Integrative deep learning analysis improves colon adenocarcinoma patient stratification at risk for mortality.

BACKGROUND: Colorectal cancers are the fourth most diagnosed cancer and the second leading cancer in number of deaths. Many clinical variables, pathological features, and genomic signatures are associated with patient risk, but reliable patient stratification in the clinic remains a challenging task. Here we assess how image, clinical, and genomic features can be combined to predict risk. METHODS: We developed and evaluated integrative deep learning models combining formalin-fixed, paraffin-embedded (FFPE) whole slide images (WSIs), clinical variables, and mutation signatures to stratify colon adenocarcinoma (COAD) patients based on their risk of mortality. Our models were trained using a dataset of 108 patients from The Cancer Genome Atlas (TCGA), and were externally validated on newly generated dataset from Wayne State University (WSU) of 123 COAD patients and rectal adenocarcinoma (READ) patients in TCGA (N = 52). FINDINGS: We first observe that deep learning models trained on FFPE WSIs of TCGA-COAD separate high-risk (OS < 3 years, N = 38) and low-risk (OS > 5 years, N = 25) patients (AUC = 0.81 ± 0.08, 5 year survival p < 0.0001, 5 year relative risk = 1.83 ± 0.04) though such models are less effective at predicting overall survival (OS) for moderate-risk (3 years < OS < 5 years, N = 45) patients (5 year survival p-value = 0.5, 5 year relative risk = 1.05 ± 0.09). We find that our integrative models combining WSIs, clinical variables, and mutation signatures can improve patient stratification for moderate-risk patients (5 year survival p < 0.0001, 5 year relative risk = 1.87 ± 0.07). Our integrative model combining image and clinical variables is also effective on an independent pathology dataset (WSU-COAD, N = 123) generated by our team (5 year survival p < 0.0001, 5 year relative risk = 1.52 ± 0.08), and the TCGA-READ data (5 year survival p < 0.0001, 5 year relative risk = 1.18 ± 0.17). Our multicenter integrative image and clinical model trained on combined TCGA-COAD and WSU-COAD is effective in predicting risk on TCGA-READ (5 year survival p < 0.0001, 5 year relative risk = 1.82 ± 0.13) data. Pathologist review of image-based heatmaps suggests that nuclear size pleomorphism, intense cellularity, and abnormal structures are associated with high-risk, while low-risk regions have more regular and small cells. Quantitative analysis shows high cellularity, high ratios of tumor cells, large tumor nuclei, and low immune infiltration are indicators of high-risk tiles. INTERPRETATION: The improved stratification of colorectal cancer patients from our computational methods can be beneficial for treatment plans and enrollment of patients in clinical trials. FUNDING: This study was supported by the National Cancer Institutes (Grant No. R01CA230031 and P30CA034196). The funders had no roles in study design, data collection and analysis or preparation of the manuscript.

Coronavirus pandemic in the South Asia region: Health policy and economy trade-off.

Background: The South Asian Association for Regional Cooperation (SAARC) covers Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan, and Sri Lanka. We conducted a comparative analysis of the trade-off between the health policies for the prevention of COVID-19 spread and the impact of these policies on the economies and livelihoods of the South Asia populations. Methods: We analyzed COVID-19 data on epidemiology, public health and health policy, health system capacity, and macroeconomic indicators from January 2020 to March 2021 to determine temporal trends by conducting joinpoint regression analysis using average weekly percent change (AWPC). Results: Bangladesh had the highest statistically significant AWPC for new COVID-19 cases (17.0; 95% CI = 7.7-27.1, P < 0.001), followed by the Maldives (12.9; 95% CI = 5.3-21.0, P < 0.001) and India (10.0; 95% CI = 8.4-11.5, P < 0.001). The AWPC for COVID-19 deaths was significant for India (6.5; 95% CI = 4.3-8.9, P < 0.001) and Bangladesh (6.1; 95% CI = 3.7-8.5, P < 0.001). Nepal (55.79%), and India (34.91%) had the second- and third-highest increase in unemployment, while Afghanistan (6.83%) and Pakistan (16.83%) had the lowest. The rate of change of real GDP had the highest decrease for Maldives (557.51%), and India (297.03%); Pakistan (46.46%) and Bangladesh (70.80%), however, had the lowest decrease. The government response stringency index for Pakistan had a see-saw pattern with a sharp decline followed by an increase in the government health policy restrictions that approximated the test-positivity trend. Conclusions: Unlike developed economies, the South Asian developing countries experienced a trade-off between health policy and their economies during the COVID-19 pandemic. South Asian countries (Nepal and India), with extended periods of lockdowns and a mismatch between temporal trends of government response stringency index and the test-positivity or disease incidence, had higher adverse economic effects, unemployment, and burden of COVID-19. Pakistan demonstrated targeted lockdowns with a rapid see-saw pattern of government health policy response that approximated the test-positivity trend and resulted in lesser adverse economic effects, unemployment, and burden of COVID-19.

