Association of conduit dimensions with perioperative outcomes and long-term quality of life after esophagectomy for malignancy.

Objective: The impact of conduit dimensions and location of esophagogastric anastomosis on long-term quality of life after esophagectomy remains unexplored. We investigated the association of these parameters with surgical outcomes and patient-reported quality of life at least 18 months after esophagectomy. Methods: We identified all patients who underwent esophagectomy for cancer from 2018 to 2020 in our institution. We reviewed each patient's initial postoperative computed tomography scan measuring the gastric conduit's greatest width (centimeters), linear staple line length (centimeters), and relative location of esophagogastric anastomosis (vertebra). Quality of life was ascertained using patient-reported outcome measures. Perioperative complications, length of stay, and mortality were collected. Multivariate regressions were performed. Results: Our study revealed that a more proximal anastomosis was linked to an increased risk of pulmonary complications, a lower recurrence rate, and greater long-term insomnia. Increased maximum intrathoracic conduit width was significantly associated with trouble enjoying meals and reflux long term after esophagectomy. A longer conduit stapled line correlated with fewer issues related to insomnia, improved appetite, less dysphagia, and significantly enhanced "social," "role," and "physical'" aspects of the patient's long-term quality of life. Conclusions: The dimensions of the gastric conduit and the height of the anastomosis may be independently associated with outcomes and long-term quality of life after esophagectomy for cancer.

Migrant well-being and undocumented status in South Korea: a cross-sectional assessment of physical, psychological, social well-being, and health behaviors.

BACKGROUND: A high burden of physical, mental, and occupational health problems among migrant workers has been well-documented, but data on undocumented migrant workers are limited and their well-being has rarely been compared to that of the general population. METHODS: Using data from a cross-sectional survey of non-professional migrant workers in South Korea in early 2021, we described their physical, psychological, social well-being and health behaviors across a wide range of outcomes, including self-rated health, occupational injury, cigarette smoking, heavy alcohol consumption, meal pattern, happiness, mental illness, social support, and social participation. The outcomes were first compared between documented and undocumented migrant workers in generalized linear regressions adjusting for potential confounders. Then, the well-being of the migrant workers was compared against that of the general population using data from the Korean Happiness Survey, which is a nationally representative survey of the South Korean general population conducted in late 2020. The parametric g-formula was performed to adjust for potential confounders. RESULTS: After adjusting for potential confounders, the undocumented migrant workers were less likely to be happy or participate in social communities, and much more likely to have anxiety or depression, smoke cigarettes, or engage in heavy alcohol consumption than the documented migrant workers. When compared to the general South Korean population, an evident social gradient emerged for happiness and mental illness; the undocumented experienced the worst outcome, followed by the documented, and then the general population. Also, the undocumented migrant workers were more likely to smoke cigarettes than the general population. CONCLUSION: The undocumented migrant workers face considerably greater challenges in terms of mental health and happiness, demonstrate higher rates of risky health behaviors such as smoking and heavy drinking, and experience a lack of social support and community integration. A stark social gradient in happiness, mental illness, and cigarette smoking exists among the documented, undocumented migrant workers and the general population in South Korea. Socio-structural factors are likely to play a crucial role in contributing to the suboptimal level of overall well-being of undocumented migrant workers. Policy-level interventions as well as interpersonal efforts are in urgent need.

Effects of uric acid on ischemic diseases, stratified by lipid levels: a drug-target, nonlinear Mendelian randomization study.

