Levels Of Severity Research Articles

Investigating the toxicity of polylactic acid microplastics on the health and physiology of freshwater fish, Cirrhinus mrigala.

The widespread presence of microplastics (MPs) in aquatic ecosystems has raised growing concerns among ecotoxicologists regarding their potential toxicity. This study explored the impacts of polylactic acid (PLA) MPs on the physiology and health of freshwater fish, Cirrhinus mrigala, by dietary exposure for 90 days. The experiment consisted of six groups: five treatment groups (0.5%, 1%, 1.5%, 2%, and 2.5% PLA-MP) and a control group (0% PLA-MP). Each group was comprised of fifteen fish, and the experiment was replicated three times. The exposure severity of PLA-MPs varied from low to high, with treatment levels ranging from 0.5% to 2.5% PLA-MPs, relative to the control group. This exposure significantly affected their growth performance. Additionally, the apparent digestibility of the SFM-based diet decreased with increasing PLA-MPs concentration. Exposure to PLA-MPs induced considerable changes in body composition, characterized by increased moisture and crude fat content and decreased ash content and crude protein. The blood profile, including MCHC, RBCs, Hb, PLT and PCV exhibited significant declines in the high treatment group (2.5% PLA-MPs), while MCH, WBCs and MCV showed notable increases. Furthermore, histopathological examination of the intestine revealed an increase in abnormalities in the intestine at 2.5% PLA-MPs level. The high treatment group (2.5% PLA-MPs) showed the lowest mineral content in the fish muscles. In summary, dietary exposure to PLA-MPs led to alterations in overall body performance across the treatment groups, ranging from low to high severity levels.

Cost benefit analysis of reducing speed limits in Athens to 30 Km/h

The objective of this research is to investigate public acceptance and socio-economic feasibility of reducing speed limit from 50 km/h to 30 km/h in certain parts of the road network of Athens, Greece. A questionnaire was developed based on the method of stated preference for various hypothetical scenarios of time, fuel consumption and the probability of road crash to investigate road user preferences towards the reduction of speed limit and identify the most important influencing factors preferences. A total of 408 respondents were asked to choose among three alternative proposals: a) Reduce the speed limit to 30 km/h throughout the urban network except on major arteries, b) Reduce the speed limit to 30 km/h throughout the urban network and c) No Reduction (do nothing). For the analysis, two binomial logistic regression models and two multinomial logistic regression models were developed. The results indicate that increase in travel time, the importance of speed in causing a crash, the number of crashes the user has been involved in, the respondent’s driving habits are the main determinants of the users’ preferences. Furthermore, a Cost Benefit Analysis (CBA) was conducted to understand the sustainability implications of the implementation of lower speed limits (i.e., 50 to 30 km/h). In terms of socio-economic impacts, the CBA considered safety-related metrics, namely the number of road casualties of all severity levels, and environmental-related metrics, namely reductions in CO2, NOx, PM levels as well as in road user surplus metrics namely travel time increase and fuel consumption reduction. Costs considered in the analysis result from increased time spent on the road. Consequently, the investment and the operational costs along with the socio-economic impact are estimated and monetized, up to the year 2030. The positive Net Present Value (NPV) and the high Internal Rate of Return (IRR), i.e. 64.5 %, obtained as well as the sensitivity analysis results, indicate the feasibility of this policy over time. Therefore, speed limit reductions are economically viable and so, should be encouraged.

