Effects of perceptions of forest change and intergroup competition on community-based conservation behaviors.

Approximately one quarter of the earth's population directly harvests natural resources to meet their daily needs. These individuals are disproportionately required to alter their behaviors in response to increasing climatic variability and global biodiversity loss. Much of the ever-ambitious global conservation agenda relies on the voluntary uptake of conservation behaviors in such populations. Thus, it is critical to understand how such individuals perceive environmental change and use conservation practices as a tool to protect their well-being. We developed a participatory mapping activity to elicit spatially explicit perceptions of forest change and its drivers across 43 mangrove-dependent communities in Pemba, Tanzania. We administered this activity along with a questionnaire regarding conservation preferences and behaviors to 423 individuals across those 43 communities. We analyzed these data with a set of Bayesian hierarchical statistical models. Perceived cover loss in 50% of a community's mangrove area drove individuals to decrease proposed limits on fuelwood bundles from 2.74 (forest perceived as intact) to 2.37 if participants believed resultant gains in mangrove cover would not be stolen by outsiders. Conversely, individuals who believed their community mangrove forests were at high risk of theft loosened their proposed harvest limits from 1.26 to 2.75 bundles of fuelwood in response to the same perceived forest decline. High rates of intergroup competition and mangrove loss were thus driving a self-reinforcing increase in unsustainable harvesting preferences in community forests in this system. This finding demonstrates a mechanism by which increasing environmental decline may cause communities to forgo conservation practices, rather than adopt them, as is often assumed in much community-based conservation planning. However, we also found that when effective boundaries were present, individuals were willing to limit their own harvests to stem such perceived decline.

Efectos de las percepciones del cambio forestal y la competencia intergrupal en los comportamientos de conservación comunitarios Resumen Aproximadamente una cuarta parte de la población mundial aprovecha directamente los recursos naturales para satisfacer sus necesidades diarias. Estos individuos se ven desproporcionadamente obligados a alterar sus comportamientos en respuesta a la creciente variabilidad climática y la pérdida de biodiversidad global. Gran parte de la ambiciosa agenda de conservación global se basa en la adopción voluntaria de comportamientos de conservación en dichas poblaciones. Por lo tanto, es fundamental comprender cómo esas personas perciben el cambio ambiental y utilizan las prácticas de conservación como herramienta para proteger su bienestar. Desarrollamos una actividad de mapeo participativo para generar percepciones espacialmente explícitas del cambio forestal y sus causantes en 43 comunidades dependientes de manglares en Pemba, Tanzania. Administramos esta actividad junto con un cuestionario sobre preferencias y comportamientos de conservación a 423 personas en esas 43 comunidades. Analizamos estos datos mediante un conjunto de modelos estadísticos jerárquicos bayesianos. La pérdida de cobertura percibida en el 50% del área de manglares de una comunidad llevó a los individuos a reducir los límites propuestos para los paquetes de leña de 2.74 (bosque percibido como intacto) a 2.37 si los participantes creían que las ganancias resultantes en la cobertura de manglares no serían robadas por personas ajenas a la comunidad. Por el contrario, las personas que creían que los bosques de manglares de su comunidad corrían un alto riesgo de robo flexibilizaron los límites de cosecha propuestos de 1.26 a 2.75 haces de leña en respuesta a la misma disminución percibida del bosque. Por lo tanto, las altas tasas de competencia entre grupos y pérdida de manglares estaban impulsando un aumento, que se auto reforzaba, en las preferencias de aprovechamiento insostenibles en los bosques comunitarios de este sistema. Este hallazgo muestra un mecanismo por el cual el creciente deterioro ambiental puede hacer que las comunidades renuncien a las prácticas de conservación, en lugar de adoptarlas, como a menudo se supone en gran parte de la planificación de la conservación basada en la comunidad. Sin embargo, también encontramos que cuando existían límites efectivos, los individuos estaban dispuestos a restringir sus propias cosechas para frenar esa disminución percibida.

Metabolic profiling of murine radiation-induced lung injury with Raman spectroscopy and comparative machine learning.

