Categories Of Illness Research Articles

End to End Leaf Detection Using Convolutional Neural Network

Global food security is seriously threatened by crop diseases, yet in many places, it is still difficult to identify them quickly because of a lack of infrastructure. In tackling this problem, recent developments in leaf-based image categorization methods have produced encouraging outcomes. This study investigates how to differentiate between healthy and unhealthy leaves using curated datasets using the Random Forest algorithm. The development of the dataset, feature extraction, classifier training, and classification are the various stages of the implementation process. To efficiently distinguish between diseased and healthy samples, the Random Forest classifier is used to train the dataset, which includes pictures of both diseased and healthy leaves. A significant obstacle to agricultural production, plant diseases have an impact on crop yield and quality. Precision farming and sustainable agriculture depend on the timely and precise identification of leaf diseases. In this work, we present a complete system that automatically detects and classifies leaf diseases using Convolutional Neural Networks (CNN), starting with picture capture. The technology offers a quicker method of identifying leaf illnesses and does away with the requirement for manual feature engineering. Our tests, carried out on several public datasets, show the model's efficacy and achieve high accuracy for a range of illness categories. These results underline the practical uses that might improve crop management and early intervention techniques. Using machine learning on massive datasets that are accessible to the public, this approach offers a scalable solution for detecting plant diseases on a large scale.

Insights on the Genetic and Phenotypic Complexities of Optic Neuropathies

Background/Objectives: Optic neuropathies are a category of illnesses that ultimately cause damage to the optic nerve, leading to vision impairment and possible blindness. Disorders such as dominant optic atrophy (DOA), Leber hereditary optic neuropathy (LHON), and glaucoma demonstrate intricate genetic foundations and varied phenotypic manifestations. This narrative review study seeks to consolidate existing knowledge on the genetic and molecular mechanisms underlying ocular neuropathies, examine genotype-phenotype correlations, and assess novel therapeutic options to improve diagnostic and treatment methodologies. Methods: A systematic literature review was performed in October 2024, utilizing PubMed, Medline, the Cochrane Library, and ClinicalTrials.gov. Search terms encompassed “optic neuropathy”, “genetic variants”, “LHON”, “DOA”, “glaucoma”, and “molecular therapies”. Studies were chosen according to established inclusion criteria, concentrating on the genetic and molecular dimensions of optic neuropathies and their therapeutic ramifications. Results: The results indicate that DOA and LHON are mostly associated with the mitochondrial dysfunction resulting from pathogenic variants in nuclear genes, mainly OPA1, and mitochondrial DNA (mtDNA) genes, respectively. Glaucoma, especially its intricate variants, is linked to variants in genes like MYOC, OPTN, and TBK1. Molecular mechanisms, such as oxidative stress and inflammatory modulation, are pivotal in disease progression. Innovative therapeutics, including gene therapy, RNA-based treatments, and antioxidants such as idebenone, exhibit promise for alleviating optic nerve damage and safeguarding vision. Conclusions: Genetic and molecular investigations have markedly enhanced our comprehension of ocular neuropathies. The amalgamation of genetic and phenotypic data is essential for customized medical strategies. Additional research is required to enhance therapeutic strategies and fill the gaps in our understanding of the underlying pathophysiology. This interdisciplinary approach shows potential for enhancing patient outcomes in ocular neuropathies.

Trends in cardiac arrest outcomes & management in children with cardiac illness category compared to non-cardiac illness category: An analysis from the AHA get with the guidelines®-resuscitation registry