Hypnosis for depression: Systematic review of randomized clinical trials with meta-analysis

IntroductionMajor Depressive Disorder (MDD) is one of the most debilitating diseases worldwide and has seen a significant increase in diagnoses during the pandemic, demanding more and better therapeutic tools to manage the post-pandemic scenario. ObjectiveThe aim of this systematic review is to provide a comprehensive analysis of the last 20 years of clinical research on Hypnotherapy (HT) to determine whether this intervention has evidence to support its recommendation for the treatment of MDD. MethodsThis review included only randomized clinical trials (RCTs) involving adult populations diagnosed with MDD, regardless of the severity level (mild, moderate, or severe) according to any validated diagnostic criteria, compared to a control group (active treatment or none), with any follow-up duration and free access to the manuscript. The bibliographic survey was conducted across seven distinct databases: MEDLINE (PubMed), Embase, CENTRAL, PsycINFO, Scopus, ScieELO, and Latin American and Caribbean Health Sciences Literature (LILACS). The risk of bias was assessed by two independent investigators using Cochrane's revised tool (RoB 2), and the final judgment was made by consensus. To better analyze the included studies, the certainty of the evidence was evaluated through the Grading of Recommendations Assessment, Development and Evaluation (GRADE). ResultsThere is not enough evidence to indicate that hypnosis-based interventions may reduce the severity of depression, which precludes the clinical recommendation of this intervention for patients in the real world, pending the production of better evidence of effectiveness and safety, although no evidence of significant adverse effects was found.

Resilience and Hassles Trajectories Among Older Adults During the COVID-19 Pandemic.

Despite higher physical vulnerability to coronavirus disease 2019 (COVID-19), older adults reported less psychological stress than younger and midlife adults during the pandemic. However, little is known about age differences in stress within later life, and most COVID-19 studies have been cross-sectional. We examined weekly hassles exposure and severity trajectories and whether these trajectories differed by age, resilience factors (higher trait resilience and education), and vulnerability factors (identifying as a woman, being a person of color, and having chronic health conditions). Community-dwelling adults aged 50+ in Oregon (Mage = 71.1, standard deviation = 7.3; 74% women, 89% non-Hispanic White) completed weekly online surveys across 8 weeks (April 28-June 22, 2020) during the COVID-19 stay-at-home mandate. A 2-part model estimated how age, resilience, and vulnerability factors predicted weekly odds of any hassle exposure and level of severity. Across time, hassles exposure decreased and the rate of severity declined, but these patterns differed by age and other demographics. The old-old (estimated at age 78) remained stable in odds of any exposure, whereas the young-old (estimated at age 64) evidenced a J-shaped curve; age did not moderate the severity slopes. Furthermore, both resilience factors were associated with exposure trajectories, whereas vulnerability factors (race/ethnicity and chronic illness) were associated with levels of hassles severity. There were age differences in patterns of hassles during the COVID-19 pandemic. Furthermore, resilience and vulnerability factors also showed complex patterns, underscoring the need for future studies to focus on age differences in well-being in later life.

Early prediction of functional mobility severity after stroke: Two key milestones

BackgroundThe prognosis for functional recovery is crucial for discharge planning, optimizing healthcare resources and improving recovery chances. Prediction tools are key but must be usable from the beginning of rehabilitation and easy to use in clinical routine. Simple mobility milestones have shown predictive potential for stroke recovery. This study aims to identify the most efficient milestones for predicting the severity level of stroke patients' mobility at an early stage of recovery. MethodsRetrospective cohort study where medical records from a general private hospital were reviewed. Through convenience sampling. The data collected included the Standardized Five Questions, Functional Status Score for the Intensive Care Unit, Berg Balance Scale, and Modified Rankin Scale, all unified by the Complexity-Based Model for Categorizing Rehabilitation, establishing severity levels. To predict mobility severity levels at reevaluations, CHAID (Chi-Square Automatic Interaction Detector) decision tree analysis and exact logistic regression analysis were employed. This involved using conventional mobility milestones alongside the clinical and demographic characteristics of the patients. Results50 patients met the criteria from admission (Evaluation 1: first week from stroke) to the first discharge point (Evaluation 2: second week from stroke). Only 25 patients remained for the second discharge point (Evaluation 3: fourth week from stroke). There were significant differences when comparing the functional assessments. The milestones “Sitting to standing” and “Sitting unsupported” were the most efficient predictors of future mobility complexity. Conclusions“Sitting balance” and “Sitting unsupported” were the best predictors. These simple milestones are feasible to assess in daily clinical practice.