Spatial characterization and quantification of CD40 expression across cancer types.

BACKGROUND: CD40, a TNF receptor family member, is expressed by a variety of immune cells and is involved in the activation of both adaptive and innate immune responses. Here, we used quantitative immunofluorescence (QIF) to evaluate CD40 expression on the tumor epithelium of solid tumors in large patient cohorts of lung, ovarian, and pancreatic cancers. METHODS: Tissue samples from nine different solid tumors (bladder, breast, colon, gastric, head and neck, non-small cell lung cancer (NSCLC), ovarian, pancreatic and renal cell carcinoma), constructed in tissue microarray format, were initially assessed for CD40 expression by QIF. CD40 expression was then evaluated on the large available patient cohorts for three of the tumor types demonstrating high CD40 positivity rate; NSCLC, ovarian and pancreatic cancer. The prognostic impact of CD40 expression on tumor cells was also investigated. RESULTS: CD40 expression on tumor cells was found to be common, with 80% of the NSCLC population, 40% of the ovarian cancer population, and 68% of the pancreatic adenocarcinoma population displaying some degree of CD40 expression on cancer cells. All of three of these cancer types displayed considerable intra-tumoral heterogeneity of CD40 expression, as well as partial correlation between expression of CD40 on tumor cells and on surrounding stromal cells. CD40 was not found to be prognostic for overall survival in NSCLC, ovarian cancer, or pancreatic adenocarcinoma. CONCLUSIONS: The high percentage of tumor cells expressing CD40 in each of these solid tumors should be considered in the development of therapeutic agents designed to target CD40.

Quantitative, Spatially Defined Expression of Leukocyte-associated Immunoglobulin-like Receptor in Non-small Cell Lung Cancer.

Targeting the interaction of leukocyte-associated immunoglobulin-like receptor-1 (LAIR-1) and its ligands has been shown to reinstate antitumor immunity. In addition, the introduction of the LAIR-1 decoy protein, LAIR-2, sensitizes previously resistant lung tumors to programmed death-1 (PD-1) blockade, indicating the potential of LAIR-1 as an alternative marker for anti-PD-1 resistance in lung cancer. Here, we assessed LAIR-1 as compared with programmed death-ligand 1 (PD-L1) expression in various tumors, with a focus on non-small cell lung cancer (NSCLC) and its histologic subtypes using multiplexed quantitative immunofluorescence (mQIF) in 287 (discovery cohort) and 144 (validation cohort) patients with NSCLC. In addition, using multispectral imaging technology on mQIF images, we evaluated the localization of LAIR-1 on various cell types. We observed that CD14+, CD68+, and CD163+ monocytes and CK+ tumor cells predominantly expressed LAIR-1 more than other cell types. Furthermore, LAIR-1 expression in the tumor compartment was significantly higher in patients with lung adenocarcinoma (LUAD) than those with lung squamous cell carcinoma subtype (**, P = 0.003). Our results indicated that high tumor LAIR-1 expression in patients with LUAD is negatively associated with OS (overall survival, HR = 2.4; *, P = 0.02) highlighting its prognostic value in LUAD but not in other subtypes. The Pearson correlation between LAIR-1 and PD-L1 is 0.31; however, mutual exclusive staining pattern (i.e., several cases were positive for LAIR-1 and negative for PD-L1) was observed. Altogether, our data suggest that the combination therapy of anti-PD-1/PD-L1 with anti-LAIR-1 or the anti-LAIR-1 monotherapy alone may be promising cancer immunotherapeutic strategies. Significance: The spatial, quantitative assessment of LAIR-1 in NSCLC shows positive association of OS with high LAIR-1+/CD68+ cell densities and negative association of OS with high LAIR-1 expression in LUAD tumor subtype.

