Higher Scores Research Articles

Quality of life and work-life balance among Nurse Educators in the Schools of Nursing in Sri Lanka

Objective: The study aimed to assess the quality of life and work-life balance (WLB) among Nurse Educators in the Schools of Nursing in Sri Lanka. Material and Methods: A descriptive cross-sectional study was conducted among 257 Nurse educators in all schools of Nursing in Sri Lanka. Data were collected by using validated and pretested self-administered questionnaires which include Socio-demographic details, a validated quality of life questionnaire (WHO QOL – BREF), and a Work Life Balance assessment scale. Data were analyzed using descriptive and inferential statistics. Results: The overall mean perceived QOL and health of the participants were 3.82 (±0.66) and 3.82 (±0.69) respectively. Participants obtained a high score for the social relationship domain (98.83/100) and a low score for the environmental domain (19.96/100) of the QOL. Findings of the work=life balance revealed an overall mean of 2.87 (SD=1.5) which indicates neither agree nor disagree with work-life interference with personal life and an overall mean of 2.53 (SD=0.96) which indicates neither agree nor disagree with personal life interference with work (PLIW). Conclusions: Nurse educators perceive a moderate level of personal life interference with work, but overall, they acknowledge work/personal life enhancement. These results highlight the complex and multifaceted nature of work-life balance among nurse educators.

A Geospatial Drug Abuse Risk Assessment and Monitoring Dashboard Tailored for School Students: Development Study With Requirement Analysis and Acceptance Evaluation.

The enormous consequences of drugs include suicides, traffic accidents, and violence, affecting the individual, family, society, and country. Therefore, it is necessary to constantly identify and monitor the drug abuse rate among school-going youth. A geospatial dashboard is vital for the monitoring of drug abuse and related crime incidence in a decision support system. This paper mainly focuses on developing MyAsriGeo, a geospatial drug abuse risk assessment and monitoring dashboard tailored for school students. It introduces innovative functionality, seamlessly orchestrating the assessment of drug abuse usage patterns and risks using multivariate student data. A geospatial drug abuse dashboard for monitoring and analysis was designed and developed in this study based on agile methodology and prototyping. Using focus group and interviews, we first examined and gathered the requirements, feedback, and user approval of the MyAsriGeo dashboard. Experts and stakeholders such as the National Anti-Drugs Agency, police, the Federal Department of Town and Country Planning, school instructors, students, and researchers were among those who responded. A total of 20 specialists were involved in the requirement analysis and acceptance evaluation of the pilot and final version of the dashboard. The evaluation sought to identify various user acceptance aspects, such as ease of use and usefulness, for both the pilot and final versions, and 2 additional factors based on the Post-Study System Usability Questionnaire and Task-Technology Fit models were enlisted to assess the interface quality and dashboard sufficiency for the final version. The MyAsriGeo geospatial dashboard was designed to meet the needs of all user types, as identified through a requirement gathering process. It includes several key functions, such as a geospatial map that shows the locations of high-risk areas for drug abuse, data on drug abuse among students, tools for assessing the risk of drug abuse in different areas, demographic information, and a self-problem test. It also includes the Alcohol, Smoking, and Substance Involvement Screening Test and its risk assessment to help users understand and interpret the results of student risk. The initial prototype and final version of the dashboard were evaluated by 20 experts, which revealed a significant improvement in the ease of use (P=.047) and usefulness (P=.02) factors and showed a high acceptance mean scores for ease of use (4.2), usefulness (4.46), interface quality (4.29), and sufficiency (4.13). The MyAsriGeo geospatial dashboard is useful for monitoring and analyzing drug abuse among school-going youth in Malaysia. It was developed based on the needs of various stakeholders and includes a range of functions. The dashboard was evaluated by a group of experts. Overall, the MyAsriGeo geospatial dashboard is a valuable resource for helping stakeholders understand and respond to the issue of drug abuse among youth.

Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation

The process of brain tumour segmentation entails locating the tumour precisely in images. Magnetic Resonance Imaging (MRI) is typically used by doctors to find any brain tumours or tissue abnormalities. With the use of region-based Convolutional Neural Network (R-CNN) masks, Grad-CAM and transfer learning, this work offers an effective method for the detection of brain tumours. Helping doctors make extremely accurate diagnoses is the goal. A transfer learning-based model has been suggested that offers high sensitivity and accuracy scores for brain tumour detection when segmentation is done using R-CNN masks. To train the model, the Inception V3, VGG-16, and ResNet-50 architectures were utilised. The Brain MRI Images for Brain Tumour Detection dataset was utilised to develop this method. This work's performance is evaluated and reported in terms of recall, specificity, sensitivity, accuracy, precision, and F1 score. A thorough analysis has been done comparing the proposed model operating with three distinct architectures: VGG-16, Inception V3, and Resnet-50. Comparing the proposed model, which was influenced by the VGG-16, to related works also revealed its performance. Achieving high sensitivity and accuracy percentages was the main goal. Using this approach, an accuracy and sensitivity of around 99% were obtained, which was much greater than current efforts.

Factors affecting nutritional status among children aged below five years in Rwanda’s Western and Southern Provinces

BackgroundThe state of a child’s nutrition is a critical indicator of their overall health and wellbeing. Public health still faces challenges from undernutrition, especially in developing nations across the globe. In Rwanda, around 33% of children aged under five years suffer from chronic undernutrition. Many factors, such as poverty, illiteracy, poor WASH practices, improper child feeding practices, and insufficient healthcare, are the leading causes of undernutrition. The study aims to assess infant and young child feeding practices, WASH, food security, and their association with the nutritional status of children under five years in Rwanda’s Western and Southern provinces.MethodsA community-based cross-sectional study design was applied to study factors affecting the nutritional status of children under five years in 439 households in the Karongi, Nyabihu, and Nyamagabe districts of Rwanda. The study assessed anemia, stunting, underweight, and wasting indicators, and collected data was analyzed using SPSS version 25.ResultsThe study findings indicate that among the children surveyed, 29.2% (128) were identified as stunted, 5.9% (26) were underweight, 2.3% (10) suffered from wasting, and 20.9% (31) had anemia. Factors associated with these conditions included larger household size [AOR = 2.108; 95% CI (1.016–4.371)], positively associated with stunting. Additionally, children from households where the head was above 60 years old were more likely to exhibit stunting [AOR = 4.809; 95% CI (1.513, 15.283)]. Furthermore, a high household dietary diversity score was positively linked to being underweight [AOR = 6.061; 95% CI (1.535,23.942)].ConclusionHousehold characteristics like size, dietary diversity, and the age of the household head affect children’s nutritional status. Improving these conditions would enhance children’s nutritional status.

APPLICATION OF POWTOON-BASED AI TECHNOLOGY IN MAKING ENTREPRENEURSHIP TEACHING MATERIALS

Technological advances with various other implications also require a greater role for the world of education, especially for teachers who use different technologies, methods and approaches in conveying material and values ​​to students. A teacher's ability to adapt to technological developments has become an inevitability that cannot be avoided. Based on the research obtained, the results show that the development of powtoon learning media has a feasibility percentage of 83,75% (feasible). Student learning outcomes were proven to have increased in obtaining a minimum score of 70 and a maximum score of 100. The gain test showed that 18 students obtained high score criteria and 9 students obtained medium score criteria. In the student response questionnaire, the percentage was 93.4% (Very Eligible). Thus, it can be stated that the Powtoon learning media is suitable for use in entrepreneurship learning. It is hoped that this research can help students and the instruments involved in the process of teaching and learning activities.

An analysis of protective health behavior and polypharmacy among older patients: a nationwide cohort study

