Childhood abuse and risk of migraine: A systematic review and meta-analysis.

BACKGROUND: Childhood abuse is associated with an increased risk of migraines. However, the literature on this association is limited. OBJECTIVE: To determine the pooled effect size of the association between childhood abuse and migraines. PARTICIPANTS AND SETTING: System review and meta-analysis. METHODS: A systematic literature search for studies published until September 20, 2023, was performed using the Embase, PubMed, and Web of Science databases. Specifically, original articles reporting the statistical effect size (odds ratio) of the association between childhood abuse and migraines were selected. Pooled odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated using random- or fixed-effects models. Publication bias was examined using funnel plots, and sensitivity analysis was used to explore the stability of the pooled results. RESULTS: Twelve studies involving 110,776 participants were included. Individuals with childhood abuse (OR = 1.60, 95 % CI: 1.49, 1.71) were at increased risk of migraine when compared with individuals with no childhood abuse. Of the different types of childhood abuse examined, sexual abuse (OR = 1.71, 95 % CI: 1.43, 2.04), physical abuse (OR = 1.47, 95 % CI: 1.38, 1.56), and emotional abuse (OR = 1.71, 95 % CI: 1.52, 1.93) were associated with an increased risk of migraine. CONCLUSIONS: Childhood abuse increases migraine risk. Multifaceted interventions to curb abuse and related behaviors can effectively reduce migraine risk. However, considering that multiple factors, such as obesity and anxiety, are causatively associated with both childhood abuse and migraines, our findings should be interpreted with caution.

Using Continuous Glucose Monitoring to Passively Classify Naturalistic Binge Eating and Vomiting Among Adults With Binge-Spectrum Eating Disorders: A Preliminary Investigation.

OBJECTIVE: Binge eating and self-induced vomiting are common, transdiagnostic eating disorder (ED) symptoms. Efforts to understand these behaviors in research and clinical settings have historically relied on self-report measures, which may be biased and have limited ecological validity. It may be possible to passively detect binge eating and vomiting using data collected by continuous glucose monitors (CGMs; minimally invasive sensors that measure blood glucose levels), as these behaviors yield characteristic glucose responses. METHOD: This study developed machine learning classification algorithms to classify binge eating and vomiting among 22 adults with binge-spectrum EDs using CGM data. Participants wore Dexcom G6 CGMs and reported eating episodes and disordered eating symptoms using ecological momentary assessment for 2 weeks. Group-level random forest models were generated to distinguish binge eating from typical eating episodes and to classify instances of vomiting. RESULTS: The binge eating model had accuracy of 0.88 (95% CI: 0.83, 0.92), sensitivity of 0.56, and specificity of 0.90. The vomiting model demonstrated accuracy of 0.79 (95% CI: 0.62, 0.91), sensitivity of 0.88, and specificity of 0.71. DISCUSSION: Results suggest that CGM may be a promising avenue for passively classifying binge eating and vomiting, with implications for innovative research and clinical applications.

Behavioral weight loss treatment for adults with binge-eating disorder: A qualitative analysis of patients' perspectives and experiences.

OBJECTIVE: Understanding the perspectives of behavioral weight loss (BWL) participants could inform whether, how, and for whom BWL should be offered. METHOD: All 126 participants in a clinical trial of BWL for adults with binge-eating disorder (BED) and overweight/obesity were contacted about a qualitative study. 45 participants, 11 of whom had dropped out of the parent study, completed qualitative interviews. The interview guide was developed using data from a survey of providers who offer Health at Every Size and other weight-neutral lifestyle interventions. Questions were phrased to invite even the most negative responses. Questions focused on participants' experiences of weight stigma during treatment, perceptions of BWL's calorie and WL goals, and opinions of BWL and weight-neutral interventions. RESULTS: We identified four themes using thematic analysis: (1) BWL did not perpetuate weight stigma. (2) Calorie and WL goals did not exacerbate participants' binge eating. (3) Patients should have the right to pursue any treatment that aligns with their personal goals. (4) BWL can improve participants' overall health. DISCUSSION: BWL participants with BED and overweight/obesity want others to have access to a program that can reduce both weight and binge eating. Participants emphasized that no treatment works for everyone, and they all agreed that BWL had not perpetuated weight stigma. Fewer than 20% of participants considered the program's calorie and WL goals to be harmful; most participants viewed those goals as achievable and helpful, and many asserted that their participation in BWL had improved their overall health. PUBLIC SIGNIFICANCE: We interviewed adults with binge-eating disorder and overweight/obesity who had participated in a behavioral weight loss (BWL) program. Our participants wanted others in their position to have access to BWL because it aims to reduce both weight and binge-eating frequency. Efforts should be made to provide patients, clinicians, and policymakers with the knowledge that supervised, evidence-based BWL is an effective and desired treatment choice for this population.