Deep learning trained on hematoxylin and eosin tumor region of Interest predicts HER2 status and trastuzumab treatment response in HER2+ breast cancer.

The current standard of care for many patients with HER2-positive breast cancer is neoadjuvant chemotherapy in combination with anti-HER2 agents, based on HER2 amplification as detected by in situ hybridization (ISH) or protein immunohistochemistry (IHC). However, hematoxylin & eosin (H&E) tumor stains are more commonly available, and accurate prediction of HER2 status and anti-HER2 treatment response from H&E would reduce costs and increase the speed of treatment selection. Computational algorithms for H&E have been effective in predicting a variety of cancer features and clinical outcomes, including moderate success in predicting HER2 status. In this work, we present a novel convolutional neural network (CNN) approach able to predict HER2 status with increased accuracy over prior methods. We trained a CNN classifier on 188 H&E whole slide images (WSIs) manually annotated for tumor Regions of interest (ROIs) by our pathology team. Our classifier achieved an area under the curve (AUC) of 0.90 in cross-validation of slide-level HER2 status and 0.81 on an independent TCGA test set. Within slides, we observed strong agreement between pathologist annotated ROIs and blinded computational predictions of tumor regions / HER2 status. Moreover, we trained our classifier on pre-treatment samples from 187 HER2+ patients that subsequently received trastuzumab therapy. Our classifier achieved an AUC of 0.80 in a five-fold cross validation. Our work provides an H&E-based algorithm that can predict HER2 status and trastuzumab response in breast cancer at an accuracy that may benefit clinical evaluations.

High Counts of CD68+ and CD163+ Macrophages in Mantle Cell Lymphoma Are Associated With Inferior Prognosis.

BACKGROUND: Lymphoma-associated macrophages (LAMs) are key components in the lymphoma microenvironment, which may impact disease progression and response to therapy. There are two major subtypes of LAMs, CD68+ M1 and CD163+ M2. M2 LAMs can be transformed from M1 LAMs, particularly in certain diffuse large B-cell lymphomas (DLBCL). While mantle cell lymphoma (MCL) is well-known to contain frequent epithelioid macrophages, LAM characterization within MCL has not been fully described. Herein we evaluate the immunophenotypic subclassification, the expression of immune checkpoint molecule PD-L1, and the prognostic impact of LAMs in MCL. MATERIALS AND METHODS: A total of 82 MCL cases were collected and a tissue microarray block was constructed. Immunohistochemical staining was performed using CD68 and CD163, and the positive cells were recorded manually in four representative 400× fields for each case. Multiplexed quantitative immunofluorescence assays were carried out to determine PD-L1 expression on CD68+ M1 LAMs and CD163+ M2 LAMs. In addition, we assessed Ki67 proliferation rate of MCL by an automated method using the QuPath digital imaging analysis. The cut-off points of optimal separation of overall survival (OS) were analyzed using the X-Tile software, the SPSS version 26 was used to construct survival curves, and the log-rank test was performed to calculate the p-values. RESULTS: MCL had a much higher count of M1 LAMs than M2 LAMs with a CD68:CD163 ratio of 3:1. Both M1 and M2 LAMs were increased in MCL cases with high Ki67 proliferation rates (>30%), in contrast to those with low Ki67 (<30%). Increased number of M1 or M2 LAMs in MCL was associated with an inferior OS. Moreover, high expression of PD-L1 on M1 LAMs had a slightly better OS than the cases with low PD-L1 expression, whereas low expression of PD-L1 on M2 LAMs had a slightly improved OS, although both were not statistically significant. CONCLUSIONS: In contrast to DLBCL, MCL had a significantly lower rate of M1 to M2 polarization, and the high levels of M1 and M2 LAMs were associated with poor OS. Furthermore, differential PD-L1 expressions on LAMs may partially explain the different functions of tumor-suppressing or tumor-promoting of M1 and M2 LAMs, respectively.

