bluuebunny/crossref_metadata_2025_split huggingface dataset:
Adolescents’ impairment due to climate anxiety is associated with self-efficacy and behavioral engagement: a cross-sectional analysis in Quebec (Canada)
Authors: Turcotte-Tremblay Anne-Marie , Fortier Gabrielle , Bélanger Richard E. , Bacque Dion Claude , Gansaonré Rabi Joel , Leatherdale Scott T. , Haddad Slim 
Published: October 2024

Abstract
Background
The current understanding of climate anxiety among adolescents is sparse. This study identifies the characteristics of adolescents living in Quebec (Canada) who experience impairments induced by climate anxiety, who have feelings of self-efficacy and who adopt pro-environment behaviors. It also characterizes the association between climate anxiety, feelings of self-efficacy, and environmental actions.

Methods
We used a cross-sectional design to analyze data from the COMPASS study on adolescent health. Participants (n = 45 362) from 113 schools in Quebec (Canada) answered questions about climate anxiety, self-efficacy, behavioral engagement, and several indicators of well-being. Data were analyzed using ordered logistic regressions adjusted for potential confounders (age, gender, school type, material deprivation, etc.).

Results
9% of adolescents reported that thinking about climate change made it difficult to sleep at least sometimes. 6% of adolescents reported that thinking about climate change interfered with their ability to get work or assignments done at least sometimes. Only 34% believed they could do something to address the problem, and 43% tried to reduce their behaviors that contribute to climate change at least sometimes. Adolescents who were boys or who were less anxious experienced less impairment and were less involved in the fight against climate change. Adolescents from less affluent families experienced more difficulty sleeping and interference with work but were not more engaged. Those with stronger school connectedness experienced less impairment and were more often involved in the fight against climate change. Adolescents who felt they could do something about climate change were more likely to try to reduce behaviors that contribute to climate change.

Conclusion
The findings are useful to identify and support vulnerable groups that are more likely to experience impairment due to climate anxiety. The feeling of climate self-efficacy was not well developed among some groups of adolescents. Improving adolescents’ beliefs in their capacity to help address climate change may be a key strategy to promote pro-environmental actions. As the threat of climate change increases, it will be important to follow the evolution of climate anxiety and engagement among adolescents.
Six clinical phenotypes with prognostic implications were identified by unsupervised machine learning in children and adolescents with SARS-CoV-2 infection: results from a German nationwide registry
Authors: Shi Yanyan , Strobl Ralf , Berner Reinhard , Armann Jakob , Scheithauer Simone , Grill Eva 
Published: October 2024

Abstract
Objective
Phenotypes are important for patient classification, disease prognostication, and treatment customization. We aimed to identify distinct clinical phenotypes of children and adolescents hospitalized with SARS-CoV-2 infection, and to evaluate their prognostic differences.

Methods
The German Society of Pediatric Infectious Diseases (DGPI) registry is a nationwide, prospective registry for children and adolescents hospitalized with a SARS-CoV-2 infection in Germany. We applied hierarchical clustering for phenotype identification with variables including sex, SARS-CoV-2-related symptoms on admission, pre-existing comorbidities, clinically relevant coinfection, and SARS-CoV-2 risk factors. Outcomes of this study were: discharge status and ICU admission. Discharge status was categorized as: full recovery, residual symptoms, and unfavorable prognosis (including consequential damage that has already been identified as potentially irreversible at the time of discharge and SARS-CoV-2-related death). After acquiring the phenotypes, we evaluated their correlation with discharge status by multinomial logistic regression model, and correlation with ICU admission by binary logistic regression model. We conducted an analogous subgroup analysis for those aged < 1 year (infants) and those aged ⩾ 1 year (non-infants).