Absolute quantification methods for Prostate-Specific antigen by Isotope-Dilution mass spectrometry.

Prostate-specific antigen (PSA) is a diagnostic marker for prostate cancer; however, because it is a macromolecular glycoprotein with complex and diverse isoforms, it is difficult to standardize clinical PSA detection results. To overcome this limitation, herein, naturally extracted PSA was characterized as free PSA (fPSA), and the PSA solution was successfully quantified by amino acid analysis coupled with isotope-dilution mass spectrometry (AAA-IDMS) and enzymatic hydrolysis-IDMS; the results could be traced to the International System of Units (SI) through absolutely quantified amino acids and peptides. After protein hydrolysis or digestion condition optimization, amino acids and signature peptides were detected by liquid chromatography-mass spectrometry with the multiple reaction monitoring mode. The mass concentrations of PSA obtained through AAA-IDMS and enzymatic hydrolysis-IDMS were (75.3 ±â€¯1.5) µg/g (k = 2) and (74.7 ±â€¯1.7) µg/g (k = 2), respectively. The PSA weighted average mass concentration was (75.0 ±â€¯1.6) µg/g (k = 2). The consistency assessment between the two methods was successfully validated, ensuring absolute quantitative accuracy. This study lays the foundation for the development of high-order reference materials for the clinical detection of PSA, which can improve the accuracy, reliability, and consistency of clinical PSA test results.

Patients' perceptions of post-treatment factors that influenced skill use after cognitive-behavioral therapy for bulimia nervosa spectrum disorders.

OBJECTIVE: Deterioration rate among patients with bulimia-spectrum eating disorders (BN-EDs) after receiving enhanced cognitive-behavioral therapy (CBT-E) remains high. Previous studies identified body image concerns, environmental triggers, lack of social support, lack of resources, comorbidity, and discontinued skill use as predictors of deterioration. However, no studies have qualitatively explored patients' perceptions of how these factors influenced their skill use and led to deterioration after receiving outpatient CBT. METHODS: This study aimed to qualitatively explore (1) what post-treatment factors patients believe contributed to deterioration, and (2) whether patients continued to practice the CBT skills they learned from treatment and identify motivators and barriers to post-treatment skill use. Twelve participants who had previously completed 16 sessions of CBT for their BN-EDs and experienced at least modest treatment responses participated in the qualitative interviews. RESULTS: Four themes were identified from the qualitative interviews. Post-treatment deterioration was primarily driven by decreased skill use due to a perceived sudden loss of accountability and continued body dissatisfaction after treatment ended. Discontinued practice of binge analysis led to decreased awareness of the relationship between poor skill use and ED behaviors. Difficulty accessing resources impeded participants from receiving external help to address challenges in skill practice, thus also contributing to deterioration. DISCUSSION: Findings suggested that outpatient treatment for BN-EDs patients should emphasize more on body image concern, and deterioration prevention for outpatient CBT-E should focus on building self-accountability to keep practicing skills after treatment ends. PUBLIC SIGNIFICANCE: This study was the first to qualitatively explore post-treatment factors influencing skill use and deterioration in patients with bulimia-spectrum eating disorders after they completed outpatient CBT. Findings indicated that decreased skill use was a primary driver of post-treatment deterioration, and that relapse prevention for outpatient CBT for BN-EDs should focus on enhancing patients' self-accountability to continue practicing therapeutic skills independently after treatment ended.

