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.

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.

Child and adolescent victims of sexual homicide in Chile between 1998 and 2021: A review of 27 cases and their autopsy reports.

Rape followed by murder against children and adolescents is one of the most serious existing crimes. The autopsies of victims of violent crimes can provide fundamental findings for the investigative process and the pursuit of justice. This research conducts a descriptive analysis of the most important findings from the autopsies of 27 cases of children and adolescents who died in Chile between 1998 and 2021 as a result of rape followed by homicide (n = 27), as well as from the judiciary rulings of these cases to gather information related to the perpetrators. It was found that the victims of this crime are mostly girls with an average age of 10, while the perpetrators are primarily single men aged 29 on average, most of whom have not finished high school. A significant relationship was found between the location of the crime and the cause of death and signs of sexual contact, the marital status of the perpetrator and the cause of death, the age of the perpetrator and signs of sexual contact, and the relationship between victim and perpetrator and signs of sexual contact.

Participation experiences of indirect victims of child sexual homicide in parole proceedings.

Few studies have examined victim participation in parole processes, particularly in countries that have specific procedures for hearing victims' statements in post-conviction proceedings. This study, through in-depth interviews, explores the experiences of seven indirect victims of child sexual homicide, identifying their needs and expectations in a justice system lacking formal mechanisms for their involvement. Results emphasize the necessity for official information for families and the consequent frustration from the absence of formal participation. Parole application becomes a new challenge to the ongoing grieving process, leading to distress responses that may require specialized care. Recommendations about formal mechanisms for victim notification, participation and support during the parole process are noted to acknowledge their experience and emotional impact.

Oligo-Carrageenan Kappa Increases Expression of Genes Encoding Proteins Involved in Photosynthesis, C, N, and S Assimilation, and Growth in Arabidopsis thaliana.

To analyze the effect of oligo-carrageenan (OC) kappa in the stimulation of growth in Arabidopsis thaliana, plants were sprayed on leaves with an aqueous solution of OC kappa at 1 mg mL-1, 5 times every 2 days and cultivated for 5 or 15 additional days. Plants treated with OC kappa showed an increase in rosette diameter, fresh and dry weight, and primary root length. Plants treated with OC kappa once and cultivated for 0 to 24 h after treatment were subjected to transcriptomic analyses to identify differentially expressed genes, mainly at 12 h after treatment. Transcripts encoding proteins involved in growth and development and photosynthesis were upregulated as well as enzymes involved in primary metabolism. In addition, plants treated with OC kappa once and cultivated for 0 to 96 h showed increased levels of transcripts encoding enzymes involved in C, N, and S assimilation at 6 and 12 h after treatment that remain increased until 96 h. Therefore, OC kappa increased the expression of genes encoding proteins involved in photosynthesis, C, N, and S assimilation, and growth in A. thaliana.

RegulonDB version 9.0: high-level integration of gene regulation, coexpression, motif clustering and beyond.

RegulonDB (http://regulondb.ccg.unam.mx) is one of the most useful and important resources on bacterial gene regulation,as it integrates the scattered scientific knowledge of the best-characterized organism, Escherichia coli K-12, in a database that organizes large amounts of data. Its electronic format enables researchers to compare their results with the legacy of previous knowledge and supports bioinformatics tools and model building. Here, we summarize our progress with RegulonDB since our last Nucleic Acids Research publication describing RegulonDB, in 2013. In addition to maintaining curation up-to-date, we report a collection of 232 interactions with small RNAs affecting 192 genes, and the complete repertoire of 189 Elementary Genetic Sensory-Response units (GENSOR units), integrating the signal, regulatory interactions, and metabolic pathways they govern. These additions represent major progress to a higher level of understanding of regulated processes. We have updated the computationally predicted transcription factors, which total 304 (184 with experimental evidence and 120 from computational predictions); we updated our position-weight matrices and have included tools for clustering them in evolutionary families. We describe our semiautomatic strategy to accelerate curation, including datasets from high-throughput experiments, a novel coexpression distance to search for 'neighborhood' genes to known operons and regulons, and computational developments.