Predicting mortality in SARS-COV-2 (COVID-19) positive patients in the inpatient setting using a novel deep neural network.

BACKGROUND: The nextwave of COVID-19 pandemic is anticipated to be worse than the initial one and will strain the healthcare systems even more during the winter months. Our aim was to develop a novel machine learning-based model to predict mortality using the deep learning Neo-V framework. We hypothesized this novel machine learning approach could be applied to COVID-19 patients to predict mortality successfully with high accuracy. METHODS: We collected clinical and laboratory data prospectively on all adult patients (≥18 years of age) that were admitted in the inpatient setting at Aga Khan University Hospital between February 2020 and September 2020 with a clinical diagnosis of COVID-19 infection. Only patients with a RT-PCR (reverse polymerase chain reaction) proven COVID-19 infection and complete medical records were included in this study. A Novel 3-phase machine learning framework was developed to predict mortality in the inpatients setting. Phase 1 included variable selection that was done using univariate and multivariate Cox-regression analysis; all variables that failed the regression analysis were excluded from the machine learning phase of the study. Phase 2 involved new-variables creation and selection. Phase 3 and final phase applied deep neural networks and other traditional machine learning models like Decision Tree Model, k-nearest neighbor models, etc. The accuracy of these models were evaluated using test-set accuracy, sensitivity, specificity, positive predictive values, negative predictive values and area under the receiver-operating curves. RESULTS: After application of inclusion and exclusion criteria (n=)1214 patients were selected from a total of 1228 admitted patients. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being septic shock (hazard ratio [HR], 4.30; 95% confidence interval [CI], 2.91-6.37), supportive treatment (HR, 3.51; 95% CI, 2.01-6.14), abnormal international normalized ratio (INR) (HR, 3.24; 95% CI, 2.28-4.63), admission to the intensive care unit (ICU) (HR, 3.24; 95% CI, 2.22-4.74), treatment with invasive ventilation (HR, 3.21; 95% CI, 2.15-4.79) and laboratory lymphocytic derangement (HR, 2.79; 95% CI, 1.6-4.86). Machine learning results showed our deep neural network (DNN) (Neo-V) model outperformed all conventional machine learning models with test set accuracy of 99.53%, sensitivity of 89.87%, and specificity of 95.63%; positive predictive value, 50.00%; negative predictive value, 91.05%; and area under the receiver-operator curve of 88.5. CONCLUSION: Our novel Deep-Neo-V model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy.

An Open-Source, Automated Tumor-Infiltrating Lymphocyte Algorithm for Prognosis in Triple-Negative Breast Cancer.

PURPOSE: Although tumor-infiltrating lymphocytes (TIL) assessment has been acknowledged to have both prognostic and predictive importance in triple-negative breast cancer (TNBC), it is subject to inter and intraobserver variability that has prevented widespread adoption. Here we constructed a machine-learning based breast cancer TIL scoring approach and validated its prognostic potential in multiple TNBC cohorts. EXPERIMENTAL DESIGN: Using the QuPath open-source software, we built a neural-network classifier for tumor cells, lymphocytes, fibroblasts, and "other" cells on hematoxylin-eosin (H&E)-stained sections. We analyzed the classifier-derived TIL measurements with five unique constructed TIL variables. A retrospective collection of 171 TNBC cases was used as the discovery set to identify the optimal association of machine-read TIL variables with patient outcome. For validation, we evaluated a retrospective collection of 749 TNBC patients comprised of four independent validation subsets. RESULTS: We found that all five machine TIL variables had significant prognostic association with outcomes (P ≤ 0.01 for all comparisons) but showed cell-specific variation in validation sets. Cox regression analysis demonstrated that all five TIL variables were independently associated with improved overall survival after adjusting for clinicopathologic factors including stage, age, and histologic grade (P ≤ 0.0003 for all analyses). CONCLUSIONS: Neural net-driven cell classifier-defined TIL variables were robust and independent prognostic factors in several independent validation cohorts of TNBC patients. These objective, open-source TIL variables are freely available to download and can now be considered for testing in a prospective setting to assess clinical utility.See related commentary by Symmans, p. 5446.

A statistically rigorous deep neural network approach to predict mortality in trauma patients admitted to the intensive care unit.

