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Correction: Biodiversity hotspot assessment in the Altai Mountains transboundary region based on Mammals and Aves
Evaluation the root canal shape among residents of Moscow region using cone beam computed tomography scanning
Correction: Maladaptive perfectionism can explain the inverse relationship between dispositional mindfulness and procrastination
A personalized recommendation algorithm for English exercises incorporating fuzzy cognitive models and multiple attention mechanisms
Retraction: An immunoinformatics and extensive molecular dynamics study to develop a polyvalent multi-epitope vaccine against cryptococcosis
The role of urinary biomarkers in the diagnosis of acute kidney injury in patients with liver cirrhosis
Correction: Disparity in neonatal abstinence syndrome by race/ethnicity, socioeconomic status, and geography, in neonates ≥ 35 weeks gestational age
Linked survey and statutory health insurance data evaluating healthcare utilization patterns and associated factors of persons with diabetes in Germany – latent class analysis
Abstract Persons with diabetes mellitus have complex healthcare needs. Existing disease management programmes (DMPs) are based on a one-size-fits-all approach. However, individuals might require more individualised care. This study aims to identify groups with different patterns of healthcare utilization among people with diabetes in Germany and factors associated with these different patterns. A cross-sectional survey was conducted among a random sample from a statutory health insurance (SHI) with diabetes (n = 1332) and linked to longitudinal SHI data. Latent class analysis was used to identify subgroups with similar patterns of healthcare utilization and factors associated with different patterns. Four patterns of healthcare utilization were identified among people with diabetes: ‘low users’ (20.8% of the total sample); ‘low users with ophthalmologist visit’ (45.2%); ‘high users’ (26.5%); and ‘high users with mental health care’ (7.5%). The classes differed significantly in age, sex, type, duration and severity of diabetes, DMP membership, diabetes training, health-related quality of life, and prevalence of depression. The ‘high users with mental health care’ class was for example younger, more female, had a lower quality of life and the highest prevalence of depression. This study may provide a first basis for thinking about targeted care in Germany beyond DMPs.
Correction: Value sets and the problem of redundancy in value set repositories
Maternal nutrition literacy and childhood obesity in food-insecure and secure households
Death toll among the Bangladeshi refugees of the 1971 war
Bangladesh achieved its independence in 1971 through a violent liberation war. To avoid persecution by the Pakistani army, 9.9 million Bangladeshi refugees escaped to India. Medicine and food supplies to these camps were not adequate to meet the necessities of such a large population of refugees. Therefore, poor condition of these camps resulted in a higher death rate among the refugees than the peacetime death rate of Bangladeshis. This paper reviews reported death tolls in several refugee camps in India as published in newspapers and peer-reviewed journals. Extrapolating these figures, we estimate the total death toll among the refugees in 1971. We also estimate the overall ‘excess death toll’, the difference between the actual death toll and the expected natural death toll among these refugees, to be approximately 562,915 deaths. The confidence interval for the estimated excess death toll among the refugees is (323,562, 802,268) deaths.
Exploring the wave’s structures to the nonlinear coupled system arising in surface geometry
The challenges of transgender and nonbinary graduate students in chemistry: A qualitative study on trans identity, science culture, and institutional support using reflexive thematic analysis
Transgender, nonbinary, two spirit, and gender-expansive students (herein trans students ) are marginalized in higher education and have significantly different college experiences than their cisgender peers. Using in-depth interviews modeled after Sista Circles methodology and applying reflexive thematic analysis, this qualitative research illuminates the nuanced experiences of trans students navigating chemistry PhD programs ( N = 10). The participants’ counterstories revealed tensions between their identities as trans people and their identities as chemists, where STEM professional culture encouraged the participants to cover and separate their transness from their graduate education. The data demonstrated that these students navigated a complicated process when choosing a graduate program and deciding whether to share their trans identities in their institutions. Participants also encountered cisnormative institutional structures, including program applications and information technology systems, which enforced usage of their legal name and gender marker data in the academy. These results highlight disparities between institutional rhetoric regarding LGBTQ+ inclusion and tangible support for trans graduate students. From grappling with the absence of supportive policies to advocating for institutional change, participants confronted systemic barriers that impeded their academic and personal growth. This study underscores the imperative for transparent and proactive support structures within STEM academic departments to foster an environment where trans individuals can thrive.
