Content Validity and Psychometric Evaluation of the Crohn's Symptom Severity (CSS) Questionnaire in Patients with Moderately to Severely Active Crohn's Disease.

INTRODUCTION: Individuals living with Crohn's disease (CD) experience burdensome symptoms. As such, it is important to measure CD symptom severity in clinical research. The goal of this study was to evaluate the content validity, psychometric performance, and score interpretability of a new patient-reported instrument, the Crohn's Symptom Severity (CSS) questionnaire, among adolescents and adults with moderately to severely active CD. METHODS: Cognitive debriefing interviews (N = 30; n = 20 adults, n = 10 adolescents) were conducted to evaluate the content validity of the CSS. Additionally, the CSS scores were evaluated for reliability and validity using data from a phase 3 randomized clinical trial of risankizumab (NCT03105128; N = 850). Meaningful within-patient change (MWPC) thresholds were estimated using anchor-based methods. RESULTS: All interview participants (n = 30/30, 100.00%) reported the CSS was easy to complete and most participants (n = 28/29, 96.55%) reported that the CSS was relevant to their experience of CD. Among the clinical trial subjects (N = 850) the following was found for the CSS: mostly acceptable item-total correlations (0.26-0.79); weak to moderate inter-item correlations (r = 0.07-0.57), good internal consistency (Cronbach's α = 0.76-0.87); intraclass correlation coefficients ranged from 0.48 to 0.70, not consistently exceeding the acceptable range for test-retest reliability (0.70); acceptable convergent validity and known-groups results; and demonstrated sensitivity to change. Analyses supported an MWPC estimate of 6-11 points. CONCLUSIONS: This study supports use of the CSS for measuring CD symptoms and sleep impact among adolescents and adults aged 16 and older with moderately to severely active CD in clinical research. TRIAL REGISTRATION: NCT03105128 (registration date 4 April 2017).

Data-independent acquisition in metaproteomics.

INTRODUCTION: Metaproteomics offers insights into the function of complex microbial communities, while it is also capable of revealing microbe-microbe and host-microbe interactions. Data-independent acquisition (DIA) mass spectrometry is an emerging technology, which holds great potential to achieve deep and accurate metaproteomics with higher reproducibility yet still facing a series of challenges due to the inherent complexity of metaproteomics and DIA data. AREAS COVERED: This review offers an overview of the DIA metaproteomics approaches, covering aspects such as database construction, search strategy, and data analysis tools. Several cases of current DIA metaproteomics studies are presented to illustrate the procedures. Important ongoing challenges are also highlighted. Future perspectives of DIA methods for metaproteomics analysis are further discussed. Cited references are searched through and collected from Google Scholar and PubMed. EXPERT OPINION: Considering the inherent complexity of DIA metaproteomics data, data analysis strategies specifically designed for interpretation are imperative. From this point of view, we anticipate that deep learning methods and de novo sequencing methods will become more prevalent in the future, potentially improving protein coverage in metaproteomics. Moreover, the advancement of metaproteomics also depends on the development of sample preparation methods, data analysis strategies, etc. These factors are key to unlocking the full potential of metaproteomics.

An ensemble machine learning model to uncover potential sites of hazardous waste illegal dumping based on limited supervision experience.

With the soaring generation of hazardous waste (HW) during industrialization and urbanization, HW illegal dumping continues to be an intractable global issue. Particularly in developing regions with lax regulations, it has become a major source of soil and groundwater contamination. One dominant challenge for HW illegal dumping supervision is the invisibility of dumping sites, which makes HW illegal dumping difficult to be found, thereby causing a long-term adverse impact on the environment. How to utilize the limited historic supervision records to screen the potential dumping sites in the whole region is a key challenge to be addressed. In this study, a novel machine learning model based on the positive-unlabeled (PU) learning algorithm was proposed to resolve this problem through the ensemble method which could iteratively mine the features of limited historic cases. Validation of the random forest-based PU model showed that the predicted top 30% of high-risk areas could cover 68.1% of newly reported cases in the studied region, indicating the reliability of the model prediction. This novel framework will also be promising in other environmental management scenarios to deal with numerous unknown samples based on limited prior experience.

