, or patient experiences and well-being. Qualitative research which seeks to understand patients' lived experiences was a notable gap in the literature. Similarly, there was a significant lack of focus on the oral and dental manifestations of scleroderma in rheumatology. Three key features were identified which would facilitate best practice in research and clinical contexts: the necessity of multidisciplinary care; the necessity of centralising patient experience; and the necessity of mitigating barriers to dental care. We conclude that increased awareness of scleroderma within dentistry and streamlining referral procedures between the disciplines of dentistry and rheumatology, to enable the early identification and management of scleroderma, are crucial.