Adaptation and tolerance mechanisms developed by mycorrhizal Bipinnula fimbriata plantlets (Orchidaceae) in a heavy metal-polluted ecosystem.

The adaptation and performance of orchid mycorrhizae in heavy metal-polluted soils have been poorly explored. In the present study, proteomic and metabolic approaches were used to detect physiological changes in orchid roots established in a heavy metal-polluted soil and to ascertain whether mycorrhizal fungi affect the metabolic responses of roots. Young Bipinnula fimbriata plantlets were established in control and heavy metal-polluted soils in a greenhouse. After 14 months, exudation of root organic acids, phenolics, percentage of mycorrhization, mineral content, and differential protein accumulation were measured. More root biomass, higher root colonization, and higher exudation rates of citrate, succinate, and malate were detected in roots growing in heavy metal-polluted soils. Higher accumulation of phosphorus and heavy metals was found inside mycorrhizal roots under metal stress. Under non-contaminated conditions, non-mycorrhizal root segments showed enhanced accumulation of proteins related to carbon metabolism and stress, whereas mycorrhizal root segments stimulated protein synthesis related to pathogen control, cytoskeleton modification, and sucrose metabolism. Under heavy metal stress, the proteome profile of non-mycorrhizal root segments indicates a lower induction of defense mechanisms, which, together with the stimulation of enzymes related to carotenoid biosynthesis and cell wall organization, may positively influence mycorrhizal fungi colonization. The results point to different metabolic strategies in mycorrhizal and non-mycorrhizal root segments that are exposed to heavy metal stress. The results indicate that root colonization by mycorrhizal fungi is stimulated to alleviate the negative effects of heavy metals in the orchids.

Cancer Survivor's History and Physical.

The number of cancer survivors is estimated by 2022 to increase to almost 18 million, in part because of improvements in earlier detection and cancer therapies, leading to longer-term survival of cancer patients. This growing number of survivors has presented challenges to the healthcare community, one of which is the need to provide to the survivor a seamless transition from the oncologist to the primary care provider (PCP). A major national initiative is under way for oncologists to provide survivorship care plans to their patients and PCPs, with the aim of communicating a complete record of cancer treatment and guiding the PCP in the future care of these cancer survivors. In caring for cancer survivors, PCPs must be familiar with the long-term and late effects, both medical and psychosocial, that are associated with common cancer treatments as well as the oncologic diagnosis itself. This article provides a unique approach to the traditional history and physical of easing the transition to primary care after completing cancer therapy for the cancer survivor. The cancer survivor-focused history and physical provide the PCP with a familiar and efficient method to clinically evaluate cancer survivors that closes the gap in this important transition of care.

RegulonDB v8.0: omics data sets, evolutionary conservation, regulatory phrases, cross-validated gold standards and more.

This article summarizes our progress with RegulonDB (http://regulondb.ccg.unam.mx/) during the past 2 years. We have kept up-to-date the knowledge from the published literature regarding transcriptional regulation in Escherichia coli K-12. We have maintained and expanded our curation efforts to improve the breadth and quality of the encoded experimental knowledge, and we have implemented criteria for the quality of our computational predictions. Regulatory phrases now provide high-level descriptions of regulatory regions. We expanded the assignment of quality to various sources of evidence, particularly for knowledge generated through high-throughput (HT) technology. Based on our analysis of most relevant methods, we defined rules for determining the quality of evidence when multiple independent sources support an entry. With this latest release of RegulonDB, we present a new highly reliable larger collection of transcription start sites, a result of our experimental HT genome-wide efforts. These improvements, together with several novel enhancements (the tracks display, uploading format and curational guidelines), address the challenges of incorporating HT-generated knowledge into RegulonDB. Information on the evolutionary conservation of regulatory elements is also available now. Altogether, RegulonDB version 8.0 is a much better home for integrating knowledge on gene regulation from the sources of information currently available.