Although uric acid-lowering agents such as xanthine oxidase inhibitors have potential cardioprotective effects, studies on their use in preventing cardiovascular diseases are lacking. We investigated the genetically proxied effects of reducing uric acid on ischemic cardiovascular diseases in a lipid-level-stratified population. We performed drug-target Mendelian randomization (MR) analyses using UK Biobank data to select genetic instruments within a uric acid-lowering gene, xanthine dehydrogenase (XDH), and construct genetic scores. For nonlinear MR analyses, individuals were stratified by lipid level. Outcomes included acute myocardial infarction (AMI), ischemic heart disease, cerebral infarction, transient cerebral ischemic attack, overall ischemic disease, and gout. We included 474,983 non-gout individuals with XDH-associated single-nucleotide polymorphisms. The XDH-variant-induced uric acid reduction was associated with reduced risk of gout (odds ratio [OR], 0.85; 95% confidence interval [CI], 0.78-0.93; P < 0.001), cerebral infarction (OR, 0.86; 95% CI, 0.75-0.98; P = 0.023), AMI (OR, 0.79; 95% CI, 0.66-0.94; P = 0.010) in individuals with triglycerides ≥ 188.00 mg/dL, and cerebral infarction in individuals with low-density lipoprotein cholesterol (LDL-C) ≤ 112.30 mg/dL (OR, 0.76; 95% CI, 0.61-0.96; P = 0.020) or LDL-C of 136.90-157.40 mg/dL (OR, 0.67; 95% CI, 0.49-0.92; P = 0.012). XDH-variant-induced uric acid reduction lowers the risk of gout, AMI for individuals with high triglycerides, and cerebral infarction except for individuals with high LDL-C, highlighting the potential heterogeneity in the protective effects of xanthine oxidase inhibitors for treating AMI and cerebral infarction depending on the lipid profiles.

Deep learning system for automated detection of posterior ligamentous complex injury in patients with thoracolumbar fracture on MRI.

This study aimed to develop a deep learning (DL) algorithm for automated detection and localization of posterior ligamentous complex (PLC) injury in patients with acute thoracolumbar (TL) fracture on magnetic resonance imaging (MRI) and evaluate its diagnostic performance. In this retrospective multicenter study, using midline sagittal T2-weighted image with fracture (± PLC injury), a training dataset and internal and external validation sets of 300, 100, and 100 patients, were constructed with equal numbers of injured and normal PLCs. The DL algorithm was developed through two steps (Attention U-net and Inception-ResNet-V2). We evaluate the diagnostic performance for PLC injury between the DL algorithm and radiologists with different levels of experience. The area under the curves (AUCs) generated by the DL algorithm were 0.928, 0.916 for internal and external validations, and by two radiologists for observer performance test were 0.930, 0.830, respectively. Although no significant difference was found in diagnosing PLC injury between the DL algorithm and radiologists, the DL algorithm exhibited a trend of higher AUC than the radiology trainee. Notably, the radiology trainee's diagnostic performance significantly improved with DL algorithm assistance. Therefore, the DL algorithm exhibited high diagnostic performance in detecting PLC injuries in acute TL fractures.

Deep learning approaches to predict 10-2 visual field from wide-field swept-source optical coherence tomography en face images in glaucoma.

Close monitoring of central visual field (VF) defects with 10-2 VF helps prevent blindness in glaucoma. We aimed to develop a deep learning model to predict 10-2 VF from wide-field swept-source optical coherence tomography (SS-OCT) images. Macular ganglion cell/inner plexiform layer thickness maps with either wide-field en face images (en face model) or retinal nerve fiber layer thickness maps (RNFLT model) were extracted, combined, and preprocessed. Inception-ResNet-V2 was trained to predict 10-2 VF from combined images. Estimation performance was evaluated using mean absolute error (MAE) between actual and predicted threshold values, and the two models were compared with different input data. The training dataset comprised paired 10-2 VF and SS-OCT images of 3,025 eyes of 1,612 participants and the test dataset of 337 eyes of 186 participants. Global prediction errors (MAEpoint-wise) were 3.10 and 3.17 dB for the en face and RNFLT models, respectively. The en face model performed better than the RNFLT model in superonasal and inferonasal sectors (P = 0.011 and P = 0.030). Prediction errors were smaller in the inferior versus superior hemifields for both models. The deep learning model effectively predicted 10-2 VF from wide-field SS-OCT images and might help clinicians efficiently individualize the frequency of 10-2 VF in clinical practice.

Assessment of the predictive power of a causal variable: An application to the Head Start impact study.

