Preservation of Topological Surface States in Millimeter-Scale Transferred Membranes.

Ultrathin topological insulator membranes are building blocks of exotic quantum matter. However, traditional epitaxy of these materials does not facilitate stacking in arbitrary orders, while mechanical exfoliation from bulk crystals is also challenging due to the non-negligible interlayer coupling therein. Here we liberate millimeter-scale films of the topological insulator Bi2Se3, grown by molecular beam epitaxy, down to 3 quintuple layers. We characterize the preservation of the topological surface states and quantum well states in transferred Bi2Se3 films using angle-resolved photoemission spectroscopy. Leveraging the photon-energy-dependent surface sensitivity, the photoemission spectra taken with 6 and 21.2 eV photons reveal a transfer-induced migration of the topological surface states from the top to the inner layers. By establishing clear electronic structures of the transferred films and unveiling the wave function relocation of the topological surface states, our work lays the physics foundation crucial for the future fabrication of artificially stacked topological materials with single-layer precision.

Use of artificial intelligence in triaging of chest radiographs to reduce radiologists' workload.

OBJECTIVES: To evaluate whether deep learning-based detection algorithms (DLD)-based triaging can reduce outpatient chest radiograph interpretation workload while maintaining noninferior sensitivity. METHODS: This retrospective study included patients who underwent initial chest radiography at the outpatient clinic between June 1 and June 30, 2017. Readers interpreted radiographs with/without a commercially available DLD that detects nine radiologic findings (atelectasis, calcification, cardiomegaly, consolidation, fibrosis, nodules, pneumothorax, pleural effusion, and pneumoperitoneum). The reading order was determined in a randomized, crossover manner. The radiographs were classified into negative and positive examinations. In a 50% worklist reduction scenario, radiographs were sorted in descending order of probability scores: the lower half was regarded as negative exams, while the remaining were read with DLD by radiologists. The primary analysis evaluated noninferiority in sensitivity between radiologists reading all radiographs and simulating a 50% worklist reduction, with the inferiority margin of 5%. The specificities were compared using McNemar's test. RESULTS: The study included 1964 patients (median age [interquartile range], 55 years [40-67 years]). The sensitivity was 82.6% (195 of 236; 95% CI: 77.5%, 87.3%) when readers interpreted all chest radiographs without DLD and 83.5% (197 of 236; 95% CI: 78.8%, 88.1%) in the 50% worklist reduction scenario. The difference in sensitivity was 0.8% (95% CI: - 3.8%, 5.5%), establishing noninferiority of 50% worklist reduction (p = 0.01). The specificity increased from 86.7% (1498 of 1728) to 90.4% (1562 of 1728) (p < 0.001) with DLD-based triage. CONCLUSION: Deep learning-based triaging may substantially reduce workload without lowering sensitivity while improving specificity. CLINICAL RELEVANCE STATEMENT: Substantial workload reduction without lowering sensitivity was feasible using deep learning-based triaging of outpatient chest radiograph; however, the legal responsibility for incorrect diagnoses based on AI-standalone interpretation remains an issue that should be defined before clinical implementation. KEY POINTS: • A 50% workload reduction simulation using deep learning-based detection algorithm maintained noninferior sensitivity while improving specificity. • The CT recommendation rate significantly decreased in the disease-negative patients, whereas it slightly increased in the disease-positive group without statistical significance. • In the exploratory analysis, the noninferiority of sensitivity was maintained until 70% of the workload was reduced; the difference in sensitivity was 0%.

Reparametrized Firth's Logistic Regressions for Dose-Finding Study With the Biased-Coin Design.