Radiation-induced lung injury (RILI) is a dose-limiting toxicity for cancer patients receiving thoracic radiotherapy. As such, it is important to characterize metabolic associations with the early and late stages of RILI, namely pneumonitis and pulmonary fibrosis. Recently, Raman spectroscopy has shown utility for the differentiation of pneumonitic and fibrotic tissue states in a mouse model; however, the specific metabolite-disease associations remain relatively unexplored from a Raman perspective. This work harnesses Raman spectroscopy and supervised machine learning to investigate metabolic associations with radiation pneumonitis and pulmonary fibrosis in a mouse model. To this end, Raman spectra were collected from lung tissues of irradiated/non-irradiated C3H/HeJ and C57BL/6J mice and labelled as normal, pneumonitis, or fibrosis, based on histological assessment. Spectra were decomposed into metabolic scores via group and basis restricted non-negative matrix factorization, classified with random forest (GBR-NMF-RF), and metabolites predictive of RILI were identified. To provide comparative context, spectra were decomposed and classified via principal component analysis with random forest (PCA-RF), and full spectra were classified with a convolutional neural network (CNN), as well as logistic regression (LR). Through leave-one-mouse-out cross-validation, we observed that GBR-NMF-RF was comparable to other methods by measure of accuracy and log-loss (p > 0.10 by Mann-Whitney U test), and no methodology was dominant across all classification tasks by measure of area under the receiver operating characteristic curve. Moreover, GBR-NMF-RF results were directly interpretable and identified collagen and specific collagen precursors as top fibrosis predictors, while metabolites with immune and inflammatory functions, such as serine and histidine, were top pneumonitis predictors. Further support for GBR-NMF-RF and the identified metabolite associations with RILI was found as CNN interpretation heatmaps revealed spectral regions consistent with these metabolites.

Stratification of tumour cell radiation response and metabolic signatures visualization with Raman spectroscopy and explainable convolutional neural network.

Reprogramming of cellular metabolism is a driving factor of tumour progression and radiation therapy resistance. Identifying biochemical signatures associated with tumour radioresistance may assist with the development of targeted treatment strategies to improve clinical outcomes. Raman spectroscopy (RS) can monitor post-irradiation biomolecular changes and signatures of radiation response in tumour cells in a label-free manner. Convolutional Neural Networks (CNN) perform feature extraction directly from data in an end-to-end learning manner, with high classification performance. Furthermore, recently developed CNN explainability techniques help visualize the critical discriminative features captured by the model. In this work, a CNN is developed to characterize tumour response to radiotherapy based on its degree of radioresistance. The model was trained to classify Raman spectra of three human tumour cell lines as radiosensitive (LNCaP) or radioresistant (MCF7, H460) over a range of treatment doses and data collection time points. Additionally, a method based on Gradient-Weighted Class Activation Mapping (Grad-CAM) was used to determine response-specific salient Raman peaks influencing the CNN predictions. The CNN effectively classified the cell spectra, with accuracy, sensitivity, specificity, and F1 score exceeding 99.8%. Grad-CAM heatmaps of H460 and MCF7 cell spectra (radioresistant) exhibited high contributions from Raman bands tentatively assigned to glycogen, amino acids, and nucleic acids. Conversely, heatmaps of LNCaP cells (radiosensitive) revealed activations at lipid and phospholipid bands. Finally, Grad-CAM variable importance scores were derived for glycogen, asparagine, and phosphatidylcholine, and we show that their trends over cell line, dose, and acquisition time agreed with previously established models. Thus, the CNN can accurately detect biomolecular differences in the Raman spectra of tumour cells of varying radiosensitivity without requiring manual feature extraction. Finally, Grad-CAM may help identify metabolic signatures associated with the observed categories, offering the potential for automated clinical tumour radiation response characterization.

A postimplant dosimetry simulation framework for robustness evaluation in permanent breast seed implant brachytherapy.

BACKGROUND: Permanent breast seed implant (PBSI) brachytherapy is a promising treatment that has the potential to be widely utilized with increased standardization, optimization, and robustness. Excellent early efficacy and very high patient acceptance were reported, however, to further evaluate and improve planning strategies, a framework to quantify plan robustness to implant uncertainties is necessary. PURPOSE: In this study, we aim to quantify clinical seed displacement using an automated algorithm and develop and validate a PBSI post-implant dosimetry simulation framework to evaluate PBSI plan robustness to implant uncertainties. METHODS AND MATERIALS: Clinical PBSI seed displacements were quantified for 63 consecutive patients. A PBSI simulator was developed in Matlab (2020) by resampling clinical seed displacements and computing a range of possible post-implant dosimetry outcomes under various seed displacement scenarios. Simulations were performed retrospectively on 63 previous clinical plans to evaluate plan robustness to seed displacement. RESULTS: Mean seed displacement for the whole cohort was 10 ± 6 mm. A clinical seed displacement database was established and a user interface was developed for the simulation framework. For all clinical plans, the median (range) value of simulated median ETV V90 in various seed displacement scenarios was 97.8% (87.5-100%). CONCLUSIONS: A PBSI postimplant dosimetry simulation framework was developed and validated. Simulation results showed that the current PTV planning margin is sufficient to provide adequate postimplant dose coverage of ETV. This simulator can be used to evaluate plan robustness to seed displacement and will facilitate future research in improving PBSI planning methods.

Comparing the performance of 2 human papillomavirus assays for a new use indication: a real-world evidence-based evaluation in the United States.