Alternations of the gut microbiota and the Firmicutes/Bacteroidetes ratio after biologic treatment in inflammatory bowel disease

BackgroundThe inflammatory bowel disease (IBD), comprising Crohn's disease (CD) and ulcerative colitis (UC) is a complex disease with multifactorial etiology. The intestinal dysbiosis have been investigated to play an important role in IBD pathogenesis and disease activity. The aim of our study was to analyze the intestinal microbiota composition in IBD across different severity levels and the impact of biologic therapy on microbiota modulation. MethodsIn this study, 27 IBD patients were recruited, including 14 patients undergoing biologic therapy for moderate to severe disease activity and 13 controls with inactive disease. The gut microbial composition was determined by 16 S ribosomal RNA gene sequencing of stool samples. ResultsBiologic therapy led to significant clinical improvement in IBD disease activity after 48 weeks. About species richness, community alpha diversity was significant lower in active CD patients and enriched gradually after biologic therapy. The beta-diversity regard to the difference of bacterial community composition showed significant difference between patients in biologic and control group. A decrease in Firmicutes and increase in Bacteroidetes abundance were observed in patients with active disease, both in CD and UC. Biologic treatment induced shifts in gut microbiota, with increased Firmicutes and decreased Bacteroidetes, as well as improved F/B ratio gradually after treatment, correlating with disease activity. ConclusionsOur study suggested that gut microbiota differences changed after biologic therapies among IBD with different disease activity, and a rising Firmicutes/Bacteroidetes ratio could be a potential predictor for disease activity and treatment response monitoring.

A genome-wide Association study of the Count of Codeine prescriptions

Opioid prescription records in existing electronic health record (EHR) databases are a potentially useful, high-fidelity data source for opioid use-related risk phenotyping in genetic analyses. Prescriptions for codeine derived from EHR records were used as targeting traits by screening 16 million patient-level medication records. Genome-wide association analyses were then conducted to identify genomic loci and candidate genes associated with different count patterns of codeine prescriptions. Both low- and high-prescription counts were captured by developing 8 types of phenotypes with selected ranges of prescription numbers to reflect potentially different levels of opioid risk severity. We identified one significant locus associated with low-count codeine prescriptions (1, 2 or 3 prescriptions), while up to 7 loci were identified for higher counts (≥ 4, ≥ 5, ≥6, or ≥ 7 prescriptions), with a strong overlap across different thresholds. We identified 9 significant genomic loci with all-count phenotype. Further, using the polygenic risk approach, we identified a significant correlation (Tau = 0.67, p = 0.01) between an externally derived polygenic risk score for opioid use disorder and numbers of codeine prescriptions. As a proof-of-concept study, our research provides a novel and generalizable phenotyping pipeline for the genomic study of opioid-related risk traits.

Association of dietary insulinemic potential with disease severity and symptoms in patients with COVID-19