IntroductionContemporary rates of survival after pediatric in-hospital CPR events and trends in survival over the last 20 years have not been compared based on illness category. We hypothesized that survival to hospital discharge for surgical-cardiac category is higher than the non-cardiac category, and rates of survival after in-hospital CPR increased over time in all categories. MethodsThe AHA Get With The Guidelines®-Resuscitation registry was queried for index CPR events in children < 18 years of age from 2000 to 2021. Categories were surgical-cardiac (in-hospital CPR event following cardiac surgery); medical-cardiac (CPR event in non-surgical cardiac disease); and non-cardiac (CPR event in patients without cardiac disease). The primary outcome was survival to hospital discharge. We compared eras 2000–2004, 2005–2009, 2010–2014, and 2015–2021 with mixed logistic regression models, including event year as a continuous predictor and site as a random effect. ResultsOf 16,241 index events, in-hospital CPR event rates by illness category were: 19 % surgical-cardiac, 18 % medical-cardiac, and 63 % non-cardiac. Surgical-cardiac category had the highest rate of survival to hospital discharge compared to medical-cardiac and non-cardiac categories (56 % vs. 44 % vs. 46 %; p < 0.001). After controlling for age, location of event, and hospital size, the odds of survival were highest for surgical-cardiac category (aOR 1.28, 95 % CI 1.17–1.41) and lower for medical-cardiac category (aOR 0.90, 0.82–0.98), compared to the non-cardiac category. Odds of survival increased for all illness categories from the 2000–2004 era to the 2015–2021 era. Rates of improvement differed among illness categories with medical-cardiac having the lowest increased odds per era. Surgical-cardiac patients had the highest rates of extracorporeal resuscitation (ECPR) (20 % across the cohort), though the greatest increase in ECPR utilization was in the non-cardiac population (52 % increased odds per era). ConclusionsOver the last 20 years, both survival to hospital discharge and ECPR use has increased in all in-hospital CPR event illness categories. Children with surgical-cardiac CPR event have higher odds of survival to hospital discharge compared to non-cardiac CPR event categories, whereas odds of survival were lowest with medical-cardiac CPR events.

Spontaneous breathing trials should be adapted for each patient according to the critical illness. A new individualised approach: the GLOBAL WEAN study.

Spontaneous breathing trials (SBT) evaluate the patient's capacity to maintain inspiratory effort after extubation. SBT practices are heterogeneous and not individualised. The objective of this study was to assess which SBT best reproduces inspiratory effort after extubation in five critical illnesses. In this multicentre randomized cross-over study, adult intensive care unit patients under invasive mechanical ventilation for at least 24-h and ready for extubation, underwent three 15-min SBTs in random order: pressure support ventilation level of 7-cmH2O with positive end-expiratory pressure (PEEP) level of 0-cmH2O (PSV7PEEP0), PSV 0-cmH2O with PEEP 0-cmH2O (PSV0PEEP0) and T-piece trial. Primary outcome was the variation of pressure-time-product per minute (PTPmin) between each SBT and 20-min after extubation. Five categories of critical illnesses were selected: abdominal surgery, brain injury, chest trauma, chronic obstructive pulmonary disease (COPD) and miscellaneous. Five hundred measures of effort from 100 patients were analysed. PTPmin (cmH2O s/min, median and interquartile range, IQR) was 256 (208-321) after extubation, 192 (127-281) at the end of PSV7PEEP0 (p < 0.001 in comparison to after extubation), 291 (235-347) at the end of PSV0PEEP0 and 262 (198-338) at the end of T-piece (both no different from after extubation). One method of SBT in patients with brain injury (PSV0PEEP0), two in abdominal surgery (PSV0PEEP0 and T-piece) and miscellaneous patients (PSV7PEEP0 and T-piece) and all three methods in chest trauma and COPD exacerbation patients replicated reasonably accurately the postextubation effort to breathe. Unassisted SBTs, namely PSV0PEEP0 and T-piece trial, are the most appropriate to replicate the postextubation effort to breathe.

Ill persons and capable workers: Constructing work ability in return-to-work negotiations after sickness absence.

In return-to-work (RTW) negotiations after sickness absence, the work ability of an individual employee becomes a shared interest for the multiple stakeholders representing both the healthcare sector and working life. In practice, the employee, employer and occupational health professionals need to reach a shared understanding of the employee's work ability to enable shared decision-making concerning the plans for sustainable RTW. Drawing on 14 video-recorded RTW negotiations, we used conversation analysis-informed membership categorization analysis to examine how the participants of RTW negotiations discuss the work ability of an employee to pursue a shared understanding of the situation. Work ability was constructed in a very situational way, using illness categories to both explain the work ability of the employee and argue for or against their ability or inability to work. Our study contributes to research on RTW by introducing a new perspective to work ability. We show how work ability is realized during RTW negotiations through interaction, and how participants leverage their cultural understanding of illness and capability when negotiating work ability. We also demonstrate how membership categorization analysis can reveal the situational and consequential aspects of illness and work ability categories.