Early reduction in anxiety sensitivity predicts greater reduction in disordered eating and trait anxiety during treatment for bulimia nervosa.

OBJECTIVE: Anxiety sensitivity (AS), the trait-like fear of symptoms of anxiety, has been associated with eating disorder (ED) pathology broadly, bulimia nervosa (BN) symptoms specifically, and the anxiety disorders that are commonly comorbid with BN. AS, especially for physical symptoms specifically, maybe a risk and maintenance factor for BN and comorbid anxiety. METHOD: Adult participants with BN (n = 44) in a clinical trial comparing CBT to mindfulness and acceptance-based treatment (MABT) reported ED symptoms, trait anxiety, and AS through treatment and follow-up. We predicted that greater early reduction (i.e., within the first month of treatment) in AS would predict greater reductions in ED symptoms and trait anxiety at post-treatment and follow-up. RESULTS: Early reductions in AS for physical concerns predicted lower ED symptoms and trait anxiety at post-treatment but not follow-up. Exploratory analyses indicated that treatment groups did not differ in either early or total change in AS, controlling for baseline AS. DISCUSSION: Early reductions in AS may be an important treatment target for BN, and may additionally support reductions in anxiety. Future research should identify which components of CBT and MABT best target AS, to deliver these components early in treatment, when they can have maximum effect. PUBLIC SIGNIFICANCE: Anxiety sensitivity, the fear of symptoms of anxiety, is associated with eating disorders (ED). In this study, participants in treatment for bulimia nervosa reported ED symptoms, trait anxiety, and anxiety sensitivity through treatment and follow-up. Greater early reductions in anxiety sensitivity predicted lower ED symptoms and trait anxiety at post-treatment. Future research should identify which elements of treatment best target anxiety sensitivity, to deliver them early in treatment.

Integrated model of ozone mass transfer and oxidation kinetic: Construction, solving and analysis.

Ozone-based advanced oxidation process (O3-AOPs) is rapidly evolving, but the surge of emerging pollutants brings new challenges for ozone oxidation research. Herein, we proposed a state-of-the-art model for simultaneously analyzing both ozone mass transfer and oxidation kinetics during ozone oxidation of emerging organic contaminants. The numerical solution and graphical representations of the integrated model were utilized to analyze the dynamics of ozone and pollutant concentration. An in-depth analysis of the integrated model revealed that the reaction rate constants in this present study were higher than previously reported apparent reaction rate constants, and catalysts were not always necessary. Finally, we developed an installable mobile application (APP) that allowed the simulation of the dynamic process for ozone oxidizing organic pollutants in the laboratory, which offered theoretical support for the selection of experimental conditions. The results of model simulation not only provide scientific explanations for counter-intuitive experimental phenomena, but also optimized experimental conditions to enhance ozone utilization.

Unveiling diverse coordination-defined electronic structures of reconstructed anatase TiO2(001)-(1 × 4) surface.

Transition metal oxides (TMOs) exhibit fascinating physicochemical properties, which originate from the diverse coordination structures between the transition metal and oxygen atoms. Accurate determination of such structure-property relationships of TMOs requires to correlate structural and electronic properties by capturing the global parameters with high resolution in energy, real, and momentum spaces, but it is still challenging. Herein, we report the determination of characteristic electronic structures from diverse coordination environments on the prototypical anatase-TiO2(001) with (1 × 4) reconstruction, using high-resolution angle-resolved photoemission spectroscopy and scanning tunneling microscopy/atomic force microscopy, in combination with density functional theory calculation. We unveil that the shifted positions of O 2s and 2p levels and the gap-state Ti 3p levels can sensitively characterize the O and Ti coordination environments in the (1 × 4) reconstructed surface, which show distinguishable features from those in bulk. Our findings provide a paradigm to interrogate the intricate reconstruction-relevant properties in many other TMO surfaces.