BackgroundThis study analyzed the relationship between protective health behaviors and polypharmacy in individuals aged 65 years and older.MethodsWe used data from a nationwide survey (KNHANES) from 2012 to 2016 in conjunction with the health insurance claims databases. A total of 3297 adults aged 65 or older were included in the study. Polypharmacy was defined as more than 30 prescription days in 6 months with five or more different drugs. Health-related behaviors (BMI, smoking, drinking, regular walking, and living alone) were extracted for 6 months before measuring polypharmacy. We used multivariable logistic regression on polypharmacy for each protective health behavior, as well as a composite score of protective health behavior. Subgroup analysis was also conducted by age and sex.ResultsAmong protective health behaviors, BMI < 25 (OR, 0.76; 95% CI, 0.66–0.88) and never smoking (OR, 0.78; 95% CI, 0.62–0.98) were associated with a lower risk of polypharmacy. Polypharmacy was significantly associated with BMI < 25 in both sex subgroups (male: OR, 0.71; 95% CI, 0.56–0.88; female: OR, 0.81; 95% CI, 0.67–0.99) and 65–79 subgroup (OR, 0.74; 95% CI, 0.63–0.86). The association between never smoking and polypharmacy was only significant in the 65–79 subgroup (OR, 0.71; 95% CI, 0.55–0.91). Participants with five protective health behaviors had a lower risk of polypharmacy than participants with zero or one health behavior, which was only statistically significant in the subgroup analysis of participants aged 65–79 years (OR, 0.52; 95% CI, 0.29–0.94).ConclusionsThis study finds that health behaviors such as obesity and smoking are associated with a higher risk of polypharmacy. Furthermore, we confirm that a high score of protective health behaviors is associated with a lower risk of polypharmacy. Our findings indicate the need for geriatric-centered management of protective health behaviors to prevent polypharmacy.

Trifecta outcomes of robotic partial nephrectomy in obese patients: A comparison of body mass index

We analyzed robotic partial nephrectomy (RPN) outcomes in obese patients based on body mass index (BMI) and trifecta achievement. We retrospectively reviewed 296 patients who underwent RPN at Kansai Medical University Hospital between 2014 and 2022. The preoperative clinical data and perioperative outcomes were evaluated. Trifecta achievement (negative surgical margin, no major complications, and no acute kidney injury on postoperative day three) and its relationship to three BMI groups (<25, 25 to <30, and ≥30) were the primary outcome. The correlation between factors in achieving trifecta and BMI was evaluated. Univariate and multivariate analyses assessed variables for achieving the trifecta with logistic regression analysis. C-statistics quantitatively evaluated the prediction accuracy. Among 296 patients, 264 (89.2%) achieved trifecta (BMI categories were <25 [89.9%], 25 to <30 [89.4%], and ≥30 [82.6%]). There was no significant BMI-related difference (p = 0.566). Intraoperative blood loss increased with the BMI (p = 0.034). Multivariate analyses showed preoperative aspects and dimensions used for anatomic (PADUA) score independently predicted trifecta failure (odds ratio 1.71; 95% confidence interval 1.32-2.20; p < 0.001). The C-statistics of the PADUA score increased with increasing BMI. Higher BMI patients had more intraoperative blood loss during RPN. However, RPN remains safe and has acceptable quality and functional outcomes. Since patients with high PADUA scores combined with a high BMI may be at risk of trifecta failure, this should be explained before RPN.

An ultrasound-based nomogram for predicting central lymph node metastasis in papillary thyroid microcarcinoma.

Performing prophylactic central lymph node dissection for papillary thyroid microcarcinoma (PTMC) patients with clinically negative lymph node metastasis remains controversial - not all patients with PTMC are suitable for active surveillance. Therefore, we aimed to establish a nomogram based on ultrasound features for predicting CLNM in PTMC. This retrospective study included 636 patients with PTMC, in which the CLNM status was pathologically confirmed. Univariate and multivariate regression analyses were conducted to screen for risk factors associated with CLNM. Then, a CLNM prediction model was established, receiver operating characteristic, calibration, and decision curve analyses were used to assess the model's performance. Five variables, including age, sex, combined CLNM status, tumor size, and capsule invasion, were included in the nomogram. The values of the area under the receiver operating characteristic curve in the training and validation datasets were 0.720 (95% confidence interval [CI], 0.649-0.791) and 0.704 (95% CI, 0.622-0.786), respectively. An ultrasound-based nomogram was successfully established, of which the predictive model shows excellent predictive performance and can be used to evaluate the status of CLNM in PTMC. Thus, patients with high nomogram scores should be considered for prophylactic central neck dissection.

