bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical Guidelines for the Treatment of Depressive Disorders IV. Medications and Other Biological Treatments
Authors: Kennedy Sidney H , Lam Raymond W , Cohen Nicole L , Ravindran Arun V ,   The CANMAT Depression Work Group
Published: June 2024

Background: The Canadian Psychiatric Association and the Canadian Network for Mood and Anxiety Treatments partnered to produce clinical guidelines for psychiatrists for the treatment of depressive disorders. Methods: A standard guidelines development process was followed. Relevant literature was identified using a computerized Medline search supplemented by review of bibliographies. Operational criteria were used to rate the quality of scientific evidence, and the line of treatment recommendations included consensus clinical opinion. This section, “Medications and Other Biological Treatments,” is 1 of 7 articles that were drafted and reviewed by clinicians. Revised drafts underwent national and international expert peer review. Results: Evidence-based recommendations are presented for 1) choosing an antidepressant, based on efficacy, tolerability, and safety; 2) the optimal use of antidepressants, including augmentation, combination, and switching strategies; 3) maintenance treatment; and 4) electroconvulsive therapy (ECT), light therapy, and additional somatic treatments. Evidence from metaanalv-ses is presented first, followed by conclusions from randomized controlled trials (RCTs) and, if appropriate, open-label data. Conclusions: There is significant evidence to support the role of selective serotonin reuptake inhibitors (SSRls), novel agents, and classic agents in the treatment of major depressive disorder (MDD). There is also evidence to support the use of somatic treatments, including ECT and light therapy, for some patients with MDD. There is limited evidence for the use of specific medications to treat subtypes of MDD. There is emerging evidence to support augmentation and combination strategies for patients previously nonresponsive to medication.
Subgroup analyses and patterns of multiple sclerosis health service utilisation: A cluster analysis
Authors: Fricke Lara Marleen , Krüger Kathrin , Trebst Corinna , Brütt Anna Levke , Dilger Elise-Marie , Eichstädt Kerstin , Flachenecker Peter , Grau Anja , Hemmerling Melissa , Hoekstra Dyon , Schaubert Kristina , Stahmann Alexander , Stahmeyer Jona Theodor , Thiele Annett , Zettl Uwe Klaus , Heidenreich Fedor , Krauth Christian 
Published: June 2024

Background Previous investigations of multiple sclerosis (MS)-related healthcare have focused on utilisation of specific individual health services (e.g. hospital care, office-based neurologists) by people with MS (PwMS). Meanwhile, little is known about possible patterns of utilisation across health services and their potential differences across patient characteristics. Objective To comprehensively analyse and identify patterns of MS-related health service utilisation and detect patient characteristics explaining such patterns. Methods In 2021, we invited all PwMS insured by the largest insurance company in Lower Saxony, Germany, to take part in an online survey. We merged respondents’ survey and health insurance claims data. We analysed MS-related health service utilisation and defined individual characteristics for subgroup analyses based on Andersen's Behavioural Model. We executed non-parametric missing value imputation and conducted hierarchical clustering to find patterns in health service utilisation. Results Of 6928 PwMS, 1935 responded to our survey and 1803 were included in the cluster analysis. We identified four distinct health service utilisation clusters: (1) regular users (n = 1130), (2) assistive care users (n = 443), (3) low users (n = 195) and (4) special services users (n = 35). Clusters differ by patient characteristics (e.g. age, impairment). Conclusion Our findings highlight the complexity of MS-related health service utilisation and provide relevant stakeholders with information allowing them to tailor healthcare planning according to utilisation patterns.
Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine
Authors: Wei Heng-Le , Yu Yu-Sheng , Wang Meng-Yao , Zhou Gang-Ping , Li Junrong , Zhang Hong , Zhou Zhengyang 
Published: June 2024

Abstract
Background
Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment.

Methods
Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine.

Results
One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders.