BACKGROUND: The US Food and Drug Administration supports innovations to facilitate new indications for high-risk human papillomavirus testing. This report describes the retrospective testing of stored specimens and analysis of existing data to efficiently and cost-effectively support a new indication for the Onclarity human papillomavirus assay (Becton, Dickinson and Company, BD Life Sciences - Integrated Diagnostic Solutions, Sparks, MD). The performance of this index test was compared with that of a predicate test, the cobas human papillomavirus assay (Roche Diagnostics, Indianapolis, IN). Both human papillomavirus assays are based on real-time polymerase chain reaction platforms that detect the presence of 14 high-risk human papillomavirus genotypes. The predicate assay reports human papillomavirus types 16 and 18 as individual results and the other 12 human papillomavirus genotypes as 1 pooled result. The index assay reports 9 independent results (human papillomavirus types 16, 18, 31, 33/58, 35/39/68, 45, 51, 52, and 56/59/66). Both the index and predicate assays are approved by the Food and Drug Administration for cervical cancer screening, but at the time that this study was initiated, the index human papillomavirus assay was not approved for use with cervical specimens collected in PreservCyt (Hologic, Inc, San Diego, CA) liquid-based cytology media. OBJECTIVE: The performance of the index human papillomavirus assay was compared with that of the predicate human papillomavirus assay for the detection of cervical intraepithelial neoplasia grades 2 or greater and 3 or greater (≥CIN2 or ≥CIN3) using PreservCyt liquid-based cytology specimens collected from women aged 21 to 65 years. In addition, the ability of the index test's extended genotyping to stratify ≥CIN2 and ≥CIN3 risks, using these specimens, was evaluated. STUDY DESIGN: The New Mexico HPV Pap Registry was used to select an age- and cytology-stratified random sample of 19,879 women undergoing opportunistic cervical screening and follow-up in routine clinical practice across New Mexico. A subset (n = 4820) of PreservCyt specimens was selected from 19,879 women for paired testing by the index and predicate human papillomavirus assays within age and cytology strata and included women with or without cervical biopsy follow-up. Point estimate differences and ratios were calculated for cervical disease detection and positivity rates, respectively, with 95% confidence intervals to determine statistical significance. The cumulative risk of ≥CIN2 or ≥CIN3, with up to 5-year follow-up, was estimated for the index assay using Kaplan-Meier methods. RESULTS: The 5-year cumulative ≥CIN3 detection rates were 5.6% for the index assay and 4.6% for the predicate assay (difference, 1.0%; 95% confidence interval, 0.5%-1.5%). The ≥CIN3 positivity rates within <1 year were 95.3% for the index assay and 94.5% for the predicate assay (ratio, 1.01; 95% confidence interval, 0.98-1.06). The ≥CIN3 cumulative positivity rates for the index and predicate assays were also similar at 5 years. Among cases of ≥CIN3, the positive agreement rates between the index and predicate assays for human papillomavirus types 16 and 18 were 100.0% (95% confidence interval, 95.0%-100.0%) and 90.9% (95% confidence interval, 62.3%-98.4%), respectively. Human papillomavirus type 16 carried the highest ≥CIN2 or ≥CIN3 risk, followed by human papillomavirus types 18/31/33/58/52/45 and human papillomavirus types 35/56/59/51/56/59/66. CONCLUSION: The index and predicate human papillomavirus assays demonstrated equivalent performance, and extended human papillomavirus genotyping, using the index assay, provided effective ≥CIN2 and ≥CIN3 risk stratification, supporting a new indication for use of the index assay with PreservCyt.

Clinical characteristics and treatment patterns of patients with episodic cluster headache: results from the United States, United Kingdom and Germany.

OBJECTIVE: To describe clinical characteristics and regional treatment patterns of episodic cluster headache (CH). METHODS: A point-in-time survey of physicians and their patients with CH was conducted in the United States, United Kingdom and Germany in 2017. RESULTS: Overall, 1012 patients with episodic CH were analyzed. Demographic and clinical findings were generally consistent across regions. Most patients were men (66.6%) and the mean age was 40.9 years. The greatest proportion of patients (38.3%) had ≤1 attack per day. The mean number of attacks per day (APD) was 2.4 and mean number of cluster periods per year was 2.6; the mean cluster period duration was 30.8 days. Most patients (69.3%) did not report a specific or predicable time when cluster periods occurred. Acute treatment was prescribed for 47.6% of patients, 10.3% of patients received preventive treatment, and 37.9% of patients received combined acute and preventive treatment; 4.2% of patients were not receiving treatment. Frequently prescribed acute treatments were sumatriptan, oxygen, and zolmitriptan; oxygen use varied considerably across countries and was prescribed least often in the United States. Frequently prescribed preventive treatments were verapamil, topiramate, and lithium. Lack of efficacy and tolerability were the most common reasons for discontinuing preventive treatment. CONCLUSIONS: We observed high use of acute treatments, but only half of patients used preventive treatments despite experiencing several cluster periods per year with multiple cluster APD. Further studies about the need for and benefits of preventive treatment for episodic CH are warranted.

