First Do No Harm: Predicting Surgical Morbidity During Humanitarian Medical Missions.

BACKGROUND: Despite good intentions, humanitarian surgical missions are unavoidably linked to some degree of complication. We hypothesized that the American College of Surgeons Surgical Risk Calculator (ACS-SRC) could estimate the risk of complications of procedures performed during the US Navy's Pacific Partnership 2015 (PP15) mission. METHODS: Patient information and surgical details recorded during PP15 were entered into the ACS-SRC. Risks of complications for each procedure were calculated. Receiver operating characteristics and Brier scores were calculated to compare the predicted outcomes to the observed complications. RESULTS: Of the 174 unique procedures performed during PP15 (representing 465 patients), 99 were found in the ACS-SRC (representing 256 patients). Risk calculations for PP15 were: 1.5% risk (IQR 0.9, 2.4) of "serious" complications and 2.0% risk (IQR 1.3, 2.8) of "any" complication. ACS-SRC specific risks were calculated as follows: pneumonia 0.1%, cardiac 0.0%, surgical site infection (SSI) 0.6%, urinary tract infection 0.2%, venous thromboembolism 0.1%, renal failure 0.0%, OR return 0.9%, and death 0.0%. The only specific ACS-SRC complication observed was "OR return" (0.35%) and SSI (0.35%). The observed PP15 rates for "serious" or "any" complications (ACS-SRC definition) were 0.70% (2/285) each. Receiver operating characteristics for ACS-SRC for predicting "serious" or "any" complication were 0.743 (p = 0.118) and 0.654 (p = 0.227), respectively. CONCLUSIONS: Although the ACS-SRC over-predicted risk compared to observed outcomes, it may offer a good starting point for humanitarian surgery risk calculation. Observed outcomes may be limited by loss-to-follow-up bias. Emphasis should be placed on establishing patient follow-up as part of humanitarian surgical mission planning and execution.

A prediction model for distinguishing lung squamous cell carcinoma from adenocarcinoma.

Accurate classification of squamous cell carcinoma (SCC) from adenocarcinoma (AC) of non-small cell lung cancer (NSCLC) can lead to personalized treatments of lung cancer. We aimed to develop a miRNA-based prediction model for differentiating SCC from AC in surgical resected tissues and bronchoalveolar lavage (BAL) samples. Expression levels of seven histological subtype-associated miRNAs were determined in 128 snap-frozen surgical lung tumor specimens by using reverse transcription-polymerase chain reaction (RT-PCR) to develop an optimal panel of miRNAs for acutely distinguishing SCC from AC. The biomarkers were validated in an independent cohort of 112 FFPE lung tumor tissues, and a cohort of 127 BAL specimens by using droplet digital PCR for differentiating SCC from AC. A prediction model with two miRNAs (miRs-205-5p and 944) was developed that had 0.988 area under the curve (AUC) with 96.55% sensitivity and 96.43% specificity for differentiating SCC from AC in frozen tissues, and 0.997 AUC with 96.43% sensitivity and 96.43% specificity in FFPE specimens. The diagnostic performance of the prediction model was reproducibly validated in BAL specimens for distinguishing SCC from AC with a higher accuracy compared with cytology (95.69 vs. 68.10%; P < 0.05). The prediction model might have a clinical value for accurately discriminating SCC from AC in both surgical lung tumor tissues and liquid cytological specimens.

A classifier integrating plasma biomarkers and radiological characteristics for distinguishing malignant from benign pulmonary nodules.

Lung cancer is primarily caused by cigarette smoking and the leading cancer killer in the USA and across the world. Early detection of lung cancer by low-dose CT (LDCT) can reduce the mortality. However, LDCT dramatically increases the number of indeterminate pulmonary nodules (PNs), leading to overdiagnosis. Having a definitive preoperative diagnosis of malignant PNs is clinically important. Using microarray and droplet digital PCR to directly profile plasma miRNA expressions of 135 patients with PNs, we identified 11 plasma miRNAs that displayed a significant difference between patients with malignant versus benign PNs. Using multivariate logistic regression analysis of the molecular results and clinical/radiological characteristics, we developed an integrated classifier comprising two miRNA biomarkers and one radiological characteristic for distinguishing malignant from benign PNs. The classifier had 89.9% sensitivity and 90.9% specificity, being significantly higher compared with the biomarkers or clinical/radiological characteristics alone (all p < 0.05). The classifier was validated in two independent sets of patients. We have for the first time shown that the integration of plasma biomarkers and radiological characteristics could more accurately identify lung cancer among indeterminate PNs. Future use of the classifier could spare individuals with benign growths from the harmful diagnostic procedures, while allowing effective treatments to be immediately initiated for lung cancer, thereby reduces the mortality and cost. Nevertheless, further prospective validation of this classifier is warranted.

Polyamines inhibit the assembly of stress granules in normal intestinal epithelial cells regulating apoptosis.