Conclusions
Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.
Social support and physical activity: does general health matter?
Authors: Lieber Sarah B. , Moxley Jerad , Mandl Lisa A. , Reid M. Carrington , Czaja Sara J. 
Published: June 2024

Abstract
Background
Physical activity levels remain suboptimal in older adults. Exploration of potentially modifiable factors such as social support is needed to inform the development and implementation of patient-oriented physical activity interventions for older adults. The impact of general health on the relationship between social support and physical activity is not well understood. We aimed to determine the association between social support and self-reported physical activity in a study of community-dwelling older adults. In addition, we examined whether self-reported general health mediates the relationship between social support and self-reported physical activity.

Method
This cross-sectional study analyzed baseline data collected as part of a randomized controlled trial comparing a digital physical activity intervention, which included social support features, with a tablet-based educational control. Adults ≥ 60 years of age were enrolled at 2 sites. Self-reported general health, social support, physical activity, and sociodemographic characteristics and comorbid conditions were assessed. Pearson and point-biserial correlations were computed to evaluate the relationship between physical activity and general health, social support, and sociodemographic features. Social support (exposure), general health (mediator), and physical activity (outcome) were incorporated into a mediation model.

Results
Among 181 participants (mean age of 70.1 years), significant correlations were found between physical activity and both general health and social support (r = -0.19 and r = 0.21, respectively; both p < 0.01). General health significantly mediated the relationship between social support and physical activity (unstandardized ß coefficient 416.9; 95% confidence interval 96.4, 842.0).

Conclusions
Augmentation of social support, particularly when coupled with other modes of health promotion to improve personal wellbeing, may be a valuable component of physical activity promotion programs. Further longitudinal research is needed to clarify the potential mechanistic pathways linking social support, general health, and physical activity to inform development of evidence-based physical activity interventions for older adults and improve downstream health-related outcomes.

Trial Registration
ClinicalTrials.gov, ClinicalTrials.gov identifier NCT03538158. Registered May 25, 2018.
Good practices on transit operation design: bus drivers’ perspective
Authors: Cordera Rubén , Novales Margarita , Orro Alfonso , Alonso Borja , dell’Olio Luigi 
Published: June 2024

AbstractUnderstanding which aspects can have the greatest influence on the performance, comfort and safety of a public transport system can be of great help in improving its operation and promoting more sustainable mobility. To find out more about these aspects, a survey was carried out among more than 180 drivers of urban bus transport systems in seven medium-sized cities in Spain. The drivers' perspective has been insufficiently taken into account in previous research, despite the fact that these workers have a high and direct knowledge of the operation and problems directly affecting the service. The data collected through this survey have been analysed using descriptive statistics and an Ordered Probit model. The results have allowed us to detect specific aspects to improve the performance and comfort of users and drivers such as the correct location and length of bus stops and enforcing regulation to ensure that buses have priority in traffic and that illegal parking does not interfere with their operations. In addition, bus lanes were seen as important by drivers to improve the comfort and safety of the service. Other factors related to safety were generally highly rated and there was little dispersion in the responses about their importance, although aspects such as proper visibility, avoiding illegal parking, and campaigns to encourage bus users to be aware of their surroundings stood out in the answers. Finally, the payment system was highlighted as very relevant to ensure the performance and comfort of users and drivers during the operation at bus stops. Improving this system was identified as the measure that could have the greatest impact on the performance of the service as a whole.
Exploring endophytic bacteria communities of Vanilla planifolia
Authors: Mahadeo Keshika , Taïbi Ahmed , Meile Jean-Christophe , Côme Bertrand , Gauvin-Bialecki Anne , Boubakri Hasna , Herrera-Belaroussi Aude , Kodja Hippolyte 
Published: June 2024

Abstract
Background
Rhizosphere bacterial community and endophytes are now known to influence plant health and response to environmental stress. Very few studies have reported the diversity of endophytic bacterial communities of Vanilla planifolia and their potential roles in promoting plant growth or contributing to aromatic quality.