Finding an adequate dose of the drug by revealing the dose-response relationship is very crucial and a challenging problem in the clinical development. The main concerns in dose-finding study are to identify a minimum effective dose (MED) in anesthesia studies and maximum tolerated dose (MTD) in oncology clinical trials. For the estimation of MED and MTD, we propose two modifications of Firth's logistic regression using reparametrization, called reparametrized Firth's logistic regression (rFLR) and ridge-penalized reparametrized Firth's logistic regression (RrFLR). The proposed methods are designed by directly reducing the small-sample bias of the maximum likelihood estimate for the parameter of interest. In addition, we develop a method on how to construct confidence intervals for rFLR and RrFLR using profile penalized likelihood. In the up-and-down biased-coin design, numerical studies confirm the superior performance of the proposed methods in terms of the mean squared error, bias, and coverage accuracy of confidence intervals.

Metformin Use and Long-term Clinical Outcomes in Kidney Transplant Recipients.

RATIONALE & OBJECTIVE: Metformin has been recommended for some patients with advanced chronic kidney disease. However, the value of metformin in kidney transplant recipients (KTRs) with pretransplant diabetes mellitus (DM) or posttransplant DM is uncertain. We investigated the clinical effects of metformin in KTRs. STUDY DESIGN: Retrospective cohort study. SETTING & PARTICIPANTS: A total of 1,995 KTRs with diabetes from 6 tertiary referral centers in the Republic of Korea. EXPOSURE: Metformin usage was defined as the use of metformin for>90 days after kidney transplantation; 1,193 KTRs were metformin users, and 802 KTRs did not use metformin. Changing usage of metformin among those exposed for >90 days was also characterized. OUTCOME: Primary outcomes were all-cause mortality and death-censored graft failure (DCGF). Secondary outcomes were biopsy-proven acute rejection (BPAR) and lactic acidosis events. ANALYTICAL APPROACH: Survival analyses were conducted using multivariable Cox regression and competing risk analyses using Fine and Gray models. Changes in metformin use over time were modeled using a time-varying covariate. Metformin usage, mean daily dose, and hemoglobin A1c (HbA1c) changes were considered in the landmark analysis to address time-varying confounding. RESULTS: Metformin use was associated with a lower risk of DCGF (adjusted hazard ratio [AHR], 0.47 [95% CI, 0.23-0.96], P=0.038); there was no significant association with all-cause mortality (AHR, 0.94 [95% CI, 0.32-2.76], P=0.915) or BPAR (AHR 0.98 [95% CI, 0.62-1.54], P=0.942). In the subgroup analysis, metformin usage was associated with a reduced risk of all-cause mortality and a lower risk of DCGF for both pretransplantation DM and posttransplant DM groups. Metformin usage was associated with a lower risk of BPAR in the posttransplant DM group, although it was less effective in the pretransplantation DM group. There was no confirmed case of metformin-associated lactic acidosis (MALA) in the present cohort. A higher dose of metformin was correlated with lower risks of DCGF and BPAR. LIMITATIONS: Data on newer antidiabetic drugs such as SGLT2 inhibitors are limited, and there is potential limited generalizability to other populations. CONCLUSIONS: Metformin usage may benefit KTRs, as evidenced by its association with a reduced risk of DCGF and the absence of MALA events. Randomized controlled trials are needed to validate these observational findings.

Modified susceptible-exposed-infectious-recovered model for assessing the effectiveness of non-pharmaceutical interventions during the COVID-19 pandemic in Seoul.

Susceptible-exposed-infectious-recovered (SEIR) models were applied to assess the effectiveness of non-pharmaceutical interventions (NPIs) and to study the dynamic behavior of the COVID-19 pandemic. Recently, SEIR models have evolved to address the change of human mobility by some NPIs for predicting the new confirmed cases. However, the models have serious limitations when applied to Seoul. Seoul has two representative quarantine policies, i.e. social distancing and the ban on gatherings. Effects of the two policies need to be reflected in different functional forms in the model because changes in human mobility do not fully reflect the ban on gatherings. Thus we propose a modified SEIR model to assess the effectiveness of social distancing, ban on gatherings and vaccination strategies. The application of the modified SEIR model was illustrated by comparing the model output with real data.

Associations of water, sanitation, and hygiene with typhoid fever in case-control studies: a systematic review and meta-analysis.