Results
The DGPI registry enrolled 6983 patients, through which we identified six distinct phenotypes for children and adolescents with SARS-CoV-2 which can be characterized by their symptom pattern: phenotype A had a range of symptoms, while predominant symptoms of patients with other phenotypes were gastrointestinal (95.9%, B), asymptomatic (95.9%, C), lower respiratory tract (49.8%, D), lower respiratory tract and ear, nose and throat (86.2% and 41.7%, E), and neurological (99.2%, F). Regarding discharge status, patients with D and E phenotype had the highest odds of having residual symptoms (OR: 1.33 [1.11, 1.59] and 1.91 [1.65, 2.21], respectively) and patients with phenotype D were significantly more likely (OR: 4.00 [1.95, 8.19]) to have an unfavorable prognosis. Regarding ICU, patients with phenotype D had higher possibility of ICU admission than staying in normal ward (OR: 4.26 [3.06, 5.98]), compared to patients with phenotype A. The outcomes observed in the infants and non-infants closely resembled those of the entire registered population, except infants did not exhibit typical neurological/neuromuscular phenotypes.

Conclusions
Phenotypes enable pediatric patient stratification by risk and thus assist in personalized patient care. Our findings in SARS-CoV-2-infected population might also be transferable to other infectious diseases.
Deep learning-based segmentation of kidneys and renal cysts on T2-weighted MRI from patients with autosomal dominant polycystic kidney disease
Authors: Sore Rémi , Cathier Pascal , Vlachomitrou Anna Sesilia , Bailleux Jérôme , Arnaud Karine , Juillard Laurent , Lemoine Sandrine , Rouvière Olivier 
Published: October 2024

Abstract
Background
Our aim was to train and test a deep learning-based algorithm for automatically segmenting kidneys and renal cysts in patients with autosomal dominant polycystic kidney disease (ADPKD).

Methods
We retrospectively selected all ADPKD patients who underwent renal MRI with coronal T2-weighted imaging at our institution from 2008 to 2022. The 20 most recent examinations constituted the test dataset, to mimic pseudoprospective enrolment. The remaining ones constituted the training dataset to which eight normal renal MRIs were added. Kidneys and cysts ground truth segmentations were performed on coronal T2-weighted images by a junior radiologist supervised by an experienced radiologist. Kidneys and cysts of the 20 test MRIs were segmented by the algorithm and three independent human raters. Segmentations were compared using overlap metrics. The total kidney volume (TKV), total cystic volume (TCV), and cystic index (TCV divided by TKV) were compared using Bland–Altman analysis.

Results
We included 164 ADPKD patients. Dice similarity coefficients ranged from 85.9% to 87.4% between the algorithms and the raters’ segmentations and from 84.2% to 86.2% across raters’ segmentations. For TCV assessment, the biases ± standard deviations (SD) were 3–19 ± 137–151 mL between the algorithm and the raters, and 22–45 ± 49–57 mL across raters. The algorithm underestimated TKV and TCV in two outliers with TCV > 2800 mL. For cystic index assessment, the biases ± SD were 2.5–6.9% ± 6.7–8.3% between the algorithm and the raters, and 2.1–9.4 ± 7.4–11.6% across raters.

Conclusion
The algorithm’s performance fell within the range of inter-rater variability, but large TKV and TCV were underestimated.

Relevance statement
Accurate automated segmentation of the renal cysts will enable the large-scale evaluation of the prognostic value of TCV and cystic index in ADPKD patients. If these biomarkers are prognostic, then automated segmentation will facilitate their use in daily routine.

Key Points

Cystic volume is an emerging biomarker in ADPKD.

The algorithm’s performance in segmenting kidneys and cysts fell within interrater variability.

The segmentation of very large cysts, under-represented in the training dataset, needs improvement.

Graphical Abstract
Machine learning-optimized non-invasive brain stimulation and treatment response classification for major depression
Authors: Albizu Alejandro , Indahlastari Aprinda , Suen Paulo , Huang Ziqian , Waner Jori L. , Stolte Skylar E. , Fang Ruogu , Brunoni Andre R. , Woods Adam J. 
Published: October 2024

Abstract
Background/Objectives
Transcranial direct current stimulation (tDCS) is a non-invasive brain stimulation intervention that shows promise as a potential treatment for depression. However, the clinical efficacy of tDCS varies, possibly due to individual differences in head anatomy affecting tDCS dosage. While functional changes in brain activity are more commonly reported in major depressive disorder (MDD), some studies suggest that subtle macroscopic structural differences, such as cortical thickness or brain volume reductions, may occur in MDD and could influence tDCS electric field (E-field) distributions. Therefore, accounting for individual anatomical differences may provide a pathway to optimize functional gains in MDD by formulating personalized tDCS dosage.

