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Rationale and Objectives: Research suggests that the semiquantitative determination of nodal (18)F-fluorodeoxyglucose (FDG) uptake on positron emission tomography (PET)/computed tomography (CT) may be useful for the assessment of mediastinal metastases in patients with non-small-cell lung carcinoma (NSCLC). The aim of this study was to evaluate the diagnostic ability of using different standardized uptake value (SUV) parameters in the detection of ipsilateral mediastinal (N2) disease. Materials and Methods: A total of 102 patients newly diagnosed with non-small-cell lung carcinoma who underwent (18)F-FDG PET/CT before surgery and had not received prior therapy were retrospectively included. All patients underwent surgical resection of the primary tumor and mediastinal lymph node dissection. On a station-based analysis, different SUV parameters (eg, mediastinal lymph node SUV, node/aorta SUV ratio, and node/liver SUV ratio) were evaluated using the histopathologic results as the reference standard. The optimal cutoff value for each SUV parameter was determined with receiver-operating characteristic curve analysis. Results: The areas under the receiver-operating characteristic curves were 0.674, 0.693, and 0.715 for node SUV, node/aorta SUV ratio, and node/liver SUV ratio, respectively (P < .05). With cutoff values of 3.15, 1.37, and 1.02 for node SUV, node/aorta SUV ratio, and node/liver SUV ratio, respectively, the sensitivity of (18)F-FDG PET/CT for N2 staging was 57.1%, 85.7%, and 71.4%, and specificity was 74.2%, 50.5%, and 61.9%. Conclusions: Compared to node SUV alone, the use of node/aorta and node/liver SUV ratios resulted in improved detection of N2 metastases. The two SUV parameters may potentially improve the diagnostic accuracy of (18)F-FDG PET/CT for the diagnosis of N2 disease in patients with non-small-cell lung carcinoma. [ABSTRACT FROM AUTHOR]

OBJECTIVE: Anomalies in error processing have been implicated in the etiology and maintenance of major depressive disorder. In particular, depressed individuals exhibit heightened sensitivity to error-related information and negative environmental cues, along with reduced responsivity to positive reinforcers. The authors examined the neural activation associated with error processing in individuals diagnosed with and without major depression and the sensitivity of these processes to modulation by monetary task contingencies. METHOD: The error-related negativity and error-related positivity components of the event-related potential were used to characterize error monitoring in individuals with major depressive disorder and the degree to which these processes are sensitive to modulation by monetary reinforcement. Nondepressed comparison subjects (N=17) and depressed individuals (N=18) performed a flanker task under two external motivation conditions (i.e., monetary reward for correct responses and monetary loss for incorrect responses) and a nonmonetary condition. After each response, accuracy feedback was provided. The error-related negativity component assessed the degree of anomaly in initial error detection, and the error positivity component indexed recognition of errors. RESULTS: Across all conditions, the depressed participants exhibited greater amplitude of the error-related negativity component, relative to the comparison subjects, and equivalent error positivity amplitude. In addition, the two groups showed differential modulation by task incentives in both components. CONCLUSIONS: These data implicate exaggerated early error-detection processes in the etiology and maintenance of major depressive disorder. Such processes may then recruit excessive neural and cognitive resources that manifest as symptoms of depression. [ABSTRACT FROM AUTHOR]

Tendon-sheath actuation mechanism is widely used in surgical robot, especially in endoscopic surgery, due to its capable of providing remote force and action transmission through long and flexible channel. However, hysteresis, backlash, nonlinear friction are the drawbacks of this mechanism. Our surgical robot use continuum manipulator which is useful in endoscopic surgery, due to its flexible and simple structure. Unlike other literatures that focus on tendon-sheath compensation only, the continuum manipulator is also taken into application level analysis. A model based feedforward motion compensation for tendon-sheath driven continuum manipulator is presented. The model is validated by using optical tracking system to trace the distal end position. Experiment result shows that the proposed model reduces the position error less than 5%.

Abstract Background Choriocarcinoma is a gestational trophoblastic tumor which causes high mortality if left untreated. MicroRNAs (miRNAs) are small non protein-coding RNAs which inhibit target gene expression. The role of miRNAs in choriocarcinoma, however, is not well understood. In this study, we examined the effect of miR-34a in choriocarcinoma. Methods MiR-34a was either inhibited or ectopically expressed transiently in two choriocarcinoma cell lines (BeWo and JEG-3) respectively. Its actions on cell invasion, proliferation and colony formation at low cell density were examined. The miR-34a putative target Notch ligand Delta-like 1 (DLL1) was identified by adoption of different approaches including: in-silico analysis, functional luciferase assay and western blotting. Real-time quantitative polymerase chain reaction was used to quantify changes in the expression of matrix proteinase in the treated cells. To nullify the effect of miR-34a ectopic expression, we activated Notch signaling through force-expression of the Notch intracellular domain in the miR-34a force-expressed cells. In addition, we studied the importance of DLL1 in BeWo cell invasion through ligand stimulation and antibody inhibition. Furthermore, the induction in tumor formation of miR-34a-inhibited BeWo cells in SCID mice was investigated. Results Transient miR-34a force-expression significantly suppressed cell proliferation and invasion in BeWo and JEG-3 cells. In silicon miRNA target prediction, luciferase functional assays and Western blotting analysis demonstrated that miR-34a regulated DLL1 expression in both cell lines. Although force-expression of miR-34a suppressed the expression of DLL1 and NOTCH1, the extent of suppression was higher in DLL1 than NOTCH1 in both cell lines. MiR-34a-mediated DLL1 suppression led to reduced matrix metallopeptidase 9 and urokinase-type plasminogen activator expression. The effect of miR-34a on cell invasion was partially nullified by Notch signaling activation. DLL1 ligand stimulated while anti-DLL1 antibody treatment suppressed cell invasion. Mice inoculated with BeWo cells transfected with miR-34a inhibitor had significantly larger xenografts and stronger DLL1 expression than those with cells transfected with the control inhibitor. Conclusions MiR-34a reduced cell proliferation and invasiveness, at least, partially through its inhibitory effect on DLL1.

Background: Aspects of reinforcement learning have been associated with specific depression symptoms and may inform the course of depressive illness., Methods: We applied support vector machines to investigate whether blood‑oxygen-level dependent (BOLD) responses linked with neural prediction error (nPE) and neural expected value (nEV) from a probabilistic learning task could forecast depression remission. We investigated whether predictions were moderated by treatment use or symptoms. Participants included 55 individuals (n = 39 female) with a depression diagnosis at baseline; 36 of these individuals completed standard cognitive behavioral therapy and 19 were followed during naturalistic course of illness. All participants were assessed for depression diagnosis at a follow-up visit., Results: Both nPE and nEV classifiers forecasted remission significantly better than null classifiers. The nEV classifier performed significantly better than the nPE classifier. We found no main or interaction effects of treatment status on nPE or nEV accuracy. We found a significant interaction between nPE-forecasted remission status and anhedonia, but not for negative affect or anxious arousal, when controlling for nEV-forecasted remission status., Limitations: Our sample size, while comparable to that of other studies, limits options for maximizing and evaluating model performance. We addressed this with two standard methods for optimizing model performance (90:10 train and test scheme and bootstrapped sampling)., Conclusions: Results support nEV and nPE as relevant biobehavioral signals for understanding depression outcome independent of treatment status, with nEV being stronger than nPE as a predictor of remission. Reinforcement learning variables may be useful components of an individualized medicine framework for depression healthcare., Competing Interests: Declaration of competing interest Dr. Brown has received consulting fees from Aya Technologies. All other authors report no financial relationships with commercial interests., (Copyright © 2024. Published by Elsevier B.V.)