Polyamines regulate multiple signaling pathways and are implicated in many aspects of cellular functions, but the exact molecular processes governed by polyamines remain largely unknown. In response to environmental stress, repression of translation is associated with the assembly of stress granules (SGs) that contain a fraction of arrested mRNAs and are thought to function as mRNA storage. Here we show that polyamines modulate the assembly of SGs in normal intestinal epithelial cells (IECs) and that induced SGs following polyamine depletion are implicated in the protection of IECs against apoptosis. Increasing the levels of cellular polyamines by ectopic overexpression of the ornithine decarboxylase gene decreased cytoplasmic levels of SG-signature constituent proteins eukaryotic initiation factor 3b and T-cell intracellular antigen-1 (TIA-1)-related protein and repressed the assembly of SGs induced by exposure to arsenite-induced oxidative stress. In contrast, depletion of cellular polyamines by inhibiting ornithine decarboxylase with α-difluoromethylornithine increased cytoplasmic eukaryotic initiation factor 3b and TIA-1 related protein abundance and enhanced arsenite-induced SG assembly. Polyamine-deficient cells also exhibited an increase in resistance to tumor necrosis factor-α/cycloheximide-induced apoptosis, which was prevented by inhibiting SG formation with silencing SG resident proteins Sort1 and TIA-1. These results indicate that the elevation of cellular polyamines represses the assembly of SGs in normal IECs and that increased SGs in polyamine-deficient cells are crucial for increased resistance to apoptosis.

Plasma microRNAs as potential biomarkers for non-small-cell lung cancer.

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death. Developing minimally invasive techniques that can diagnose NSCLC, particularly at an early stage, may improve its outcome. Using microarray platforms, we previously identified 12 microRNAs (miRNAs) the aberrant expressions of which in primary lung tumors are associated with early-stage NSCLC. Here, we extend our previous research by investigating whether the miRNAs could be used as potential plasma biomarkers for NSCLC. We initially validated expressions of the miRNAs in paired lung tumor tissues and plasma specimens from 28 stage I NSCLC patients by real-time quantitative reverse transcription PCR, and then evaluated diagnostic value of the plasma miRNAs in a cohort of 58 NSCLC patients and 29 healthy individuals. The altered miRNA expressions were reproducibly confirmed in the tumor tissues. The miRNAs were stably present and reliably measurable in plasma. Of the 12 miRNAs, five displayed significant concordance of the expression levels in plasma and the corresponding tumor tissues (all r>0.850, all P<0.05). A logistic regression model with the best prediction was defined on the basis of the four genes (miRNA-21, -126, -210, and 486-5p), yielding 86.22% sensitivity and 96.55% specificity in distinguishing NSCLC patients from the healthy controls. Furthermore, the panel of miRNAs produced 73.33% sensitivity and 96.55% specificity in identifying stage I NSCLC patients. In addition, the genes have higher sensitivity (91.67%) in diagnosis of lung adenocarcinomas compared with squamous cell carcinomas (82.35%) (P<0.05). Altered expressions of the miRNAs in plasma would provide potential blood-based biomarkers for NSCLC.

Aldehyde dehydrogenase 1 A1-positive cell population is enriched in tumor-initiating cells and associated with progression of bladder cancer.

Aldehyde dehydrogenase 1 A1 (ALDH1A1) has recently been suggested as a marker for cancer stem or stem-like cancer cells of some human malignancies. The purpose of this study was to investigate the stem cell-related function and clinical significance of the ALDH1A1 in bladder urothelial cell carcinoma. Aldefluor assay was used to isolate ALDH1A1+ cells from bladder cancer cells. Stem cell characteristics of the ALDH1A1+ cells were then investigated by in vitro and in vivo approaches. Immunohistochemistry was done for evaluating ALDH1A1 expression on 22 normal bladder tissues and 216 bladder tumor specimens of different stage and grade. The ALDH1A1+ cancer cells displayed higher in vitro tumorigenicity compared with isogenic ALDH1A1- cells. The ALDH1A1+ cancer cells could generate xenograft tumors that resembled the histopathologic characteristics and heterogeneity of the parental cells. High ALDH1A1 expression was found in 26% (56 of 216) of human bladder tumor specimens and significantly related to advanced pathologic stage, high histologic grade, recurrence and progression, and metastasis of bladder urothelial cell carcinomas (all P < 0.05). Furthermore, ALDH1A1 expression was inversely associated with cancer-specific and overall survivals of the patients (P = 0.027 and 0.030, respectively). Therefore, ALDH1A1+ cell population could be enriched in tumor-initiating cells. ALDH1A1 may serve as a useful marker for monitoring the progression of bladder tumor and identifying bladder cancer patients with poor prognosis who might benefit from adjuvant and effective treatments.

Detecting genomic aberrations by fluorescence in situ hybridization with quantum dots-labeled probes.

Detection of genomic alterations of cancer genes by fluorescent in situ hybridization (FISH) will provide important information for cancer diagnosis and therapy. To effectively and reliably detect the genomic changes, we prepared novel FISH probes by directly conjugating genomic DNA of genes to semiconductor quantum dot fluorophores (QDs). The generated QD-genomic probes are substantially more photostable than the probes labeled with organic dye and show high intensity in both metaphase and interphase cell. The directly labeling probes allow detection of genomic targets in a fast and simple FISH procedure with high sensitivity and specificity. Furthermore, application of the QD-genomic probes in lung cancer specimens can reliably visualize gene amplification in cancer cells. These results suggest that the QD-FISH probes may offer an effective approach to analyze cancer-related genomic aberrations in basic research and clinical applications.

Using grid techniques for drug target identification.

The completion of the sequencing of the human genome has opened an unprecedented opportunity in the discovery of novel drug targets for disease therapy. However, one of the major challenges facing the drug discovery community is the expanding of data and the need of large-scale computational power in a collaborative environment. Grid techniques can present an architectural framework that aims to provide access to heterogeneous resources in a secure, reliable and scalable manner across various administrative boundaries for drug discovery, which has been a promising strategy for solving large-scale problems in modern pharmaceutical R&D. In this review, we discuss the current applications of Grid technology in drug target protein identification process; and an overview of drug target discovery system architecture, focusing in particular on the data manager service system architecture is also proposed.