RegulonDB version 7.0: transcriptional regulation of Escherichia coli K-12 integrated within genetic sensory response units (Gensor Units).

RegulonDB (http://regulondb.ccg.unam.mx/) is the primary reference database of the best-known regulatory network of any free-living organism, that of Escherichia coli K-12. The major conceptual change since 3 years ago is an expanded biological context so that transcriptional regulation is now part of a unit that initiates with the signal and continues with the signal transduction to the core of regulation, modifying expression of the affected target genes responsible for the response. We call these genetic sensory response units, or Gensor Units. We have initiated their high-level curation, with graphic maps and superreactions with links to other databases. Additional connectivity uses expandable submaps. RegulonDB has summaries for every transcription factor (TF) and TF-binding sites with internal symmetry. Several DNA-binding motifs and their sizes have been redefined and relocated. In addition to data from the literature, we have incorporated our own information on transcription start sites (TSSs) and transcriptional units (TUs), obtained by using high-throughput whole-genome sequencing technologies. A new portable drawing tool for genomic features is also now available, as well as new ways to download the data, including web services, files for several relational database manager systems and text files including BioPAX format.

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.

Acute Radiation-Induced Hematopoietic Depletion Does Not Alter the Onset or Severity of Pneumonitis in Mice.

Survival from partial-body irradiation (PBI) may be limited by the development of the late lung injury response of pneumonitis. Herein we investigated the hypothesis that acute hematopoietic depletion alters the onset and severity of lung disease in a mouse model. To establish depletion, C3H/HeJ mice received 8 Gy PBI with shielding of only the tibiae, ankles and feet. One week after irradiation, blood lymphocyte and neutrophil counts were each significantly reduced (P < 0.04) in these mice compared to levels in untreated controls or in mice receiving 16 Gy to the whole thorax only. All 8 Gy PBI mice survived to the experimental end point of 16 weeks postirradiation. To determine whether the hematopoietic depletion affects lung disease, groups of mice received 8 Gy PBI plus 8 Gy whole-thorax irradiation (total lung dose of 16 Gy) or 16 Gy whole-thorax irradiation only. The weight loss, survival to onset of respiratory distress (P = 0.17) and pneumonitis score (P = 0.96) of mice that received 8 Gy PBI plus 8 Gy whole-thorax irradiation were not significantly different from those of mice receiving 16 Gy whole-thorax irradiation only. Mice in respiratory distress from PBI plus whole-thorax irradiation had significantly reduced (P = 0.02) blood monocyte counts compared to levels in distressed, whole-thorax irradiated mice, and symptomatic pneumonitis was associated with increased blood neutrophil counts (P = 0.04) relative to measures from irradiated, non-distressed mice. In conclusion, survivable acute hematopoietic depletion by partial-body irradiation did not alter the onset or severity of lethal pneumonitis in the C3H/HeJ mouse model.

Distinguishing septic shock from non-septic shock in postsurgical patients using gene expression.

OBJECTIVES: To obtain a gene expression signature to distinguish between septic shock and non-septic shock in postoperative patients, since patients with both conditions show similar signs and symptoms. METHODS: Differentially expressed genes were selected by microarray analysis in the discovery cohort. These genes were evaluated by quantitative real time polymerase chain reactions in the validation cohort to determine their reliability and predictive capacity by receiver operating characteristic curve analysis. RESULTS: Differentially expressed genes selected were IGHG1, IL1R2, LCN2, LTF, MMP8, and OLFM4. The multivariate regression model for gene expression presented an area under the curve value of 0.922. These genes were able to discern between both shock conditions better than other biomarkers used for diagnosis of these conditions, such as procalcitonin (0.589), C-reactive protein (0.705), or neutrophils (0.605). CONCLUSIONS: Gene expression patterns provided a robust tool to distinguish septic shock from non-septic shock postsurgical patients and shows the potential to provide an immediate and specific treatment, avoiding the unnecessary use of broad-spectrum antibiotics and the development of antimicrobial resistance, secondary infections and increase health care costs.