Background: This study aimed to assess the impact of the COVID-19 pandemic on the disease burden of Taiwan's notifiable infectious diseases (NIDs). We compared disease burdens between the pandemic and pre-pandemic year of 2020 (with non-pharmaceutical interventions (NPIs)) and 2010 (without NPIs), respectively, to understand the overall pandemic impact on NIDs in Taiwan., Methods: Forty-three national NIDs were analyzed using the Statistics of Communicable Diseases and Surveillance Report by estimating the premature death and disability via different transmission categories, sex, and age groups. The study evaluated the impact of diseases by assessing the years lost due to death (YLLs), the duration of living with disability (YLDs), and the overall disability-adjusted life years (DALYs) by measuring both the severity of the illness and its duration., Results: Taiwan recorded 1,577 (2010) and 1,260 (2020) DALYs per million population and lost 43 NIDs, decreasing 317 DALYs per million population. Tuberculosis, HIV/AIDS and acute hepatitis B/D were the leading causes of DALYs, accounting for 89% (2010) and 77% (2020)., Conclusion: Overall, this study provided the first insight of changes in disease burdens in NIDs between pre- and post-COVID-19 based on a nationwide viewpoint for further preventive measures and interventions to be focused on specific diseases by associated health administrations and policies., Competing Interests: The authors declare that they have no competing interests., (© 2024 Yang et al.)

Spontaneous pneumothorax usually presents as a medical emergency and requires prompt attention and treatment. In patients with underlying lung diseases, it is often associated with prolonged hospitalization, persistent air leak and also a high rate of recurrence. It brings considerable clinical burden to patients and therefore advancement of spontaneous pneumothorax management is eagerly anticipated. In recent years, conservative approach with avoidance of invasive treatment has risen to be a main consideration for primary spontaneous pneumothorax (PSP) patients who are clinically stable with minimal symptoms. The body of evidence in secondary spontaneous pneumothorax (SSP) group is less robust compared with that in PSP group. Non-surgical treatment in SSP is becoming more common due to concerns about morbidity and mortality after surgical pleurodesis as patients are usually older with more underlying medical diseases. Until last year, there have been no updates on the international recommendation of pneumothorax management since the British Thoracic Society (BTS) guideline published in 2010. The latest 2023 BTS guideline on pleural diseases provides us a good opportunity to review the latest development and literature of the care for patients with spontaneous pneumothorax. This article will explore the goals of pneumothorax treatment including air evacuation, cessation of persistent air leak and prevention of recurrence., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-415/coif). The authors have no conflicts of interest to declare., (2024 Journal of Thoracic Disease. All rights reserved.)

Objectives: This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making., Study Design: Retrospective cohort study conducted at a single tertiary hospital., Patients: This study included children who were admitted to the pediatric ICU at the National Taiwan University Hospital between 2010 and 2019 due to pneumonia., Methodology: Two prediction models were developed using tree-structured machine learning algorithms. The primary outcomes were ICU mortality and 24-h ICU mortality. A total of 33 features, including demographics, underlying diseases, vital signs, and laboratory data, were collected from the electronic health records. The machine learning models were constructed using the development data set, and performance matrices were computed using the holdout test data set., Results: A total of 1231 ICU admissions of children with pneumonia were included in the final cohort. The area under the receiver operating characteristic curves (AUROCs) of the ICU mortality model and 24-h ICU mortality models was 0.80 (95% confidence interval [CI], 0.69-0.91) and 0.92 (95% CI, 0.86-0.92), respectively. Based on feature importance, the model developed in this study tended to predict increased mortality for the subsequent 24 h if a reduction in the blood pressure, peripheral capillary oxygen saturation (SpO 2 ), or higher partial pressure of carbon dioxide (PCO2) were observed., Conclusions: This study demonstrated that the machine learning models for predicting ICU mortality and 24-h ICU mortality in children with pneumonia have the potential to support decision-making, especially in resource-limited settings., (© 2024 Wiley Periodicals LLC.)

Exposure and cognitive-based therapies are both effective for PTSD, but knowledge of which intervention is best for which patient is lacking. This lack of knowledge is particularly noticeable for group treatments, as no study has examined whether responses to different group therapies are associated with different pretreatment characteristics. Here, we explored whether pretreatment levels of three types of psychological characteristics-PTSD symptom clusters, posttraumatic cognitions, and emotion regulation difficulties-were associated with symptom reduction during group-delivered cognitive versus exposure-based PTSD treatment. Participants were Veterans with PTSD drawn from two previous clinical trials: one of group CPT (GCPT; n  = 32) and the other of group-based exposure therapy (GBET; n  = 21). Growth curve modeling was used to identify pretreatment variables that predicted weekly PTSD symptom changes during each therapy. Higher posttraumatic cognitions at pretreatment predicted steeper PTSD symptom reduction during GCPT but not GBET. Additionally, symptom reduction during each therapy was associated with different pretreatment emotion regulation difficulties: difficulties with goal-directed behavior for GBET and lack of emotional clarity and limited access to emotion regulation strategies for GCPT. These findings suggest that assigning Veterans to a group PTSD therapy that better matches their pretreatment psychological profile might facilitate a better therapeutic response.

The noradrenaline (NA) system is one of the brain's major neuromodulatory systems; it originates in a small midbrain nucleus, the locus coeruleus (LC), and projects widely throughout the brain. 1 , 2 The LC-NA system is believed to regulate arousal and attention 3 , 4 and is a pharmacological target in multiple clinical conditions. 5 , 6 , 7 Yet our understanding of its role in health and disease has been impeded by a lack of direct recordings in humans. Here, we address this problem by showing that electrochemical estimates of sub-second NA dynamics can be obtained using clinical depth electrodes implanted for epilepsy monitoring. We made these recordings in the amygdala, an evolutionarily ancient structure that supports emotional processing 8 , 9 and receives dense LC-NA projections, 10 while patients (n = 3) performed a visual affective oddball task. The task was designed to induce different cognitive states, with the oddball stimuli involving emotionally evocative images, 11 which varied in terms of arousal (low versus high) and valence (negative versus positive). Consistent with theory, the NA estimates tracked the emotional modulation of attention, with a stronger oddball response in a high-arousal state. Parallel estimates of pupil dilation, a common behavioral proxy for LC-NA activity, 12 supported a hypothesis that pupil-NA coupling changes with cognitive state, 13 , 14 with the pupil and NA estimates being positively correlated for oddball stimuli in a high-arousal but not a low-arousal state. Our study provides proof of concept that neuromodulator monitoring is now possible using depth electrodes in standard clinical use., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: Acute respiratory infections (ARIs) are common in children. We developed machine learning models to predict pediatric ARI pathogens at admission., Methods: We included hospitalized children with respiratory infections between 2010 and 2018. Clinical features were collected within 24 h of admission to construct models. The outcome of interest was the prediction of 6 common respiratory pathogens, including adenovirus, influenza virus types A and B, parainfluenza virus (PIV), respiratory syncytial virus (RSV), and Mycoplasma pneumoniae (MP). Model performance was estimated using area under the receiver operating characteristic curve (AUROC). Feature importance was measured using Shapley Additive exPlanation (SHAP) values., Results: A total of 12,694 admissions were included. Models trained with 9 features (age, event pattern, fever, C-reactive protein, white blood cell count, platelet count, lymphocyte ratio, peak temperature, peak heart rate) achieved the best performance (AUROC: MP 0.87, 95% CI 0.83-0.90; RSV 0.84, 95% CI 0.82-0.86; adenovirus 0.81, 95% CI 0.77-0.84; influenza A 0.77, 95% CI 0.73-0.80; influenza B 0.70, 95% CI 0.65-0.75; PIV 0.73, 95% CI 0.69-0.77). Age was the most important feature to predict MP, RSV and PIV infections. Event patterns were useful for influenza virus prediction, and C-reactive protein had the highest SHAP value for adenovirus infections., Conclusion: We demonstrate how artificial intelligence can assist clinicians identify potential pathogens associated with pediatric ARIs upon admission. Our models provide explainable results that could help optimize the use of diagnostic testing. Integrating our models into clinical workflows may lead to improved patient outcomes and reduce unnecessary medical costs., (Copyright © 2023. Published by Elsevier B.V.)

