Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.

PURPOSE: There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize that this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared with the commonly used research cohorts that are meticulously collected. MATERIALS AND METHODS: Genetic counselors (GCs) collect family history when patients (ie, probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the TP53 gene. Our clinical counseling-based (CCB) cohort consists of 3,297 individuals across 124 families (522 cases of single primary cancer and 125 cases of multiple primary cancers). We applied our software suite LFSPRO to make risk predictions and assessed performance in discrimination using AUC and in calibration using observed/expected (O/E) ratio. RESULTS: For prediction of deleterious TP53 mutations, we achieved an AUC of 0.78 (95% CI, 0.71 to 0.85) and an O/E ratio of 1.66 (95% CI, 1.53 to 1.80). Using the LFSPRO.MPC model to predict the onset of the second cancer, we obtained an AUC of 0.70 (95% CI, 0.58 to 0.82). Using the LFSPRO.CS model to predict the onset of different cancer types as the first primary, we achieved AUCs between 0.70 and 0.83 for sarcoma, breast cancer, or other cancers combined. CONCLUSION: We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests that better risk counseling may be achieved by GCs using these already-developed mathematical models.

LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious Germline TP53 Mutations.

PURPOSE: LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the TP53 gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components and further visualize the risk profiles of their patients to aid the decision-making process. METHODS: LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing risk model that predicts cancer-specific risks for the first primary and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. On receiving the family history as input, LFSPROShiny renders the family into a pedigree and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. RESULTS: We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. CONCLUSION: Since December 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at the MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.

Validating risk prediction models for multiple primaries and competing cancer outcomes in families with Li-Fraumeni syndrome using clinically ascertained data at a single institute.

Purpose: There exists a barrier between developing and disseminating risk prediction models in clinical settings. We hypothesize this barrier may be lifted by demonstrating the utility of these models using incomplete data that are collected in real clinical sessions, as compared to the commonly used research cohorts that are meticulously collected. Patients and methods: Genetic counselors (GCs) collect family history when patients (i.e., probands) come to MD Anderson Cancer Center for risk assessment of Li-Fraumeni syndrome, a genetic disorder characterized by deleterious germline mutations in the TP53 gene. Our clinical counseling-based (CCB) cohort consists of 3,297 individuals across 124 families (522 cases of single primary cancer and 125 cases of multiple primary cancers). We applied our software suite LFSPRO to make risk predictions and assessed performance in discrimination using area under the curve (AUC), and in calibration using observed/expected (O/E) ratio. Results: For prediction of deleterious TP53 mutations, we achieved an AUC of 0.81 (95% CI, 0.70 - 0.91) and an O/E ratio of 0.96 (95% CI, 0.70 - 1.21). Using the LFSPRO.MPC model to predict the onset of the second cancer, we obtained an AUC of 0.70 (95% CI, 0.58 - 0.82). Using the LFSPRO.CS model to predict the onset of different cancer types as the first primary, we achieved AUCs between 0.70 and 0.83 for sarcoma, breast cancer, or other cancers combined. Conclusion: We describe a study that fills in the critical gap in knowledge for the utility of risk prediction models. Using a CCB cohort, our previously validated models have demonstrated good performance and outperformed the standard clinical criteria. Our study suggests better risk counseling may be achieved by GCs using these already-developed mathematical models.

LFSPROShiny: an interactive R/Shiny app for prediction and visualization of cancer risks in families with deleterious germline TP53 mutations.

Purpose: LFSPRO is an R library that implements risk prediction models for Li-Fraumeni syndrome (LFS), a genetic disorder characterized by deleterious germline mutations in the TP53 gene. To facilitate the use of these models in clinics, we developed LFSPROShiny, an interactive R/Shiny interface of LFSPRO that allows genetic counselors (GCs) to perform risk predictions without any programming components, and further visualize the risk profiles of their patients to aid the decision-making process. Methods: LFSPROShiny implements two models that have been validated on multiple LFS patient cohorts: a competing-risk model that predicts cancer-specific risks for the first primary, and a recurrent-event model that predicts the risk of a second primary tumor. Starting with a visualization template, we keep regular contact with GCs, who ran LFSPROShiny in their counseling sessions, to collect feedback and discuss potential improvement. Upon receiving the family history as input, LFSPROShiny renders the family into a pedigree, and displays the risk estimates of the family members in a tabular format. The software offers interactive overlaid side-by-side bar charts for visualization of the patients' cancer risks relative to the general population. Results: We walk through a detailed example to illustrate how GCs can run LFSPROShiny in clinics, from data preparation to downstream analyses and interpretation of results with an emphasis on the utilities that LFSPROShiny provides to aid decision making. Conclusion: Since Dec 2021, we have applied LFSPROShiny to over 100 families from counseling sessions at MD Anderson Cancer Center. Our study suggests that software tools with easy-to-use interfaces are crucial for the dissemination of risk prediction models in clinical settings, hence serving as a guideline for future development of similar models.

An Introductory Tutorial on Cardiovascular Pharmacogenetics for Healthcare Providers.