In a study attempting to estimate a causal effect of a causal variable, an assessment of the predictive power of the causal variable can shed light on the heterogeneity around its average effect. Using data from the Head Start Impact Study, a randomized controlled trial of the Head Start, a nation-wide early childhood education program in the United States, we provide a parallel comparison between measures of average effect and predictive power of the Head Start on five cognitive outcomes. We observed that one year of the Head Start increased scores for all five outcomes, with effect sizes ranging from 0.12 to 0.19 standard deviations. Percent variation explained by the Head Start ranged from 0.56 to 1.62%. For binary versions of the outcomes, the overall pattern remained; the Head Start on average improved the outcomes by meaningful magnitudes. In contrast, in a fully adjusted model, the Head Start only improved area under the curve (AUC) by less than 1% and its influence on the variance of predicted probabilities was negligible. The Head-Start-only model only achieved AUC ranging from 50.22 to 55.24%. Negligible predictive power despite the significant average effect suggests that the heterogeneity in effects may be large. The average effect estimates may not generalize well to different populations or different Head Start program settings. Assessment of the predictive power of a causal variable in randomized data should be a routine practice as it can provide helpful information on the causal effect and especially its heterogeneity.

Quantitative Measurement of Pneumothorax Using Artificial Intelligence Management Model and Clinical Application.

Artificial intelligence (AI) techniques can be a solution for delayed or misdiagnosed pneumothorax. This study developed, a deep-learning-based AI model to estimate the pneumothorax amount on a chest radiograph and applied it to a treatment algorithm developed by experienced thoracic surgeons. U-net performed semantic segmentation and classification of pneumothorax and non-pneumothorax areas. The pneumothorax amount was measured using chest computed tomography (volume ratio, gold standard) and chest radiographs (area ratio, true label) and calculated using the AI model (area ratio, predicted label). Each value was compared and analyzed based on clinical outcomes. The study included 96 patients, of which 67 comprised the training set and the others the test set. The AI model showed an accuracy of 97.8%, sensitivity of 69.2%, a negative predictive value of 99.1%, and a dice similarity coefficient of 61.8%. In the test set, the average amount of pneumothorax was 15%, 16%, and 13% in the gold standard, predicted, and true labels, respectively. The predicted label was not significantly different from the gold standard (p = 0.11) but inferior to the true label (difference in MAE: 3.03%). The amount of pneumothorax in thoracostomy patients was 21.6% in predicted cases and 18.5% in true cases.

Localization-adjusted diagnostic performance and assistance effect of a computer-aided detection system for pneumothorax and consolidation.

While many deep-learning-based computer-aided detection systems (CAD) have been developed and commercialized for abnormality detection in chest radiographs (CXR), their ability to localize a target abnormality is rarely reported. Localization accuracy is important in terms of model interpretability, which is crucial in clinical settings. Moreover, diagnostic performances are likely to vary depending on thresholds which define an accurate localization. In a multi-center, stand-alone clinical trial using temporal and external validation datasets of 1,050 CXRs, we evaluated localization accuracy, localization-adjusted discrimination, and calibration of a commercially available deep-learning-based CAD for detecting consolidation and pneumothorax. The CAD achieved image-level AUROC (95% CI) of 0.960 (0.945, 0.975), sensitivity of 0.933 (0.899, 0.959), specificity of 0.948 (0.930, 0.963), dice of 0.691 (0.664, 0.718), moderate calibration for consolidation, and image-level AUROC of 0.978 (0.965, 0.991), sensitivity of 0.956 (0.923, 0.978), specificity of 0.996 (0.989, 0.999), dice of 0.798 (0.770, 0.826), moderate calibration for pneumothorax. Diagnostic performances varied substantially when localization accuracy was accounted for but remained high at the minimum threshold of clinical relevance. In a separate trial for diagnostic impact using 461 CXRs, the causal effect of the CAD assistance on clinicians' diagnostic performances was estimated. After adjusting for age, sex, dataset, and abnormality type, the CAD improved clinicians' diagnostic performances on average (OR [95% CI] = 1.73 [1.30, 2.32]; p < 0.001), although the effects varied substantially by clinical backgrounds. The CAD was found to have high stand-alone diagnostic performances and may beneficially impact clinicians' diagnostic performances when used in clinical settings.

Ultrasonic-Assisted Extraction of Cannabidiolic Acid from Cannabis Biomass.