Results
In this study, the composition and diversity of the Vanilla rhizosphere bacterial community were explored by analyzing rhizosphere soil and root tissue samples as well as green pods of three accessions of Vanilla planifolia grown on different types of substrates (compost and leaf litter). In addition, the endophytic bacterial diversity of roots and green pods as well as the evolution of endophytic bacteria after the curing process of vanilla green pods were analyzed based on a metabarcoding approach. The results showed that bacterial species richness and diversity were higher in the compost. The analysis of the soil bacterial composition displayed that Halomonas, Pseudoalteromonas, Enterobacter and Bradyrhizobium were the most abundant genera. Moreover, the results indicated that the soil bacterial community structure was linked to the host plant genotype. Regarding the roots endophytic bacteria composition, the genera Halomonas, Pseudoalteromonas, Bacillus and Carboxydocella genera were present in all samples, independently from the substrate nature. Several genera including Bacillus, Bradyrhizobium, Burkholderia and Halomonas were transmitted internally from the roots to the green pods. The curing process reduced the bacterial richness and bacterial diversity associated with the green pods. Halomonas, Pseudoalteromonas, Bacillus, and Carboxydocella are the dominant genera in the pods after the curing process.

Conclusions
This study provides an overview of changes of the bacterial communities dynamics especially endophytic in the roots and the green pods. It highlighted bacterial genera (Halomonas, Pseudoalteromonas, Bacillus, and Carboxydocella) potentially implicated in the formation of aroma compounds of vanilla beans.
T cell-mediated Immune response and correlates of inflammation and their relationship with COVID-19 clinical severity: not an intuitive guess
Authors: Pena Nathalia Mantovani , Santana Luiz Claudio , Hunter James R , Blum Vinicius Fontanesi , Vergara Tania , Gouvea Celso , Leal Elcio , Bellei Nancy , Schechter Mauro , Diaz Ricardo Sobhie 
Published: June 2024

Abstract
Background
Predictors of the outcome of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection remain to be fully determined. We evaluated selected viral characteristics and immunological responses that might predict and/or correlate to the clinical outcome of COVID-19.

Methods
For individuals developing divergent clinical outcomes, the magnitude and breadth of T cell-mediated responses were measured within 36 h of symptom onset. Peripheral Blood Mononuclear Cells (PBMCs) were subjected to in vitro stimulation with SARS-CoV-2-based peptides. In addition, SARS-CoV-2 sequences were generated by metagenome, and HLA typing was performed using Luminex technology.

Findings
CD4+ T cell activation was negatively correlated with SARS-CoV-2 basal viral load in patients with severe COVID-19 (p = 0·043). The overall cellular immune response, as inferred by the IFN-γ signal, was higher at baseline for patients who progressed to mild disease compared to patients who progressed to severe disease (p = 0·0044). Subjects with milder disease developed higher T cell responses for MHC class I and II-restricted peptides (p = 0·033).

Interpretation
Mounting specific cellular immune responses in the first days after symptom onset, as inferred by IFN-γ magnitude in the ELISPOT assay, may efficiently favor a positive outcome. In contrast, progression to severe COVID-19 was accompanied by stronger cellular immune responses, higher CD4 + T cell activation, and a higher number of in silico predicted high-affinity class I HLA alleles.
Elixhauser comorbidity method in predicting death of Spanish inpatients with asplenia and pneumococcal pneumonia
Authors: Gea-Izquierdo Enrique , Ruiz-Urbaez Rossana , Hernández-Barrera Valentín , Stich Michael , Gil-de-Miguel Ángel 
Published: June 2024

Abstract
Background
Pneumococcal pneumonia (PP) is a serious infection caused by Streptococcus pneumoniae (pneumococcus), with a wide spectrum of clinical manifestations. The aim of this study was to analyze the comorbidity factors that influenced the mortality in patients with asplenia according to PP.