Pharmacogenetics can improve clinical outcomes by reducing adverse drug effects and enhancing therapeutic efficacy for commonly used drugs that treat a wide range of cardiovascular diseases. One of the major barriers to the clinical implementation of cardiovascular pharmacogenetics is limited education on this field for current healthcare providers and students. The abundance of pharmacogenetic literature underscores its promise, but it can also be challenging to learn such a wealth of information. Moreover, current clinical recommendations for cardiovascular pharmacogenetics can be confusing because they are outdated, incomplete, or inconsistent. A myriad of misconceptions about the promise and feasibility of cardiovascular pharmacogenetics among healthcare providers also has halted clinical implementation. Therefore, the main goal of this tutorial is to provide introductory education on the use of cardiovascular pharmacogenetics in clinical practice. The target audience is any healthcare provider (or student) with patients that use or have indications for cardiovascular drugs. This tutorial is organized into the following 6 steps: (1) understand basic concepts in pharmacogenetics; (2) gain foundational knowledge of cardiovascular pharmacogenetics; (3) learn the different organizations that release cardiovascular pharmacogenetic guidelines and recommendations; (4) know the current cardiovascular drugs/drug classes to focus on clinically and the supporting evidence; (5) discuss an example patient case of cardiovascular pharmacogenetics; and (6) develop an appreciation for emerging areas in cardiovascular pharmacogenetics. Ultimately, improved education among healthcare providers on cardiovascular pharmacogenetics will lead to a greater understanding for its potential in improving outcomes for a leading cause of morbidity and mortality.

Adverse Cardiovascular Events Associated With Cyclin-Dependent Kinase 4/6 Inhibitors in Patients With Metastatic Breast Cancer.

Background Cyclin-dependent kinase (CDK) 4 and 6 inhibitors have significantly improved survival in patients with hormone receptor-positive metastatic breast cancer. There are few data regarding the epidemiology of cardiovascular adverse events (CVAEs) with these therapies. Methods and Results Using the OneFlorida Data Trust, adult patients without prior cardiovascular disease who received at least 1 CDK4/6 inhibitor were included in the analysis. CVAEs identified from International Classification of Diseases, Ninth and Tenth Revisions (ICD-9/10) codes included hypertension, atrial fibrillation(AF)/atrial flutter (AFL), heart failure/cardiomyopathy, ischemic heart disease, and pericardial disease. Competing risk analysis (Fine-Gray model) was used to determine the association between CDK4/6 inhibitor therapy and incident CVAEs. The effect of CVAEs on all-cause death was studied using Cox proportional hazard models. Propensity-weight analyses were performed to compare these patients to a cohort of patients treated with anthracyclines. A total of 1376 patients treated with CDK4/6 inhibitors were included in the analysis. CVAEs occurred in 24% (35.9 per 100 person-years). CVAEs were slightly higher in patients who received CKD4/6 inhibitors compared with anthracyclines (P=0.063), with higher death rate associated with the development of AF/AFL or cardiomyopathy/heart failure in the CDK4/6 group. The development of cardiomyopathy/heart failure and AF/AFL was associated with increased all-cause death (adjusted hazard ratio [HR], 4.89 [95% CI, 2.98-8.05]; and 5.88 [95% CI, 3.56-9.73], respectively). Conclusions CVAEs may be more common with CDK4/6 inhibitors than previously recognized, with increased death rates in these patients who develop AF/AFL or heart failure. Further research is needed to definitively determine cardiovascular risk associated with these novel anticancer treatments.

Personalized Risk Prediction for Cancer Survivors: A Bayesian Semi-parametric Recurrent Event Model with Competing Outcomes.

Multiple primary cancers are increasingly more frequent due to improved survival of cancer patients. Characteristics of the first primary cancer largely impact the risk of developing subsequent primary cancers. Hence, model-based risk characterization of cancer survivors that captures patient-specific variables is needed for healthcare policy making. We propose a Bayesian semi-parametric framework, where the occurrence processes of the competing cancer types follow independent non-homogeneous Poisson processes and adjust for covariates including the type and age at diagnosis of the first primary. Applying this framework to a historically collected cohort with families presenting a highly enriched history of multiple primary tumors and diverse cancer types, we have derived a suite of age-to-onset penetrance curves for cancer survivors. This includes penetrance estimates for second primary lung cancer, potentially impactful to ongoing cancer screening decisions. Using Receiver Operating Characteristic (ROC) curves, we have validated the good predictive performance of our models in predicting second primary lung cancer, sarcoma, breast cancer, and all other cancers combined, with areas under the curves (AUCs) at 0.89, 0.91, 0.76 and 0.68, respectively. In conclusion, our framework provides covariate-adjusted quantitative risk assessment for cancer survivors, hence moving a step closer to personalized health management for this unique population.

Genome-wide CRISPR/Cas9 screen identifies etoposide response modulators associated with clinical outcomes in pediatric AML.