People with cluster headache (CH) experience headache attacks of excruciating stabbing pain, usually on one side of the head around the eye. These headache attacks typically last between 15 min and 3 h, and come in clusters (or bouts) occurring up to several times a day for a few weeks or months at a time. This greatly impacts a patient's quality of life.We surveyed doctors and their patients across the United States, the United Kingdom and Germany, looking at symptoms that occurred during CH attacks, how long the headache attacks lasted, how often the patient had them, and what medicines were being given.Our results showed that patients with CH suffered from clusters (bouts) of headache attacks several times a year. Nearly, a third of patients had a wrong diagnosis before being diagnosed with CH. Patients experienced stress, agitation, restlessness, difficulty relaxing and depression during a headache attack, especially those who had more CH attacks each day.Although many patients were taking medication, only half of patients were prescribed medicines to prevent their headache attack from starting. Side effects and the medicines not working were the most common reasons patients stopped taking medicine to prevent their headache attacks. The differences seen in medicines prescribed between countries suggest differences in guidance, or in doctors' awareness of current medication guidelines. Further studies about the need for and benefits of medicines to prevent CH attacks are needed.

The interdependence of relational and material wealth inequality in Pemba, Zanzibar.

The extent of inequality in material wealth across different types of societies is well established. Less clear, however, is how material wealth is associated with relational wealth, and the implications of such associations for material wealth inequality. Theory and evidence suggest that material wealth both guides, and is patterned by, relational wealth. While existing comparative studies typically assume complementarity between different types of wealth, such associations may differ for distinct kinds of relational wealth. Here, we first review the literature to identify how and why different forms of relational wealth may align. We then turn to an analysis of household-level social networks (food sharing, gender-specific friendship and gender-specific co-working networks) and material wealth data from a rural community in Pemba, Zanzibar. We find that (i) the materially wealthy have most relational ties, (ii) the associations between relational and material wealth-as well as relational wealth more generally-are patterned by gender differences, and (iii) different forms of relational wealth have similar structural properties and are closely aligned. More broadly, we show how examining the patterning of distinct types of relational wealth provides insights into how and why inequality in material wealth remains muted in a community undergoing rapid economic change. This article is part of the theme issue 'Evolutionary ecology of inequality'.

Risk stratification of HPV-positive results using extended genotyping and cytology: Data from the baseline phase of the Onclarity trial.

BACKGROUND: Optimizing the balance between colposcopy referrals and the detection of high-grade cervical intraepithelial neoplasia (CIN) during cervical cancer screening requires robust triage strategies. We evaluated the performance of extended HPV genotyping (xGT), in combination with cytology triage, and compared it to previously published performance data for high-grade CIN detection by HPV16/18 primary screening in combination with p16/Ki-67 dual staining (DS). METHODS AND MATERIALS: The baseline phase of the Onclarity trial enrolled 33,858 individuals, yielding 2978 HPV-positive participants. Risk values for ≥CIN3 were determined for Onclarity result groupings corresponding to HPV16, not HPV16 but HPV18 or 31, not HPV16/18/31 but HPV33/58 or 52, not HPV16/18/31/33/58/52 but HPV35/39/68 or 45 or 51 or 56/59/66 across all cytology categories. Published data from the IMPACT trial for HPV16/18 plus DS was utilized as a comparator during ROC analyses. RESULTS: There were 163 ≥ CIN3 cases detected. The ≥CIN3 risk stratum hierarchy (% risk of ≥CIN3) that resulted from this analysis included: >LSIL (39.4%); HPV16, ≤LSIL (13.3%); HPV18/31, ≤LSIL (5.9%); HPV33/58/52/45, ASC-US/LSIL (2.4%); HPV33/58/52, NILM (2.1%); HPV35/39/68/51/56/59/66, ASC-US/LSIL (0.9%); and HPV45/35/39/68/51/56/59/66, NILM (0.6%). For ≥CIN3 ROC analysis, the optimal cutoff for sensitivity versus specificity was approximated between not HPV16 but HPV18 or 31, any cytology (≥CIN3 sensitivity = 85.9% and colposcopy-to- ≥ CIN3 = 7.4) and not HPV16/18/31 but HPV33/58/52, NILM (≥CIN3 sensitivity = 94.5% and colposcopy-to- ≥CIN3 = 10.8). HPV16/18 with DS triage showed a sensitivity of 94.3%, with a colposcopy-to- ≥ CIN3 ratio of 11.4. CONCLUSIONS: xGT performed similarly compared to HPV primary screening plus DS for detection of high-grade CIN. xGT provides results that stratify risk in a flexible and reliable manner for colposcopy risk thresholds set by different guidelines or organizations.