Methods
Discharge reports from the Spanish Minimum Basic Data Set (MBDS) was used to retrospectively analyze patients with asplenia and PP, from 1997 to 2021. Elixhauser Comorbidity Index (ECI) was calculated to predict in-hospital mortality (IHM).

Results
97,922 patients with asplenia were included and 381 cases of PP were identified. The average age for men was 63.87 years and for women 65.99 years. In all years, ECI was larger for splenectomized than for non-splenectomized patients, with men having a higher mean ECI than women. An association was found between risk factors ECI, splenectomy, age group, sex, pneumococcal pneumonia, and increased mortality (OR = 0.98; 95% CI: 0.97–0.99; p < 0.001). The IHM increased steadily with the number of comorbidities and index scores in 1997–2021.

Conclusions
Asplenia remain a relevant cause of hospitalization in Spain. Comorbidities reflected a great impact in patients with asplenia and PP, which would mean higher risk of mortality.
A deep learning-powered diagnostic model for acute pancreatitis
Authors: Zhang Chi , Peng Jin , Wang Lu , Wang Yu , Chen Wei , Sun Ming-wei , Jiang Hua 
Published: June 2024

Abstract
Background
Acute pancreatitis is one of the most common diseases requiring emergency surgery. Rapid and accurate recognition of acute pancreatitis can help improve clinical outcomes. This study aimed to develop a deep learning-powered diagnostic model for acute pancreatitis.

Materials and methods
In this investigation, we enrolled a cohort of 190 patients with acute pancreatitis who were admitted to Sichuan Provincial People’s Hospital between January 2020 and December 2021. Abdominal computed tomography (CT) scans were obtained from both patients with acute pancreatitis and healthy individuals. Our model was constructed using two modules: (1) the acute pancreatitis classifier module; (2) the pancreatitis lesion segmentation module. Each model’s performance was assessed based on precision, recall rate, F1-score, Area Under the Curve (AUC), loss rate, frequency-weighted accuracy (fwavacc), and Mean Intersection over Union (MIOU).

Results
Upon admission, significant variations were observed between patients with mild and severe acute pancreatitis in inflammatory indexes, liver, and kidney function indicators, as well as coagulation parameters. The acute pancreatitis classifier module exhibited commendable diagnostic efficacy, showing an impressive AUC of 0.993 (95%CI: 0.978–0.999) in the test set (comprising healthy examination patients vs. those with acute pancreatitis, P < 0.001) and an AUC of 0.850 (95%CI: 0.790–0.898) in the external validation set (healthy examination patients vs. patients with acute pancreatitis, P < 0.001). Furthermore, the acute pancreatitis lesion segmentation module demonstrated exceptional performance in the validation set. For pancreas segmentation, peripancreatic inflammatory exudation, peripancreatic effusion, and peripancreatic abscess necrosis, the MIOU values were 86.02 (84.52, 87.20), 61.81 (56.25, 64.83), 57.73 (49.90, 68.23), and 66.36 (55.08, 72.12), respectively. These findings underscore the robustness and reliability of the developed models in accurately characterizing and assessing acute pancreatitis.

Conclusion
The diagnostic model for acute pancreatitis, driven by deep learning, exhibits excellent efficacy in accurately evaluating the severity of the condition.

Trial Registration
This is a retrospective study.
Survival status and predictors of mortality among cervical cancer patients at oncologic centers in Addis Ababa, Ethiopia: a follow up study
Authors: Sifer Samuel Dessu 
Published: June 2024

Abstract
Background
Cervical cancer (CC) ranks as the third most commonly diagnosed cancer and the fourth leading cause of cancer-related deaths among women globally. In Addis Ababa, there is a shortage of available evidence concerning the phenomenon of survival time and its predictors among women diagnosed with CC. Therefore, this study aimed to assess the survival status and predictors of mortality among CC patients at oncologic centers in Addis Ababa, Ethiopia.