Methods
To address the dosing variability of tDCS, we examined a subsample of sixteen active-tDCS participants’ data from the larger ELECT clinical trial (NCT01894815). With this dataset, individualized neuroimaging-derived computational models of tDCS current were generated for (1) classifying treatment response, (2) elucidating essential stimulation features associated with treatment response, and (3) computing a personalized dose of tDCS to maximize the likelihood of treatment response in MDD.

Results
In the ELECT trial, tDCS was superior to placebo (3.2 points [95% CI, 0.7 to 5.5; P = 0.01]). Our algorithm achieved over 90% overall accuracy in classifying treatment responders from the active-tDCS group (AUC = 0.90, F1 = 0.92, MCC = 0.79). Computed precision doses also achieved an average response likelihood of 99.981% and decreased dosing variability by 91.9%.

Conclusion
These findings support our previously developed precision-dosing method for a new application in psychiatry by optimizing the statistical likelihood of tDCS treatment response in MDD.
Systemic lupus erythematosus: pathogenesis and targeted therapy
Authors: Su Xu , Yu Hui , Lei Qingqiang , Chen Xuerui , Tong Yanli , Zhang Zhongyang , Yang Wenyong , Guo Yuanbiao , Lin Liangbin 
Published: October 2024

AbstractSystemic lupus erythematosus (SLE) is a multifaceted autoimmune disorder characterized by dysregulated immune responses and autoantibody production, which affects multiple organs and varies in clinical presentation and disease severity. The development of SLE is intricate, encompassing dysregulation within the immune system, a collapse of immunological tolerance, genetic susceptibilities to the disease, and a variety of environmental factors that can act as triggers. This review provides a comprehensive discussion of the pathogenesis and treatment strategies of SLE and focuses on the progress and status of traditional and emerging treatment strategies for SLE. Traditional treatment strategies for SLE have mainly employed non-specific approaches, including cytotoxic and immunosuppressive drugs, antimalarials, glucocorticoids, and NSAIDs. These strategies are effective in mitigating the effects of the disease, but they are not a complete cure and are often accompanied by adverse reactions. Emerging targeted therapeutic drugs, on the other hand, aim to control and treat SLE by targeting B and T cells, inhibiting their activation and function, as well as the abnormal activation of the immune system. A deeper understanding of the pathogenesis of SLE and the exploration of new targeted treatment strategies are essential to advance the treatment of this complex autoimmune disease.
Telephone Survey–Reported Perceptions of Telehealth Visits Among Black and Non-Black Patients Diagnosed With Cancer
Authors: Fridman Ilona , Neslund-Dudas Christine , Barrow Lauren C.J. , Dunn Matthew R. , Jones Regina , Kinlaw Alan C. , Smith Angela B. , Stein Jacob N. , Tam Samantha , Wood William A. , Lafata Jennifer Elston 
Published: October 2024

PURPOSE
Empirical evidence underscores both benefits of telehealth visits and persistent disparities in use for Black adults. Guided by the Technology Acceptance Model, we evaluated perceptions regarding telehealth visits among Black and Non-Black adults receiving cancer care from two academic health systems.

METHODS
Between April 2022 and October 2023, a survey was conducted among adult patients treated for cancer in the past 3 years. Survey recruitment was stratified by visit type (telehealth or in-person) and race. Before a scheduled oncology visit, respondents completed a telephone survey to assess their perceptions of usefulness, ease of use, and attitudes toward telehealth visits. Frequency differences were estimated using chi-square tests, and prevalence differences using logistic regression.

RESULTS

The survey was completed by 773 respondents (42% Black) with a 15% response rate. Black respondents were younger and less likely to report male sex, being married, having a college education, comfortable income, or confidence in medical and e-literacy. Compared with Non-Black respondents, Black respondents perceived telehealth visits as less useful for determining health needs (37%
v
48%) and asking questions (9%
v
15%). They thought that telehealth visits were less easy with regard to understanding their health care provider (14%
v
21%) and the ability to connect (23%
v
30%). Black respondents expressed more concern about internet access (26%
v
15%), access to electronic devices (17%
v
9%), and finding assistance for connecting (24%
v
12%). They also found telehealth visits to be less private (19%
v
34%).