BACKGROUND: Trauma patients admitted to critical care are at high risk of mortality because of their injuries. Our aim was to develop a machine learning-based model to predict mortality using Fahad-Liaqat-Ahmad Intensive Machine (FLAIM) framework. We hypothesized machine learning could be applied to critically ill patients and would outperform currently used mortality scores. METHODS: The current Deep-FLAIM model evaluates the statistically significant risk factors and then supply these risk factors to deep neural network to predict mortality in trauma patients admitted to the intensive care unit (ICU). We analyzed adult patients (≥18 years) admitted to the trauma ICU in the publicly available database Medical Information Mart for Intensive Care III version 1.4. The first phase selection of risk factor was done using Cox-regression univariate and multivariate analyses. In the second phase, we applied deep neural network and other traditional machine learning models like Linear Discriminant Analysis, Gaussian Naïve Bayes, Decision Tree Model, and k-nearest neighbor models. RESULTS: We identified a total of 3,041 trauma patients admitted to the trauma surgery ICU. We observed that several clinical and laboratory-based variables were statistically significant for both univariate and multivariate analyses while others were not. With most significant being serum anion gap (hazard ratio [HR], 2.46; 95% confidence interval [CI], 1.94-3.11), sodium (HR, 2.11; 95% CI, 1.61-2.77), and chloride (HR, 2.11; 95% CI, 1.69-2.64) abnormalities on laboratories, while clinical variables included the diagnosis of sepsis (HR, 2.03; 95% CI, 1.23-3.37), Quick Sequential Organ Failure Assessment score (HR, 1.52; 95% CI, 1.32-3.76). And Systemic Inflammatory Response Syndrome criteria (HR. 1.41; 95% CI, 1.24-1.26). After we used these clinically significant variables and applied various machine learning models to the data, we found out that our proposed DNN outperformed all the other methods with test set accuracy of 92.25%, sensitivity of 79.13%, and specificity of 94.16%; positive predictive value, 66.42%; negative predictive value, 96.87%; and area under the curve of the receiver-operator curve of 0.91 (1.45-1.29). CONCLUSION: Our novel Deep-FLAIM model outperformed all other machine learning models. The model is easy to implement, user friendly and with high accuracy. LEVEL OF EVIDENCE: Prognostic study, level II.

PD-L1 Protein Expression on Both Tumor Cells and Macrophages are Associated with Response to Neoadjuvant Durvalumab with Chemotherapy in Triple-negative Breast Cancer.

PURPOSE: In both the IMpassion 130 trial in the metastatic setting and in Keynote 522 in the neoadjuvant setting, patients with triple-negative breast cancer (TNBC) showed benefit from PD-1 axis immunotherapy. Here, we assess PD-L1 expression on both tumor and immune cells using quantitative immunofluorescence to assess association with benefit from neoadjuvant durvalumab concurrent with chemotherapy in TNBC. EXPERIMENTAL DESIGN: Pretreatment core needle biopsies (n = 69) were obtained from patients who participated in a phase I/II clinical trial (NCT02489448). The final analysis included 45 patients [pathologic complete response (pCR) = 18, non-pCR = 27] due to technical issues and insufficient tissue. Slides were stained using a previously validated Ultivue DNA-based Ultimapper kit (CD8, CD68, PD-L1, Cytokeratin/Sox10, and Hoechst counterstain). The PD-L1 expression was analyzed by molecular compartmentalization without segmentation using AQUA software (version 3.2.2.1) in three tissue compartments including tumor (cytokeratin-positive cells), CD68+ cells, and overall stroma. RESULTS: In patients with pCR, PD-L1 expression was significantly higher in tumor cells, in CD68+ cells and in the stroma compared with patients non-pCR. There was no difference in the amount of CD68+ cells in the tumor or stromal compartments between cases with pCR and non-pCR. CONCLUSIONS: Expression of PD-L1 in tumor cells, immune cells in stroma, and colocalized with CD68+ cells is associated with higher rates of pCR to durvalumab and chemotherapy in TNBC.

Immune Cell PD-L1 Colocalizes with Macrophages and Is Associated with Outcome in PD-1 Pathway Blockade Therapy.