Welcome To #Spoonietok: Understanding and Supporting Disability Expertise, Storytelling Abilities, and Collective Information Practices on Tiktok and Beyond

ABSTRACTThis poster reports developing dissertation research, exploring the individual and collective storytelling practices of people experiencing difficult to diagnose chronic pain and fatigue (CSSs) on TikTok to understand and ultimately support these embodied, creative, and collective storytelling abilities on and off‐app. Ongoing data collection and analysis using constant comparison, guided by critical disability studies sensitizing concepts, of 150 videos and their top‐20 comments, and interviews with 6 community members indicate that people with CSSs are using TikTok affordances to tell and scaffold complex micro‐stories sharing disabled expertise; creatively visualize invisible illness; use intimate, documentary cinematography; tell composite stories using skits and collapsed time; and make audio, visual, and community/collective mimetic alterations. This poster shares a pragmatic sampling approach using algorithm training, and results from visualization activities during interviews which indicate a messy overlapping landscape of what I call algorithmically mediated online health communit(ies) (AMOHCs), which have slippery boundaries, where health information is found serendipitously, and people connect across specific chronic illness categories. This dissertation‐in‐progress is developing a set of disability‐specific storytelling abilities, which interview participants indicate are transferable to off‐app settings; and upcoming codesign workshops creating storytelling support materials with community members will further distill disability storytelling support needs.

Comparison of predictive models using logistic regression, classification tree, and structural equation model for severe dengue.

The aim of this study was to compare the predictive performance of three statistical models-logistic regression, classification tree, and structural equation model (SEM)-in predicting severe dengue illness. We adopted modified classification of dengue illness severity based on WHO 1997 guideline. Predictive models were constructed using demographic factors and laboratory indicators on the day of fever occurrence. We developed statistical predictive models using data from two hospital cohorts in Thailand, consisting of 257 Thai children. Different predictive models for each category of severe dengue illness were developed employing logistic regression, classification tree, and SEM. The probability of discrimination of each model for severe output of disease was analyzed with external validation data sets from 55 and 700 patients not used in model development. From external validation using predictors on the day of presentation to the hospital, the area under the receiver operating characteristic curve was between 0.65 and 0.84 for the regression model. It was between 0.73 and 0.85 for SEM models. Classification tree models showed good results of sensitivity, ranging from 0.95 to 0.99. However, they showed poor specificity ranging from 0.10 to 0.44. Our study showed that SEM is comparable to logistic regression or classification tree, which was widely used for more severe form of dengue prediction.

Tinea capitis and its associated factors among school children in Gondar town northwest, Ethiopia

IntroductionTinea capitis is a global public health concern with a unique therapeutic challenge and mostly affects children. The burden is double in developing countries. There is no study on school-going children from the urban residence in Ethiopia.ObjectiveTo determine the prevalence of Tinea capitis and its associated factors among school children in Gondar town northwest, Ethiopia 2021.MethodsAn institution-based cross-sectional study was conducted among school children in Gondar town from November 20 to December 20, 2021. Data was collected through an interviewer-administered structured questionnaire. A stratified simple random sampling technique was employed. Then data were entered into EPI Info version 7.22 and transferred to Statistical package for social science (SPSS) version 22 for further analysis. The binary logistic regression model was employed to identify factors associated with tinea capitis, and the strength of association and statistical significance was declared using the adjusted odds ratios with its corresponding 95% CI, and p-value ≤ 0.05 respectively.ResultThe prevalence of tinea capitis among school children in Gondar town was found to be 29.4%. Public school student (AOR = 2.79 95% CI: 1.34–5.87), widowed mother (AOR = 6.09, 95% CI: 1.83–11.23), students in the age group of 5–8 years (AOR = 3.79 95% CI: 1.68–8.55), animal contact (AOR = 2.61 95% CI: 1.15–5.90), and family similar illness category (AOR = 8.49 95% CI: 3.73–11.39) have risen the likelihood of tinea capitis among school children.ConclusionsThe prevalence of tinea capitis was higher. Young age, children from widowed marital status, illiterate mother, history of share blades, animal contact, a family similar illness, and lower number of living rooms are important factors contributing to tinea capitis among school children. Health education for the mother on the mode of transmission, prevention, and improve the low socioeconomic status of the parent is crucial.Key termsChildren, Ethiopia, Gondar, School age, Tinea capitis