Methods
A facility-based retrospective cohort study was conducted among records of women with cervical cancer enrolled from the 1st of January 2017 to the 30th of December 2022 among 252 cervical cancer patients. Data were collected using a pretested, structured data collection checklist by trained data collectors. The Kaplan–Meier survival curve was used to estimate the survival time of the respondents. The Cox multivariable regression model was carried out to identify predictors of CC. Variables with P-value < 0.05 in multivariable analysis were declared as statistically significant.

Results
The cumulative proportion of surviving at the end of the 10th and 20th month was 99.6% (95%CI: 97.02, 99.94) and 96.99% (95%CI: 93.41, 98.64), respectively. Similarly, it was 92.67% (95%CI: 87.65, 95.70), 85.9% (95%CI: 78.68, 90.94), 68.0% (95%CI: 57.14, 76.66) and 18.27% (8.38, 31.16) at the end of 30th, 40th, 50th and 60th monthly respectively. The overall median survival time was 54 months (95%CI: 52.6, 55.4). The incidence of death among a cohort of women with CC was 7.34 per 1000 person months. Being anemic (AHR: 4.77; 95%CI: 1.93, 11.77; P-value: 0.001), took a single cancer treatment (AHR: 1.92; 95%CI: 1.01, 3.64; P-value: 0.046) and HIV sero status positive (AHR: 2.05; 95%CI: 1.01, 4.19; P-value: 0.048) were statistically significant in multivariable cox proportional hazard model.

Conclusion and recommendation
Anemia, treatment initiation and HIV-sero status were independent predictors of mortality among women admitted with CC. It is imperative to enhance early screening initiatives and treatment resources for CC, alongside fostering public awareness through collaboration with various media outlets concerning preventive measures, screening procedures, and treatment alternatives for CC.
Liquid-based cytology specimens for next-generation sequencing in lung adenocarcinoma: challenges and evaluation of targeted therapy
Authors: Xiao Xiaoyue , Sun ZiHan , Liang Shuo , Li Weihua , Guo HuiQin , Zhao Huan , Zhao LinLin , Ma HaiYue , Sun Yue , Wang Cong , Chang XinXiang , Zhang ZhiHui 
Published: June 2024

Abstract
Background
To explore challenges of liquid-based cytology (LBC) specimens for next-generation sequencing (NGS) in lung adenocarcinoma and evaluate the efficacy of targeted therapy.

Methods
A retrospective analysis was conducted on the NGS test of 357 cases of advanced lung adenocarcinoma LBC specimens and compared with results of histological specimens to assess the consistency. The impact of tumor cellularity on NGS test results was evaluated. The utility of epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) was collected. Clinical efficacy evaluation was performed and survival curve analysis was conducted using the Kaplan-Meier method.

Results
There were 275 TKI-naive and 82 TKI-treated specimens, the mutation rates of cancer-related genes detected in both groups were similar (86.2% vs. 86.6%). The EGFR mutation rate in the TKI treated group was higher than that in the TKI-naive group (69.5% > 54.9%, P = 0.019). There was no significant difference in the EGFR mutation frequency among different tumor cellularity in the TKI-naive group. However, in the TKI treated group, the frequency of EGFR sensitizing mutation and T790M resistance mutation in specimens with < 20% tumor cellularity was significantly lower than that in specimens with ≥ 20% tumor cellularity. Among 22 cases with matched histological specimens, 72.7% (16/22) of LBC specimens were completely consistent with results of histological specimens. Among 92 patients with EGFR-mutant lung adenocarcinoma treated with EGFR-TKIs in the two cohorts, 88 cases experienced progression, and the median progression-free survival (PFS) was 12.1 months.

Conclusions
Cytological specimens are important sources for gene detection of advanced lung adenocarcinoma. When using LBC specimens for molecular testing, it is recommended to fully evaluate the tumor cellularity of the specimens.