CONCLUSION
Racial differences in telehealth ease of use and usefulness perceptions highlight the need for oncology practices to couple connectivity support with effective communication strategies to avoid disparities in oncology telehealth services.
The Effect of Pexidartinib on Neuropathic Pain via Influences on Microglia and Neuroinflammation in Mice
Authors: Yang Liuyue , Gomm Ashley , Bai Ping , Ding Weihua , Tanzi Rudolph E. , Wang Changning , Shen Shiqian , Zhang Can 
Published: October 2024

BACKGROUND:
Chronic pain is a debilitating medical condition that lacks effective treatments. Increasing evidence suggests that microglia and neuroinflammation underlie pain pathophysiology, which therefore supports a potential strategy for developing pain therapeutics. Here, our study is testing the hypothesis that the promise of pain amelioration can be achieved using the small-molecule pexidartinib (PLX-3397), a previously food and drug administration (FDA)-approved cancer medicine and a colony-stimulating factor-1 receptor (CSF-1R) inhibitor that display microglia-depleting properties.

METHOD:
We used the previously reported chronic constriction injury (CCI) mouse model, in which PLX-3397 or vehicle was orally administrated to mice daily for 21 days, then applied to the CCI model, followed by PLX-3397 or vehicle administration for an additional 28 days. Additionally, we examined microglia-related neuroinflammation markers using positron emission tomography (PET) neuroimaging and immunofluorescence (IF).

RESULTS:
We showed that PLX-3397 significantly ameliorated pain-related behavioral changes throughout the entire experimental period after CCI (vehicle versus PLX-3397 at day 14, effect size: 2.57, P = .002). Microglia changes were first analyzed by live-animal PET neuroimaging, revealing PLX-3397-associated reduction of microglia by probing receptor-interacting serine/threonine-protein kinase 1 (RIPK1), a protein primarily expressed in microglia, which were further corroborated by postmortem immunohistochemistry (IHC) analysis using antibodies for microglia, including ionized Ca2+ binding adaptor molecule 1 (Iba-1) (somatosensory cortex, hindlimb area; vehicle versus PLX-3397, effect size 3.6, P = .011) and RIPK1 (somatosensory cortex, hindlimb area; vehicle versus PLX-3397, effect size 2.9, P = .023. The expression of both markers decreased in the PLX-3397 group. Furthermore, we found that PLX-3397 led to significant reductions in various proteins, including inducible nitric oxide synthase (iNOS) (somatosensory cortex, hindlimb area; vehicle versus PLX-3397, effect size: 2.3, P = .048), involved in neuroinflammation through IHC.

CONCLUSIONS:
Collectively, our study showed PLX-3397-related efficacy in ameliorating pain linked to the reduction of microglia and neuroinflammation in mice. Furthermore, our research provided new proof-of-concept data supporting the promise of testing PLX-3397 as an analgesic.
The Employer Perspective
Authors: Reed Rachelle , McAvoy Cayla , Gallo Paul M. , Newsome A'Naja M. 
Published: October 2024

Apply It!
By reading this article, you will:
• Gather insight from key industry stakeholders on the latest fitness trends identified in the 2025 Trends report.
• Learn how industry stakeholders use the Trends report to inform their strategic planning and decision-making processes.
• Understand alignment of 2025 Trends report and real-world trends, through the perspective of industry stakeholders.
A Simple and Optimal Policy Design with Safety Against Heavy-Tailed Risk for Stochastic Bandits
Authors: Simchi-Levi David , Zheng Zeyu , Zhu Feng 
Published: October 2024