Reconstruction of Raman Spectra of Biochemical Mixtures Using Group and Basis Restricted Non-Negative Matrix Factorization.

Raman spectroscopy is a useful tool for obtaining biochemical information from biological samples. However, interpretation of Raman spectroscopy data in order to draw meaningful conclusions related to the biochemical make up of cells and tissues is often difficult and could be misleading if care is not taken in the deconstruction of the spectral data. Our group has previously demonstrated the implementation of a group- and basis-restricted non-negative matrix factorization (GBR-NMF) framework as an alternative to more widely used dimensionality reduction techniques such as principal component analysis (PCA) for the deconstruction of Raman spectroscopy data as related to radiation response monitoring in both cellular and tissue data. While this method provides better biological interpretability of the Raman spectroscopy data, there are some important factors which must be considered in order to provide the most robust GBR-NMF model. We here evaluate and compare the accuracy of a GBR-NMF model in the reconstruction of three mixture solutions of known concentrations. The factors assessed include the effect of solid versus solutions bases spectra, the number of unconstrained components used in the model, the tolerance of different signal to noise thresholds, and how different groups of biochemicals compare to each other. The robustness of the model was assessed by how well the relative concentration of each individual biochemical in the solution mixture is reflected in the GBR-NMF scores obtained. We also evaluated how well the model can reconstruct original data, both with and without the inclusion of an unconstrained component. Overall, we found that solid bases spectra were generally comparable to solution bases spectra in the GBR-NMF model for all groups of biochemicals. The model was found to be relatively tolerant of high levels of noise in the mixture solutions using solid bases spectra. Additionally, the inclusion of an unconstrained component did not have a significant effect on the deconstruction, on the condition that all biochemicals in the mixture were included as bases chemicals in the model. We also report that some groups of biochemicals achieve a more accurate deconstruction using GBR-NMF than others, likely due to similarity in the individual bases spectra.

Detection of high-grade cervical neoplasia using extended genotyping: Performance data from the longitudinal phase of the Onclarity trial.

OBJECTIVES: The Onclarity cervical cancer screening trial was designed to establish the clinical validity of the Onclarity HPV assay for extended genotyping (xGT) during detection of high-grade cervical neoplasia grades 2 or 3 (≥CIN2 or ≥CIN3). Here, three-year follow up data is presented to evaluate the overall efficacy of these screening strategies, compared to the baseline data. METHODS: At baseline 29,513 women, ≥25 years, had evaluable cytology and valid high-risk HPV results. Women with atypical squamous cells-undetermined significance or worse cytology or a positive HPV test were referred for colposcopy/biopsy. Participants that did not reach the study end point (treatment for ≥CIN2) continued into the longitudinal phase that included the same protocol as baseline. RESULTS: The three-year cumulative incident risk (CIR) for ≥CIN3 in HPV-negative women was 0.15% [95%CI: 0.06, 0.26] and for HPV- and cytology-negative women was 0.12% [95% CI: 0.03,0.23]. HPV16 carried the highest baseline and three-year ≥CIN3 CIR, followed by HPV31 and HPV18. At least one year of genotype-specific persistence increased ≥CIN3 risk for xGT results compared to genotype non-persistence, HPV clearance, or new infection over the same time period. Risk-based screening with immediate colposcopy for HPV16/18/31 and further xGT triage resulted in better ≥CIN3 sensitivity (79.2% versus 72.3%; relative difference of 6.9 [95%CI: 3.3, 10.4]) and a lower colposcopy/≥CIN3 ratio (9.2 versus 11.2; relative difference of -1.9 [95%CI: -2.6, -1.3]) when compared to primary HPV16/18-based screening. CONCLUSIONS: An HPV-negative result offers the same assurance of no disease over three years of follow up as that offered by a negative co-testing result. xGT facilitates risk-based screening and persistence tracking and can help optimize disease detection during screening without excessive colposcopic procedures.

Raman spectroscopy and convolutional neural networks for monitoring biochemical radiation response in breast tumour xenografts.