The Role of Self-Compassion and Social Anxiety in the Relationship Between Cognitive Distortions and Emotional Eating.

This study aimed to examine the mediating role of self-compassion and social anxiety in the relationship between cognitive distortions and emotional eating. The research was carried out on 406 adult individuals between the ages of 18-25 living in different regions of Turkey. To measure research variables, Liebowitz social anxiety, thought types, self-sensitivity, and Turkish emotional eating scale were used. The scales were distributed to the participants online. The data were analyzed through the SPSS program. In the study, it was determined that self-compassion predicted emotional eating negatively and social anxiety predicted emotional eating positively. According to the results of the serial mediation analysis, it was determined that self-compassion and social anxiety mediated the relationship between cognitive distortions and emotional eating separately.

An assessment of global land susceptibility to wind erosion based on deep-active learning modelling and interpretation techniques.

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model's output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model's predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model's output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion.

A qualitative study of hermeneutic analysis in individuals with schizophrenia, psychiatrists and psychiatric nurses writings on selected narratives.

WHAT IS KNOWN ON THE SUBJECT?: In daily psychiatric practice, understanding individuals often involves formulating the clinical case, which may lead to viewing them solely as symptoms to be solved. Consequently, it can be challenging to see the individual beyond their symptoms. However, trying to understand the individual by his/her writings about narratives can offer broader perspectives and extend beyond being a medium for patients to express their experiences. On understanding writing, hermeneutics will be a resourceful method. WHAT THE PAPER ADDS TO EXISTING KNOWLEDGE?: This study aims to propose a reevaluation of the nature of therapeutic communication among individuals with schizophrenia, psychiatrists, and psychiatric nurses by interpreting their writings on the same written narratives through a hermeneutic analysis. Our study has unique features compared to previous studies on communication between mental health professionals and patients. We directly investigated the act of understanding through the writings of the three groups of participants. The participants were also engaged in reading and writing about the narratives instead of relying on clinical interviews or self-report questionnaires. To the best of our knowledge, our study is the first hermeneutic study to examine the nature of reflection while all three groups encounter the same narratives as readers. IMPLICATIONS FOR PRACTICE?: The results point to the need for psychiatrists/psychiatric nurses to develop new perspectives and skills in understanding/interacting with individuals with schizophrenia. Our study could be a pioneer sample in applying hermeneutics as a prevailing method to mental health nursing practice. ABSTRACT: INTRODUCTION: Understanding individuals with schizophrenia is an essential but challenging phenomenon in psychiatry. AIM/QUESTION: This study proposes a reevaluation of the nature of therapeutic communication among individuals with schizophrenia, psychiatrists and psychiatric nurses by interpreting their writings on the same written narratives through a hermeneutic analysis. METHOD: In this study, employing a qualitative descriptive methodology utilising hermeneutic analysis, participants read the five narratives, wrote down their opinions on each narratives and answered nine questions to express their own personal writing experiences. RESULTS: Participants from each group acted as both 'readers' and 'authors' while rewriting the selected five short narratives based on their own sense of readings. The expression, rewriting refers to the act of reading and written interpretations of participants and researchers in this study. While individuals with schizophrenia primarily focused on the text (narrative) in their readings and writings, psychiatrists and psychiatric nurses tended to focus on reader (themselves) in their interpretations. DISCUSSION: Although reading should be approached as a process of discovery rather than merely seeking predetermined knowledge, psychiatrists and psychiatric nurses read the narratives as if the author were a patient or as the signs of an illness. IMPLICATIONS FOR PRACTICE: The results point to the need for psychiatrists/psychiatric nurses to develop new perspectives and skills in understanding and interacting with individuals with schizophrenia.