AimCOVID-19 has threatened people's lives in world. Studies showed that level of insulin is related to function of immune system and level of inflammation in body. In this study we aimed to evaluate the association between dietary insulinemic potential and level of inflammatory biomarkers, severity and symptoms of COVID-19. MethodsParticipants of this cross-sectional study were 684 people who recovered from COVID-19. Dietary intake of participants was assessed using 168-item food frequency questionnaire during telephone interview, then dietary insulinemic index and load (DII and DIL) calculated for each participants. Data related to study outcomes, include levels of inflammatory biomarkers, disease symptoms, lung infection, saturation of peripheral oxygen, Respiratory Rate, need for respiratory support, duration of disease, hospitalization, recovery from disease and need for respiratory support and score of life satisfaction were collected through an interview with participants and review of their medical records. ResultsAfter controlling for confounders, participants in the highest quartile of DIL had a significantly higher risk of severe COVID-19 (OR: 2.66, 95% CI: 1.01–7.04, Ptrend=0.035), risk of hospitalization (OR: 3.44, 95% CI: 2.27-7.64, Ptrend>0.001), fever (OR: 2.91, 95% CI: 8.08–1.05, Ptrend=0.042), chills (OR: 4.68, 95% CI: 1.77–12.37, Ptrend=0.002) and lower risk of weakness (OR: 0.28, 95% CI: 0.10–0.74, Ptrend=0.012) and myalgia (OR:0.26, CI: 0.10–0.45, Ptrend=0.006) compared to the lowest quartile. In addition our findings showed a significant positive association between DII and risk of fever (OR: 1.71 95% CI: 2.84–1.03, Ptrend=0.049). Our finding failed to find a significand association between DII and risk of sever COVID-19 and hospitalization. Moreover we did not find significant association between DII/DIL and duration of disease, other symptoms and outcomes. ConclusionsOur findings showed that higher DIL was associated with greater risk of sever COVID-19 and hospitalization. More detailed cohort studies are needed to confirm the findings.

Developing Model-Agnostic Meta-Learning Enabled Lightbgm Model Asthma Level Prediction in Smart Healthcare Modeling

Millions of people across the world suffer from the chronic respiratory condition known as asthma. Predicting the severity of asthma based on a variety of personal and environmental characteristics might yield useful information for preventative measures. LightGBM model is a gradient-boosted model with the potential for great accuracy, but it requires careful hyperparameter adjustment to reach its full potential. Common tuning techniques have a hard time generalizing to new data distributions. The dataset was used, and its many subsets were used to represent various demographics and geographic areas. LightGBM was configured with hyperparameters, trained on a sample dataset, and then verified for each job. To quickly adjust to new tasks, the MAML method sought to find the optimal values for its hyperparameters. After the meta-training step was complete, the generalizability of the hyperparameters was tested on new data. After including MAML for hyperparameter adjustment, the Light- GBM model showed a gain of 7% in accuracy, coming in at 98.5%. Predictions of severe asthma had a crucially high 97.8 percent degree of accuracy. The model’s recall rate for severe asthma levels was 97.4%, demonstrating its capacity to reliably detect and anticipate important instances. An F1-score of 97.1%, a metric that averages the accuracy and recall of a model, is indicative of good overall performance.. For gradient-boosted model applications like asthma level prediction, MAML provides a viable path for hyperparameter adjustment. Although there are obstacles to be overcome, this method has the potential to greatly improve the flexibility and precision of predictive healthcare models. More effective implementations and a wider range of applications can be explored in future studies.

LABORATORY PARAMETERS IN PATIENTS WITH CHRONIC CEREBRAL ISCHEMIA DEPENDING ON THE PRESENCE OF CONCOMITANT CHRONIC OBSTRUCTIVE PULMONARY DISEASE

This study investigates the laboratory parameters in patients with chronic cerebral ischemia (CCI) in relation to the presence of concomitant chronic obstructive pulmonary disease (COPD). CCI is characterized by a progressive reduction in cerebral blood flow, while COPD can exacerbate hypoxia, potentially worsening brain ischemia. The research evaluates blood biomarkers, including inflammatory markers and oxygenation indices, to determine the impact of COPD on CCI patients. Results suggest that patients with both CCI and COPD present with more severe inflammatory responses and lower oxygenation levels, which may influence the progression of cerebral ischemia and overall prognosis.