Looking for Crumbs in the Obesity Forest: Anti-obesity Interventions and Obesity-Associated Cardiometabolic Traits in the Mexican Population. History and Systematic Review With Meta-Analyses.

Mexicans and Mexican Americans share culture, genetic background, and predisposition for chronic complications associated with obesity and diabetes making imperative efficacious treatments and prevention. Obesity has been treated for centuries focused-on weight loss while other treatments on associated conditions like gout, diabetes (T2D), and hypertriglyceridemia. To date, there is no systematic review that synthesizes the origin of obesity clinics in Mexico and the efforts to investigate treatments for obesity tested by randomized clinical trials (RCT). We conducted systematic searches in Pubmed, Scopus, and Web of Science to retrieve anti-obesity RCT through 2019 and without an inferior temporal limit. The systematic review included RCT of anti-obesity treatments in the Mexican adult population, covering alternative medicine, pharmacological, nutritional, behavioral, and surgical interventions reporting metabolism-associated traits such as BMI, weight, waist circumference, triglycerides, glucose, among others. Only the studies with at least 3 months of treatment were included in the meta-analyses in order to reduce placebo effects. We found 634 entries, after removal of duplicates and screening the studies based on eligibility criteria, we analyzed 43 national, and 2 multinational-collaborative studies. Most of the national studies had small sample sizes, and the implemented strategies do not have replications in the population. The nutrition/behavioral interventions were difficult to blind, and most studies have medium-to-high risk of bias. Nutritional/behavioral interventions and medications showed effects on BMI, waist circumference, and blood pressure. Simple measures like pure water instead of sweet beverages decrease triglycerides and systolic blood pressure. Dark chocolate showed the highest effect for BMI and high blood pressure, and treatment with insulin increased weight in those with T2D. The study of obesity in Mexico has been on-going for more than four decades, the interest on RCT just increased until this millennium, but with small sample sizes and lack of replication. The interventions affect different cardiometabolic associated traits, which should be analyzed in detail in the population living near the Mexico-U.S. border; therefore, bi-national collaboration is desirable to disentangle the cultural effects on this population's treatment response. Systematic Review Registration: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42020221436, identifier: CRD42020221436.

Fungal and Bacterial Microbiome Associated with the Rhizosphere of Native Plants from the Atacama Desert.

The rhizosphere microbiome is key in survival, development, and stress tolerance in plants. Salinity, drought, and extreme temperatures are frequent events in the Atacama Desert, considered the driest in the world. However, little information of the rhizosphere microbiome and its possible contribution to the adaptation and tolerance of plants that inhabit the desert is available. We used a high-throughput Illumina MiSeq sequencing approach to explore the composition, diversity, and functions of fungal and bacterial communities of the rhizosphere of Baccharis scandens and Solanum chilense native plants from the Atacama Desert. Our results showed that the fungal phyla Ascomycota and Basidiomycota and the bacterial phyla Actinobacteria and Proteobacteria were the dominant taxa in the rhizosphere of both plants. The linear discriminant analysis (LDA) effect size (LefSe) of the rhizosphere communities associated with B. scandens showed the genera Penicillium and Arthrobacter were the preferential taxa, whereas the genera Oidiodendron and Nitrospirae was the preferential taxa in S. chilense. Both plant showed similar diversity, richness, and abundance according to Shannon index, observed OTUs, and evenness. Our results indicate that there are no significant differences (p = 0.1) between the fungal and bacterial communities of both plants, however through LefSe, we find taxa associated with each plant species and the PCoA shows a separation between the samples of each species. This study provides knowledge to relate the assembly of the microbiome to the adaptability to drought stress in desert plants.

Mycorrhizal Fungi Isolated from Native Terrestrial Orchids from Region of La Araucanía, Southern Chile.