BACKGROUND: Water, sanitation, and hygiene (WASH) play a pivotal role in controlling typhoid fever, as it is primarily transmitted through oral-fecal pathways. Given our constrained resources, staying current with the most recent research is crucial. This ensures we remain informed about practical insights regarding effective typhoid fever control strategies across various WASH components. We conducted a systematic review and meta-analysis of case-control studies to estimate the associations of water, sanitation, and hygiene exposures with typhoid fever. METHODS: We updated the previous review conducted by Brockett et al. We included new findings published between June 2018 and October 2022 in Web of Science, Embase, and PubMed. We used the Risk of Bias in Non-Randomized Studies of Interventions (ROBINS-I) tool for risk of bias (ROB) assessment. We classified WASH exposures according to the classification provided by the WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation, and Hygiene (JMP) update in 2015. We conducted the meta-analyses by only including studies that did not have a critical ROB in both Bayesian and frequentist random-effects models. RESULTS: We identified 8 new studies and analyzed 27 studies in total. Our analyses showed that while the general insights on the protective (or harmful) impact of improved (or unimproved) WASH remain the same, the pooled estimates of OR differed. Pooled estimates of limited hygiene (OR = 2.26, 95% CrI: 1.38 to 3.64), untreated water (OR = 1.96, 95% CrI: 1.28 to 3.27) and surface water (OR = 2.14, 95% CrI: 1.03 to 4.06) showed 3% increase, 18% decrease, and 16% increase, respectively, from the existing estimates. On the other hand, improved WASH reduced the odds of typhoid fever with pooled estimates for improved water source (OR = 0.54, 95% CrI: 0.31 to 1.08), basic hygiene (OR = 0.6, 95% CrI: 0.38 to 0.97) and treated water (OR = 0.54, 95% CrI: 0.36 to 0.8) showing 26% decrease, 15% increase, and 8% decrease, respectively, from the existing estimates. CONCLUSIONS: The updated pooled estimates of ORs for the association of WASH with typhoid fever showed clear changes from the existing estimates. Our study affirms that relatively low-cost WASH strategies such as basic hygiene or water treatment can be an effective tool to provide protection against typhoid fever in addition to other resource-intensive ways to improve WASH. TRIAL REGISTRATION: PROSPERO 2021 CRD42021271881.

Electrocardiogram-based deep learning algorithm for the screening of obstructive coronary artery disease.

BACKGROUND: Information on electrocardiogram (ECG) has not been quantified in obstructive coronary artery disease (ObCAD), despite the deep learning (DL) algorithm being proposed as an effective diagnostic tool for acute myocardial infarction (AMI). Therefore, this study adopted a DL algorithm to suggest the screening of ObCAD from ECG. METHODS: ECG voltage-time traces within a week from coronary angiography (CAG) were extracted for the patients who received CAG for suspected CAD in a single tertiary hospital from 2008 to 2020. After separating the AMI group, those were classified into ObCAD and non-ObCAD groups based on the CAG results. A DL-based model adopting ResNet was built to extract information from ECG data in the patients with ObCAD relative to those with non-ObCAD, and compared the performance with AMI. Moreover, subgroup analysis was conducted using ECG patterns of computer-assisted ECG interpretation. RESULTS: The DL model demonstrated modest performance in suggesting the probability of ObCAD but excellent performance in detecting AMI. The AUC of the ObCAD model adopting 1D ResNet was 0.693 and 0.923 in detecting AMI. The accuracy, sensitivity, specificity, and F1 score of the DL model for screening ObCAD were 0.638, 0.639, 0.636, and 0.634, respectively, while the figures were up to 0.885, 0.769, 0.921, and 0.758 for detecting AMI, respectively. Subgroup analysis showed that the difference between normal and abnormal/borderline ECG groups was not notable. CONCLUSIONS: ECG-based DL model showed fair performance for assessing ObCAD and it may serve as an adjunct to the pre-test probability in patients with suspected ObCAD during the initial evaluation. With further refinement and evaluation, ECG coupled with the DL algorithm may provide potential front-line screening support in the resource-intensive diagnostic pathways.