Neutralizing antibodies to SARS-CoV-2 variants of concern: a pediatric surveillance study
Early detection of esophageal cancer: Evaluating AI algorithms with multi-institutional narrowband and white-light imaging data
Esophageal cancer is one of the most common cancers worldwide, especially esophageal squamous cell carcinoma, which is often diagnosed at a late stage and has a poor prognosis. This study aimed to develop an algorithm to detect tumors in esophageal endoscopy images using innovative artificial intelligence (AI) techniques for early diagnosis and detection of esophageal cancer. We used white light and narrowband imaging data collected from Gachon University Gil Hospital, and applied YOLOv5 and RetinaNet detection models to detect lesions. The models demonstrated high performance, with RetinaNet achieving a precision of 98.4% and sensitivity of 91.3% in the NBI dataset, and YOLOv5 attaining a precision of 93.7% and sensitivity of 89.9% in the WLI dataset. The generalizability of these models was further validated using external data from multiple institutions. This study demonstrates an effective method for detecting esophageal tumors through AI-based esophageal endoscopic image analysis. These efforts are expected to significantly reduce misdiagnosis rates, enhance the effective diagnosis and treatment of esophageal cancer, and promote the standardization of medical services.
Retinal neurodegeneration, neuroretinal rim analysis and choroid thickness in pseudoexfoliation syndrome with spectral domain optical coherence tomography
Genetic, clinical, lifestyle and sociodemographic risk factors for head and neck cancer: A UK Biobank study
Introduction Despite a steady decline in tobacco smoking, head and neck cancer (HNC) incidence rates are on the rise. Therefore, novel risk factors for HNC are needed to identify at-risk patients at an early stage. Here, we used genetic, clinical, lifestyle, and sociodemographic data from UK Biobank (UKB) to evaluate the relative importance of known risk factors for HNC and identify novel predictors of HNC risk. Methods All participants in the UKB between 2006 and 2021 were stratified into HNC cases and controls at baseline (cases: n = 534; controls: n = 501833) or during follow-up (cases: n = 1587; controls: n = 500246). A cross-sectional description of risk factors (clinical characteristics, lifestyle and sociodemographic) for HNC at baseline was performed, followed by multivariate Cox regression analysis (adjusted for age and sex) and gradient boosting machine learning to determine the relative importance of predictors (phenotypic predictors and SNPs) of HNC development after baseline. Results In addition to known risk factors for HNC (age, male sex, smoking and alcohol consumption habits, occupation), we show that smoking cessation at ≤ 40 years of age is the strongest predictor of HNC risk. Although SNPs may play a role in HNC development, a predictive model containing phenotypic variables and SNPs (C-index 0.75) did not significantly outperform a model containing the phenotypic predictors alone (C-index 0.73). Conclusion Taken together, this study demonstrates that phenotypic variables such as past tobacco smoking habits, occupation, facial pain, education, pulmonary function, and anthropometric measures can be used to predict HNC risk.
Effect of the location and severity of partial ureteral obstruction on urinary system stone disease formation
Optimizing an automated sleep detection algorithm using wrist-worn accelerometer data for individuals with chronic pain
Objective To optimize a wrist-worn accelerometer-based, automated sleep detection methodology for chronic pain populations. Patients and methods A cohort of 16 patients with chronic pain underwent free-living observation for one week before participating in an Interdisciplinary Pain Management Program. Patients wore ActiGraph GT9X devices and maintained a sleep diary, documenting their nightly bedtimes and wake times. To derive sleep quality measures from accelerometry data, the Tudor-Locke sleep detection algorithm was employed. However, this algorithm had not been validated for chronic pain patients. Therefore, a sensitivity analysis of the algorithm’s parameters was conducted, identifying a set of parameters which maximized the agreement between sleep periods identified by the algorithm and sleep periods identified by participant’s sleep logs, which were considered ground truth. Sleep measures derived when using the optimized parameters were then compared against sleep measures derived using the default parameters. Results Our optimized parameter set achieved a mean sleep detection agreement of 67% with participant’s sleep logs, while the default parameter set achieved a mean agreement of 50%. Statistically significant differences were observed between sleep measures from the optimal and default parameter sets (P < .001). These findings suggest an optimized parameter set should be favored for chronic pain populations. Conclusion The Tudor-Locke algorithm provides automated sleep detection for accelerometry data; however, caution must be exercised when applying the algorithm to populations beyond its validated scope. In this manuscript, we provide an empirically optimized parameter set for applying this algorithm to adults with chronic pain.