Prognoses Associated With Palliative Performance Scale Scores in Modern Palliative Care Practice

The Palliative Performance Scale (PPS) is one of the most widely used prognostic tools for patients with serious illness. However, current prognostic estimates associated with PPS scores are based on data that are over a decade old. To generate updated prognostic estimates by PPS score, care setting, and illness category, and examine how well PPS predicts short- and longer-term survival. This prognostic study was conducted at a large academic medical center with robust inpatient and outpatient palliative care practices using electronic health record data linked with data from California Vital Records. Eligible participants included patients who received a palliative care consultation between January 1, 2018, and December 31, 2020. Data analysis was conducted from November 2022 to February 2024. Palliative care consultation with a PPS score documented. The primary outcomes were predicted 1-, 6-, and 12-month mortality and median survival of patients by PPS score in the inpatient and outpatient settings, and performance of the PPS across a range of survival times. In subgroup analyses, mortality risk by PPS score was estimated in patients with cancer vs noncancer illnesses and those seen in-person vs by video telemedicine in the outpatient setting. Overall, 4779 patients (mean [SD] age, 63.5 [14.8] years; 2437 female [51.0%] and 2342 male [49.0%]) had a palliative care consultation with a PPS score documented. Of these patients, 2276 were seen in the inpatient setting and 3080 were seen in the outpatient setting. In both the inpatient and outpatient settings, 1-, 6-, and 12-month mortality were higher and median survival was shorter for patients with lower PPS scores. Prognostic estimates associated with PPS scores were substantially longer (2.3- to 11.7-fold) than previous estimates commonly used by clinicians. The PPS had good ability to discriminate between patients who lived and those who died in the inpatient setting (integrated time-dependent area under the curve [iAUC], 0.74) but its discriminative ability was lower in the outpatient setting (iAUC, 0.67). The PPS better predicted 1-month survival than longer-term survival. Mortality rates were higher for patients with cancer than other serious illnesses at most PPS levels. In this prognostic study, prognostic estimates associated with PPS scores were substantially longer than previous estimates commonly used by clinicians. Based on these findings, an online calculator was updated to assist clinicians in reaching prognostic estimates that are more consistent with modern palliative care practice and specific to the patient's setting and diagnosis group.

Virtual Reality Therapy Could be a Promising Alternative for Anxiety – A Review

Anxiety disorders are the major mental illnesses globally, characterized by a persistent and variable clinical course. These disorders probably have a severe impact on a person’s ability to function personally, socially, and professionally. A person suffering from such disorders could affect the standard of living. Anxiety disorders are classified within the vague category of neurotic illnesses. General disorders such as apprehension, fearfulness, restlessness, and agoraphobia are among the current categories of anxiety disorders. Anxiety is advantageous from an evolutionary standpoint since it promotes survival by motivating individuals to avoid hazardous environments. The frequency of symptoms associated with anxiety that are associated with each other is around 2 to 4% in the general population, and it may rise up to 20% with non-psychotic conditions. Then present systematic review aims to assess virtual reality therapy’s (VRT) effectiveness in treating anxiety disorders. Our aim is to highlight the existing research to ascertain the effectiveness of VRT in alleviating symptoms of anxiety as well as its possible advantages and constraints in comparison to conventional therapeutic approaches. The review will further explore the fundamental mechanics of VRT and pinpoint any lacuna in the existing literature that necessitates more attention. Furthermore, these reviews are significant because they provide insightful information about how VRT could be used to treat anxiety and evaluate the effectiveness of VRT as a therapeutic intervention.

COVID-19 in patients with thymic epithelial tumors with or without Good’s syndrome: a single-center retrospective study