Background: Obesity increases surgical risks in various abdominal surgeries and its impact on open pancreaticoduodenectomy (OPD) and minimally invasive pancreaticoduodenectomy (MIPD) remains unknown. This study aimed to compare the surgical outcomes of OPD and MIPD in obese and non-obese patients by propensity score matching (PSM) analysis during the implementation of MIPD., Methods: We retrospectively reviewed all pancreaticoduodenectomies from December 2014 to May 2021. Obesity was defined as body mass index > 25 kg/m 2 according to World Health Organization International Obesity Task Force. PSM was used to minimize the selection bias of MIPD., Results: Among 462 pancreaticoduodenectomies (339 OPDs, 123 MIPDs), there were 313 patients in the non-obese group (MIPD: 78, OPD: 235) and 149 patients in the obese group (MIPD: 45, OPD: 104). After PSM, there were 70 MIPD/106 OPD patients in the non-obese group and 38 MIPD/54 OPD patients in the obese group. The obese MIPD patients had more fluid collection (36.8% vs 9.8%, p = 0.002), a higher Clavien-Dindo (CD) grade (p = 0.007), more major complications (42.1% vs 14.8%, p = 0.004), and longer operative times (306 min vs 264 min, p < 0.001) than the obese OPD patients. The non-obese MIPD patients had lower CD grades (p = 0.02), longer operative times (294 vs 264 min, p < 0.001), and less blood loss (100 mL vs 200 mL) than the non-obese OPD patients. MIPD was a strong predictor of major complication (CD ≥ 3) in obese patients (odds ratio 3.11, 95% CI: 1.40-6.95, p = 0.005)., Conclusions: Minimally invasive approaches deteriorate the CD grade, fluid collection, and major complications in obese patients undergoing pancreaticoduodenectomy during the initial development period. Non-obese patients may benefit from MIPD over OPD in terms of less blood loss and lower CD grades. The impact of BMI on MIPD should be considered when assessing the surgical risks., (© 2022. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: The association between human epidermal growth factor receptor-2 (HER2) amplification and brain metastasis (BM) in patients having colorectal cancer (CRC) has been suggested but not yet established. This study investigated the expression patterns of HER2, its association with BM, and its prognostic value in patients having CRC., Methods: We retrospectively identified 99 patients having metastatic CRC (mCRC) and BM (the BM cohort) and compared them with a cohort of 249 patients having mCRC and without BM (the stage IV cohort) by propensity score matching. Immunohistochemical studies of HER2 on all available paraffin-embedded tumour samples, either from the primary tumour, the metastasis (brain and/or extracranial sites) or both, were performed and analysed. HER2 fluorescent in situ hybridisation was applied when necessary. The expression of HER2 was compared and correlated with survival., Results: HER2 amplifications were detected in 16 (18.4%) of 87 and 9 (3.6%) of 249 patients who had specimens available in the BM and stage IV cohorts, respectively (P < .001). After propensity score matching, HER2 amplification was significantly associated with BM (odds ratio: 5.38, P = .003). HER2 heterogeneity was frequently observed not only at the single tumour level but also in paired tumour samples. A marginally significant longer survival since BM was found in patients having HER2-amplified mCRC than in those without (P = .07)., Conclusions: HER2 amplification was significantly associated with BM in patients having mCRC and might have prognostic value for survival since BM. Given the heterogeneity of HER2 expression, the testing of HER2 status on available tissues from both primary and metastatic tumours should be encouraged., Competing Interests: Conflict of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Physiological warmup plays an important role in reducing the injury risk in different sports. In response to the associated temperature increase, the muscle and tendon soften and become easily stretched. In this study, we focused on type I collagen, the main component of the Achilles tendon, to unveil the molecular mechanism of collagen flexibility upon slight heating and to develop a model to predict the strain of collagen sequences. We used molecular dynamics approaches to simulate the molecular structures and mechanical behavior of the gap and overlap regions in type I collagen at 307 K, 310 K, and 313 K. The results showed that the molecular model in the overlap region is more sensitive to temperature increases. Upon increasing the temperature by 3 degrees Celsius, the end-to-end distance and Young's modulus of the overlap region decreased by 5% and 29.4%, respectively. The overlap region became more flexible than the gap region at higher temperatures. GAP-GPA and GNK-GSK triplets are critical for providing molecular flexibility upon heating. A machine learning model developed from the molecular dynamics simulation results showed good performance in predicting the strain of collagen sequences at a physiological warmup temperature. The strain-predictive model could be applied to future collagen designs to obtain desirable temperature-dependent mechanical properties., Competing Interests: The authors declare no conflict of interest., (© 2023 The Authors.)

Objective: To identify and characterize clinical subgroups of hospitalized Coronavirus Disease 2019 (COVID-19) patients., Materials and Methods: Electronic health records of hospitalized COVID-19 patients at NewYork-Presbyterian/Columbia University Irving Medical Center were temporally sequenced and transformed into patient vector representations using Paragraph Vector models. K-means clustering was performed to identify subgroups., Results: A diverse cohort of 11 313 patients with COVID-19 and hospitalizations between March 2, 2020 and December 1, 2021 were identified; median [IQR] age: 61.2 [40.3-74.3]; 51.5% female. Twenty subgroups of hospitalized COVID-19 patients, labeled by increasing severity, were characterized by their demographics, conditions, outcomes, and severity (mild-moderate/severe/critical). Subgroup temporal patterns were characterized by the durations in each subgroup, transitions between subgroups, and the complete paths throughout the course of hospitalization., Discussion: Several subgroups had mild-moderate severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections but were hospitalized for underlying conditions (pregnancy, cardiovascular disease [CVD], etc.). Subgroup 7 included solid organ transplant recipients who mostly developed mild-moderate or severe disease. Subgroup 9 had a history of type-2 diabetes, kidney and CVD, and suffered the highest rates of heart failure (45.2%) and end-stage renal disease (80.6%). Subgroup 13 was the oldest (median: 82.7 years) and had mixed severity but high mortality (33.3%). Subgroup 17 had critical disease and the highest mortality (64.6%), with age (median: 68.1 years) being the only notable risk factor. Subgroups 18-20 had critical disease with high complication rates and long hospitalizations (median: 40+ days). All subgroups are detailed in the full text. A chord diagram depicts the most common transitions, and paths with the highest prevalence, longest hospitalizations, lowest and highest mortalities are presented. Understanding these subgroups and their pathways may aid clinicians in their decisions for better management and earlier intervention for patients., (© The Author(s) 2022. Published by Oxford University Press on behalf of the American Medical Informatics Association. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Background: Nitric oxide-releasing drugs are used for cardiovascular diseases; however, their effects on the tumor immune microenvironment are less clear. Therefore, this study explored the impact of nitric oxide donors on tumor progression in immune-competent mice., Methods: The effects of three different nitric oxide-releasing compounds (SNAP, SNP, and ISMN) on tumor growth were studied in tumor-bearing mouse models. Three mouse tumor models were used: B16F1 melanoma and LL2 lung carcinoma in C57BL/6 mice, CT26 colon cancer in BALB/c mice, and LL2 lung carcinoma in NOD/SCID mice. After nitric oxide treatment, splenic cytokines and lymphocytes were analyzed by cytokine array and flow cytometry, and tumor-infiltrating lymphocytes in the TME were analyzed using flow cytometry and single-cell RNA sequencing., Results: Low doses of three exogenous nitric oxide donors inhibited tumor growth in two immunocompetent mouse models but not in NOD/SCID immunodeficient mice. Low-dose nitric oxide donors increase the levels of splenic cytokines IFN-γ and TNF-α but decrease the levels of cytokines IL-6 and IL-10, suggesting an alteration in Th2 cells. Nitric oxide donors increased the number of CD8 + T cells with activation gene signatures, as indicated by single-cell RNA sequencing. Flow cytometry analysis confirmed an increase in infiltrating CD8 + T cells and dendritic cells. The antitumor effect of nitric oxide donors was abolished by depletion of CD8 + T cells, indicating the requirement for CD8 + T cells. Tumor inhibition correlated with a decrease in a subtype of protumor macrophages and an increase in a subset of Arg1-positive macrophages expressing antitumor gene signatures. The increase in this subset of macrophages was confirmed by flow cytometry analysis. Finally, the combination of low-dose nitric oxide donor and cisplatin induced an additive cancer therapeutic effect in two immunocompetent animal models. The enhanced therapeutic effect was accompanied by an increase in the cells expressing the gene signature of NK cell., Conclusions: Low concentrations of exogenous nitric oxide donors inhibit tumor growth in vivo by regulating T cells and macrophages. CD8 + T cells are essential for antitumor effects. In addition, low-dose nitric oxide donors may be combined with chemotherapeutic drugs in cancer therapy in the future., (© 2023. The Author(s).)