Machine learning analysis of serum cholesterol's impact on knee osteoarthritis progression.

The controversy surrounding whether serum total cholesterol is a risk factor for the graded progression of knee osteoarthritis (KOA) has prompted this study to develop an authentic prediction model using a machine learning (ML) algorithm. The objective was to investigate whether serum total cholesterol plays a significant role in the progression of KOA. This cross-sectional study utilized data from the public database DRYAD. LASSO regression was employed to identify risk factors associated with the graded progression of KOA. Additionally, six ML algorithms were utilized in conjunction with clinical features and relevant variables to construct a prediction model. The significance and ranking of variables were carefully analyzed. The variables incorporated in the model include JBS3, Diabetes, Hypertension, HDL, TC, BMI, SES, and AGE. Serum total cholesterol emerged as a significant risk factor for the graded progression of KOA in all six ML algorithms used for importance ranking. XGBoost algorithm was based on the combined best performance of the training and validation sets. The ML algorithm enables predictive modeling of risk factors for the progression of the KOA K-L classification and confirms that serum total cholesterol is an important risk factor for the progression of KOA.

Psychological well-being and needs of parents and carers of children and young people with mental health difficulties: a quantitative systematic review with meta-analyses.

QUESTION: For parents of children and young people (CYP) with diagnosed mental health difficulties, what are the levels of parents' well-being and psychological need? STUDY SELECTION AND ANALYSIS: Medline, PsycINFO, EMBASE, AMED, CINAHL, Web of Science and Cochrane Library of Registered Trials were searched from inception to June 2023. INCLUSION CRITERIA: parents of CYP aged 5-18 years with formal mental health diagnosis. Data were extracted from validated measures of well-being or psychological needs with established cut-off points or from a controlled study. FINDINGS: 32 of the 73 310 records screened were included. Pooled means showed clinical range scores for one measure of depression, and all included measures of anxiety, parenting stress and general stress. Meta-analyses showed greater depression (g=0.24, 95% CI 0.11 to 0.38) and parenting stress (g=0.34, 95% CI 0.20 to 0.49) in parents of CYP with mental health difficulties versus those without. Mothers reported greater depression (g=0.42, 95% CI 0.18 to 0.66) and anxiety (g=0.73, 95% CI 0.27 to 1.18) than fathers. Narrative synthesis found no clear patterns in relation to CYP condition. Rates of parents with clinically relevant levels of distress varied. Typically, anxiety, parenting stress and general stress scored above clinical threshold. Quality appraisal revealed few studies with a clearly defined control group, or attempts to control for important variables such as parent gender. CONCLUSIONS: The somewhat mixed results suggest clinical anxiety, parenting and general stress may be common, with sometimes high depression. Assessment and support for parents of CYP with mental health problems is required. Further controlled studies, with consideration of pre-existing parental mental health difficulties are required. PROSPERO REGISTRATION NUMBER: CRD42022344453.

Solution to a case involving the interpretation of trace degraded DNA mixtures.

DNA mixture analysis poses a significant challenge in forensic genetics, particularly when dealing with degraded and trace amount DNA samples. Multi-SNPs (MNPs) are genetic markers similar to microhaplotypes but with smaller molecular sizes (< 75 bp), making them theoretically more suitable for analyzing degraded and trace amount samples. In this case report, we investigated a cold case involving a campstool stored for over a decade, aiming to detect and locate the suspect's DNA. We employed both conventional capillary electrophoresis-based short tandem repeat (CE-STR) analysis and next-generation sequencing-based multi-SNP (NGS-MNP) analysis. The typing results and deconvolution of the mixed CE-STR profiles were inconclusive regarding the presence of the suspect's DNA in the mixed samples. However, through NGS-MNP analysis and presence probability calculations, we determined that the suspect's DNA was present in the samples from Sect. 4-1 with a probability of 1-8.41 × 10- 6 (99.999159%). This evidence contradicted the suspect's statement and aided in resolving the case. Our findings demonstrate the significant potential of MNP analysis for examining degraded and trace amount DNA mixtures in forensic investigations.