Trends and Factors Affecting Functional Limitations on Activities of Daily Living Performance Among Korean Adults With Disabilities, 2008–2020: A Longitudinal Study

ABSTRACT Background Loss of the ability to perform activities of daily living (ADLs) leads to negative health outcomes such as reduced quality of life, institutionalization, and mortality. In Korea, the proportion of older adults with disabilities is increasing along with rapid population aging. Therefore, providing a comprehensive approach to the prevention and management of ADL limitations in people with disabilities is necessary. This can be accomplished by understanding the trends and factors affecting these limitations over time. Purpose This study was developed to examine the longitudinal trend and factors affecting ADL limitations over time among people with disabilities in Korea. Methods Data from 346 people with disabilities in the 2008–2020 Korean Welfare Panel Study were used. Bivariate analysis and a Kendall trend test were performed to determine the longitudinal trends for ADL limitations, and multiple logistic regression was used to evaluate whether relevant variables could predict these limitations. Results The prevalence of ADL limitations among people with disabilities increased by 16.5% over the 12 years of the study. The highest rate of increase in these limitations over time was found in people with mental disorders (27.8%), those over 65 years of age (27.3%), and those with depression (25.6%). In multiple logistic regression, the odds ratios of the variables were slightly different at each survey wave. However, severe disability and low educational level were consistently found to be associated with ADL limitations over time. Conclusions The findings provide evidence of a significant relationship between level of disability and/or educational status and ADL limitations in people with disabilities in Korea. To prevent the development of ADL limitations in people with disabilities, comprehensive identification of longitudinal trends and factors affecting ADL limitations is necessary. Early intervention, including integrated services such as home rehabilitation services to prevent ADL limitations, especially for disabled people with severe disabilities and low educational levels, has the potential to delay ADL limitations.

Application and expansion of an algorithm predicting attention-deficit/hyperactivity disorder and impairment in a predominantly White sample.

Current assessment protocols for attention-deficit/hyperactivity disorder (ADHD) focus heavily on a set of highly overlapping symptoms, with well-validated factors like cognitive disengagement syndrome (CDS), executive function (EF), age, sex, and race and ethnicity generally being ignored. Using machine learning techniques, the current study aimed to validate recent findings proposing a subset of ADHD symptoms that, together, predict ADHD diagnosis, severity, and impairment level better than the full symptom list, while also testing whether the inclusion of the factors listed above could further increase accuracy. Parents of 1,922 children (50.1% male) aged 6-17 years completed rating scales of ADHD, CDS, EF, and impairment. Results suggested nine symptoms as most important in predicting outcomes: (a) has difficulty sustaining attention in tasks or play activities; (b) does not follow through on instructions and fails to finish work; (c) avoids tasks (e.g., schoolwork, homework) that require sustained mental effort; (d) is often easily distracted; (e) has difficulty organizing tasks and activities; (f) is often forgetful in daily activities; (g) fidgets with hands or feet or squirms in seat; (h) interrupts/intrudes on others; and (i) shifts around excessively or feels restless or hemmed in. The abbreviated algorithm achieved accuracy rates that did not significantly differ compared to an algorithm comprising all 18 symptoms in predicting impairment, while also demonstrating excellent discriminative ability in predicting ADHD diagnosis. Adding CDS and EF to the abbreviated algorithm further improved the prediction of global impairment. Continued refinement of screening tools will be key to ensuring access to clinical services for youth at risk for ADHD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Using machine learning modeling to identify childhood abuse victims on the basis of personality inventory responses

Trauma is very common and associated with significant co-morbidity world-wide, particularly PTSD and frequently other mental health disorders. However, it can be challenging to identify victims of abuse as self-reports can be difficult to elicit due to emotional distress. Better confirmation of a history of significant mistreatment can assist significantly in treatment planning. We evaluate an alternate approach based on machine-learning techniques applied to personality inventory data (Minnesota Personality Inventory, Adolescent Version; MMPI-A) obtained concurrently to examine convergence with reports of past trauma exposure. The Childhood Trauma Questionnaire (CTQ) was administered to 733 child and adolescent inpatients. Statistical and information-theory measures showed that each type of abuse – sexual, physical, and emotional – had a unique “fingerprint” of MMPI-A profiles. In contrast to our previous findings in terms of specific correlations with IQ, individuals positive for Sexual abuse had the fewest MMPI-A elevations, followed by Physical abuse, while those reporting Emotional abuse had the greatest number of elevations. We developed an initial classifier Machine Learning (ML) model for predicting a history of abuse that demonstrates equivalent sensitivity compared to other widely used screening measures. In addition, we show via PCA and cluster analysis that the different levels of severity of emotional abuse present with unique mixtures of personality trait characteristics. Thus, this type of ML mediated analysis could permit at-scale detection of those at potential high risk of a history of abuse by use of real-time information, using a variety of nontransparent data sources.