With the quickly growing population of patients receiving dialysis treatment in Taiwan in recent years, concerns about whether more incidence of inguinal hernia exists in dialysis patients are increasing. In Taiwan, peritoneal dialysis (PD) and hemodialysis (HD) are the 2 most common dialysis types. Therefore, the relationship between dialysis type and inguinal hernia occurrence needs to be evaluated and compared. Our retrospective cohort study included a study population total of 3891 patients diagnosed with end stage renal disease (ESRD) under the HD or PD procedure from 2001 to 2009 from the Longitudinal Health Insurance Database. Also, International Statistical Classification of Diseases and Related Health Problems 9th Revision codes were used to identify ESRD and hernia occurrence. Cox proportional-hazards regression model was applied to measure the risk factors to the hernia occurrence. During the follow-up periods of 3 years, the number of hernia occurrences was 44 (1.13%), 1 (0.03%), and 8 (0.21%) with inguinal, femoral, and ventral hernias, respectively. Only the dialysis type revealed significantly increased hernia risk because PD would increase hernia risk 7 times (adjusted hazard ratio [aHR] = 6.98, 95% CI = 3.59-13.25) than HD. If the patients received PD and shifted to HD later, the risk of hernia was 5 times (aHR = 4.98, 95% CI = 2.29-10.85) than patients with HD. Patients with ESRD receiving PD or PD-HD shift were risk factors of inguinal hernia occurrence. The results may help clinicians increase the alert of possible risk factors and complications at the beginning of dialysis treatment in patients with ESRD., Competing Interests: The authors have no funding and conflicts of interest to disclose., (Copyright © 2022 the Author(s). Published by Wolters Kluwer Health, Inc.)

Background: The aim of the study was to evaluate the feasibility of convolutional neural network (CNN)-based deep learning (DL) algorithms to dichotomize shoulder ultrasound (US) images with or without supraspinatus calcific tendinopathy (SSCT)., Methods: This was a retrospective study pertaining to US examinations that had been performed by 18 physiatrists with 3-20 years of experience. 133,619 US images from 7836 consecutive patients who had undergone shoulder US examinations between January 2017 and June 2019 were collected. Only images with longitudinal or transverse views of supraspinatus tendons (SSTs) were included. During the labeling process, two physiatrists with 6-and 10-year experience in musculoskeletal US independently classified the images as with or without SSCT. DenseNet-121, a pre-trained model in CNN, was used to develop a computer-aided system to identify US images of SSTs with and without calcifications. Testing accuracy, sensitivity, and specificity calculated from the confusion matrix was used to evaluate the models., Results: A total of 2462 images were used for developing the DL algorithm. The longitudinal-transverse model developed with a CNN-based DL algorithm was better for the diagnosis of SSCT when compared with the longitudinal and transverse models (accuracy: 91.32%, sensitivity: 87.89%, and specificity: 94.74%)., Conclusion: The developed DL model as a computer-aided system can assist physicians in diagnosing SSCT during the US examination., Competing Interests: Prof. Wen-Shiang Chen, an editorial board member at Journal of Medical Ultrasound, had no roles in the peer review process of or decision to publish this article. The other authors declared no conflicts of interest in writing this paper., (Copyright: © 2022 Journal of Medical Ultrasound.)

Background: Treatment dropout has been problematic with evidence-based treatments for posttraumatic stress disorder (PTSD), including cognitive processing therapy (CPT). This study sought to evaluate whether CPT group contributed to symptom improvement among treatment completers and non-completers., Methods: Sixty-one Iraq and Afghanistan combat Veterans self-selected CPT group or treatment as usual (TAU) forming a convenience sample. Defining treatment completion as attending at least nine sessions: 18 completed treatment, 20 dropped-out (DOs); 20 completed TAU, 3 lost to TAU follow-up., Results: Multiple Regression revealed significant pre-post-treatment improvement, the Clinician-Administered PTSD Scale (CAPS-IV, F(5, 40.1) = 2.53, p = 0.0436). Reviewing DOs' last available PTSD Checklist-Military Version scores before leaving treatment, six achieved clinically significant improvement of >10 points; seven a clinically reliable change of 5-10 points., Conclusion: These findings highlight that CPT group may be effective at reducing trauma-related symptoms among treatment completers and dropouts and point to the utility of a clinical definition of good treatment end-state., (© 2022 Wiley Periodicals LLC.)

Aims: Premature atrial complexes (PACs) have been reported to increase the risk of adverse cardiovascular outcomes. Beta blockers at low dosages may help to reduce PAC symptoms, but it is unclear whether they can improve long-term outcomes., Methods: Patients enrolled from a Holter cohort in a medical referral center were stratified into high-burden (≥100 beats/24 h) and low-burden (<100 beats/24 h) sub-cohorts, and propensity score matching between treatment groups and non-treatment groups was conducted for each sub-cohort., Results: In the high-burden sub-cohort, after propensity score matching, the treatment group and non-treatment group respectively had 208 and 832 patients. The treatment group had significantly lower mortality rates than the non-treatment group [hazard ratio (HR) = 0.521, 95% confidence interval (CI) = 0.294-0.923, p = 0.025], but there was no difference in new stroke (HR = 0.830, 95% CI = 0.341-2.020, p = 0.681), and new atrial fibrillation (HR = 1.410, 95% CI = 0.867-2.292, p = 0.167) events. In the low-burden sub-cohort, after propensity score matching, there were 614 patients in the treatment group and 1,228 patients in the non-treatment group. Compared to the non-treatment group, up to 40% risk reduction in mortality was found in the treatment group (HR = 0.601, 95% CI = 0.396-0.913, p = 0.017), but no differences in new stroke (HR =0.969, 95% CI = 0.562-1.670, p = 0.910) or atrial fibrillation (HR = 1.074, 95% CI = 0.619-1.863, p = 0.800) were found., Conclusions: Beta blockers consistently decreased long-term mortality in high-burden and low-burden patients. Interestingly, this effect was not achieved through reduction of new-onset stroke or AF, and further research is warranted., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Huang, Lee, Huang, Chiu, Su and Liu.)