Industrial hemp (Cannabis spp.) has many compounds of interest with potential medical benefits. Of these compounds, cannabinoids have come to the center of attention, specifically acidic cannabinoids. The focus is turning toward acidic cannabinoids due to their lack of psychotropic activity. Cannabis plants produce acidic cannabinoids with hemp plants producing low levels of psychotropic cannabinoids. As such, utilization of hemp for acidic cannabinoid extraction would eliminate the need for decarboxylation prior to extraction as a source for the cannabinoids. The use of solvent-based extraction is ideal for obtaining acidic cannabinoids as their solubility in solvents such as supercritical CO2 is limited due to the high pressure and temperature required to reach their solubility constants. An alternative method designed to increase solubility is ultrasonic-assisted extraction. In this protocol, the impact of solvent polarity (acetonitrile 0.46, ethanol 0.65, methanol 0.76, and water 1.00) and concentration (20%, 50%, 70%, 90%, and 100%) on ultrasonic-assisted extraction efficiency has been examined. Results show that water was the least effective and acetonitrile was the most effective solvent examined. Ethanol was further examined since it has the lowest toxicity and is generally regarded as safe (GRAS). Surprisingly, 50% ethanol in water is the most effective ethanol concentration for extracting the highest amount of cannabinoids from hemp. The increase in cannabidiolic acid concentration was 28% when compared to 100% ethanol, and 23% when compared to 100% acetonitrile. While it was determined that 50% ethanol is the most effective concentration for our application, the method has also been demonstrated to be effective with alternative solvents. Consequently, the proposed method is deemed effective and rapid for extracting acidic cannabinoids.

Distributional effects on children's cognitive and social-emotional outcomes in the Head Start Impact Study: A quantile regression approach.

Heterogeneity in treatment effects of the Head Start, a federally funded early childhood development program in the United States, has previously been found in the Head Start Impact Study (HSIS), a nationally representative randomized controlled trial. While individual characteristics have been extensively examined as sources of effect heterogeneity, treatment effects may vary as a function of outcome distribution (i.e., distributional effect). Using quantile regressions, we investigated distributional effects of the Head Start on eight child developmental outcomes for first year, second year, third year, and the 3rd grade year follow-up in the HSIS data. For PPVT and Applied Problems, the effects varied substantially across quantiles in the first follow-up, but they were positive overall. The effects at the lower quantiles were larger and were sustained beyond the first follow-up (PPVT [95% CI] at 10th and 90th quantiles: 8.74 [6.22, 11.27], 3.32 [0.82, 5.81]) in the first follow-up and 5.72 [2.66, 8.77], -1.66 [-3.69, 0.37] in the second follow-up). For Behavior Problems, the effects were only positive for the lower quantiles in the first follow-up, but they became null in the latter follow-ups. For Letter-Word Identification, Spelling, and Pre-Academic, the effects were positive in the first follow-up with moderate variation across quantiles. In the second follow-up, only the effects at the lower quantiles were statistically significant, although they faded in the latter follow-ups. For Oral Comprehension and Social Skills, effects were null for all follow-ups. The Head Start had meaningful distributional effects for a range of child developmental outcomes, and distributional effects should be routinely assessed for better understanding of child developmental programs.

Assessment of heterogeneous Head Start treatment effects on cognitive and social-emotional outcomes.

Head Start is a federally funded, nation-wide program in the U.S. for enhancing school readiness of children aged 3-5 from low-income families. Understanding heterogeneity in treatment effects (HTE) is an important task when evaluating programs, but most attempts to explore HTE in Head Start have been limited to subgroup analyses that rely on average treatment effects by subgroups. This study applies an extension of multilevel modelling, complex variance modelling, to data from a randomized controlled trial of Head Start, Head Start Impact Study (HSIS). The treatment effects on the variance, in addition to the mean, of nine cognitive and social-emotional outcomes were assessed for 4,442 children aged 3-4 years who were followed until their 3rd grade year. Head Start had positive short-term effects on the means of multiple cognitive outcomes while having no effect on the means of social-emotional outcomes. Head Start reduced the variances of multiple cognitive and one social-emotional outcomes, meaning that substantial HTE exists. In particular, the increased mean and decreased variance reflect the ability of Head Start to improve the outcomes and reduce their variability. Exploratory secondary analyses suggested that larger benefits for children with Spanish as a primary language and low parental educational level partly explained the reduced variability, but the HTE remained and the variability was reduced even within these subgroups. Routinely monitoring the treatment effects on the variance, in addition to the mean, would lead to a more comprehensive program evaluation that describes how a program performs on average and on the entire distribution.