Association of Abdominal Aortic Calcification and Clinical Outcomes in LDH Patients Treated with Endoscopic Lumbar Discectomy.

To determine any significant associations between abdominal aortic calcification (AAC) and clinical manifestations of pain symptoms following endoscopic lumbar discectomy. Patients sequentially presenting with a history of unilateral radiculopathy with or without back pain, and with magnetic resonance imaging (MRI)-confirmed diagnosis of posterolateral disc herniation between August 2021 and December 2023 were eligible for inclusion in the study. All patients underwent endoscopic discectomy at our center during the study period. AAC stages were classified based on the Kauppila classification system using lateral lumbar radiographs. Between August 2021 and December 2023 a total of 120 patients were included in the study for analysis, of whom 82 (68.3%) exhibited mild AAC and 38 (31.7%) had moderate-severe AAC. Analyses using the multivariate linear regression model revealed a significant correlation between AAC comorbidity and postoperative clinical outcomes. At 1 year post-surgery, the mean change in leg pain following endoscopic lumbar discectomy was significantly less for patients with moderate-severe AAC (5.0 points) than for patients with mild ACC (p < 0.001). Even after adjusting for relevant confounders, this difference remained significant. Similar results were also observed in the postoperative improvement of back pain, the Oswestry Disability Index score, and the physical functioning, role physical, and bodily pain components of the 36-item Short Form Health Survey questionnaire. There was no significant difference in the rate of repeat surgery or post-surgical new-onset back pain between patients with different levels of severity of AAC at 1 year post-surgery. There is a significant association between the severity of AAC and clinical outcomes among patients with lumbar disk herniation who underwent endoscopic lumbar discectomy. AAC may serve as a prognostic factor in predicting surgical outcomes and guiding management strategies for patients with lumbar disk herniation following endoscopic lumbar discectomy.

A Consensus on Staphylococcus aureus Exacerbated Atopic Dermatitis and the Need for a Novel Treatment.

The skin microbiome is essential for skin barrier function because it inhibits pathogen colonization, and decreased microbiome diversity correlates with increased Staphylococcus aureus (S. aureus) burden and atopic dermatitis (AD) severity. Managing S. aureuss-driven AD in clinical practice remains problematic due to complications such as AD exacerbation, impetigo, abscesses, and invasive infections. This project used a modified Delphi process comprising face-to-face discussions followed by a blinded vote to define 5 final consensus statements. A panel of 6 pediatric dermatologists developed a consensus on S. aureus-driven AD exacerbation, challenges in current treatments for AD with secondary bacterial infections, and new developments to improve patient care and outcomes. The panel's 5 consensus statements provide recommendations for dermatologists, pediatricians, and healthcare providers treating patients with secondary infected AD. These recommendations underscore the importance of recognizing and managing S. aureus skin infection in AD clinical practice and promoting antibiotic stewardship to mitigate resistance. The panel defined a significant unmet need for a single topical AD therapy effective against all symptoms, including pruritus, S. aureus-driven AD exacerbation, infection, and inflammation, across AD severity levels. J Drugs Dermatol. 2024;23(10):825-832. doi:10.36849/JDD.8240.