Tumour cells exhibit altered metabolic pathways that lead to radiation resistance and disease progression. Raman spectroscopy (RS) is a label-free optical modality that can monitor post-irradiation biomolecular signatures in tumour cells and tissues. Convolutional Neural Networks (CNN) perform automated feature extraction directly from data, with classification accuracy exceeding that of traditional machine learning, in cases where data is abundant and feature extraction is challenging. We are interested in developing a CNN-based predictive model to characterize clinical tumour response to radiation therapy based on their degree of radiosensitivity or radioresistance. In this work, a CNN architecture is built for identifying post-irradiation spectral changes in Raman spectra of tumour tissue. The model was trained to classify irradiated versus non-irradiated tissue using Raman spectra of breast tumour xenografts. The CNN effectively classified the tissue spectra, with accuracies exceeding 92.1% for data collected 3 days post-irradiation, and 85.0% at day 1 post-irradiation. Furthermore, the CNN was evaluated using a leave-one-out- (mouse, section or Raman map) validation approach to investigate its generalization to new test subjects. The CNN retained good predictive accuracy (average accuracies 83.7%, 91.4%, and 92.7%, respectively) when little to no information for a specific subject was given during training. Finally, the classification performance of the CNN was compared to that of a previously developed model based on group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF) classification. We found that CNN yielded higher classification accuracy, sensitivity, and specificity in mice assessed 3 days post-irradiation, as compared with the GBR-NMF-RF approach. Overall, the CNN can detect biochemical spectral changes in tumour tissue at an early time point following irradiation, without the need for previous manual feature extraction. This study lays the foundation for developing a predictive framework for patient radiation response monitoring.

Practical guide to coproduction in conservation science.

We considered a series of conservation-related research projects on the island of Pemba, Tanzania, to reflect on the broad significance of Beier et al.'s recommendations for linking conservation science with practical conservation outcomes. The implementation of just some of their suggestions can advance a successful coproduction of actionable science by small research teams. Key elements include, first, scientists and managers working together in the field to ensure feedback in real time; second, questions jointly identified by managers and researchers to facilitate engaged collaboration; third, conducting research at multiple sites, thereby broadening managers' abilities to reach multiple stakeholders; and fourth, establishing a multidisciplinary team because most of the concerns of local managers require input from multiple disciplines.

Consideramos una serie de proyectos de investigación relacionados con la conservación en la isla de Pemba, Tanzania, para reflexionar sobre la importancia de las recomendaciones de Beier et al. para vincular las ciencias de la conservación con sus resultados prácticos. La implementación de sólo algunas de sus sugerencias puede impulsar una coproducción exitosa de ciencia práctica hecha por pequeños equipos de investigación. Los elementos clave incluyen, primero, a los científicos y administrados trabajando juntos en el campo para asegurar respuestas en tiempo real; segundo, preguntas identificadas en conjunto por los administradores y los investigadores para facilitar la colaboración participativa; tercero, realizar investigaciones en sitios diferentes y ampliar con esto las habilidades de los administradores para llegar a múltiples actores; y cuarto, establecer un equipo multidisciplinario ya que la mayoría de los intereses de los administradores locales requieren información de múltiples disciplinas.

Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.

OBJECTIVE: In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murine lungs with the goal of setting the foundation for a predictive disease model. METHODS: Analysis of Raman tissue data is achieved through a combination of techniques. We first distinguish between tissue measurements and air pockets in the lung by using group and basis restricted non-negative matrix factorization. We then analyze the tissue spectra using sparse multinomial logistic regression to discriminate between fibrotic gradings. Model validation is achieved by splitting the data into a training set containing 70% of the data and a test set with the remaining 30%; classification accuracy is used as the performance metric. We also explore several other potential classification tasks wherein the response considered is the grade of pneumonitis and fibrosis sickness. RESULTS: A classification accuracy of 91.6% is achieved on the test set of fibrotic gradings, illustrating the ability of Raman measurements to detect differing levels of fibrotic disease among the murine lungs. It is also shown via further modeling that coarser consideration of fibrotic grading via binning (ie. 'Low', 'Medium', 'High') does not degrade performance. Finally, we consider preliminary models for pneumonitis discrimination using the same methodologies.

Radiation treatment response and hypoxia biomarkers revealed by machine learning assisted Raman spectroscopy in tumour cells and xenograft tissues.

Recent advancements in anatomical imaging of tumours as treatment targets have led to improvements in RT. However, it is unlikely that improved anatomical imaging alone will be the sole driver for new advances in personalised RT. Biochemically based radiobiological information is likely to be required for next-generation improvements in the personalisation of radiotherapy dose prescriptions to individual patients. In this paper, we use Raman spectroscopy (RS), an optical technique, to monitor individual biochemical response to radiation within a tumour microenvironment. We spatially correlate individual biochemical responses to augmentatively derived hypoxic maps within the tumour microenvironment. Furthermore, we pair RS with a data analytical framework combining (i) group and basis restricted non-negative matrix factorization (GBR-NMF), (ii) a random forest (RF) classifier, (iii) and a feature metric importance calculation method, Shapley Additive exPlanations (SHAP), in order to ascertain the relative importance of individual biochemicals in describing the overall biological response as observed with RS. The current study found that the GBR-NMF-RF-SHAP model helped identify a wide range of radiation response biomarkers and hypoxia indicators (e.g., glycogen, lipids, DNA, amino acids) in H460 human lung cancer cells and H460 xenografts. Correlations between the hypoxic regions and Raman chemical biomarkers (e.g., glycogen, alanine, and arginine) were also identified in H460 xenografts. To summarize, GBR-NMF-RF-SHAP combined with RS can be applied to monitor the RT-induced biochemical response within cellular and tissue environments. Individual biochemicals were identified that (i) contributed to overall biological response to radiation, and (ii) spatially correlated with hypoxic regions of the tumour. RS combined with our analytical pipeline shows promise for further understanding of individual biochemical dynamics in radiation response for use in cancer therapy.