'A Meaningful Difference, but Not Ultimately the Difference I Would Want': A Mixed-Methods Approach to Explore and Benchmark Clinically Meaningful Changes in Aphasia Recovery.

INTRODUCTION: Outcome measurement instruments (OMIs) are used to gauge the effects of treatment. In post-stroke aphasia rehabilitation, benchmarks for meaningful change are needed to support the interpretation of patient outcomes. This study is part of a research programme to establish minimal important change (MIC) values (the smallest change above which patients perceive themselves as importantly changed) for core OMIs. As a first step in this process, the views of people with aphasia and clinicians were explored, and consensus was sought on a threshold for clinically meaningful change. METHODS: Sequential mixed-methods design was employed. Participants included people with post-stroke aphasia and speech pathologists. People with aphasia were purposively sampled based on time post-stroke, age and gender, whereas speech pathologists were sampled according to their work setting (hospital or community). Each participant attended a focus group followed by a consensus workshop with a survey component. Within the focus groups, experiences and methods for measuring meaningful change during aphasia recovery were explored. Qualitative data were transcribed and analysed using reflexive thematic analysis. In the consensus workshop, participants voted on thresholds for meaningful change in core outcome constructs of language, communication, emotional well-being and quality of life, using a six-point rating scale (much worse, slightly worse, no change, slightly improved, much improved and completely recovered). Consensus was defined a priori as 70% agreement. Voting results were reported using descriptive statistics. RESULTS: Five people with aphasia (n = 4, > 6 months after stroke; n = 5, < 65 years; n = 3, males) and eight speech pathologists (n = 4, hospital setting; n = 4, community setting) participated in one of four focus groups (duration: 92-112 min). Four themes were identified describing meaningful change as follows: (1) different for every single person; (2) small continuous improvements; (3) measured by progress towards personally relevant goals; and (4) influenced by personal factors. 'Slightly improved' was agreed as the threshold of MIC on the anchor-rating scale (75%-92%) within 6 months of stroke, whereas after 6 months there was a trend towards supporting 'much improved' (36%-66%). CONCLUSION: Our mixed-methods research with people with aphasia and speech pathologists provides novel evidence to inform the definition of MIC in aphasia rehabilitation. Future research will aim to establish MIC values for core OMIs. PATIENT OR PUBLIC CONTRIBUTION: This work is the result of engagement between people with lived experience of post-stroke aphasia, including people with aphasia, family members, clinicians and researchers. Engagement across the research cycle was sought to ensure that the research tasks were acceptable and easily understood by participants and that the outcomes of the study were relevant to the aphasia community. This engagement included the co-development of a plain English summary of the results. Advisors were remunerated in accordance with Health Consumers Queensland guidelines. Interview guides for clinicians were piloted by speech pathologists working in aphasia rehabilitation.

Suicide deaths associated with climate change-induced heat anomalies in Australia: a time series regression analysis.

BACKGROUND: Although environmental determinants play an important role in suicide mortality, the quantitative influence of climate change-induced heat anomalies on suicide deaths remains relatively underexamined. OBJECTIVE: The objective is to quantify the impact of climate change-induced heat anomalies on suicide deaths in Australia from 2000 to 2019. METHODS: A time series regression analysis using a generalised additive model was employed to explore the potentially non-linear relationship between temperature anomalies and suicide, incorporating structural variables such as sex, age, season and geographic region. Suicide deaths data were obtained from the Australian National Mortality Database, and gridded climate data of gridded surface temperatures were sourced from the Australian Gridded Climate Dataset. FINDINGS: Heat anomalies in the study period were between 0.02°C and 2.2°C hotter than the historical period due to climate change. Our analysis revealed that approximately 0.5% (264 suicides, 95% CI 257 to 271) of the total 50 733 suicides within the study period were attributable to climate change-induced heat anomalies. Death counts associated with heat anomalies were statistically significant (p value 0.03) among men aged 55+ years old. Seasonality was a significant factor, with increased deaths during spring and summer. The relationship between high heat anomalies and suicide deaths varied across different demographic segments. CONCLUSIONS AND IMPLICATIONS: This study highlights the measurable impact of climate change-induced heat anomalies on suicide deaths in Australia, emphasising the need for increased climate change mitigation and adaptation strategies in public health planning and suicide prevention efforts focusing on older adult men. The findings underscore the importance of considering environmental factors in addition to individual-level factors in understanding and reducing suicide mortality.