A comparison of the chemical compositions and nutrient values of the sanim and srithong varieties of the farmed golden apple snails (Gastropoda; Ampullariidae; Pomacea canaliculata)

The comparison of the Sanim (SN) and Srithong (ST) varieties of the farmed apple snail Pomacea canaliculata as an alternative protein source has shown that ST contained more hydroxyproline and possessed a greater firmness than SN. Nevertheless, both varieties yielded similar protein contents of 20.90–23.07 % and exhibited similar texture, color, cooking yields and cooking losses after cooking. Good palatability with high liking score was obtained from both snail meat samples, particularly color, texture, and overall attributes. Snail meats possessed good nutritive value with an amount of protein and low fat content, whereas SN contained higher protein and total energy(P < 0.05). Other nutritionally useful chemicals (such as calcium, sodium, iron, vitamins B1 and B2) are present. Sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) results discovered that myosin heavy chain, paramyosin, and actin were the prevalent protein components. Differences in nitrogen content distribution were observed between SN and ST, in which alkaline-soluble protein was the major fraction. Amino acid composition and fatty acid content varied regarding snail varieties with a large amount of essential amino acids and good source of MUFA and PUFA. Therefore, both farmed SN and ST snails could be an effective alternative protein source with a significant amount of nutritive value.

Spatial transcriptome and single-cell reveal the role of nucleotide metabolism in colorectal cancer progression and tumor microenvironment

BackgroundThe intricacies of nucleotide metabolism within tumor cells specific to colorectal cancer (CRC) remain insufficiently characterized. A nuanced examination of particular tumor clusters and their dynamic interplay with the tumor microenvironment (TME) may yield profound insights into these therapeutically auspicious communicative networks.MethodsBy integrating ten types of single-cell enrichment scoring methods, we carried out enrichment analysis on CRC cell types, which was validated through four additional single-cell cohorts. Groups of tumor cells were determined using the average values of the scores. Using cellphonedb, monocle, inferCNV, SCENIC, and Cytotrace, functional analyses were performed. Utilizing the RCTD approach, single-cell groupings were mapped onto spatial transcriptomics, analyzing cell dependency and pathway activity to distinguish between tumor cell subtypes. Differential expression analysis identified core genes in nucleotide metabolism, with single-cell and spatial transcriptomics analyses elucidating the function of these genes in tumor cells and the immune microenvironment. Prognostic models were developed from bulk transcriptome cohorts to forecast responses to immune therapy. Laboratory experiments were conducted to verify the biological function of the core gene.ResultsNucleotide metabolism is significantly elevated in tumor cells, dividing them into two groups: NUhighepi and NUlowepi. The phenotype NUhighepi was discerned to exhibit pronounced malignant attributes. Utilizing the analytical tool stlearn for cell-to-cell communication assessment, it was ascertained that NUhighepi engages in intimate interactions with fibroblasts. Corroborating this observation, spatial transcriptome cell interaction assessment through MISTy unveiled a particular reliance of NUhighepi on fibroblasts. Subsequently, we pinpointed NME1, a key gene in nucleotide metabolism, affirming its role in thwarting metastasis via in vitro examination. Utilizing multiple machine learning algorithms, a stable prognostic model (NRS) has been developed, capable of predicting survival and responses to immune therapy. In addition, targeted drugs have been identified for both high and low scoring groups. Laboratory experiments have revealed that NME1 can inhibit the proliferation and invasion of CRC tumor cells.ConclusionOur study elucidates the potential pro-tumor mechanism of NUhighepi and the role of NME1 in inhibiting metastasis, further deepening the understanding of the role of nucleotide metabolism in colorectal cancer, and providing valuable targets for disrupting its properties.

The predictive ability of Mayo adhesive probability score for evaluating intraoperative bleeding in standard percutaneous nephrolithotomy in adult patients.