Objective: A common difficulty at the end of life (EOL) is to determine an appropriate service model, such as hospice share care (HSC), hospice inpatient care (HIC) and hospice home care (HHC). This study aimed to recommend the appropriate hospice delivery model based on the physical, psychosocial and spiritual needs of patients referred for hospice care., Methods: This cohort study included patients who received only one kind of hospice delivery model between 2006 and 2020. Data were analysed with descriptive statistics, Fisher's exact test, non-parametric analysis of variance, Kaplan-Meier curves and Cox proportional hazards model that determined the patients' clinical characteristics for a hospice delivery model and overall survival., Results: A total of 8874 hospice patients were recruited, of which 7076 (79.7%) were HSC patients, 918 (10.4%) were HIC patients and 880 (9.9%) were HHC patients. There were significant differences in the physical symptoms and demographic, psychosocial and spiritual factors among the three groups (p<0.001). The patients who received the HHC were less to have dyspnoea (18.5%) and dysphagia (28.7%). The HIC patients showed higher severity of symptoms and experienced greater psychosocial distress (73.2%). The HSC is appropriate for noncancer patients . Patients with cancer were associated with less dyspnoea (32.4%) and dysphagia (46.5%). Patients with lung cancer who received the HHC had better survival than those who received other types of hospice care (HR=0.75, 95% CI: 0.66 to 0.86, p<0.001)., Conclusions: This study provides guidance regarding the appropriate hospice service model, based on individualised palliative needs, targeting improvement in EOL care., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Air pollution is a well-known contributor to asthma. Air toxics are hazardous air pollutants that cause or may cause serious health effects. Although individual air toxics have been associated with asthma, only a limited number of studies have specifically examined combinations of air toxics associated with the disease. We geocoded air toxic levels from the US National Air Toxics Assessment (NATA) to residential locations for participants of our AiRway in Asthma (ARIA) study. We then applied Data-driven ExposurE Profile extraction (DEEP), a machine learning-based method, to discover combinations of early-life air toxics associated with current use of daily asthma controller medication, lifetime emergency department visit for asthma, and lifetime overnight hospitalization for asthma. We discovered 20 multi-air toxic combinations and 18 single air toxics associated with at least 1 outcome. The multi-air toxic combinations included those containing acrylic acid, ethylidene dichloride, and hydroquinone, and they were significantly associated with asthma outcomes. Several air toxic members of the combinations would not have been identified by single air toxic analyses, supporting the use of machine learning-based methods designed to detect combinatorial effects. Our findings provide knowledge about air toxic combinations associated with childhood asthma.

Importance: Major depressive disorder is prevalent and impairing. Parsing neurocomputational substrates of reinforcement learning in individuals with depression may facilitate a mechanistic understanding of the disorder and suggest new cognitive therapeutic targets., Objective: To determine associations among computational model-derived reinforcement learning parameters, depression symptoms, and symptom changes after treatment., Design, Setting, and Participants: In this mixed cross-sectional-cohort study, individuals performed reward and loss variants of a probabilistic learning task during functional magnetic resonance imaging at baseline and follow-up. A volunteer sample with and without a depression diagnosis was recruited from the community. Participants were assessed from July 2011 to February 2017, and data were analyzed from May 2017 to May 2021., Main Outcomes and Measures: Computational model-based analyses of participants' choices assessed a priori hypotheses about associations between components of reward-based and loss-based learning with depression symptoms. Changes in both learning parameters and symptoms were then assessed in a subset of participants who received cognitive behavioral therapy (CBT)., Results: Of 101 included adults, 69 (68.3%) were female, and the mean (SD) age was 34.4 (11.2) years. A total of 69 participants with a depression diagnosis and 32 participants without a depression diagnosis were included at baseline; 48 participants (28 with depression who received CBT and 20 without depression) were included at follow-up (mean [SD] of 115.1 [15.6] days). Computational model-based analyses of behavioral choices and neural data identified associations of learning with symptoms during reward learning and loss learning, respectively. During reward learning only, anhedonia (and not negative affect or arousal) was associated with model-derived learning parameters (learning rate: posterior mean regression β = -0.14; 95% credible interval [CrI], -0.12 to -0.03; outcome sensitivity: posterior mean regression β = 0.18; 95% CrI, 0.02 to 0.37) and neural learning signals (moderation of association between striatal prediction error and expected value signals: t97 = -2.10; P = .04). During loss learning only, negative affect (and not anhedonia or arousal) was associated with learning parameters (outcome shift: posterior mean regression β = -0.11; 95% CrI, -0.20 to -0.01) and disrupted neural encoding of learning signals (association with subgenual anterior cingulate prediction error signals: r = -0.28; P = .005). Symptom improvement following CBT was associated with normalization of learning parameters that were disrupted at baseline (reward learning rate: posterior mean regression β = 0.15; 90% CrI, 0.001 to 0.41; loss outcome shift: posterior mean regression β = 0.42; 90% CrI, 0.09 to 0.77)., Conclusions and Relevance: In this study, the mapping of reinforcement learning components to symptoms of major depression revealed mechanistic features associated with these symptoms and points to possible learning-based therapeutic processes and targets.

Intracortical brain-computer interfaces (iBCIs) translate neural activity into control commands, thereby allowing paralyzed persons to control devices via their brain signals. Recurrent neural networks (RNNs) are widely used as neural decoders because they can learn neural response dynamics from continuous neural activity. Nevertheless, excessively long or short input neural activity for an RNN may decrease its decoding performance. Based on the temporal attention module exploiting relations in features over time, we propose a temporal attention-aware timestep selection (TTS) method that improves the interpretability of the salience of each timestep in an input neural activity. Furthermore, TTS determines the appropriate input neural activity length for accurate neural decoding. Experimental results show that the proposed TTS efficiently selects 28 essential timesteps for RNN-based neural decoders, outperforming state-of-the-art neural decoders on two nonhuman primate datasets (R2=0.76±0.05 for monkey Indy and CC=0.91±0.01 for monkey N). In addition, it reduces the computation time for offline training (reducing 5-12%) and online prediction (reducing 16-18%). When visualizing the attention mechanism in TTS, the preparatory neural activity is consecutively highlighted during arm movement, and the most recent neural activity is highlighted during the resting state in nonhuman primates. Selecting only a few essential timesteps for an RNN-based neural decoder provides sufficient decoding performance and requires only a short computation time.

Copper oxide particles of various sizes and constituent phases were used to form conductive circuits by means of photonic sintering. With the assistance of extremely low-energy-density xenon flash pulses (1.34 J/cm 2 ), a mixture of nano/submicron copper oxide particles can be reduced in several seconds to form electrical conductive copper films or circuits exhibiting an average thickness of 6 μm without damaging the underlying polymeric substrate, which is quite unique compared to commercial nano-CuO inks whose sintered structure is usually 1 μm or less. A mixture of submicron/nano copper oxide particles with a weight ratio of 3:1 and increasing the fraction of Cu 2 O in the copper oxide both decrease the electrical resistivity of the reduced copper. Adding copper formate further improved the continuity of interconnects and, thereby, the electrical conductance. Exposure to three-pulse low-energy-density flashes yields an electrical resistivity of 64.6 μΩ·cm. This study not only shed the possibility to use heat-vulnerate polymers as substrate materials benefiting from extremely low-energy light sources, but also achieved photonic-sintered thick copper films through the adoption of submicron copper oxide particles.

It is known that the adhesive property and mechanical strength of an apolar polymer can be improved by grafting with polar side chains, whereas the underlying mechanism is still elusive. In this work, the equilibrium structure and mechanical moduli of the melt of graft copolymers have been explored by dissipative particle dynamics. Due to the strong immiscibility of the non-polar backbone and polar side chains, nano-clusters of side chains formed and acted as physical crosslinkers. Moreover, non-affinity adsorption of polar side chains in the melt to the wall was observed, revealing an improvement in the adhesion property. Subjecting graft copolymers to cyclic deformation, the storage and loss moduli were acquired, and they grew with increasing grafting density. The melt strength in terms of the crossover frequency ascended with more side chains on the backbone. Our findings reveal that the strengthening of the mechanical properties of graft copolymers can be attributed to the formation of weakly cross-linked structures, thus offering an insight into the molecular design to aid the development of stronger graft copolymers.