IntroductionThymic epithelial tumors (TETs) are rare neoplasms often associated with immune-related disorders. Patients with Good’s syndrome (GS), an adult-acquired TET-related immunodeficiency, are at a high risk of mortality due to infectious diseases. This study aims to examine COVID-19 occurrence and severity in TET patients, with or without GS.MethodsClinical records of TET patients referred to the Regional Coordinating Center for Rare Tumors of Campania Region were retrospectively collected. During the observation period, elapsing from March 2020 to April 2023, the following data were collected: occurrence of SARS-CoV-2 infection; COVID-19 severity, according to the National Institute of Health (NIH) illness categories; COVID-19 treatment. COVID-19 occurrence and severity were assessed in the overall population and correlated with the presence of GS and/or other immune-related dysregulations.ResultsOverall, 47 TET patients were included in the study; 27 of these (57.4%) had GS. All participants had received a full cycle of mRNA vaccine for SARS-CoV2., Thirty-one patients (66.0%) experienced COVID-19, of whom 18 (58.0%) had previously received a diagnosis of GS. No significant association of GS and/or other immune-related dysregulations with SARS-CoV-2 infection occurrence was detected (Fisher’s exact test p = 1 and p = 0.3587, respectively). Among patients with GS, 8 (45.0%) reported a COVID-19 severity score of ≥ 3; whereas, only 1 of the 13 patients without GS (7.7%) had a severity score of ≥ 3. The correlation between presence of GS and COVID-19 severity (score 1 or 2 vs. ≥ 3) was statistically significant (p = 0.0448). No statistically significant association between COVID-19 severity and other immune-related syndromes were found (p = 1). Of note, all the hospitalized patients for NIH 4 and 5 COVID-19 had GS.ConclusionsOur data suggest that TET patients, especially those with GS, require a careful multidisciplinary monitoring for SARS-CoV-2 infection, in order to establish tailored treatments and prophylactic protocols.

Association of EEG characteristics with outcomes following pediatric ICU cardiac arrest: A secondary analysis of the ICU-RESUScitation trial

Background and ObjectivesThere are limited tools available following cardiac arrest to prognosticate neurologic outcomes. Prior retrospective and single center studies have demonstrated early EEG features are associated with neurologic outcome. This study aimed to evaluate the prognostic value of EEG for pediatric in-hospital cardiac arrest (IHCA) in a prospective, multicenter study. MethodsThis cohort study is a secondary analysis of the ICU-Resuscitation trial, a multicenter randomized interventional trial conducted at 18 pediatric and pediatric cardiac ICUs in the United States. Patients who achieved return of circulation (ROC) and had post-ROC EEG monitoring were eligible for inclusion. Patients < 90 days old and those with pre-arrest Pediatric Cerebral Performance Category (PCPC) scores > 3 were excluded. EEG features of interest included EEG Background Category, and presence of focal abnormalities, sleep spindles, variability, reactivity, periodic and rhythmic patterns, and seizures. The primary outcome was survival to hospital discharge with favorable neurologic outcome. Associations between EEG features and outcomes were assessed with multivariable logistic regression. Prediction models with and without EEG Background Category were developed and receiver operator characteristic curves compared. ResultsOf the 1129 patients with an index cardiac arrest who achieved ROC in the parent study, 261 had EEG within 24 h of ROC, of which 151 were evaluable. The cohort included 57% males with a median age of 1.1 years (IQR 0.4, 6.8). EEG features including EEG Background Category, sleep spindles, variability, and reactivity were associated with survival with favorable outcome and survival, (all p < 0.001). The addition of EEG Background Category to clinical models including age category, illness category, PRISM score, duration of CPR, first documented rhythm, highest early post-arrest arterial lactate improved the prediction accuracy achieving an AUROC of 0.84 (CI 0.77–0.92), compared to AUROC of 0.76 (CI 0.67–0.85) (p = 0.005) without EEG Background Category. ConclusionThis multicenter study demonstrates the value of EEG, in the first 24 h following ROC, for predicting survival with favorable outcome after a pediatric IHCA.

Anhedonia as a Potential Transdiagnostic Phenotype With Immune-Related Changes in Recent-Onset Mental Health Disorders