Prediction of disease progression indicators in prostate cancer patients receiving HDR-brachytherapy using Raman spectroscopy and semi-supervised learning: a pilot study.

This work combines Raman spectroscopy (RS) with supervised learning methods-group and basis restricted non-negative matrix factorisation (GBR-NMF) and linear discriminant analysis (LDA)-to aid in the prediction of clinical indicators of disease progression in a cohort of 9 patients receiving high dose rate brachytherapy (HDR-BT) as the primary treatment for intermediate risk (D'Amico) prostate adenocarcinoma. The combination of Raman spectroscopy and GBR-NMF-sparseLDA modelling allowed for the prediction of the following clinical information; Gleason score, cancer of the prostate risk assessment (CAPRA) score of pre-treatment biopsies and a Ki67 score of < 3.5% or > 3.5% in post treatment biopsies. The three clinical indicators of disease progression investigated in this study were predicted using a single set of Raman spectral data acquired from each individual biopsy, obtained pre HDR-BT treatment. This work highlights the potential of RS, combined with supervised learning, as a tool for the prediction of multiple types of clinically relevant information to be acquired simultaneously using pre-treatment biopsies, therefore opening up the potential for avoiding the need for multiple immunohistochemistry (IHC) staining procedures (H&E, Ki67) and blood sample analysis (PSA) to aid in CAPRA scoring.

Effect of Galcanezumab on Total Pain Burden in Patients Who Had Previously Not Benefited from Migraine Preventive Medication (CONQUER Trial): A Post Hoc Analysis.

INTRODUCTION: In evaluating therapies for migraine prevention, emphasis is placed on frequency and less attention is paid to duration or severity. Total pain burden (TPB) combines frequency, duration, and severity of migraine headache, and has the potential to further characterize the benefit of preventive treatment using a single composite measure. TPB was previously used to characterize response to galcanezumab (GMB) in patients with migraine. In this post hoc analysis we assessed the impact of GMB in lowering TPB in patients who had previously not benefited from two to four categories of migraine preventive medication. METHODS: CONQUER trial patients (N = 462), 18-75 years old who had previously not benefited from two to four categories of migraine preventive medication, were randomized (1:1) to monthly placebo or GMB 120 mg with 240 mg loading dose. For each patient, monthly TPB in severity-weighted hours was calculated by multiplying migraine headache duration (hours) by maximum severity for each migraine headache day, then summing these daily scores over the month for the monthly score. Changes from baseline in monthly TPB across months 1-3 were analyzed. Spearman correlations between TPB and scores on the Migraine-Specific Quality-of-Life Questionnaire (MSQ) total and Migraine Disability Assessment Scale (MIDAS) were assessed at baseline. RESULTS: Mean (SD) baseline monthly TPB was 192.1 (158.3) and 188.2 (197.4) severity-weighted hours for GMB-treated and placebo-treated patients, respectively. Across the 3-month double-blind period, GMB-treated patients experienced significantly greater mean reductions from baseline in monthly TPB compared with placebo-treated patients, both for mean change (GMB - 82.7, placebo - 15.8, p < 0.001) and percentage change (GMB - 38.6%, placebo 9.4%, p < 0.001). Furthermore, baseline TPB correlated with MSQ score (r = - 0.39) and MIDAS score (r = 0.40), suggesting good association of TPB with functional and disability outcomes. CONCLUSION: GMB reduced mean TPB in patients who had previously not benefited from two to four categories of migraine preventive medication. TRIAL REGISTRATION: NCT03559257.

Research integrity in clinical trials: innocent errors and spin versus scientific misconduct.