Prediabetes and diabetes mellitus (preDM/DM) have become alarmingly prevalent among youth in recent years. However, simple questionnaire-based screening tools to reliably assess diabetes risk are only available for adults, not youth. As a first step in developing such a tool, we used a large-scale dataset from the National Health and Nutritional Examination Survey (NHANES) to examine the performance of a published pediatric clinical screening guideline in identifying youth with preDM/DM based on American Diabetes Association diagnostic biomarkers. We assessed the agreement between the clinical guideline and biomarker criteria using established evaluation measures (sensitivity, specificity, positive/negative predictive value, F-measure for the positive/negative preDM/DM classes, and Kappa). We also compared the performance of the guideline to those of machine learning (ML) based preDM/DM classifiers derived from the NHANES dataset. Approximately 29% of the 2858 youth in our study population had preDM/DM based on biomarker criteria. The clinical guideline had a sensitivity of 43.1% and specificity of 67.6%, positive/negative predictive values of 35.2%/74.5%, positive/negative F-measures of 38.8%/70.9%, and Kappa of 0.1 (95%CI: 0.06-0.14). The performance of the guideline varied across demographic subgroups. Some ML-based classifiers performed comparably to or better than the screening guideline, especially in identifying preDM/DM youth (p = 5.23 × 10 -5 ).We demonstrated that a recommended pediatric clinical screening guideline did not perform well in identifying preDM/DM status among youth. Additional work is needed to develop a simple yet accurate screener for youth diabetes risk, potentially by using advanced ML methods and a wider range of clinical and behavioral health data.

In order to accurately diagnose the health of high-order statically indeterminate structures, most existing structural health monitoring (SHM) methods require multiple sensors to collect enough information. However, comprehensive data collection from multiple sensors for high degree-of-freedom structures is not typically available in practice. We propose a method that reconciles the two seemingly conflicting difficulties. Takens' embedding theorem is used to augment the dimensions of data collected from a single sensor. Taking advantage of the success of machine learning in image classification, high-dimensional reconstructed attractors were converted into images and fed into a convolutional neural network (CNN). Attractor classification was performed for 10 damage cases of a 3-story shear frame structure. Numerical results show that the inherently high dimension of the CNN model allows the handling of higher dimensional data. Information on both the level and the location of damage was successfully embedded. The same methodology will allow the extraction of data with unsupervised CNN classification to be consistent with real use cases.

The focus of this paper was designing and demonstrating bus structure FBG sensor networks using intensity wavelength division multiplexing (IWDM) techniques and a gated recurrent unit (GRU) algorithm to increase the capability of multiplexing and the ability to detect Bragg wavelengths with greater accuracy. Several Fiber Bragg grating (FBG) sensors are coupled with power ratios of 90:10 and 80:10, respectively in the suggested experimental setup. We used the latest IWDM multiplexing technique for the proposed scheme, as the IWDM system increases the number of sensors and allows us to alleviate the limited operational region drawback of conventional wavelength division multiplexing (WDM). However, IWDM has a crosstalk problem that causes high-sensor signal measurement errors. Thus, we proposed the GRU model to overcome this crosstalk or overlapping problem by converting the spectral detection problem into a regression problem and considered the sequence of spectral features as input. By feeding this sequential spectrum dataset into the GRU model, we trained the GRU system until we achieved optimal efficiency. Consequently, the well-trained GRU model quickly and accurately identifies the Bragg wavelength of each FBG from the overlapping spectra. The Bragg wavelength detection performance of our proposed GRU model is tested or validated using different numbers of FBG sensors, such as 3-FBG, 5-FBG, 7-FBG, and 10-FBG, separately. As a result, the experiment result proves that the well-trained GRU model accurately identifies each FBG Bragg wavelength, and even the number of FBG sensors increase, as well as the spectra of FBGs, which are partially or fully overlapped. Therefore, to boost the detection efficiency, reliability, and to increase the multiplexing capabilities of FBG sensor networks, the proposed sensor system is better than the other previously proposed methods.

Social influences on decision-making are particularly pronounced during adolescence and have both protective and detrimental effects. To evaluate how responsiveness to social signals may be linked to substance use in adolescents, we used functional neuroimaging and a gambling task in which adolescents who have and have not used substances (substance-exposed and substance-naïve, respectively) made choices alone and after observing peers' decisions. Using quantitative model-based analyses, we identify behavioral and neural evidence that observing others' safe choices increases the subjective value and selection of safe options for substance-naïve relative to substance-exposed adolescents. Moreover, the effects of observing others' risky choices do not vary by substance exposure. These results provide neurobehavioral evidence for a role of positive peers (here, those who make safer choices) in guiding adolescent real-world risky decision-making., Competing Interests: The authors declare no competing interest., (Copyright © 2020 the Author(s). Published by PNAS.)

Viable microalgae occur in the air. Whether they can survive the stresses such as UV, desiccation and freezing temperatures at high altitudes during long distance dispersal is rarely studied. If yes, what mechanisms confer the tolerance? Four freshwater airborne green microalgae were isolated from Dongsha Atoll in the South China Sea, classified as Scenedesmus sp. DSA1, Coelastrella sp. DSA2, Coelastrella sp. DSA3 and Desmodesmus sp. DSA6 based on their morphologies and ITS sequences. Their survival rates under UV stress were tightly correlated with their cell wall thickness. All the four airborne green microalgae survived the air-dry stress on benchtop followed by - 20 °C freeze-desiccation stress for 4 weeks, but not the two waterborne green microalgae Desmodesmus sp. F5 and Neodesmus sp. UTEX 2219-4 used as controls. Three of the four airborne microalgae survived the lyophilization treatment, excluding Desmodesmus sp. DSA6 and the two waterborne microalgae. The four airborne microalgae produced carotenoids under prolonged stress conditions, which might help detoxify the reactive oxygen species generated under environmental stresses and shield from the high-light stress in the air. Characterization of these airborne microalgae may help answer how the descendants of green algae survived on the land about 450 MYA.

Industrial scale microalgal cell disruption requires low cost, high efficiency and structural conservation of biomolecules for biorefinery. Many cultivated microalgae have thick walls and these walls are barriers for efficient cell disruption. Until recently, despite the high biodiversity of microalgae, little attention has been paid to thin-wall microalgal species in the natural environment for the production and recovery of valuable biomolecules. Instead of developing high power cell disruption devices, utilization of thin-wall species would be a better approach. The present paper describes a simple device that was assembled to evaluate the viability and effectiveness of biomolecule extraction from both thin- and thick-wall species as a proof of concept. This device was tested with high-pressure gases including N 2 , CO 2 plus N 2 , and air as the disruption force. The highest nitrogen pressure, 110 bar, was not able to disrupt the thick-wall microalgal cells. On the other hand, the thin-wall species was disrupted to different degrees using different pressures and treatment durations. In the same treatment duration, higher nitrogen pressure gave better cell disruption efficiency than the lower pressure. However, in the same pressure, longer treatment duration did not give better efficiency than the shorter duration. High pressure CO 2 treatments resulted in low soluble protein levels in the media. The best conditions to disrupt the thin-wall microalgal cells were 110 bar N 2 or air for 1 min among these tests. In these conditions, not only were the disruption efficiencies high, but also the biomolecules were well preserved., (Copyright © 2019 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.)

Background: Aberrant emotion processing is a hallmark of posttraumatic stress disorder (PTSD), with neurobiological models suggesting both heightened neural reactivity and diminished habituation to aversive stimuli. However, empirical work suggests that these response patterns may be specific to subsets of those with PTSD. This study investigates the unique contributions of PTSD symptom clusters (re-experiencing, avoidance and numbing, and hyperarousal) to neural reactivity and habituation to negative stimuli in combat-exposed veterans., Methods: Ninety-five combat-exposed veterans (46 with PTSD) and 53 community volunteers underwent functional magnetic resonance imaging while viewing emotional images. This study examined the relationship between symptom cluster severity and hemodynamic responses to negative compared with neutral images (NEG>NEU)., Results: Veterans exhibited comparable mean and habituation-related responses for NEG>NEU, relative to civilians. However, among veterans, habituation, but not mean response, was differentially related to PTSD symptom severity. Hyperarousal symptoms were related to decreased habituation for NEG>NEU in a network of regions, including superior and inferior frontal gyri, ventromedial prefrontal cortex, superior and middle temporal gyri, and anterior insula. In contrast, re-experiencing symptoms were associated with increased habituation in a similar network. Furthermore, re-experiencing severity was positively related to amygdalar functional connectivity with the left inferior frontal gyrus and dorsal anterior cingulate cortex for NEG>NEU., Conclusions: These results indicate that hyperarousal symptoms in combat-related PTSD are associated with decreased neural habituation to aversive stimuli. These impairments are partially mitigated in the presence of re-experiencing symptoms, such that during exposure to negative stimuli, re-experiencing symptoms are positively associated with amygdalar connectivity to prefrontal regions implicated in affective suppression., (Published by Elsevier Inc.)