BackgroundChronic low-grade inflammation is observed across mental disorders and is associated with difficult-to-treat-symptoms of anhedonia and functional brain changes, reflecting a potential transdiagnostic dimension. Previous investigations have focused on distinct illness categories in people with enduring illness, but few have explored inflammatory changes. We sought to identify an inflammatory signal and the associated brain function underlying anhedonia among young people with recent-onset psychosis and recent-onset depression. MethodsResting-state functional magnetic resonance imaging, inflammatory markers, and anhedonia symptoms were collected from 108 (mean [SD] age = 26.2 [6.2] years; female = 50) participants with recent-onset psychosis (n = 53) and recent-onset depression (n = 55) from the European Union/Seventh Framework Programme–funded PRONIA (Personalised Prognostic Tools for Early Psychosis Management) study. Time series were extracted using the Schaefer atlas, defining 100 cortical regions of interest. Using advanced multimodal machine learning, an inflammatory marker model and a functional connectivity model were developed to classify participants into an anhedonic group or a normal hedonic group. ResultsA repeated nested cross-validation model using inflammatory markers classified normal hedonic and anhedonic recent-onset psychosis/recent-onset depression groups with a balanced accuracy of 63.9% and an area under the curve of 0.61. The functional connectivity model produced a balanced accuracy of 55.2% and an area under the curve of 0.57. Anhedonic group assignment was driven by higher levels of interleukin 6, S100B, and interleukin 1 receptor antagonist and lower levels of interferon gamma, in addition to connectivity within the precuneus and posterior cingulate. ConclusionsWe identified a potential transdiagnostic anhedonic subtype that was accounted for by an inflammatory profile and functional connectivity. Results have implications for anhedonia as an emerging transdiagnostic target across emerging mental disorders.

Neuro-stimulating effect of Citri Reticulata Pericarpium Viride essential oil through regulating Glu/NMDAR on olfactory bulb to improve anxiety-like behavior

Ethnopharmacological relevanceCitri Reticulata Pericarpium Viride (also known Qing-Pi or QP) is a plant in the Rutaceae family, QP is a traditional Qi-regulating medicine in Chinese medicine that is compatible with other Chinese medicine components and has extensive clinical practice in treating anxiety and depression. Reports on the pharmacological effects of QP have demonstrated its neuroprotective effects and antioxidant capacities. Numerous pharmacological benefits of QP are attributed to its antioxidant abilities. Anxiety disorders are a broadly defined category of mental illnesses. Oxidative stress and an imbalance in the antioxidant defense system are typical pathological features of these disorders. Aim of the studyThe aim of this study was to evaluate the effects of QP essential oil on anxiety using animal models and investigate the underlying neurobiological mechanisms. Materials and methodsThis study aimed to develop an animal model of anxiety using chronic restraint stress and investigate the effects of inhalation of Citri Reticulata Pericarpium Viride essential oil on anxiety-like behavior, olfactory function, and olfactory bulb neurogenesis in mice with anxiety. ResultsThe results showed that long-term chronic restraint stimulation caused a decrease in olfactory function, significant anxiety-like behavior, and a notable reduction in the number of neurons in the olfactory bulb. However, inhalation of Citri Reticulata Pericarpium Viride essential oil reversed these effects, improving the olfactory function, neuro-stimulating effect, alleviating anxiety-like behavior, and regulating theta (4–12Hz) oscillation in the hippocampus DG area. These effects were associated with changes in the expression levels of glutamate receptor NMDAR and NeuN in olfactory bulb. ConclusionsThe study revealed that mice with anxiety induced by chronic restraint stress exhibited significant olfactory dysfunction, providing strong evidence for the causal relationship between anxiety disorders and olfactory dysfunction. Moreover, QP essential oil has the potential to be developed as a therapeutic drug for anxiety disorders, in addition to its role as a complementary anxiolytic.

SCDC-Net: Detection and Classification of Skin Cancer using Deep q Neural Network with Hybrid Features

The increment in radiation in atmosphere causing wide spread of skin cancer disease around the globe. Cancer of the skin is one of the most lethal forms of the illness, and it is responsible for millions of deaths annually all over the world. Because of this, the early identification of skin cancer using computer-aided diagnostic (CAD) technology might potentially save a person's life. However, skin cancer classification of performance is affected by hair artifacts and improper segmentation of lesion. The conventional methods were failed to achieve higher performance due to improper feature analysis. Therefore, to achieve the robust performance, this work was developed the transfer learning-based skin cancer detection and classification (SCDC-Net). Initially, UG-Net model was introduced to eliminate hair from skin lesions by enhancing the region of interest, which is developed by combining U-shaped deep learning network (U-Net) with generative adversarial networks (GAN). In addition, Hybrid U-Net (HU-Net) perform the segmentation operation for identifying the disease effected region of interest. Further, the features are extracted using gray level cooccurrence matrix (GLCM) matrix and discrete wavelet transform (DWT), which extracts the color, texture, statistical features. Finally, these combined characteristics are used to train a deep q neural network (DQNN) model, which then classifies a number of different skin cancer illness categories. The simulations conducted on real-time ISIC-2019 dataset proved that proposed SCDC-Net outperformed in all performance measures as compared to existing methods.