Evaluation of a technology-enhanced, integrated community health and wellness program for seniors (HWePS): protocol of a non-randomized comparison trial.

BACKGROUND: Healthy aging for all in the community is a shared public health agenda for countries with aging populations, but there is a lack of empirical evidence on community-wide preventive models that promote the health of older people residing in socially-disadvantaged communities. The Health and Wellness Program for Seniors (HWePS) is a technology-enhanced, multi-level, integrated health equity intervention model. This study evaluates the effect of the HWePS on the health and well-being of older adults residing in urban, low-income communities.  METHODS/DESIGN: HWePS is a prospective, non-randomized comparison trial conducted in an intervention and a control neighborhood (dong) in Seoul, South Korea, over 12 months. Older people who reside in the small areas and meet the inclusion/exclusion criteria are eligible to participate. The multi-level, multi-faceted HWePS intervention is a preventive community care model for older residents guided by the expanded chronic care model, the comprehensive health literacy intervention model, and the Systems for Person-centered Elder Care model along with health equity frameworks. HWePS consists of four components: a health literacy intervention based on individual and community needs assessments, personalized (self-)care management featuring nurse coaching and peer support, a healthy-living and healthy-aging community initiative, and information and communication technology (ICT) systems. The primary outcomes are self-reported health and health-related quality of life. Outcome assessors and data analysts are blinded to group assignment. Process evaluation will be also conducted. DISCUSSION: As a multi-level health equity project, HWePS has adopted a novel study design that simultaneously targets individual- and community-level factors known to contribute to health inequality in later life in the community. The study will provide insights into the effectiveness and implementation process of an integrated, multi-level, preventive community care model, which in turn can help improve the health outcomes of older residents and reduce disparities in underserved urban communities. TRIAL REGISTRATION: ISRCTN29103760. Registered 2 September 2021, https://www.isrctn.com/ISRCTN29103760.

Confined Monolayer Ag As a Large Gap 2D Semiconductor and Its Momentum Resolved Excited States.

2D materials have intriguing quantum phenomena that are distinctively different from their bulk counterparts. Recently, epitaxially synthesized wafer-scale 2D metals, composed of elemental atoms, are attracting attention not only for their potential applications but also for exotic quantum effects such as superconductivity. By mapping momentum-resolved electronic states using time-resolved and angle-resolved photoemission spectroscopy (ARPES), we reveal that monolayer Ag confined between bilayer graphene and SiC is a large gap (>1 eV) 2D semiconductor, consistent with ab initio GW calculations. The measured valence band dispersion matches the GW quasiparticle band structure. However, the conduction band dispersion shows an anomalously large effective mass of 2.4 m0. Possible mechanisms for this large enhancement in the "apparent mass" are discussed.

Dopamine dysregulation in psychotic relapse after antipsychotic discontinuation: an [18F]DOPA and [11C]raclopride PET study in first-episode psychosis.

Although antipsychotic drugs are effective for relieving the psychotic symptoms of first-episode psychosis (FEP), psychotic relapse is common during the course of the illness. While some FEPs remain remitted even without medication, antipsychotic discontinuation is regarded as the most common risk factor for the relapse. Considering the actions of antipsychotic drugs on presynaptic and postsynaptic dopamine dysregulation, this study evaluated possible mechanisms underlying relapse after antipsychotic discontinuation. Twenty five FEPs who were clinically stable and 14 matched healthy controls were enrolled. Striatal dopamine activity was assessed as Kicer value using [18F]DOPA PET before and 6 weeks after antipsychotic discontinuation. The D2/3 receptor availability was measured as BPND using [11C]raclopride PET after antipsychotic discontinuation. Healthy controls also underwent PET scans according to the corresponding schedule of the patients. Patients were monitored for psychotic relapse during 12 weeks after antipsychotic discontinuation. 40% of the patients showed psychotic relapse after antipsychotic discontinuation. The change in Kicer value over time significantly differed between relapsed, non-relapsed patients and healthy controls (Week*Group: F = 4.827, df = 2,253.193, p = 0.009). In relapsed patients, a significant correlation was found between baseline striatal Kicer values and time to relapse after antipsychotic discontinuation (R2 = 0.518, p = 0.018). BPND were not significantly different between relapsed, non-relapsed patients and healthy controls (F = 1.402, df = 2,32.000, p = 0.261). These results suggest that dysfunctional dopamine autoregulation might precipitate psychotic relapse after antipsychotic discontinuation in FEP. This finding could be used for developing a strategy for the prevention of psychotic relapse related to antipsychotic discontinuation.