PURPOSE OF REVIEW: High-quality research underpins the best healthcare practice. This article focuses on analyzing the current literature to promote research integrity across clinical trials. RECENT FINDINGS: Recent admissions of questionable practices by researchers have undermined practitioner and public confidence. There is limited evidence specifically for ethical and professional standards in clinical trials to guide researchers and institutions to embed integrity into research practice. SUMMARY: Unintentional errors and spin in research are not uncommon as training in design and conduct of clinical trials is not part of health education for medical and allied health professions. There is unfamiliarity with procedures, such as prospective registration, a priori documentation of statistical analysis plans, openness in data sharing, and so forth. This, combined with the academic culture of secrecy, has led to an environment where scientific suspicion, instead of trust, is the norm. Existing science integrity documents are devoid of specific recommendations about how to translate any guidance into clinical trial practice. There is a need for constructive, supportive and multidisciplinary approaches based on open dialogue and continuous training, targeting the research environment. Research integrity now needs to take centre stage to re-instill confidence in randomized trial evidence to inform clinical practice.

Raman spectroscopy and supervised learning as a potential tool to identify high-dose-rate-brachytherapy induced biochemical profiles of prostate cancer.

High-dose-rate-brachytherapy (HDR-BT) is an increasingly attractive alternative to external beam radiation-therapy for patients with intermediate risk prostate cancer. Despite this, no bio-marker based method currently exists to monitor treatment response, and the changes which take place at the biochemical level in hypo-fractionated HDR-BT remain poorly understood. The aim of this pilot study is to assess the capability of Raman spectroscopy (RS) combined with principal component analysis (PCA) and random-forest classification (RF) to identify radiation response profiles after a single dose of 13.5 Gy in a cohort of nine patients. We here demonstrate, as a proof-of-concept, how RS-PCA-RF could be utilised as an effective tool in radiation response monitoring, specifically assessing the importance of low variance PCs in complex sample sets. As RS provides information on the biochemical composition of tissue samples, this technique could provide insight into the changes which take place on the biochemical level, as result of HDR-BT treatment.

Group and Basis Restricted Non-Negative Matrix Factorization and Random Forest for Molecular Histotype Classification and Raman Biomarker Monitoring in Breast Cancer.

Raman spectroscopy is a non-invasive optical technique that can be used to investigate biochemical information embedded in cells and tissues exposed to ionizing radiation used in cancer therapy. Raman spectroscopy could potentially be incorporated in personalized radiation treatment design as a tool to monitor radiation response in at the metabolic level. However, tracking biochemical dynamics remains challenging for Raman spectroscopy. Here we developed a novel analytical framework by combining group and basis restricted non-negative matrix factorization and random forest (GBR-NMF-RF). This framework can monitor radiation response profiles in different molecular histotypes and biochemical dynamics in irradiated breast cancer cells. Five subtypes of; human breast cancer (MCF-7, BT-474, MDA-MB-230, and SK-BR-3) and normal cells derived from human breast tissue (MCF10A) which had been exposed to ionizing radiation were tested in this framework. Reference Raman spectra of 20 biochemicals were collected and used as the constrained Raman biomarkers in the GBR-NMF-RF framework. We obtained scores for individual biochemicals corresponding to the contribution of each Raman reference spectrum to each spectrum obtained from the five cell types. A random forest classifier was then fitted to the chemical scores for performing molecular histotype classifications (HER2, PR, ER, Ki67, and cancer versus non-cancer) and assessing the importance of the Raman biochemical basis spectra for each classification test. Overall, the GBR-NMF-RF framework yields classification results with high accuracy (>97%), high sensitivity (>97%), and high specificity (>97%). Variable importance calculated in the random forest model indicated high contributions from glycogen and lipids (cholesterol, phosphatidylserine, and stearic acid) in molecular histotype classifications.

Factors that Influence the Reported Sensitivity of Rapid Antigen Testing for SARS-CoV-2.

Tests that detect the presence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) antigen in clinical specimens from the upper respiratory tract can provide a rapid means of coronavirus disease 2019 (COVID-19) diagnosis and help identify individuals who may be infectious and should isolate to prevent SARS-CoV-2 transmission. This systematic review assesses the diagnostic accuracy of SARS-CoV-2 antigen detection in COVID-19 symptomatic and asymptomatic individuals compared to quantitative reverse transcription polymerase chain reaction (RT-qPCR) and summarizes antigen test sensitivity using meta-regression. In total, 83 studies were included that compared SARS-CoV-2 rapid antigen-based lateral flow testing (RALFT) to RT-qPCR for SARS-CoV-2. Generally, the quality of the evaluated studies was inconsistent; nevertheless, the overall sensitivity for RALFT was determined to be 75.0% (95% confidence interval: 71.0-78.0). Additionally, RALFT sensitivity was found to be higher for symptomatic vs. asymptomatic individuals and was higher for a symptomatic population within 7 days from symptom onset compared to a population with extended days of symptoms. Viral load was found to be the most important factor for determining SARS-CoV-2 antigen test sensitivity. Other design factors, such as specimen storage and anatomical collection type, also affect the performance of RALFT. RALFT and RT-qPCR testing both achieve high sensitivity when compared to SARS-CoV-2 viral culture.