Mayo adhesive probability (MAP) score is one of the commonest tool to predict the adherence status of perirenal fat. The association between MAP score and intraoperative hemorrhage in patients undergoing micropercutaneous nephrolithotomy was comfirmed in a recent well designed study. We aimed to investigate if MAP score may predict the possibility for significant intraoperative bleeding in supine percutaneous nephrolithotomy (PCNL) performed with a 26 Fr rigid nephroscope. In this observational retrospective study, demographic, clinical, laboratory, radiological, perioperative (operation duration, hospitalization, intraoperative bleeding, success) and MAP score (perinephric fat stranding, posterior perinephric fat thickness and total MAP score) data of all patients who underwent supine PCNL between June 2021 and July 2023 were evaluated. The patients were divided into 2 groups according to their MAP scores (79 [54.1%] patients with MAP score < 3 [low MAP score] and 67 [54.1%] patients with MAP score ≥ 3 [high MAP score]). The rate of patients with intraoperative bleeding was 47.8% in the patient group with a high MAP score, while it was 22.8% in the patient group with a low MAP score (p = 0.002). In multivariate logistic regression analysis, anticoagulant drug use history (OR = 2.525; 95% CI = 1.025-6.224; p = 0.044), presence of multiple stones (OR = 3.015; 95% CI = 1.205-7.543; p = 0.018), calyx localization of the stone (OR = 2.871;95% CI = 1.166-7.068; p = 0.022), higher renal parenchymal thickness (OR = 1.119; 95% CI = 1.049-1.193; p = 0.001) and MAP score > 3 (OR = 3.486; 95% CI = 1.579-7.696; p = 0.002) were defined as independent risk factors for significant intraoperative bleeding. In clinical practice, the MAP score can be used to predict bleeding before PCNL.

Comparing Patients’ Perceptions of Dry Eye Disease Between Spanish- and English-Speaking Patients in the United States

Purpose It is well-known that patients’ perceptions of their disease can impact management strategies and disease outcomes. Limited knowledge exists on such perceptions in dry eye disease (DED) and the role of language in these perceptions. Herein, we compared the perceptions about DED between Spanish- and English-speaking patients. Methods This cross-sectional study included 146 patients with DED who underwent ophthalmic evaluation and completed questionnaires assessing their perceptions of DED on a 10-point scale during their routine appointments. Perceptions included opinions on the level of satisfaction with understanding of DED, ease of following doctor’s advice, effectiveness of treatment, satisfaction with DED care, and outlook on DED. Perceptions were categorized as low (scores 0–2), moderate (scores 3–7), and high (scores 8–10). The percentage of patients with high perception scores were then compared between Spanish- and English-speaking patients. Results There were 48 Spanish speakers and 98 English speakers. Overall, high scores of DED perceptions were identified in 47.9% for satisfaction with the level of understanding of DED, 72.6% for ease of following doctor’s advice, 52.1% for helpfulness of DED treatment, 64.4% for satisfaction with DED care, and 52.1% for optimistic outlook on DED. High scores for satisfaction with the level of understanding of DED were significantly lower in Spanish speakers (27.1%) than English speakers (58.2%, p < .001). No significant differences were observed in other perceptions between Spanish- and English-speaking participants. Conclusions Spanish-speaking subjects reported lower satisfaction with their understanding of DED than English speakers. Clinicians should provide health services and educational materials in the patient’s preferred language to minimize barriers to understanding their disease.

Preoperative albumin-bilirubin score predicts short-term outcomes and long-term prognosis in colorectal cancer patients undergoing radical surgery.

The albumin-bilirubin (ALBI) score is a serum biochemical indicator of liver function and has been proven to have prognostic value in a variety of cancers. In colorectal cancer (CRC), a high ALBI score tends to be associated with poorer survival. To investigate the correlation between the preoperative ALBI score and outcomes in CRC patients who underwent radical surgery. Patients who underwent radical CRC surgery between January 2011 and January 2020 at a single clinical center were included. The ALBI score was calculated by the formula (log10 bilirubin × 0.66) + (albumin × -0.085), and the cutoff value for grouping patients was -2.8. The short-term outcomes, overall survival (OS), and disease-free survival (DFS) were calculated. A total of 4025 CRC patients who underwent radical surgery were enrolled in this study, and there were 1908 patients in the low ALBI group and 2117 patients in the high ALBI group. Cox regression analysis revealed that age, tumor size, tumor stage, ALBI score, and overall complications were independent risk factors for OS; age, tumor stage, ALBI score, and overall complications were identified as independent risk factors for DFS. A high preoperative ALBI score is correlated with adverse short-term outcomes, and the ALBI score is an independent risk factor for OS and DFS in patients with CRC undergoing radical surgery.