PURPOSE: Programmed death ligand 1 (PD-L1) is expressed in tumor cells and immune cells, and both have been associated with response to anti-PD-1 axis immunotherapy. Here, we examine the expression of PD-L1 to determine which cell type carries the predictive value of the test. EXPERIMENTAL DESIGN: We measured the expression of PD-L1 in multiple immune cells with two platforms and confocal microscopy on three retrospective Yale NSCLC cohorts (425 nonimmunotherapy-treated cases and 62 pembrolizumab/nivolumab/atezolizumab-treated cases). The PD-L1 level was selectively measured in different immune cell subsets using two multiplexed quantitative immunofluorescence panels, including CD56 for natural killer cells, CD68 for macrophages, and CD8 for cytotoxic T cells. RESULTS: PD-L1 was significantly higher in macrophages in both tumor and stromal compartment compared with other immune cells. Elevated PD-L1 in macrophages was correlated with high PD-L1 level in tumor as well as CD8 and CD68 level (P < 0.0001). High PD-L1 expression in macrophages was correlated with better overall survival (OS; P = 0.036 by cell count/P = 0.019 by molecular colocalization), while high PD-L1 expression in tumor cells was not. CONCLUSIONS: In nearly 500 non-small cell lung cancer (NSCLC) cases, the predominant immune cell type that expresses PD-L1 is CD68+ macrophages. The level of PD-L1 in macrophages is significantly associated with the level of PD-L1 in tumor cells and infiltration by CD8+ T cells, suggesting a connection between high PD-L1 and "hot" tumors. In anti-PD-1 axis therapy-treated patients, high levels of PD-L1 expression in macrophages are associated with longer OS and may be responsible for the predictive effect of the marker.

An open source automated tumor infiltrating lymphocyte algorithm for prognosis in melanoma.

Assessment of tumor infiltrating lymphocytes (TILs) as a prognostic variable in melanoma has not seen broad adoption due to lack of standardization. Automation could represent a solution. Here, using open source software, we build an algorithm for image-based automated assessment of TILs on hematoxylin-eosin stained sections in melanoma. Using a retrospective collection of 641 melanoma patients comprising four independent cohorts; one training set (N = 227) and three validation cohorts (N = 137, N = 201, N = 76) from 2 institutions, we show that the automated TIL scoring algorithm separates patients into favorable and poor prognosis cohorts, where higher TILs scores were associated with favorable prognosis. In multivariable analyses, automated TIL scores show an independent association with disease-specific overall survival. Therefore, the open source, automated TIL scoring is an independent prognostic marker in melanoma. With further study, we believe that this algorithm could be useful to define a subset of patients that could potentially be spared immunotherapy.

Quantitative Assessment of CMTM6 in the Tumor Microenvironment and Association with Response to PD-1 Pathway Blockade in Advanced-Stage Non-Small Cell Lung Cancer.

INTRODUCTION: CKLF like MARVEL transmembrane domain containing 6 (CMTM6) has been described as a programmed death ligand 1 (PD-L1) regulator at the protein level by modulating stability through ubiquitination. In this study, we describe the patterns of CMTM6 expression and assess its association with response to programmed cell death 1 pathway blockade in NSCLC. METHODS: We used multiplexed quantitative immunofluorescence to determine the expression of CMTM6 and PD-L1 in 438 NSCLCs represented in tissue microarrays, including in two independent retrospective cohorts of immunotherapy-treated (n = 69) and non-immunotherapy-treated (n = 258) patients and a third collection of EGFR- and KRAS-genotyped tumors (n = 111). RESULTS: Tumor and stromal CMTM6 expression was detected in approximately 70% of NSCLCs. CMTM6 expression was not associated with clinical features or EGFR/KRAS mutational status and showed a modest correlation with T-cell infiltration (R2 < 0.40). We found a significant correlation between CMTM6 and PD-L1, which was higher in the stroma (R2 = 0.51) than in tumor cells (R2 = 0.35). In our retrospective NSCLC cohort, neither CMTM6 nor PD-L1 expression alone significantly predicted immunotherapy outcomes. However, high CMTM6 and PD-L1 coexpression in the stromal and CD68 compartments (adjusted hazard ratio = 0.38, p = 0.03), but not in tumor cells (p = 0.15), was significantly associated with longer overall survival in treated patients but was not observed in the absence of immunotherapy. CONCLUSION: This study supports the mechanistic role for CMTM6 in stabilization of PD-L1 in patient tumors and suggests that high coexpression of CMTM6 and PD-L1, particularly in stromal immune cells (macrophages), might identify the greatest benefit from programmed cell death 1 axis blockade in NSCLC.