Predictive roles of cognitive biases in health anxiety: A machine learning approach.

Prior work suggests that cognitive biases may contribute to health anxiety. Yet there is little research investigating how biased attention, interpretation, and memory for health threats are collectively associated with health anxiety, as well as the relative importance of these cognitive processes in predicting health anxiety. This study aimed to build a prediction model for health anxiety with multiple cognitive biases as potential predictors and to identify the biased cognitive processes that best predict individual differences in health anxiety. A machine learning algorithm (elastic net) was performed to recognise the predictors of health anxiety, using various tasks of attention, interpretation, and memory measured across behavioural, self-reported, and computational modelling approaches. Participants were 196 university students with a range of health anxiety severity from mild to severe. The results showed that only the interpretation bias for illness and the attention bias towards symptoms significantly contributed to the prediction model of health anxiety, with both biases having positive weights and the former being the most important predictor. These findings underscore the central role of illness-related interpretation bias and suggest that combined cognitive bias modification may be a promising method for alleviating health anxiety.

Enhanced PM2.5 estimation across China: An AOD-independent two-stage approach incorporating improved spatiotemporal heterogeneity representations.

In China, population growth and aging have partially negated the public health benefits of air pollution control measures, underscoring the ongoing need for precise PM2.5 monitoring and mapping. Despite its prevalence, the satellite-derived Aerosol Optical Depth (AOD) method for estimating PM2.5 concentrations often encounters significant spatial data gaps. Additionally, current research still needs better representation of PM2.5 spatiotemporal heterogeneity. Addressing these challenges, we developed a two-stage model employing the Extreme Gradient Boosting (XGBoost) algorithm. By incorporating improved spatiotemporal factors, we achieved high-precision and full-coverage daily 1-km PM2.5 mappings across China for the year 2020 without utilizing AOD products. Specifically, Model 1 develops improved temporal encodings and a terrain classification factor (DC), while Model 2 constructs an enhanced spatial autocorrelation term (Ps) by integrating observed and estimated values. Notably, Model 2 excelled in 10-fold sample-based cross-validation, achieving a coefficient of determination of 0.948, a mean absolute error of 3.792 µg/m³, a root mean square error of 7.144 µg/m³, and a mean relative error of 14.171%. Feature importance and Shapley Additive exPlanations (SHAP) analyses determined the relative importance of predictors in model training and outcome prediction, while correlation analysis identified strong links between improved temporal encodings, PM2.5 concentrations, and significant meteorological factors. Two-way Partial Dependence Plots (PDPs) further explored the interactions among these factors and their impact on PM2.5 levels. Compared to traditional methods, improved temporal encodings align more closely with seasonal variations and synergize more effectively with meteorological factors. Besides, the structured nature of DC aids in model training, while the improved Ps more effectively captures PM2.5's spatial autocorrelation, outperforming traditional Ps. Overall, this study effectively represents spatiotemporal information, thereby boosting model accuracy and enabling seamless large-scale PM2.5 estimations. It provides deep insights into variables and models, providing significant implications for future air pollution research.

The "Hungry Judge" effect on prostate MRI reporting: Chronobiological trends from 35'004 radiologist interpretations.