Objectives: Perineural invasion (PNI) is a poor prognostic pathologic feature of oral squamous cell carcinoma (OSCC). The mechanisms of PNI remain poorly understood, and nerve-tumour interactions have been implicated for its pathogenesis., Design and Setting: Systematic investigation of nerve-tumour interactions was performed using fresh human peripheral nerve. In vitro and in vivo models were used to determine the ability of human peripheral nerves to enhance OSCC migration/invasion. Retrospective cohort study was also carried out in one medical centre from 2001 to 2009., Participants: 314 T1-2 OSCC patients., Main Outcome Measures: In the transwell migration/invasion assay, the cells in five representative fields were counted. In the nerve implantation model, tumour size was estimated. PNI quantification by PNI focus number was carried out in the OSCC patients to correlate with cervical lymph node metastasis and oncologic outcomes., Results: The transwell migration/invasion assay demonstrated that human peripheral nerves, compared with subcutaneous soft tissue, significantly enhanced the migration/invasion abilities of OSCC. Moreover, the enhanced migration was dose-dependent with increased length or number of peripheral nerve segments. The nerve implantation model showed that human peripheral nerve also enhanced OSCC growth in vivo. Finally, increased PNI focus number was found dose-dependently associated with increased cervical lymph node metastasis and decreased 5-year disease-specific survival rates., Conclusions: These results clearly indicated the presence of nerve-tumour interaction that involved paracrine influences leading to aggressiveness of OSCC. Further investigations are required to explore key cell types and molecules involved in nerve-tumour interactions for future therapeutic targeting of PNI in OSCC., (© 2019 John Wiley & Sons Ltd.)

Background: In substance-dependent individuals, drug deprivation and drug use trigger divergent behavioral responses to environmental cues. These divergent responses are consonant with data showing that short- and long-term adaptations in dopamine signaling are similarly sensitive to state of drug use. The literature suggests a drug state-dependent role of learning in maintaining substance use; evidence linking dopamine to both reinforcement learning and addiction provides a framework to test this possibility., Methods: In a randomized crossover design, 22 participants with current cocaine use disorder completed a probabilistic loss-learning task during functional magnetic resonance imaging while on and off cocaine (44 sessions). Another 54 participants without Axis I psychopathology served as a secondary reference group. Within-drug state and paired-subjects' learning effects were assessed with computational model-derived individual learning parameters. Model-based neuroimaging analyses evaluated effects of drug use state on neural learning signals. Relationships among model-derived behavioral learning rates (α+, α-), neural prediction error signals (δ+, δ-), cocaine use, and desire to use were assessed., Results: During cocaine deprivation, cocaine-dependent individuals exhibited heightened positive learning rates (α+), heightened neural positive prediction error (δ+) responses, and heightened association of α+ with neural δ+ responses. The deprivation-enhanced neural learning signals were specific to successful loss avoidance, comparable to participants without psychiatric conditions, and mediated a relationship between chronicity of drug use and desire to use cocaine., Conclusions: Neurocomputational learning signals are sensitive to drug use status and suggest that heightened reinforcement by successful avoidance of negative outcomes may contribute to drug seeking during deprivation. More generally, attention to drug use state is important for delineating substrates of addiction., (Published by Elsevier Inc.)

Background: Within the dual systems perspective, high reward sensitivity and low punishment sensitivity in conjunction with deficits in cognitive control may contribute to high levels of risk taking, such as substance use., Objective: The current study examined whether the individual components of effortful control (inhibitory control, attentional control, and activation control) serve as regulators and moderate the association between reward or punishment sensitivity and substance use behaviors., Method: A total of 1,808 emerging adults from a university setting (Mean age = 19.48; 72% female) completed self-report measures of reward and punishment sensitivity, effortful control, and substance use., Results: Findings indicated significant two-way interactions for punishment sensitivity and inhibitory control for alcohol and marijuana use. The form of these interactions revealed a significant negative association between punishment sensitivity and alcohol and marijuana use at low levels of inhibitory control. No significant interactions emerged for reward sensitivity or other components of effortful control., Conclusions: The current findings provide preliminary evidence suggesting the dual systems theorized to influence risk taking behavior interact to make joint contributions to health risk behaviors such as substance use in emerging adults.

To explore the effects of virtual reality (VR) and augmented reality (AR) in the treatment of claustrophobia, the potential effects of VR and AR on induced anxiety were investigated in this paper. During the experiment, 34 subjects were randomly selected and distributed in AR and VR scenes in a sequence. The skin conductance and heart rates of the subjects were measured throughout the entire process, and the anxiety scale was used to assess the subjective anxiety when the task in each scene was completed. The results showed the following: (1) AR and VR scenes led to feelings of discomfort, but the subjective anxiety scores obtained in the two scenes were not significantly different; (2) the skin conductance level of the subjects significantly increased from the baseline when the subjects entered the experimental scene but remained active in the two scenes without showing significant difference between the scenes; and (3) the heart rate index significantly increased from the baseline after the subjects entered the scene and then gradually decreased. The heart rates of the subjects significantly increased again when the anxiety-induced event was triggered. However, no significant difference was observed between AR and VR scenes. AR and VR have induced obvious anxiety, which was reflected in the subjective and objective physiological indicators. However, no significant difference was found in the effects of AR and VR on the induced anxiety. Considering the cost of building two scenes and other factors, AR was more suitable for the treatment of claustrophobia than VR.

[This corrects the article DOI: 10.1371/journal.pone.0194605.].

Carica papaya L. is an important economic crop worldwide and is used as a model plant for sex-determination research. To study the different flower sex types, we screened sex-related genes using alternative splicing sequences (AS-seqs) from a transcriptome database of the three flower sex types, i.e., males, females, and hermaphrodites, established at 28 days before flowering using 15 bacterial artificial chromosomes (BACs) of C. papaya L. After screening, the cDNA regions of the three sex-related loci, including short vegetative phase-like (CpSVPL), the chromatin assembly factor 1 subunit A-like (CpCAF1AL), and the somatic embryogenesis receptor kinase (CpSERK), which contained eight sex-related single-nucleotide polymorphisms (SNPs) from the different sex types of C. papaya L., were genotyped using high-resolution melting (HRM). The three loci were examined regarding the profiles of the third whorl, as described below. CpSVPL, which had one SNP associated with the three sex genotypes, was highly expressed in the male and female sterile flowers (abnormal hermaphrodite flowers) that lacked the fourth whorl structure. CpCAF1AL, which had three SNPs associated with the male genotype, was highly expressed in male and normal hermaphrodite flowers, and had no AS-seqs, whereas it exhibited low expression and an AS-seqs in intron 11 in abnormal hermaphrodite flowers. Conversely, carpellate flowers (abnormal hermaphrodite flowers) showed low expression of CpSVPL and AS-seqs in introns 5, 6, and 7 of CpSERK, which contained four SNPs associated with the female genotype. Specifically, the CpSERK and CpCAF1AL loci exhibited no AS-seq expression in the third whorl of the male and normal hermaphrodite flowers, respectively, and variance in the AS-seq expression of all other types of flowers. Functional mapping of the third whorl of normal hermaphrodites indicated no AS-seq expression in CpSERK, low CpSVPL expression, and, for CpCAF1AL, high expression and no AS-seq expression on XYh-type chromosomes.