Employment, Economic, and Sociodemographic Factors Associated with Changes in Smoking and Drinking Behaviors during the COVID-19 Pandemic in South Korea.

The societal disruptions resulting from the coronavirus disease 2019 (COVID-19) pandemic may have caused changes in smoking and alcohol consumption. Using data from the Koreans' Happiness Survey, a nationally representative survey in South Korea, we (1) described population-level smoking and drinking behaviors; (2) assessed changes in smoking and drinking behaviors during the COVID-19 pandemic; and (3) identified employment, economic, and sociodemographic factors associated with these changes using multinomial logistic regression. The overall amount of smoking and drinking decreased during the pandemic, but the changes were heterogeneous across subgroups. Male gender, receipt of the basic living allowance, self-employment, unemployment, and chronic disease status were associated with increased smoking, while higher household income, temporary worker status, living with someone (versus alone), and having fewer offline friends were associated with decreased smoking. Male gender, self-employment, living alone, having more offline friends, and chronic disease status were associated with increased drinking, while younger age, male gender, low and high household income (i.e., a U-shaped relationship), long-term rent with a deposit, temporary worker status, and chronic disease status were associated with decreased drinking. Our findings provide evidence on changes in smoking and drinking during the COVID-19 pandemic in South Korea and differential changes across subgroups.

Antioxidant and lipid-reducing effects of Rosa rugosa root extract in 3T3-L1 cell.

Rosa rugosa root is traditionally known to be effective in the treatment of diabetes in Korea. R. rugosa root-specific compounds also show antioxidant effects, and could reduce lipid and fat accumulation, however, the underlying mechanism has not been clarified. In present study, the antioxidant and lipid-reducing effects of a 50% ethanol extract of R. rugosa (REE) were investigated differentiated mouse preadipocytes (3T3-L1 cells). REE showed strong radical scavenging activities and inhibitory effect of total lipid accumulation and triglyceride levels in differentiated 3T3-L1 cells. In addition, REE treatment reduced the mRNA and protein levels of adipogenesis and lipogenesis markers. This REE-promoted lipid reduction was caused by downregulation of peroxisome proliferator activated receptor gamma (PPARγ), CCAAT/enhancer binding protein alpha (C/EBPα), and sterol regulatory element binding protein1 (SREBP1c) and down regulation of ERK expression. Overall, these results demonstrate the potential of REE for development of a drug in the medical treatment of lipid-associated disorders.

Treatment effect heterogeneity in the head start impact study: A systematic review of study characteristics and findings.

There have been consistent efforts to assess treatment effect heterogeneity (TEH) of Head Start using the data from the Head Start Impact Study (HSIS), a randomized controlled trial of a federally funded child development program for a nationally representative sample of low-income parents and their 3- and 4-year-old children in the United States. Including 28 studies on TEH of Head Start, this review found that multiple high-risk subgroups (e.g., children with lower cognitive abilities, Spanish-speaking dual language learners) experienced larger gains across a range of developmental and parental outcomes, but mixed results for several subgroups. Most studies focused on subgroup analyses, cognitive and social-emotional outcomes, and short-term effects. Further studies on distributional effects, health and parental outcomes, and long-term effects are warranted. Finally, suggestions for future research on TEH of Head Start are discussed, which are applicable to other child development programs and policy evaluations.

Association Between USMLE Step 1 Scores and In-Training Examination Performance: A Meta-Analysis.