Overall assessment for selected markers from high-throughput data.

Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined P $$ P $$ -value by conventional meta-analysis, that ignore the non-random selection are clearly biased, producing over-optimistic significance. We use the false-discovery rate (FDR) concept to develop a selection-adjusted FDR (sFDR) as an overall assessment measure. We describe the link between the overall assessment and other concepts such as replicability and meta-analysis. Some simulation studies and two real metabolomic datasets are considered to illustrate the application of sFDR in high-throughput data analyses.

QTc interval prolongation due to spinal anesthesia in patients with and without diabetes: an observational study.

BACKGROUND: Spinal anesthesia and autonomic neuropathy (caused by diabetes) prolong the QTc interval. Changes in the duration of the QTc interval following subarachnoid blockade in patients with diabetes have not been evaluated. We hypothesized that after subarachnoid blockade, QTc interval prolongation would be greater in patients with diabetes than in those without. Accordingly, we compared the QTc interval, T wave peak-to-end interval (Tp-e interval), blood pressure, heart rate, and heart rate variability before and after spinal anesthesia in patients with and without diabetes. METHODS: This prospective observational study (Clinical Research Information Service identifier: KCT0004897) was conducted in a tertiary university hospital and included 24 patients with diabetes mellitus (DM group) and 24 patients without it (control group) who were scheduled for spinal anesthesia. The QTc interval, Tp-e interval, heart rate variability, blood pressure, and heart rate were measured before (T1) and 1 (T2), 5 (T3), and 10 min (T4) following subarachnoid blockade. RESULTS: Ten minutes following subarachnoid blockade, the QTc intervals of patients in the DM group were significantly longer than the baseline values, whereas the change in the QTc interval in the control group was not significant (p < 0.0001 vs. p = 0.06). CONCLUSION: Spinal anesthesia caused a more significant prolongation of the QTc interval in patients with diabetes than in those without.

Time-resolved ARPES Determination of a Quasi-Particle Band Gap and Hot Electron Dynamics in Monolayer MoS2.

The electronic structure and dynamics of 2D transition metal dichalcogenide (TMD) monolayers provide important underpinnings both for understanding the many-body physics of electronic quasi-particles and for applications in advanced optoelectronic devices. However, extensive experimental investigations of semiconducting monolayer TMDs have yielded inconsistent results for a key parameter, the quasi-particle band gap (QBG), even for measurements carried out on the same layer and substrate combination. Here, we employ sensitive time- and angle-resolved photoelectron spectroscopy (trARPES) for a high-quality large-area MoS2 monolayer to capture its momentum-resolved equilibrium and excited-state electronic structure in the weak-excitation limit. For monolayer MoS2 on graphite, we obtain QBG values of ≈2.10 eV at 80 K and of ≈2.03 eV at 300 K, results well-corroborated by the scanning tunneling spectroscopy (STS) measurements on the same material.

Application of N Descriptors Proposed by the International Association for the Study of Lung Cancer in Clinical Staging.