Characteristics, management and outcome of patients with late-arrival STEMI in the Acute Coronary Syndrome Israeli Surveys (ACSIS)

IntroductionPatients with ST-elevation myocardial infarction (STEMI) and late arrival (>12 h) after symptom onset, are at high risk for mortality and heart failure and represent a challenge for management. We aimed to define patient characteristics, management, and outcome of late-arrival STEMI in Israel over the last 20 years. MethodsWe analyzed data of late-arrival STEMI (12–48 h and > 48 h) from the biennial acute coronary syndrome Israeli Surveys (ACSIS), as well as time-dependent changes [early (2000–2010) Vs. late (2013–2021) period]. ResultsData regarding time from symptom onset to hospital arrival was available in 6,466 STEMI patients. Of these, 9.6 % arrived 12–48 h and 3 % >48 h from symptom onset. Late-arrival patients were more likely to be older women with diabetes and high GRACE score and less likely to have prior myocardial infarction.In recent years, 95 % of patients arriving 12–48 h and 96 % of those arriving > 48 h had coronary angiography, as opposed to 75 % and 77 % in the early years (p = 0.007). Percutaneous coronary intervention (PCI) increased from 60 % and 55 % respectively to 85 % (p ≤ 0.001).TIMI-3 flow after primary PCI was 89–92 %, irrespective of arrival time. Late arrival patients (12–48 h but not > 48 h) who had PCI had better adjusted 1-year survival, HR 0.49 (95 %CI 0.29–0.82), p = 0.01. ConclusionsLate-arrival STEMI patients have higher risk characteristics. Most late-arrival patients undergo coronary angiography and PCI and have TIMI-3 flow after primary PCI. In patients arriving 12–48 h after symptom onset PCI is associated with better survival.

Evaluation of OCT biomarker changes in treatment-naive neovascular AMD using a deep semantic segmentation algorithm.

To determine real-life quantitative changes in OCT biomarkers in a large set of treatment naive patients in a real-life setting undergoing anti-VEGF therapy. For this purpose, we devised a novel deep learning based semantic segmentation algorithm providing the first benchmark results for automatic segmentation of 11 OCT features including biomarkers for neovascular age-related macular degeneration (nAMD). Training of a Deep U-net based semantic segmentation ensemble algorithm for state-of-the-art semantic segmentation performance which was used to analyze OCT features prior to, after 3 and 12 months of anti-VEGF therapy. High F1 scores of almost 1.0 for neurosensory retina and subretinal fluid on a separate hold-out test set with unseen patients. The algorithm performed worse for subretinal hyperreflective material and fibrovascular PED, on par with drusenoid PED, and better in segmenting fibrosis. In the evaluation of treatment naive OCT scans, significant changes occurred for intraretinal fluid (mean: 0.03 µm3 to 0.01 µm3, p < 0.001), subretinal fluid (0.08 µm3 to 0.01 µm3, p < 0.001), subretinal hyperreflective material (0.02 µm3 to 0.01 µm3, p < 0.001), fibrovascular PED (0.12 µm3 to 0.09 µm3, p = 0.02) and central retinal thickness C0 (225.78 µm3 to 169.40 µm3). The amounts of intraretinal fluid, fibrovascular PED, and ERM were predictive of poor outcome. The segmentation algorithm allows efficient volumetric analysis of OCT scans. Anti-VEGF provokes most potent changes in the first 3 months while a gradual loss of RPE hints at a progressing decline of visual acuity. Additional research is required to understand how these accurate OCT predictions can be leveraged for a personalized therapy regimen.

Early pregnancy associations with Gestational Diabetes: methods and cohort results of the Hoosier Moms Cohort