Abstract 43: Development and Internal Validation of Risk Scores to Predict Survival to Discharge Following Pediatric In-Hospital Cardiac Arrest

Introduction: In-hospital cardiac arrest (IHCA) in the pediatric population is associated with poor survival and neurological outcomes. We aimed to develop and internally validate a risk score to predict survival to discharge in pediatric IHCA patients. Methods: For model development and validation, we included pediatric IHCA patients captured in the Get With The Guidelines® registry (provided by the American Heart Association) between 2005 and 2021. We used logistic regression (LR), classification and regression trees (CART), and artificial neural networks (ANN) to develop models using 70% of the data and validate them using the remaining 30% of the data. The discrimination of the models was compared based on the area under the receiver operating characteristic curve (AUC), calibration based on the ratio of observed to expected (O: E), and predictive accuracy based on percent survival in each risk group. Results: We included 6141 pediatric patients (mean age 4.82 years, 41.3% infants, 54.7% male). Most patients survived to hospital discharge (60.9%). We developed separate models for infants vs. older children using LR, CART, and ANN. The least absolute shrinkage and selection operator (LASSO) identified age, illness category, acyanotic cardiac malformation, hepatic insufficiency, hypotension/hypoperfusion, metabolic/electrolyte abnormality, metastatic/hematologic malignancy, renal insufficiency, septicemia, and pediatric cerebral performance score on admission as important predictor variables for analysis. Table 1 shows a comparison of model parameters. Logistic regression models were found to be better at classifying survivors into risk categories with higher specificity and discrimination. Conclusion: Pediatric patients experiencing IHCA can be classified into low, moderate, and high-risk categories using a simple risk score and easily identified pre-arrest variables. These risk scores can support clinical decisions, facilitate research, and help monitor the quality of medical services.

Traditional ethnobotanical knowledge and ethnomedicinal use of plants in the Tropical Rift Valley of Ethiopia

IntroductionA wide range of floral resources are used in Ethiopia for primary healthcare. Unfortunately, due to inadequate documentation and the fact that they were verbally passed down from one generation to the next, these indigenous medicinal practices are being forgotten. The study's goal was to identify and record indigenous peoples' traditional knowledge of plant usage in the Abala Abaya District of Wolaita, Ethiopia. MethodsThe collection of ethnobotanical data used a cross-sectional research design involving focus group discussions and semistructured questionnaires between January 2021 and April 2022. From each selected kebele (ward; smallest administrative unit), 50 informants (a total of 200) were selected as representative sample using a systematic random sampling technique. ResultsMore than 50 different human and animal illnesses have been documented to be treated by seventy-two species of plants grouped in thirty-two families. The most abundant families were Poaceae and Asteraceae (8 taxa in each). The two most often used plant parts were leaf (29%) and herbs (54%), respectively. Oral administration was the most typical mode of delivery (75%). For treating stomachache in the category of gastrointestinal illnesses, Hagenia abyssinica (Bruce) J.F.Gmel got the greatest fidelity level scores (FL = 100%). ConclusionsIn Abala Abaya District, there is broad access to traditional medicinal plants that can treat ailments in both humans and animals. This study, therefore, might be a baseline piece of information for further botanical related studies in the region. Plants with the highest FL values are highly recommended for novel drug discovery.

Mental health literacy and its associated factors among traditional healers toward mental illness in Northeast, Ethiopia: A mixed approach study.