Background Patients with N1 or N2 non-small cell lung cancer exhibit prognostic heterogeneity. To refine the current N staging system, new N stages were proposed by the International Association for the Study of Lung Cancer. However, those proposed new N stages have not been validated. Purpose To evaluate the prognostic performance of the proposed N descriptors for clinical staging. Materials and Methods Participants with non-small cell lung cancer without distant metastasis from January 2010 to December 2014 were retrospectively included. Each patient's clinical N (cN) stage was assigned to one of seven categories (cN0, cN1a, cN1b, cN2a1, cN2a2, cN2b, cN3). The 5-year overall survival rates were estimated with the Kaplan-Meier method. The adjusted hazard ratios (HRs) and their 95% CIs were estimated by using a multivariable Cox proportional hazard model. Ad hoc analyses according to lymph node (LN) size were performed. Results A total of 1271 patients (median age, 66 years; interquartile range, 59-73 years; 812 men) were included. The 5-year overall survival rates were 77.3%, 53.7%, 36.0%, 29.2%, 34.4%, 18.0%, and 12.4% for stages cN0, cN1a, cN1b, cN2a1, cN2a2, cN2b, and cN3, respectively. Patients with cN2b disease had a worse prognosis than patients with cN2a disease (HR, 1.53; 95% CI: 1.06, 2.22; P = .02). There was no prognostic difference between cN1b and cN1a (HR, 1.13; 95% CI: 0.61, 2.09; P = .71); however, there was a difference between cN1 subgroups when stratified by LN size (≥2 cm; HR, 2.26; 95% CI: 1.16, 4.44; P = .02). Within cN2a disease, there were no differences between cN2a1 and cN2a2 (HR, 0.98; 95% CI: 0.61, 1.56; P = .93) or between subgroups according to LN size (HR, 0.74; 95% CI: 0.40, 1.37; P = .34). Conclusion A survival difference was observed between single- and multistation involvement among cN2 disease. The number of involved lymph node stations in patients with cN1 disease and the presence of skip metastasis in patients with cN2 disease were not associated with survival differences. © RSNA, 2021 Online supplemental material is available for this article.

Sleep deprivation impairs learning and memory by decreasing protein O-GlcNAcylation in the brain of adult zebrafish.

Sleep is an evolutionarily conserved physiological process implicated in the consolidation of learning and memory (L/M). Here, we report that sleep deprivation (SD)-induced cognitive deficits in zebrafish are mediated through reduction in O-GlcNAcylation of brain. Microarray-based gene expression profiling of zebrafish brain demonstrated significant changes in genes involved in metabolism by SD or fear conditioning (FC), compared to the control group. In particular, it was observed that a marked decrease in the number of genes involved in carboxylic acid and organic acid metabolic processes in the brains of SD group compared to control group. SD downregulated O-GlcNAc transferase (OGT) and O-GlcNAcylation, while the expression of O-GlcNAcase was upregulated. FC activated protein kinase A (PKA) and phosphorylated cAMP response element binding protein (p-CREB), an effect that was greatly inhibited by SD. Moreover, FC upregulated expressions of OGT and increased O-GlcNAcylation in the brains of normal but not SD zebrafish. Intriguingly, upregulation of O-GlcNAcylation by glucosamine restored defects in L/M functions and PKA/p-CREB activity in SD group. Our findings highlight the O-GlcNAcylation changes in the brain during the L/M process and further provide a foundation for future studies seeking the molecular and biochemical mechanisms by which HBP of glucose metabolism affects cognitive function.

Prenatal heavy metal exposures and atopic dermatitis with gender difference in 6-month-old infants using multipollutant analysis.