AIM: To investigate the associations between the hour of the day and Prostate Imaging-Reporting and Data System (PI-RADS) scores assigned by radiologists in prostate MRI reports. MATERIALS AND METHODS: Retrospective single-center collection of prostate MRI reports over an 8-year period. Mean PI-RADS scores assigned between 0800 and 1800 h were examined with a regression model. RESULTS: A total of 35'004 prostate MRI interpretations by 26 radiologists were included. A significant association between the hour of day and mean PI-RADS score was identified (ß2 = 0.005, p < 0.001), with malignant scores more frequently assigned later in the day. CONCLUSION: These findings suggest chronobiological factors may contribute to variability in radiological assessments. Though the magnitude of the effect is small, this may potentially add variability and impact diagnostic accuracy.

Physiological modeling of the metaverse of the Mycobacterium tuberculosis ß-CA inhibition mechanism.

Tuberculosis (TB) is an infectious disease that primarily affects the lungs of humans and accounts for Mycobacterium tuberculosis (Mtb) bacteria as the etiologic agent. In this study, we introduce a computational framework designed to identify the important chemical features crucial for the effective inhibition of Mtb ß-CAs. Through applying a mechanistic model, we elucidated the essential features pivotal for robust inhibition. Using this model, we engineered molecules that exhibit potent inhibitory activity and introduce relevant novel chemistry. The designed molecules were prioritized for synthesis based on their predicted pKi values via the QSAR (Quantitative Structure-Activity Relationship) model. All the rationally designed and synthesized compounds were evaluated in vitro against different carbonic anhydrase isoforms expressed from the pathogen Mtb; moreover, the off-target and widely human-expressed CA I and II were also evaluated. Among the reported derivatives, 2, 4, and 5 demonstrated the most valuable in vitro activity, resulting in promising candidates for the treatment of TB infection. All the synthesized molecules exhibited favorable pharmacokinetic and toxicological profiles based on in silico predictions. Docking analysis confirmed that the zinc-binding groups bind effectively into the catalytic triad of the Mtb ß-Cas, supporting the in vitro outcomes with these binding interactions. Furthermore, molecules with good prediction accuracies according to previously established mechanistic and QSAR models were utilized to delve deeper into the realm of systems biology to understand their mechanism in combating tuberculotic pathogenesis. The results pointed to the key involvement of the compounds in modulating immune responses via NF-κß1, SRC kinase, and TNF-α to modulate granuloma formation and clearance via T cells. This dual action, in which the pathogen's enzyme is inhibited while modulating the human immune machinery, represents a paradigm shift toward more effective and comprehensive treatment approaches for combating tuberculosis.

Calibration of variant effect predictors on genome-wide data masks heterogeneous performance across genes.

In silico variant effect predictions are available for nearly all missense variants but played a minimal role in clinical variant classification because they were deemed to provide only supporting evidence. Recently, the ClinGen Sequence Variant Interpretation (SVI) Working Group updated recommendations for variant effect prediction use. By analyzing control pathogenic and benign variants across all genes, they were able to compute evidence strength for predictor score intervals with some intervals generating moderate, strong, or even very strong evidence. However, this genome-wide approach could obscure heterogeneous predictor performance in different genes. We quantified the gene-by-gene performance of two top predictors, REVEL and BayesDel, by analyzing control variants in each predictor score interval in 3,668 disease-relevant genes. Approximately 10% of intervals had sufficient control variants for analysis, and ∼70% of these intervals exceeded the maximum number of incorrect predictions implied by the SVI recommendations. These trending discordant intervals arose owing to the divergence of the gene-specific distribution of predictions from the genome-wide distribution, suggesting that gene-specific calibration is needed in many cases. Approximately 22% of ClinVar missense variants of uncertain significance in genes we analyzed (REVEL = 100,629, BayesDel = 71,928) had predictions in trending discordant intervals. Thus, genome-wide calibrations could result in many variants receiving inappropriate evidence strength. To facilitate a review of the SVI's calibrations, we developed a web application enabling visualization of gene-specific predictions and trending concordant and discordant intervals.