Background Gestational Diabetes (GDM) raises the risk of adverse perinatal outcomes and long-term risk of type 2 diabetes. There is currently a lack of comprehensive GDM prediction models based on more than simple clinical features. Objective The objective of this study was to collect a comprehensive set of clinical, sociodemographic, biobehavioral, and genomic features in a prospective high-risk cohort for GDM, to discover novel predictive and therapeutic targets for GDM during early pregnancy. Study design The Hoosier Moms Cohort was a prospective observational study of pregnant individuals, with a singleton gestation &lt;20 weeks. The study protocol included 2 visits during pregnancy and one at delivery. Psychosocial, dietary, social, and demographic characteristics were collected in addition to maternal and newborn samples. Developing GDM was the primary outcome. Univariate associations with GDM for continuous variables were analyzed using either two-sample t-test or Wilcoxon Rank Sum test, and categorical variables using either chi-square or Fishers exact test. Multiple logistic regression was performed for independent associations with GDM. Results A total of 411 participants were recruited, with complete data available for 391. Patients were on average 30 years of age, had a mean body mass index (BMI) of 28, and 17% were of Hispanic ethnicity. Additionally, 54% reported a family history of diabetes, with 4% reporting a personal prior history of GDM. A total of 39 participants (10.0%) developed GDM. Compared to those that did not, participants who developed GDM had a significantly higher baseline BMI (31.6 vs 27.2, p=0.003), HbA1c (5.24 vs 5.07, p&lt;0.001), triglycerides (156.8 vs 134.2, p=0.022), and random blood glucose (85.90 vs 79.96, p=0.025) at the initial visit. Those with GDM were more likely to have a prior history of gestational diabetes (28.21% vs 1.96%, p&lt;0.001), and current chronic hypertension (12.82% vs 1.9%, p=0.003). Additionally, they scored higher on a validated insomnia questionnaire (9.62 vs 7.80, p=0.028). A significant association was found between GDM and 3 previously reported genetic markers (p&lt;0.01). Individuals with high polygenic risk scores for type 2 diabetes were not more likely to have a GDM diagnosis. Through stepwise logistic regression, prior history of GDM, current diagnosis of hypertension, insomnia, and BMI were independently associated with GDM (odds ratio, 95% confidence intervals: 14.98, 4.49-50.02; 10.94, 2.32-51.69; 1.11, 1.01-1.22; 1.09, 1.03-1.16, respectively). Conclusion The Hoosier Moms Cohort identified that participants with a previous GDM diagnosis, chronic hypertension, elevated BMI, and insomnia have significantly increased odds of developing GDM in a diverse cohort of participants. These factors will be integrated into a machine learning model with multi-omics data to develop a comprehensive predictor for GDM.

Music Sentiment Analysis and its Application in Music Therapy Based on AI Technology

Music therapy, enriched by the integration of AI technology, represents a cutting-edge approach to harnessing the therapeutic power of music for mental and emotional well-being. AI algorithms are employed to analyze individual preferences, emotional states, and physiological responses, enabling the creation of personalized music interventions. These interventions can range from mood-enhancing playlists to dynamically generated compositions tailored to the specific needs of the listener. This paper introduces an Optimized Sentimental n-gram Classifier (OSC) model tailored for application in the context of music therapy. Leveraging artificial intelligence (AI) technology and sentiment analysis techniques, the OSC model aims to enhance the understanding and classification of sentiments expressed during music therapy sessions. The OSC model uses the n-gram classifier for the estimation of the feature vector in the music speech signal. The classifier model comprises of the Artificial Intelligence (AI) for the evaluation of the music therapy for the sentimental analysis. Through extensive experimentation and evaluation, the OSC model demonstrates high accuracy, precision, recall, and F1 scores across multiple iterations, indicating its effectiveness in accurately predicting sentiments and classifying sessions. The model's robust performance suggests its potential to assist therapists in better understanding participants' emotional states and tailoring interventions accordingly. By providing a valuable tool for sentiment analysis in music therapy, the OSC model contributes to advancing the integration of AI technology into healthcare practices, with implications for improving patient outcomes and well-being.

Implementation and adoption of SOAP-M and SBAR at a German anesthesiology department – a single-center survey study

BackgroundChecklists are a common tool used in order to mitigate risks caused by human factors and can facilitate the safe induction of anesthesia as well as handovers. SBAR (Situation, Background, Assessment, Recommendation) is a checklist recommended by the WHO and DGAI for handovers, while SOAP-M (Suction, Oxygen, Airway, Pharmaceuticals, Monitoring) is a checklist for the induction of anesthesia. This study investigates the implementation and adoption of these two checklists.MethodsWe conducted a single-center online survey one year after the implementation of SOAP-M and SBAR at a university hospital’s anesthesiology department, using scales from three validated questionnaires to assess safety attitudes as well as the behavior of staff and the perceived usefulness of the checklists.ResultsStaff with a high score in general attitude towards patient safety, as determined by the safety attitudes questionnaire, considered both checklists useful additions to their work environment. Nurses and physicians (p = 0.102) as well as groups divided according to work experience (p = 0.077) showed no significant differences in using SOAP-M and SBAR. Perceived usefulness was significantly higher (p < 0.001) among users of the checklists, and the same goes for positive reinforcement (p < 0.001), social cues (p = 0.0215) and goal cues (p = 0.0252).ConclusionSOAP-M and SBAR are perceived as useful checklists for patient handovers and anesthesia induction by tertiary referral hospital’s employees with high score in general safety attitude and were therefore commonly used one year after their introduction. No significant difference in checklist adoption between occupations as well as groups divided according to work experience could be found. Perceived usefulness is significantly higher among users of the checklist, who feel using the checklists provides more support.