BACKGROUND: Prenatal exposure to heavy metals during critical developmental phases has been implicated in allergic phenotypes. However, few studies have been conducted on the gender-specific association of prenatal heavy metal exposure with atopic dermatitis (AD) in infants. OBJECTIVE: To examine the gender-specific association of prenatal exposure to multiple heavy metals with AD incidence in 6-month-old infants using data from the Mothers and Children's Environmental Health (MOCEH). METHODS: We evaluated 738 mother-child pairs from the MOCEH study, an ongoing prospective birth cohort. The concentrations of three heavy metals (lead, mercury and cadmium) in maternal blood samples were measured during early and late pregnancy. Each quartile of heavy metal concentration was used to consider the possible nonlinear association with AD. For assessing the multi-pollutant model, we constructed the multivariate regression model including all three heavy metals at both early and late pregnancy. Further, the group Lasso model was used to perform the variable selection with categorized exposures and assess the effect of multiple pollutants including their pairwise interactions. RESULTS: A total of 200 incident cases of AD were diagnosed in 6-month-old infants. In the multivariate regression model of the boy group, adjusted odds ratios comparing the second, third and fourth quartile of lead exposure in boys with the first quartile were 1.83 (95% CI: 1.00, 3.38), 1.04 (0.91, 3.32) and 2.40 (1.18, 4.90), respectively. However, the only second quartile of lead exposure compared to first quartile was significantly associated with AD in girls. In addition, the results of the group Lasso model were similar with the results of multivariate regression model. CONCLUSION: The results suggest that lead exposure in late pregnancy increases risk of AD in 6-month-old boys although the strength of association is weak. Further studies are needed to confirm the susceptibility window and gender differences in lead-induced AD.

Effects of Local Anesthetic Volume (Standard Versus Low) on Incidence of Hemidiaphragmatic Paralysis and Analgesic Quality for Ultrasound-Guided Superior Trunk Block After Arthroscopic Shoulder Surgery.

BACKGROUND: Relative to interscalene block, superior trunk block (STB) provides comparable analgesia and a reduced risk of hemidiaphragmatic paralysis. However, the incidence of hemidiaphragmatic paralysis remains high when a standard volume (15 mL) of local anesthetic is used. This study aimed to evaluate the effects of local anesthetic volume of STB on the incidence of phrenic nerve palsy, as well as its analgesic efficacy following arthroscopic shoulder surgery. METHODS: Patients scheduled for elective arthroscopic shoulder surgery were randomized to receive ultrasound-guided STB using either 5- or 15-mL 0.5% ropivacaine before general anesthesia. The primary outcome was the incidence of hemidiaphragmatic paralysis at 30 minutes after block. The secondary outcomes were pulmonary function, grade of sensory and motor blockade, pain score, opioid consumption, adverse effects, and satisfaction. RESULTS: Relative to standard-volume STB, low-volume STB was associated with a lower incidence of hemidiaphragmatic paralysis after block (14.3 [4.8%-30.3%] vs 65.7 [46.8%-80.9%]; difference 51.4% [95% confidence intervals {CIs}, 29.0%-67.1%]; P < .0001) and at the postanesthesia care unit (9.4% vs 50.0%; difference 40.6 [95% CI, 18.9%-57.7%]; P = .0004). Pulmonary function was also better preserved in the low-volume group than in the standard-volume group. The extent of the sensory and motor blocks was significantly different between the groups. Pain-related outcomes, satisfaction, and any adverse events were not significantly different between the groups. CONCLUSIONS: Low-volume STB provided a lower incidence of hemidiaphragmatic paralysis with no significant difference in analgesic efficacy relative to standard-volume STB for arthroscopic shoulder surgery.

Occurrence of metabolic diseases associated with antipsychotic use among Korean patients with schizophrenia.

OBJECTIVE: Metabolic side effects of antipsychotics significantly affect adherence to medication. We aimed to identify factors associated with the occurrence of metabolic diseases among Korean patients with schizophrenia (SCZ) from the national health insurance system database. We evaluated the frequency of antidiabetic and antihyperlipidemic use after diagnosis of SCZ according to typical or atypical antipsychotic use. MATERIALS AND METHODS: Among the 43,800 patients diagnosed with SCZ between 2008 and 2012, 29,591 patients who had no metabolic diseases before the diagnosis were included in the analysis to investigate the occurrence of metabolic diseases associated with antipsychotic use. The associations between the development of metabolic diseases and patient characteristics were evaluated using logistic regression analysis. RESULTS: Use of both typical and atypical antipsychotics (multivariate-adjusted odds ratio (OR), 1.2513; 95% confidence interval (CI), 1.0953 - 1.4294) was associated with higher incidence of metabolic diseases than without their use. Among the atypical antipsychotics, use of clozapine (multivariate-adjusted OR, 1.1959; 95% CI, 1.0086 - 1.4179) and quetiapine (multivariate-adjusted OR, 1.1284; 95% CI, 1.0446 - 1.2189) showed higher incidence of metabolic diseases compared to that without their use. Among the patients using ≥ 1 type of antidiabetic or antihyperlipidemic agents within 6 years after diagnosis of SCZ, the proportion of patients using only atypical antipsychotics was greater than those using only typical antipsychotics. CONCLUSION: The use of both typical and atypical antipsychotics, and clozapine and quetiapine treatment, may be associated with the occurrence of metabolic diseases in patients with SCZ. Additional prospective studies with accurate dosage information are needed to validate our findings.

Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs.

Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer performance for the detection of lung cancers on chest radiographs. Materials and Methods Among patients diagnosed with lung cancers between January 2010 and December 2014, 117 patients (median age, 69 years; interquartile range [IQR], 64-74 years; 57 women) were retrospectively identified in whom lung cancers were visible on previous chest radiographs. For the healthy control group, 234 patients (median age, 58 years; IQR, 48-68 years; 123 women) with normal chest radiographs were randomly selected. Nine observers reviewed each chest radiograph, with and without a DLAD. They detected potential lung cancers and determined whether they would recommend chest CT for follow-up. Observer performance was compared with use of the area under the alternative free-response receiver operating characteristic curve (AUC), sensitivity, and rates of chest CT recommendation. Results In total, 105 of the 117 patients had lung cancers that were overlooked on their original radiographs. The average AUC for all observers significantly rose from 0.67 (95% confidence interval [CI]: 0.62, 0.72) without a DLAD to 0.76 (95% CI: 0.71, 0.81) with a DLAD (P < .001). With a DLAD, observers detected more overlooked lung cancers (average sensitivity, 53% [56 of 105 patients] with a DLAD vs 40% [42 of 105 patients] without a DLAD) (P < .001) and recommended chest CT for more patients (62% [66 of 105 patients] with a DLAD vs 47% [49 of 105 patients] without a DLAD) (P < .001). In the healthy control group, no difference existed in the rate of chest CT recommendation (10% [23 of 234 patients] without a DLAD and 8% [20 of 234 patients] with a DLAD) (P = .13). Conclusion Using a deep learning-based automatic detection algorithm may help observers reduce the number of overlooked lung cancers on chest radiographs, without a proportional increase in the number of follow-up chest CT examinations. © RSNA, 2020 Online supplemental material is available for this article.

Toxicological assessments of an ethanol extract complex of Descurainia sophia and Peucedanum praeruptorum: Subacute oral toxicity and genotoxicity studies.

An ethanol extract complex of Descurainia sophia seeds and Peucedanum praeruptorum roots, called BP10A, has antitumor potential against colorectal cancer. In the present study, we evaluated the 28-day oral toxicity and the genotoxicity of BP10A. The subacute toxicity test was done through oral administration to mice. ICR mice (n = 10) received daily oral BP10A doses of 0, 500, 1000 and 2000 mg/kg for 28 consecutive days. During administration, general clinical signs, food consumption, organ weights, and hematologic, biochemical and histopathological parameters in male and female mice were assessed. No significant adverse effects up to the highest dose (2000 mg/kg) were found. The genotoxicity was evaluated using a battery of tests, including an in vitro bacterial reverse mutation (Ames) test, an in vivo micronucleus test using bone marrow cells in ICR mice and a chromosomal aberration test using CHL/IU cells. BP10A did not show any genotoxic signs in the Ames (up to 5000 µg/plate), micronucleus (up to 5000 mg/kg) and the chromosomal aberration tests (550-1750 µg/mL). Therefore, BP10A was considered safe based on the subacute toxicity and genotoxicity results, indicating that it is a useful pharmaceutical material with no adverse toxicity.