PURPOSE: On February 12, 2020, the sponsors of the United States Medical Licensing Examination announced that Step 1 will transition to pass/fail scoring in 2022. Step 1 performance has historically carried substantial weight in the evaluation of residency applicants and as a predictor of subsequent subject-specific medical knowledge. Using a systematic review and meta-analysis, the authors sought to determine the association between Step 1 scores and in-training examination (ITE) performance, which is often used to assess knowledge acquisition during residency. METHOD: The authors systematically searched Medline, EMBASE, and Web of Science for observational studies published from 1992 through May 10, 2020. Observational studies reporting associations between Step 1 and ITE scores, regardless of medical or surgical specialty, were eligible for inclusion. Pairs of researchers screened all studies, evaluated quality assessment using a modified Newcastle-Ottawa Scale, and extracted data in a standardized fashion. The primary endpoint was the correlation of Step 1 and ITE scores. RESULTS: Of 1,432 observational studies identified, 49 were systematically reviewed and 37 were included in the meta-analysis. Overall study quality was low to moderate. The pooled estimate of the correlation coefficient was 0.42 (95% confidence interval [CI]: 0.36, 0.48; P < .001), suggesting a weak-to-moderate positive correlation between Step 1 and ITE scores. The random-effects meta-regression found the association between Step 1 and ITE scores was weaker for surgical (versus medical) specialties (beta -0.25 [95% CI: -0.41, -0.09; P = .003]) and fellowship (versus residency) training programs (beta -0.25 [95% CI: -0.47, -0.03; P = .030]). CONCLUSIONS: The authors identified a weak-to-moderate positive correlation between Step 1 and ITE scores based on a meta-analysis of low-to-moderate quality observational data. With Step 1 scoring transitioning to pass/fail, the undergraduate and graduate medical education communities should continue to develop better tools for evaluating medical students.

Patterning of individual variability in neurocognitive health among South African women exposed to childhood maltreatment.

There are individual differences in health outcomes following exposure to childhood maltreatment, yet constant individual variance is often assumed in analyses. Among 286 Black, South African women, the association between childhood maltreatment and neurocognitive health, defined here as neurocognitive performance (NP), was first estimated assuming constant variance. Then, without assuming constant variance, we applied Goldstein's method (Encyclopedia of statistics in behavioral science, Wiley, 2005) to model "complex level-1 variation" in NP as a function of childhood maltreatment. Mean performance in some tests of information processing speed (Digit-symbol, Stroop Word, and Stroop Color) lowered with increasing severity of childhood maltreatment, without evidence of significant individual variation. Conversely, we found significant individual variation by severity of childhood maltreatment in tests of information processing speed (Trail Making Test) and executive function (Color Trails 2 and Stroop Color-Word), in the absence of mean differences. Exploratory results suggest that the presence of individual-level heterogeneity in neurocognitive performance among women exposed to childhood maltreatment warrants further exploration. The methods presented here may be used in a person-centered framework to better understand vulnerability to the toxic neurocognitive effects of childhood maltreatment at the individual level, ultimately informing personalized prevention and treatment.

Antioxidant and Anti-Obesity Activities of Polygonum cuspidatum Extract through Alleviation of Lipid Accumulation on 3T3-L1 Adipocytes.

Natural products are widely used due to their various biological activities which include antiinflammatory, antioxidant, and anti-obesity effects. In this study, we determined the antioxidative and anti-obesity effects of Polygonum cuspidatum 50% ethanol extract (PEE). The antioxidative effect of PEE was evaluated using its radical scavenging activity, total phenolic content, and reducing power. The anti-obesity effect of PEE was investigated using 3T3-L1 adipocytes. The antioxidative activity of PEE was progressively increased in various concentrations, mainly due to the presence of phenolic compounds. PEE also alleviated lipid accumulation on 3T3-L1 adipocytes and downregulated the mRNA and protein production of adipogenesis-related (SREBP-1c, PPARγ, C/EBPα) and lipogenesis-related (aP2, FAS, ACC) markers. Furthermore, we found that the inhibitory effect on lipid accumulation via PEE was caused by the alleviation of NF-κB, p38 MAPK, ERK1/2, and JNK at the protein level. Taken together, our results imply that PEE is a potential antioxidant that can prevent obesityassociated disorders.