Combined interpretation of objective firearm evidence comparison algorithms using Bayesian networks.

Traditionally, firearm and toolmark examiners manually evaluate the similarity of features on two bullets using comparison microscopy. Advances in microscopy have made it possible to collect 3D topographic data, and several automated comparison algorithms have been introduced for the comparison of bullet striae using these data. In this study, open-source approaches for cross-correlation, congruent matching profile segments, consecutive matching striations, and a random forest model were evaluated. A statistical characterization of these automated approaches was performed using four datasets of consecutively manufactured firearms to provide a challenging comparison scenario. Each automated approach was applied to all samples in a pairwise fashion, and classification performance was compared. Based on these findings, a Bayesian network was empirically learned and constructed to leverage the strengths of each individual approach, model the relationship between the automated results, and combine them into a posterior probability for the given comparison. The network was evaluated similarly to the automated approaches, and the results were compared. The developed Bayesian network classified 99.6% of the samples correctly, and the resultant probability distributions were significantly separated more so than the automated approaches when used in isolation.

[Treatment of clavicle fracture: Interpretation of the 2023 American Academy of Orthopaedic Surgeons (AAOS) Clinical Practice Guidelines].

Clavicle fracture is a common orthopedic injury, accounting for approximately 2.6%-4% of all adult skeletal fractures. In 2023, the American Academy of Orthopaedic Surgeons (AAOS) developed evidence-based treatment guidelines for clavicle fractures, which include 4 recommendations and 10 options. This article, based on a thorough review of the guidelines, discusses the clinical treatment of clavicle fractures, aiming to share advancements and the latest diagnostic and therapeutic considerations with orthopedic colleagues to enhance treatment outcomes.

From Photography to Radiology: How Physicians Leveraged Early Hospital X-ray Machines to Supplant Photographers.

At the end of the nineteenth century, the advent of x-ray machines fueled American medicine's reliance on technology, transforming hospitals and the medical profession. X-ray manufacturers pursued the nascent hospital market as competition and patent feuds accelerated x-ray machine modifications. Hospitals incorporated clunky new machines and employed x-ray photographers, but as the unruly apparatus stabilized, physicians joining the new specialty of radiology discounted the toils of machine troubleshooting and promoted their medically qualified x-ray interpretations. This article frames early medical radiography in terms of boundary work, highlighting how discourse among physicians, x-ray photographers, and hospital administrators vied to establish a privileged demarcation between radiological science and photographic craft. Ultimately, radiologists supplanted x-ray photographers by leveraging the automation of x-ray machines and capitalizing on the epistemic shift from photographic objectivity to qualified interpretations. By focusing on this overlooked aspect of x-ray incorporation into hospitals, this work provides a unique perspective on how harnessing mechanization and authoritative medical interpretations can shift professional boundaries.

An intervention study on college students' employment anxiety based on interpretation bias modification: A randomized controlled experiment.

External environmental factors and internal cognitive bias affect college students' anxiety while job hunting. The current study is an intervention study on alleviating employment anxiety among college students through an Interpretation Bias Modification (IBM) computer-based intervention. A total of 79 valid participants were recruited. The Interpretation Bias Modification (IBM) group participants were required to complete employment-related IBM Internet training twice a week for three weeks. The placebo control group participants were required to complete neutral Internet training at the same frequency. The waiting list control group did not undergo any training. The groups were tested at three time points: prior to the intervention (pre-test), immediately after (post-test), and one month after the intervention (one-month follow-up). The IBM intervention group [F(2, 72) = 31.68, p < 0.001] showed greater significance in reducing employment anxiety than participants in the placebo control group [F(2, 72) = 9.83, p < 0.001] from the pre-test to one-month follow-up. There was no significant difference in employment anxiety among the waiting-list control group over time. The IBM intervention for college students can effectively decrease employment anxiety and reduce interpretation bias, which can be maintained at the one-month follow-up.