Facial feedback manipulation influences the automatic detection of unexpected emotional body expressions.

Unexpected or changing facial expressions are known to be able to engage more automatic processing than frequently occurring facial expressions, thereby inducing a neural differential wave response known as expression mismatch negativity (EMMN). Recent studies have shown that EMMN can be modulated by the observer's facial feedback (i.e., feedback from their own facial movements). A similar EMMN activity has been discovered for body expressions, but thus far only a few emotion types have been investigated. It is unknown whether the EMMNs evoked by body expressions can be influenced by facial feedback. To explore this question, we recorded EEG activity of 29 participants in the reverse oddball paradigm. Here two unexamined categories of body expressions were presented, happy and sad, placed in two paired stimulus sequences: in one the happy body was presented with a probability of 80% (standards) while the sad body was presented with a probability of 20% (deviants), and in the other the probabilities were reversed. The facial feedback was manipulated by different pen holding conditions (i.e., participants holding the pen with the teeth, lips, or nondominant hand). The nonparametric cluster permutation test revealed significant happy and sad body-related EMMN (bEMMN) activities. The happy-bEMMN were more negative than sad-bEMMN within the range of 100-150 ms. Additionally, the bEMMN amplitude of both emotions is modulated by the facial feedback conditions. These results expand the range of emotional types applicable to bEMMN and provide evidence for the validity of the facial feedback hypothesis across emotional carriers.

Seeking the neural representation of statistical properties in print during implicit processing of visual words.

Statistical learning (SL) plays a key role in literacy acquisition. Studies have increasingly revealed the influence of distributional statistical properties of words on visual word processing, including the effects of word frequency (lexical level) and mappings between orthography, phonology, and semantics (sub-lexical level). However, there has been scant evidence to directly confirm that the statistical properties contained in print can be directly characterized by neural activities. Using time-resolved representational similarity analysis (RSA), the present study examined neural representations of different types of statistical properties in visual word processing. From the perspective of predictive coding, an equal probability sequence with low built-in prediction precision and three oddball sequences with high built-in prediction precision were designed with consistent and three types of inconsistent (orthographically inconsistent, orthography-to-phonology inconsistent, and orthography-to-semantics inconsistent) Chinese characters as visual stimuli. In the three oddball sequences, consistent characters were set as the standard stimuli (probability of occurrence p = 0.75) and three types of inconsistent characters were set as deviant stimuli (p = 0.25), respectively. In the equal probability sequence, the same consistent and inconsistent characters were presented randomly with identical occurrence probability (p = 0.25). Significant neural representation activities of word frequency were observed in the equal probability sequence. By contrast, neural representations of sub-lexical statistics only emerged in oddball sequences where short-term predictions were shaped. These findings reveal that the statistical properties learned from long-term print environment continues to play a role in current word processing mechanisms and these mechanisms can be modulated by short-term predictions.

ConLBS: An Attack Investigation Approach Using Contrastive Learning with Behavior Sequence.

Attack investigation is an important research field in forensics analysis. Many existing supervised attack investigation methods rely on well-labeled data for effective training. While the unsupervised approach based on BERT can mitigate the issues, the high degree of similarity between certain real-world attacks and normal behaviors makes it challenging to accurately identify disguised attacks. This paper proposes ConLBS, an attack investigation approach that combines the contrastive learning framework and multi-layer transformer network to realize the classification of behavior sequences. Specifically, ConLBS constructs behavior sequences describing behavior patterns from audit logs, and a novel lemmatization strategy is proposed to map the semantics to the attack pattern layer. Four different augmentation strategies are explored to enhance the differentiation between attack and normal behavior sequences. Moreover, ConLBS can perform unsupervised representation learning on unlabeled sequences, and can be trained either supervised or unsupervised depending on the availability of labeled data. The performance of ConLBS is evaluated in two public datasets. The results show that ConLBS can effectively identify attack behavior sequences in the cases of unlabeled data or less labeled data to realize attack investigation, and can achieve superior effectiveness compared to existing methods and models.