Mycorrhizal interactions of orchids are influenced by several environmental conditions. Hence, knowledge of mycorrhizal fungi associated with orchids inhabiting different ecosystems is essential to designing recovery strategies for threatened species. This study analyzes the mycorrhizal associations of terrestrial orchids colonizing grassland and understory in native ecosystems of the region of La Araucanía in southern Chile. Mycorrhizal fungi were isolated from peloton-containing roots and identified based on the sequence of the ITS region. Their capacities for seed germination were also investigated. We detected Tulasnella spp. and Ceratobasidium spp. in the pelotons of the analyzed orchids. Additionally, we showed that some Ceratobasidium isolates effectively induce seed germination to differing degrees, unlike Tulasnella spp., which, in most cases, fail to achieve protocorm growth. This process may underline a critical step in the life cycle of Tulasnella-associated orchids, whereas the Ceratobasidium-associated orchids were less specific for fungi and were effectively germinated with mycorrhizal fungi isolated from adult roots.

In Situ Rhizosphere Microbiome in Antarctic Vascular Plants, Modulated by Soil Condition.

Antarctic soils are considered young soils; therefore, the microbiota associated with Antarctic vascular plants play a critical role in their productivity. In this research, we compared the microbiota from three different soil conditions using a 16S rRNA and internal transcribed spacer rRNA gene amplicon approach for bacterial and fungal communities.

Anesthesia outside the Operating Room in a Patient with Mitochondrial Disease.

Mitochondrial dysfunction comprehends a wide range of genetic disorders. These patients' precarious metabolic balance makes its management difficult. Furthermore, the same systems affected by mitochondrial disease can be altered by many of the frequently used anesthetic agents. Each patient has to be evaluated individually according to their comorbidities and anesthetic requirements.

[Consenso experto en tiempos de COVID-19: aplicaciones del método Delphi en materia de salud].

La epidemia de COVID-19 ha modificado la cultura de la comunicación. La solución para los problemas de salud puede ser asertiva cuando es consensuada. El método Delphi es una herramienta de consenso que emplea rondas de listas de preguntas para recopilar información del conocimiento de un panel de expertos que analizan planteamientos y posibles soluciones a problemas. Se basa en la premisa de que, con la libertad del anonimato, la inteligencia combinada mejora el juicio individual y captura la opinión colectiva experta. El proceso del método es muy flexible, pues las rondas de preguntas pueden realizarse de manera presencial o remota. En este artículo se describe cómo implementar el método Delphi convencional en tiempos de confinamiento, y se analizan la utilidad y las limitaciones del método para su uso por expertos en salud para la resolución de problemas de tratamiento, diagnóstico o administrativos. Las tecnologías actuales para recolectar los datos permiten gran flexibilidad en el formato de los cuestionarios y facilitan la recopilación de la opinión experta. Gracias a su adaptabilidad, el método Delphi se está convirtiendo en una estrategia popular que involucra los ámbitos cualitativo y cuantitativo.

The Endophytic Fungus Chaetomium cupreum Regulates Expression of Genes Involved in the Tolerance to Metals and Plant Growth Promotion in Eucalyptus globulus Roots.

The endophytic strain Chaetomium cupreum isolated from metal-contaminated soil was inoculated in Eucalyptus globulus roots to identify genes involved in metal stress response and plant growth promotion. We analyzed the transcriptome of E. globulus roots inoculated with C. cupreum. De novo sequencing, assembly, and analysis were performed to identify molecular mechanisms involved in metal stress tolerance and plant growth promotion. A total of 393,371,743 paired-end reads were assembled into 135,155 putative transcripts. It was found that 663 genes significantly changed their expression in the presence of treatment, of which 369 were up-regulated and 294 were down-regulated. We found differentially expressed genes (DEGs) encoding metal transporters, transcription factors, stress and defense response proteins, as well as DEGs involved in auxin biosynthesis and metabolism. Our results showed that the inoculation of C. cupreum enhanced tolerance to metals and growth promotion on E. globulus. This study provides new information to understand molecular mechanisms involved in plant-microbe interactions under metals stress.