Practicality, Validity, and Responsiveness of Using the Proxy Version of the Child Health Utility–9 Dimensions With Children Aged 2 to 5 Years

ObjectivesThis study aimed to assess the practicality, validity, and responsiveness of the proxy Child Health Utility–9 Dimensions (CHU9D) in children aged 2 to 5 years. MethodsWe used data from the Barrier Enhancement for Eczema Prevention trial, a UK randomized controlled trial testing whether daily emollients in infancy could prevent eczema in high-risk infants. The main parent/carer completed the proxy CHU9D using developers’ additional guidance for completion in those younger than 5 years and the Patient-Oriented Eczema Measure (POEM) at ages 2, 3, 4, and 5 years. Practicality was assessed by completion rates. Construct validity assessed whether CHU9D could discriminate between those with/without eczema and between eczema severity levels on POEM. Responsiveness was determined by ability to discriminate between 3 groups: (1) those whose POEM score deteriorated ≥3 points, (2) those whose change was not clinically important (−2.9 to 2.9 points), and (3) those whose POEM score improved ≥3 points. Analysis was conducted in Stata 17. ResultsOf 1394 children participating in the Barrier Enhancement for Eczema Prevention trial, study questionnaires were completed by 1212 (87%), 981 (70%), 990 (71%), and 976 (70%) at 2, 3, 4, and 5 years. Of these the CHU9D was completed by 1066 (88.0%), 685 (69.8%), 925 (93.4%), and 923 (94.6%), respectively. Mean utility at all time points was approximately 0.934 (range 0.443-1). For construct validity, very small differences in the CHU9D between known groups were observed (P < .01). A total of 801 participants had responsiveness data: 13% deteriorated, 72% had nonclinically important change, and 15% improved. Mean utility change (standardized response mean) for these groups was −0.0198 (0.21), 0.0041 (0.05), and 0.0175 (0.21) showing small change and small responsiveness. ConclusionsProxy CHU9D in 2- to 5-year-old children shows potential but further research is needed.

Nonparametric Testing of Body Mass Index as a Predictor of Severe Noma: Assessing Potential Association.

Noma, a devastating facial disease, primarily affects impoverished populations in low- and middle-income countries, often leading to severe disfigurement or fatality if untreated. Studies suggest malnutrition is a significant risk factor for noma, yet the association between body mass index (BMI) in kilograms per square meter and tissue damage severity remains underexplored. This study aimed to investigate the relationship between BMI and tissue damage severity among noma cases in Ethiopia. A retrospective analysis of medical records from Facing Africa's database covering noma cases treated between 2007 and 2019 was conducted. A standardized data collection template recorded demographic and clinical data, including patient demographics, BMI, and tissue damage severity levels. JASP statistical software was used for analysis. Analysis of 228 noma cases revealed a mean BMI of 17.8 kg/m2, ranging from 11.5 to 28.3 kg/m2. Females exhibited a higher mean BMI (18.3 kg/m2) than males (16.9 kg/m2). Differences in BMI distributions between major (67) and minor (30) tissue damage categories were observed, with "minor" cases having a slightly higher mean BMI. The study found a statistically significant association between BMI and tissue damage severity (P = 0.03), indicating that tissue damage due to noma worsened as BMI decreased. Nonparametric tests confirmed this association's significance. This study highlights low BMI as a predictor of severe noma. Public health interventions should focus on malnutrition to reduce noma severity and psychosocial and functional morbidity and improve outcomes. However, additional factors influencing the progression of noma and the severity of associated tissue damage require further investigation.

Knee joint dysfunction in the patients immediately before arthroplasty was well reflected by locomotive syndrome, not physical frailty.