Impact of Methamphetamine Abuse: A Rare Case of Rapid Cerebral Aneurysm Growth with Review of Literature.

Methamphetamine or "meth" is a sympathomimetic amine of the amphetamine-type substances (ATS) class with an extremely high potential for abuse. Illicitly abused neurostimulants like cocaine and meth predispose patients to the aneurysmal formation with reported rupture at a younger age and in much smaller sized aneurysms. However, very rapid growth of aneurysm within less than 2 weeks with methamphetamine abuse is very rarely observed or reported. In this report, we present a patient with repeated and recurrent meth abuse who demonstrated rapid growth of a pericallosal aneurysm over the period of less than two weeks. The pathophysiology of stroke related to meth and ATS abuse is multifactorial with hypertension, tachycardia, and vascular disease postulated as major mechanisms. The rapid growth of an aneurysm has a high risk of aneurysmal rupture and SAH, which is a neurosurgical emergency and therefore warrants careful consideration and close monitoring. This case confirms the dynamic temporal effects of methamphetamine use on intracranial vessels and this specific neurostimulants association to rapid aneurysmal formation. In light of vascular pathologies the possibility of drug-induced pseudoaneurysm should also be considered in young patients with history of meth abuse.

Effect of dichloromethane fraction of Areca catechu nut on monoamines associated behaviors and tyramine pressor sensitivity in rodents.

The current study was aimed at investigating the effect of Areca catechu nut dichloromethane fraction (7 mg/kg) on monoamines (serotonin and dopamine) modulation (5-hydroxytryptophan-induced tremors and phenylethylamine-induced stereotypes) and its interaction with tyramine (cheese effect). The dichloromethane fraction caused pronounced increase in 5-HTP-induced tremors (50%) with negligible PEA-induced stereotypes (20%). Additionally, it did not produce a significant increase in the tyramine pressor effects. These results suggest that the dichloromethane fraction of A. catechu nut primarily elevates serotonin levels (probably via monoamine oxidase A inhibition) and does not induce cheese effect.

Studies on anti-inflammatory and analgesic activities of betel nut in rodents.

ETHNOPHARMACOLOGICAL RELEVANCE: Areca catechu, commonly known as betel nut, is very famous for its medicinal use in multiple disorders. It is also popular as a remedy against inflammatory disorders in the Unani (Greco-Arab) system of medicine. OBJECTIVE OF THE STUDY: This study was aimed at investigating the anti-inflammatory and analgesic activities of the crude extract of Areca catechu and its respective fractions. MATERIALS AND METHODS: Paw edema, formalin-induced nociception and acetic acid-induced writhing assays were carried out in vivo. Free radical scavenging activity of the plant extract was performed in vitro. RESULTS: Preliminary experiments using a single dose (100 mg/kg) of Areca catechu and its respective fractions demonstrated an anti-inflammatory effect on carrageenan-induced edema in mice and rats, the aqueous fraction being distinctly more effective. When studied on prostaglandin E2 (PGE2), arachidonic acid, histamine, or serotonin (5HT)-induced edema in rats, Areca catechu and its aqueous fraction markedly repressed only the PGE2 and arachidonic acid-induced inflammation. When studied for analgesic activity, the crude extract and its aqueous fraction produced a dose-dependent (10-100 mg/kg) inhibitory effect on formalin-induced nociception in mice and acetic acid-induced writhing in rats, similar to aspirin. In DPPH assay, Areca catechu and its aqueous fraction exhibited free radical scavenging activity with respective IC(50) values of 5.34 µg/ml (4.93-5.78, CI; 95%, n=5) and 7.28 µg/ml (6.04-7.95, n=4), like that of rutin with IC(50) value of 4.75 µg/ml (3.89-5.42, n=4). CONCLUSION: These results indicate the anti-inflammatory and analgesic effects of Areca catechu and provide a rationale for its medicinal use in inflammatory disorders.