Currently, the biggest issue facing the entire world is mental health. According to the Ethiopian Ministry of Health, nearly one-fourth of the community is experiencing any of the mental illness categories. Most of the cases were treated in religious and traditional institutions, which the community most liked to be treated. However, there were very limited studies conducted to show the level of mental health literacy among traditional healers. The study aimed to assess the level of mental health literacy and its associated factors among traditional healers toward mental illness found in Northeast, Ethiopia from September 1-30/2022. A mixed approach cross-sectional study design was carried out on September 130, 2022, using simple random sampling with a total sample of 343. Pretested, structured questionnaires and face-to-face interviews were utilized for data collection. The level of Mental Health Literacy (MHL) was assessed using the 35 mental health literacy (35-MHLQ) scale. The semi-structured checklist was used for the in-depth interview and the FGD for the qualitative part. Data was entered using Epi-data version 4.6 and, then exported to SPSS version 26 for analysis. The association between outcome and independent variables was analyzed with bivariate and multivariable linear regression. P-values < 0.05 were considered statistically significant. Thematic analysis was used to analyze the qualitative data, and the findings were then referenced with the findings of the quantitative data. The findings of this study showed that the sample of traditional healers found in Dessie City scored a total mean of mental health literacy of 91.81 ± 10:53. Age (β = -0.215, 95% CI (-0.233, -0.05), p = 0.003, informal educational status (β = -5.378, 95% CI (-6.505, -0.350), p = 0.029, presence of relative with a mental disorder (β = 6.030, 95% CI (0.073, 7.428),p = 0.046, getting information on mental illness (β = 6.565, 95% CI (3.432, 8.680), p = <0.001, and mental health training (β = 4.889, 95% CI (0.379, 6.455), p = 0.028 were variables significantly associated with mental health literacy. Traditional healers provide a variety of explanations for the causes of mental illness, including biological, psychological, and supernatural ones. The mean score of the Mental Health Literacy Scale (MHLS) is lower among traditional healers compared with other studies. Age, informal educational status, family history of mental illness, getting information on mental illness, and mental health training were significantly associated with mental health literacy. Therefore, different works to improve the levels of mental health literacy among traditional healers are essential.

Modified DeeplabV3+ with multi-level context attention mechanism for colonoscopy polyp segmentation

The development of automated methods for analyzing medical images of colon cancer is one of the main research fields. A colonoscopy is a medical treatment that enables a doctor to look for any abnormalities like polyps, cancer, or inflammatory tissue inside the colon and rectum. It falls under the category of gastrointestinal illnesses, and it claims the lives of almost two million people worldwide. Video endoscopy is an advanced medical imaging approach to diagnose gastrointestinal disorders such as inflammatory bowel, ulcerative colitis, esophagitis, and polyps. Medical video endoscopy generates several images, which must be reviewed by specialists. The difficulty of manual diagnosis has sparked research towards computer-aided techniques that can quickly and reliably diagnose all generated images. The proposed methodology establishes a framework for diagnosing coloscopy diseases. Endoscopists can lower the risk of polyps turning into cancer during colonoscopies by using more accurate computer-assisted polyp detection and segmentation. With the aim of creating a model that can automatically distinguish polyps from images, we presented a modified DeeplabV3+ model in this study to carry out segmentation tasks successfully and efficiently. The framework's encoder uses a pre-trained dilated convolutional residual network for optimal feature map resolution. The robustness of the modified model is tested against state-of-the-art segmentation approaches. In this work, we employed two publicly available datasets, CVC-Clinic DB and Kvasir-SEG, and obtained Dice similarity coefficients of 0.97 and 0.95, respectively. The results show that the improved DeeplabV3+ model improves segmentation efficiency and effectiveness in both software and hardware with only minor changes.

FAKTOR YANG MEMPENGARUHI KESEHATAN MENTAL PENDUDUK DKI JAKARTA PADA MASA PANDEMI COVID-19 BERDASARKAN DETERMINAN KESEHATAN PUBLIK PERKOTAAN

Mental health is one of the most neglected categories of illness in the health sector. The COVID-19 pandemic exacerbated this condition. Jakarta Provincial Health Office has also highlighted the mental health condition among Jakarta residents both before and during the pandemic through the high number of cases of severe mental disorders in Jakarta. Research on the factors affecting the mental health of Jakarta residents during the COVID-19 pandemic is still limited. This study uses a quantitative approach. This study uses Partial Least Square (PLS) path modeling as an analytical method by using smartPLS software. The study aims to identify the factors of mental health in Jakarta during the COVID-19 pandemic based on urban public health determinants. Three determinants of urban public health are positively related to the mental health of Jakarta residents during the COVID-19 pandemic. The result shows that the social determinant, represented by a healthy lifestyle, contributed significantly to the mental health of Jakarta residents during the COVID-19 pandemic, followed by the economic determinant, represented by healthy food consumption, and the built environment/infrastructure determinant, represented by the provision of green open spaces.