We study the stochastic multi-armed bandit problem and design new policies that enjoy both optimal regret expectation and light-tailed risk for regret distribution. We first find that any policy that obtains the optimal instance-dependent expected regret could incur a heavy-tailed regret tail risk that decays slowly with T. We then focus on policies that achieve optimal worst-case expected regret. We design a novel policy that (i) enjoys the worst-case optimality for regret expectation and (ii) has the worst-case tail probability of incurring a regret larger than any regret threshold that decays exponentially with respect to T. The decaying rate is proved to be optimal for all worst-case optimal policies. Our proposed policy achieves a delicate balance between doing more exploration at the beginning of the time horizon and doing more exploitation when approaching the end, compared with standard confidence-bound-based policies. We also enhance the policy design to accommodate the “any-time” setting where T is unknown a priori, highlighting “lifelong exploration”, and prove equivalently desired policy performances as compared with the “fixed-time” setting with known T. From a managerial perspective, we show through numerical experiments that our new policy design yields similar efficiency and better safety compared to celebrated policies. Our policy design is preferable especially when (i) there is a risk of underestimating the volatility profile, or (ii) there is a challenge of tuning policy hyper-parameters. We conclude by extending our proposed policy design to the stochastic linear bandit setting that leads to both worst-case optimality in terms of regret expectation and light-tailed risk on regret distribution. This paper was accepted by J. George Shanthikumar, data science. Funding: The work of D. Simchi-Levi and F. Zhu is partially supported by the MIT Data Science Laboratory. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03512 .
Political Districting to Minimize County Splits
Authors: Shahmizad Maral , Buchanan Austin 
Published: October 2024

Political Districting to Minimize County Splits When dividing a state into districts for elections, one traditional criterion is that political subdivisions like counties and cities should not be divided unnecessarily. Some states go as far as to say that the number of county splits should be minimized, but previously there was no scalable exact method for determining this. With new integer programming techniques, Shahmizad and Buchanan exactly compute this minimum number for all states and district types (congressional, state senate, state house) across the USA.
Clinicopathological Features of Membranous Nephropathy Complicated by IgA Nephropathy: A Retrospective Analysis of Seven Cases
Authors: Xu Beilei , Bao Qiuhong , Shen Wei , Hong Quxia 
Published: October 2024

Aims/Background Both membranous nephropathy (MN) and immunoglobulin A nephropathy (IgAN) are immune complex-mediated glomerular diseases, but the concurrent occurrence of these two conditions in the same patient is not common, a phenomenon that is currently not supported by clinical data in terms of treatment and prognosis. This study explores the clinical and pathological characteristics, as well as the treatment outcomes, of patients affected by MN and IgAN simultaneously. Methods The clinical data, pathological features, and diagnostic and therapeutic information of seven cases of MN complicated by IgAN, treated between December 2015 and December 2022, were retrospectively analyzed. Results Among the seven cases, there were two male and five female patients, with an average age of 57.3 ± 9.2 years. All patients presented with clinical manifestations of proteinuria and edema upon admission, with an average 24-hour urine protein of 3716.6 ± 1519.4 mg/24 h. Phospholipase A2 receptor (PLA2R) expression was detected in all seven cases, and nephrotic syndrome was clinically diagnosed in five cases. Additionally, all seven cases showed microscopic hematuria, with intermittent gross hematuria in two cases. All seven patients included in this study underwent renal biopsy. After disease staging, the patients had MN stages I–III and IgAN stages II–III. Pathological findings revealed abnormal glomerular basement membrane (GBM) and diffuse immunoglobulin G (IgG) deposition in the subepithelial space, predominantly of the IgG4 subtype. Simultaneously, there was diffuse mesangial zone deposition of immunoglobulin A (IgA) to varying degrees, co-localization of complement component C3 and IgA, and mesangial cell proliferation. Treatment strategies included angiotensin-converting enzyme inhibitors (ACEI) or angiotensin receptor blockers (ARB) in combination with steroids or immunosuppressive therapies such as tacrolimus, cyclophosphamide, and rituximab. After 2–6 months of treatment, all patients achieved complete remission with a favourable prognosis. Conclusion MN accompanied by IgAN tends to occur more frequently in middle-aged and elderly individuals, with a relatively low incidence. The latent feature of the comorbidities manifests as a form of IgAN superimposed on the background of MN. Utilizing ACEI or ARB in combination with steroids or various immunosuppressive therapies represents a potentially effective treatment strategy.