Evaluating the public's readiness to combat vector-borne disease threats in Jazan, Saudi Arabia: A cross-sectional survey.

Vector-borne diseases (VBDs) pose a significant public health challenge in Saudi Arabia, particularly in the Jazan region. This study aimed to assess the knowledge, attitudes, and practices regarding VBDs among the population of Jazan and to identify factors associated with these variables. A cross-sectional survey was conducted among 642 adult residents of Jazan using a convenience random sampling technique. The survey tool consisted of 6 domains: demographics, knowledge of VBDs, preventive practices, care-seeking behavior, knowledge of specific VBDs, and attitudes towards VBDs. Data were analyzed using descriptive statistics, chi-square tests, t tests, ANOVA, and multivariable logistic regression. The majority of participants (60.0%) had high knowledge scores, while (75.5%) and (77.7%) had high attitude and practice scores, respectively. However, knowledge gaps were identified in specific areas, such as the transmission of leishmaniasis and Rift Valley fever. Gender was a significant predictor of both knowledge and practice scores, with males having higher knowledge and females demonstrating higher levels of preventive practices. Age, education, income, and working status were also associated with knowledge scores. The findings highlight the need for targeted interventions and educational campaigns to address the identified gaps in knowledge, attitudes, and practices. Future research should focus on exploring the effectiveness of different intervention strategies and investigating the integration of VBD prevention and control measures into existing healthcare systems. By employing a multi-disciplinary approach, evidence-based strategies can be developed to prevent and control VBDs, ultimately improving public health outcomes in Jazan and other endemic regions worldwide.

Development and Validation of an Interpretable Machine Learning Model for Early Prognosis Prediction in ICU Patients with Malignant Tumors and Hyperkalemia.

This study aims to develop and validate a machine learning (ML) predictive model for assessing mortality in patients with malignant tumors and hyperkalemia (MTH). We extracted data on patients with MTH from the Medical Information Mart for Intensive Care-IV, version 2.2 (MIMIC-IV v2.2) database. The dataset was split into a training set (75%) and a validation set (25%). We used the Least Absolute Shrinkage and Selection Operator (LASSO) regression to identify potential predictors, which included clinical laboratory indicators and vital signs. Pearson correlation analysis tested the correlation between predictors. In-hospital death was the prediction target. The Area Under the Curve (AUC) and accuracy of the training and validation sets of 7 ML algorithms were compared, and the optimal 1 was selected to develop the model. The calibration curve was used to evaluate the prediction accuracy of the model further. SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) enhanced model interpretability. 496 patients with MTH in the Intensive Care Unit (ICU) were included. After screening, 17 clinical features were included in the construction of the ML model, and the Pearson correlation coefficient was <0.8, indicating that the correlation between the clinical features was small. eXtreme Gradient Boosting (XGBoost) outperformed other algorithms, achieving perfect scores in the training set (accuracy: 1.000, AUC: 1.000) and high scores in the validation set (accuracy: 0.734, AUC: 0.733). The calibration curves indicated good predictive calibration of the model. SHAP analysis identified the top 8 predictive factors: urine output, mean heart rate, maximum urea nitrogen, minimum oxygen saturation, minimum mean blood pressure, maximum total bilirubin, mean respiratory rate, and minimum pH. In addition, SHAP and LIME performed in-depth individual case analyses. This study demonstrates the effectiveness of ML methods in predicting mortality risk in ICU patients with MTH. It highlights the importance of predictors like urine output and mean heart rate. SHAP and LIME significantly enhanced the model's interpretability.