Disproportionate reactions to unexpected stimuli in the environment are a cardinal symptom of posttraumatic stress disorder (PTSD). Here, we test whether these heightened responses are associated with disruptions in distinct components of reinforcement learning. Specifically, using functional neuroimaging, a loss-learning task, and a computational model-based approach, we assessed the mechanistic hypothesis that overreactions to stimuli in PTSD arise from anomalous gating of attention during learning (i.e., associability). Behavioral choices of combat-deployed veterans with and without PTSD were fit to a reinforcement learning model, generating trial-by-trial prediction errors (signaling unexpected outcomes) and associability values (signaling attention allocation to the unexpected outcomes). Neural substrates of associability value and behavioral parameter estimates of associability updating, but not prediction error, increased with PTSD during loss learning. Moreover, the interaction of PTSD severity with neural markers of associability value predicted behavioral choices. These results indicate that increased attention-based learning may underlie aspects of PTSD and suggest potential neuromechanistic treatment targets.

Biochemical detection plays a critical role in many analytical fields. For example, blood samples include many proteins with relevance to disease diagnosis and therapeutic monitoring. Foods and beverages contain a large number of chemicals and compounds which must be quantified and characterized to ensure their compliance with safety standards. Detecting trace amounts of contaminants in ambient air or water samples is essential in monitoring the environment and protecting human health. Therefore, effective techniques for performing the rapid and reliable detection of targeted analytes are required. Compared to conventional macroscale devices, microfluidic systems have many advantages, including a greater sensitivity, a faster response time, a reduced sample and reagent consumption, and a greater portability. Accordingly, many microfluidic systems for sample detection have been proposed in recent years. The performance of such devices relies on the target analyte being present in a sufficient concentration to enable its detection. In many biomedical, food testing and environmental applications, the detection limit was restricted. Thus, the sample must first be concentrated before the detection process is carried out. Accordingly, this review provides a comprehensive review of recent advances for sample preconcentration with emphasis on utilizing ion concentration polarization (ICP) effects in micro/nanofluidics platforms. We start with a brief introduction regarding the importance of preconcentration using micro/nanofluidics platforms, followed by in-depth discussions of the ICP effects for the preconcentration and applications to biomedical analysis, food testing and environmental monitoring. Finally, the article concludes with a brief perspective on the future development of the field., (© 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.)

The developmental period of adolescence is characterized by increasing incidence of health risk behaviors (HRBs). Based on theoretical models that emphasize the moderating role of cognitive control, this study examined how neural correlates of cognitive control and risk sensitivity interact to predict HRBs among late adolescents (17-20 years). Neuroimaging data indicate that risk-related hemodynamic activity in the anterior insula during anticipation of uncertain outcomes predicts HRBs among late adolescents exhibiting greater dorsal anterior cingulate cortex (dACC) activity during a cognitive interference task but not among late adolescents requiring less dACC activity. These results present neural evidence for a significant moderating effect of cognitive control on the link between risk sensitivity and HRBs among late adolescents., (© 2016 The Authors. Journal of Research on Adolescence © 2016 Society for Research on Adolescence.)

In data-driven phenotyping, a core computational task is to identify medical concepts and their variations from sources of electronic health records (EHR) to stratify phenotypic cohorts. A conventional analytic framework for phenotyping largely uses a manual knowledge engineering approach or a supervised learning approach where clinical cases are represented by variables encompassing diagnoses, medicinal treatments and laboratory tests, among others. In such a framework, tasks associated with feature engineering and data annotation remain a tedious and expensive exercise, resulting in poor scalability. In addition, certain clinical conditions, such as those that are rare and acute in nature, may never accumulate sufficient data over time, which poses a challenge to establishing accurate and informative statistical models. In this paper, we use infectious diseases as the domain of study to demonstrate a hierarchical learning method based on ensemble learning that attempts to address these issues through feature abstraction. We use a sparse annotation set to train and evaluate many phenotypes at once, which we call bulk learning. In this batch-phenotyping framework, disease cohort definitions can be learned from within the abstract feature space established by using multiple diseases as a substrate and diagnostic codes as surrogates. In particular, using surrogate labels for model training renders possible its subsequent evaluation using only a sparse annotated sample. Moreover, statistical models can be trained and evaluated, using the same sparse annotation, from within the abstract feature space of low dimensionality that encapsulates the shared clinical traits of these target diseases, collectively referred to as the bulk learning set., (Copyright © 2017 Elsevier Inc. All rights reserved.)

The current review is guided by the theoretical perspective that emphasizes the regulating role of executive functioning (Carver et al., 2009) and presents studies that elucidate the ways that executive functioning (inhibition and working memory) explain individual differences in adolescent substance use independently or by regulating the reactive system (reward and punishment sensitivity). Behavioral studies indicate that main effects of executive functioning on adolescent substance use are often nonsignificant or weak in effect sizes. In contrast, emerging evidence suggests consistent and stronger regulating effects of executive functioning over reward and punishment sensitivity. Functional neuroimaging studies reveal significant associations between executive functioning task-related hemodynamic responses and substance use with strong effect sizes. There is also direct evidence from studies testing statistical interactions of the regulating effects of EF-related brain activation, and indirect evidence in studies examining functional connectivity, temporal discounting, and reinforced control. We note key future directions and ways to address limitations in existing work., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

The clinical diagnosis and symptoms of major depressive disorder (MDD) have been closely associated with impairments in reward processing. In particular, various studies have shown blunted neural and behavioral responses to the experience of reward in depression. However, little is known about whether depression affects individuals' valuation of potential rewards during decision-making, independent from reward experience. To address this question, we used a gambling task and a model-based analytic approach to measure two types of individual sensitivity to reward values in participants with MDD: 'risk preference,' indicating how objective values are subjectively perceived, and 'inverse temperature,' determining the degree to which subjective value differences between options influence participants' choices. On both of these measures of value sensitivity, participants with MDD were comparable to non-psychiatric controls. In addition, both risk preference and inverse temperature were stable over four laboratory visits and comparable between the groups at each visit. Neither valuation measure varied with severity of clinical symptoms in MDD. These data suggest intact and stable value processing in MDD during risky decision-making.

Biomass and lutein productivities of two thermotolerant microalgae were assessed in tropical outdoor conditions in all four seasons. Generally, addition of bicarbonate at 0.2g/L every two days or 2% CO2 did not enhance the productivities compared to the controls, and the productivities in the spring were higher than in the fall. Durations of effective irradiance positively correlated to the productivity of Coelastrella sp. F50 well, but not for Desmodesmus sp. F2. The ineffective light intensity was below 5000 lux (about 106μmol/m(2)s). The productivities produced in the 17cm diameter bottles were 1.5- to 1.9-fold higher than that in the 27cm ones. Lutein content, about 0.5% in biomass on average, did not change significantly grown in different carbon sources or seasons. The annual productivities of the microalgal biomass and lutein in one hectare were estimated to be 33tons and 180kg, respectively, using the non-optimized photobioreactor cultivation., (Copyright © 2016 Elsevier Ltd. All rights reserved.)

Semisynthetic triterpenoids such as bardoxolone methyl (methyl-2-cyano 3,12-dioxooleano-1,9-dien-28-oate; CDDO-Me) (4) are potent inducers of antioxidant and anti-inflammatory signaling pathways, including those regulated by the transcription factor Nrf2. However, the reversible nature of the interaction between triterpenoids and thiols has hindered attempts to identify pharmacologically relevant targets and characterize the sites of interaction. Here, we report a shortened synthesis and SAR profiling of 4, enabling the design of analogues that react irreversibly with model thiols, as well as the model protein glutathione S-transferase P1, in vitro. We show that one of these analogues, CDDO-epoxide (13), is comparable to 4 in terms of cytotoxicity and potency toward Nrf2 in rat hepatoma cells and stably modifies specific cysteine residues (namely, Cys-257, -273, -288, -434, -489, and -613) within Keap1, the major repressor of Nrf2, both in vitro and in living cells. Supported by molecular modeling, these data demonstrate the value of 13 for identifying site(s) of interaction with pharmacologically relevant targets and informing the continuing development of triterpenoids as novel drug candidates.