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.

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.

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.

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.

A multi-center retrospective analysis of the effect of DPP4 inhibitors on progression-free survival in advanced airway and colorectal cancers.

Cluster of differentiation 26 (CD26), also known as dipeptidyl peptidase IV (DPP4), is a cell surface protein with exopeptidase activity and is expressed by most cell types. CD26/DPP4 is a multifunctional molecule with diverse biological effects, including regulatory effects on tumor growth, invasion and metastasis, and is a potential novel therapeutic target for selected cancers. In this study, we retrospectively analyzed diabetic patients with concurrent advanced airway or colorectal cancer to examine the effect of DPP4-inhibitors on progression-free survival (PFS). We performed a multi-center retrospective review of patients with advanced colorectal or airway (lung, head and neck) cancer and a concurrent diagnosis of diabetes. The control group included patients on metformin and a sulfonylurea, and the study group included patients on metformin and a DPP4 inhibitor. Ninety-six patients were eligible for the study. The cancers progressed in 23.7% of patients treated with DPP4 inhibitors compared to 50.9% of patients in the control group with an odds ratio of 0.303 [95% confidence interval (CI): 0.106-0.809] and P=0.010. There was a statistically significant improvement in PFS in the study group as compared to the control group, hazard ratio=0.42 (95% CI: 0.21-0.84) and P=0.014. There was a trend toward improvement in overall survival, although this effect was not statistically significant (P=0.11). Exposure to DPP4 inhibitors in the study group led to higher PFS in patients with advanced colorectal and airway cancers. Additional investigations with larger patient cohorts are needed to validate the relationship between DPP4 inhibition and the clinical outcome of selected malignancies.

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.

Denileukin Diftitox (Ontak) as Maintenance Therapy for Peripheral T-Cell Lymphomas: Three Cases with Sustained Remission.

Peripheral T-cell lymphomas (PTCL) are rare but markedly aggressive forms of non-Hodgkin's lymphoma (NHL). They carry a poor prognosis, with current therapeutic approach being generally ineffective. The most employed first-line treatment is CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone), which still results in high rates of relapses. Denileukin diftitox is a fusion protein combining the cytotoxic portion of the diphtheria toxin and the receptor-binding domain of the interleukin-2 (IL-2) molecule, thereby targeting cells expressing the IL-2 receptor, including both T-cell and B-cell lymphomas. It has been approved for the treatment of cutaneous T-cell lymphomas, and it has documented activity in PTCL both as a single agent and as part of combination therapy. This report documents three cases of PTCL where denileukin diftitox has been used as long-term maintenance therapy after complete remission was achieved. While the overall survival rate of patients with advanced stage, refractory PTCL is generally poor (with median overall survival of 5.5 months), the three patients described in this report are all experiencing an ongoing complete remission for more than four years.

Disfunción de prótesis mitral en paciente con síndrome de Bland-White-Garland / Dysfunction protesis mitral in a patient with white garland syndrome Bland-White-Garland

Las anomalías coronarias tienen una incidencia en la población general de 0.2 a 1.2%; de ellas la más frecuente es el origen anómalo de la coronaria izquierda del tronco de la arteria pulmonar, conocido como ALCAPA (anomalous left coronary artery from pulmonary artery). Ésta es una rara anomalía que representa el 0.25 a 0.5% del total de cardiopatías. La implantación de la arteria coronaria izquierda desde el tronco de la arteria pulmonar también conocida como síndrome de Bland-White-Garland (BWG) es la anomalía coronaria más frecuente. Reporte de caso: Se presenta caso de mujer de 51 años de edad, quien ingresó de forma electiva para cateterismo cardiaco como protocolo prequirúrgico de recambio valvular por disfunción de prótesis mitral donde como hallazgo se encontró inmovilidad de uno de los discos; la paciente tenía antecedente de sustitución valvular mitral a los 15 años por prolapso congénito. Durante el cateterismo cardiaco se evidencia el implante anómalo de la coronaria izquierda, situación que no había sido detectada en el primer evento quirúrgico, presentada en sesión médico-quirúrgica y siendo aceptada para recambio valvular más reconexión coronaria, llevándose procedimiento sin complicaciones. Conclusiones: Aunque el síndrome BWG es una enfermedad con rara presentación en la edad adulta, el conocimiento de esta anomalía congénita es importante, ya que el diagnóstico precoz y el tratamiento pueden prevenir daños irreversibles al miocardio.

Coronary anomalies have an incidence in the general population of 0.2 to 1.2 %, of which the most common is the anomalous origin of the left coronary artery from the main pulmonary artery, known as ALCAPA (anomalous left coronary artery from pulmonary artery). It is a rare anomaly representing 0.25 to 0.5 % of all heart disease. The implementation of the left coronary artery from the main pulmonary artery also known as Bland-White-Garland syndrome is the most common coronary anomaly. Case Report: Female 51-year-old show, who electively income for preoperative cardiac catheterization and Protocol were valve replacement for mitral prosthesis dysfunction where was found immobility of a disk. Immobility was found, patient had a history of mitral valve replacement for 15 years for congenital prolapse, during cardiac catheterization of anomalous left coronary implant situation that had not been detected in the first surgical event, presented in medical and surgical session and being accepted for coronary valve replacement reconnection is more evidence, taking procedure without complications. Conclusions: Although BWG syndrome is a rare disease presenting in adulthood, knowledge of this congenital anomaly is important because early diagnosis and treatment can prevent irreversible damage to the myocardium.

Immunodominant role of CCHA subunit of Concholepas hemocyanin is associated with unique biochemical properties.

Hemocyanin, the oxygen transporter metallo-glycoprotein from mollusks, shows strong relationship between its notable structural features and intrinsic immunomodulatory effects. Here we investigated the individual contribution of CCHA and CCHB subunits from Concholepas hemocyanin (CCH) to in vivo humoral immune response and their pre-clinical evaluation as immunotherapeutic agent in a mice bladder cancer model, in relation to their biochemical properties. To this end, subunits were purified and well characterized. Homogeneous subunits were obtained by anionic exchange chromatography, and its purity assessed by electrophoretic and immunochemical methods. While each CCH subunit contains eight functional units showing partial cross reaction, the vibrational spectral analysis showed several spectral differences, suggesting structural differences between them. In addition, we demonstrated differences in the carbohydrate content: CCHA had a 3.6% w/w sugar with both N- and O-linked moieties. In turn, CCHB had a 2.5% w/w sugar with N-linked, while O-linked moieties were nearly absent. Considering these differences, it was not possible to predict a priori whether the immunogenic and immunotherapeutic properties of subunits might be similar. Surprisingly, both subunits by itself induced a humoral response, and showed an antitumor effect in the bladder carcinoma cell line MBT-2. However, when immunologic parameters were analyzed, CCHA showed better efficiency than CCHB. No allergic reactions or any toxic effects were observed in mice treated with CCHA, sustaining its potential therapeutic use. Our study supports that CCHA subunit accounts for the most important features involved in the immunogenicity of CCH, such as better hydrophilicity and higher content of carbohydrates.