We assessed physical function by three different methods in patients with knee osteoarthritis just before total knee arthroplasty (TKA) and investigated the relationship between impairment of mobility and preoperative factors. All patients scheduled to undergo TKA at our hospital were assessed for basic attributes, clinical assessment, radiography, whole-body mode DXA, knee muscle strength. And frailty, sarcopenia, and locomotive syndrome (LS) were evaluated. Among 204 patients (213 knees),172 women, mean age 75.0 years, the overall distribution in frailty was <no: 14.6%, pre-frailty: 58.5%, and frailty: 26.8%>; in sarcopenia <no: 93.3%, yes: 3.4%, and severe: 3.4%>; and in LS <Stage 0: 0%, Stage 1: 3.3%, Stage 2: 11.4%, and stage 3: 85.3%>. Eighty-seven percent of the patients with frailty and 92% with LS stage 3 did not suffer from sarcopenia. Statistically significant relationships were observed between sarcopenia and frailty, while no relationship between LS and frailty or LS and sarcopenia. Multivariate analysis of related factors with severity level for frailty and LS revealed statistically significant correlations for frailty with gait speed and LS with KSS and muscle strength. In the patients with knee OA immediately before arthroplasty, physical frailty and LS was not related with by sarcopenia. Knee joint dysfunction without sarcopenia was well reflected by LS, not by physical frailty.

Local adaptation, plasticity, and evolved resistance to hypoxic cold stress in high-altitude deer mice

A fundamental question in evolutionary biology concerns the relative contributions of phenotypic plasticity vs. local adaptation (genotypic specialization) in enabling wide-ranging species to inhabit diverse environmental conditions. Here, we conduct a long-term hypoxia acclimation experiment to assess the relative roles of local adaptation and plasticity in enabling highland and lowland deer mice (Peromyscus maniculatus) to sustain aerobic thermogenesis at progressively increasing elevations. We assessed the relative physiological performance capacities of highland and lowland natives as they were exposed to progressive, stepwise increases in hypoxia, simulating the gradual ascent from sea level to an elevation of 6,000 m. The final elevation of 6,000 m far exceeds the highest attainable elevations within the species' range, and therefore tests the animals' ability to tolerate levels of hypoxia that surpass the prevailing conditions within their current distributional limits. Our results demonstrate that highland natives exhibit superior thermogenic capacities at the most severe levels of hypoxia, suggesting that the species' broad fundamental niche and its ability to inhabit such a broad range of elevational zones is attributable to genetically based local adaptation, including evolved changes in plasticity. Transcriptomic and physiological measurements identify evolved changes in the acclimation response to hypoxia that contribute to the enhanced thermogenic capacity of highland natives.

Deep learning approaches for assessing pediatric sleep apnea severity through SpO2 signals

Pediatric Sleep Apnea–Hypopnea (SAH) presents a significant health challenge, particularly in diagnostic contexts, where conventional Polysomnography (PSG) testing, although effective, can be distressing for children. Addressing this, our research proposes a less invasive method to assess pediatric SAH severity by analyzing blood oxygen saturation (SpO2) signals. We adopted two advanced deep learning architectures, namely ResNet-based and attention-augmented hybrid CNN-BiGRU models, to process SpO2 signals in a one-dimensional (1D) format for Apnea–Hypopnea Index (AHI) estimation in pediatric subjects. Employing the CHAT dataset, which includes 844 SpO2 signals, the data was partitioned into training (60%), testing (30%), and validation (10%) sets. A predefined validation subset was randomly selected to ensure the models' robustness via a threefold cross-validation approach. Comparative analysis revealed that while the ResNet model attained an average accuracy of 72.9% across four SAH severity categories with a kappa score of 0.57, the CNN-BiGRU-Attention model demonstrated superior performance, achieving an average accuracy of 75.95% and a kappa score of 0.63. This distinction underscores our method's efficacy in both estimating AHI and categorizing SAH severity levels with notable precision. Further, to evaluate diagnostic capabilities, the models were benchmarked against common AHI thresholds (1, 5, and 10 events/hour) in each test fold, affirming their effectiveness in identifying pediatric SAH. This study marks a significant advance in the field, offering a non-invasive, child-friendly alternative for pediatric SAH diagnosis. Although challenges persist in accurately estimating AHI, particularly in severe cases, our findings represent a critical stride towards improving diagnostic processes in pediatric SAH.