Etoposide is used to treat a wide range of malignant cancers, including acute myeloid leukemia (AML) in children. Despite the use of intensive chemotherapeutic regimens containing etoposide, a significant proportion of pediatric patients with AML become resistant to treatment and relapse, leading to poor survival. This poses a pressing clinical challenge to identify mechanisms underlying drug resistance to enable effective pharmacologic targeting. We performed a genome-wide CRISPR/Cas9 synthetic-lethal screening to identify functional modulators of etoposide response in leukemic cell line and integrated results from CRISPR-screen with gene expression and clinical outcomes in pediatric patients with AML treated with etoposide-containing regimen. Our results confirmed the involvement of well-characterized genes, including TOP2A and ABCC1, as well as identified novel genes such as RAD54L2, PRKDC, and ZNF451 that have potential to be novel drug targets. This study demonstrates the ability for leveraging CRISPR/Cas9 screening in conjunction with clinically relevant endpoints to make meaningful discoveries for the identification of prognostic biomarkers and novel therapeutic targets to overcome treatment resistance.

Genetic Contributors of Efficacy and Adverse Metabolic Effects of Chlorthalidone in African Americans from the Genetics of Hypertension Associated Treatments (GenHAT) Study.

Hypertension is a leading risk factor for cardiovascular disease mortality. African Americans (AAs) have the highest prevalence of hypertension in the United States, and to alleviate the burden of hypertension in this population, better control of blood pressure (BP) is needed. Previous studies have shown considerable interpersonal differences in BP response to antihypertensive treatment, suggesting a genetic component. Utilizing data from 4297 AA participants randomized to chlorthalidone from the Genetics of Hypertension Associated Treatments (GenHAT) study, we aimed to identify variants associated with the efficacy of chlorthalidone. An additional aim was to find variants that contributed to changes in fasting glucose (FG) in these individuals. We performed genome-wide association analyses on the change of systolic and diastolic BP (SBP and DBP) over six months and FG levels over 24 months of treatment. We sought replication in the International Consortia of Pharmacogenomics Studies. We identified eight variants statistically associated with BP response and nine variants associated with FG response. One suggestive LINC02211-CDH9 intergenic variant was marginally replicated with the same direction of effect. Given the impact of hypertension in AAs, this study implies that understanding the genetic background for BP control and glucose changes during chlorthalidone treatment may help prevent adverse cardiovascular events in this population.

A ten-gene DNA-damage response pathway gene expression signature predicts gemtuzumab ozogamicin response in pediatric AML patients treated on COGAAML0531 and AAML03P1 trials.

Gemtuzumab ozogamicin (GO) is an anti-CD33 monoclonal antibody linked to calicheamicin, a DNA damaging agent, and is a well-established therapeutic for treating acute myeloid leukemia (AML). In this study, we used LASSO regression modeling to develop a 10-gene DNA damage response gene expression score (CalDDR-GEx10) predictive of clinical outcome in pediatric AML patients treated with treatment regimen containing GO from the AAML03P1 and AAML0531 trials (ADE + GO arm, N = 301). When treated with ADE + GO, patients with a high CalDDR-GEx10 score had lower complete remission rates (62.8% vs. 85.5%, P = 1.7 7 * 10-5) and worse event-free survival (28.7% vs. 56.5% P = 4.08 * 10-8) compared to those with a low CalDDR-GEx10 score. However, the CalDDR-GEx10 score was not associated with clinical outcome in patients treated with standard chemotherapy alone (ADE, N = 242), implying the specificity of the CalDDR-GEx10 score to calicheamicin-induced DNA damage response. In multivariable models adjusted for risk group, FLT3-status, white blood cell count, and age, the CalDDR-GEx10 score remained a significant predictor of outcome in patients treated with ADE + GO. Our findings present a potential tool that can specifically assess response to calicheamicin-induced DNA damage preemptively via assessing diagnostic leukemic cell gene expression and guide clinical decisions related to treatment using GO.

Evolution of physico-chemical properties of Dicranopteris linearis-derived activated carbon under various physical activation atmospheres.

This work emphasizes the effect of the physical activation using CO2 and steam agents on the physicochemical properties of activated carbon produced from Dicranopteris linearis (D. linearis), a fern species widely distributed across tropic and subtropic ecoregions. The D. linearis-derived chars produced under pyrolysis at 400 °C for 1 h were activated in various CO2-steam proportions. As revealed by the IR and Raman spectra, the structure of the activated chars was heavily dependent on the relative proportion of CO2 and steam. The total specific surface area (SSA) of the activated chars proportionally increased with the increase in steam proportion and was comparable to the values of commercial activated char products. Specifically, the activation under CO2- and steam-saturated conditions has correspondingly resulted in SSA increasing from 89 to 653 m2g-1 and from 89 to 1015 m2g-1. Steam also enhanced the development of mesoporous structures of the D. linearis-derived char products, thereby extending their potential applications, particularly for industries that require high rigidity in the product such as pharmaceutical and cosmetic sectors.

Global Proteomic Profiling of Pediatric AML: A Pilot Study.

Acute Myeloid Leukemia (AML) is a heterogeneous disease with several recurrent cytogenetic abnormalities. Despite genomics and transcriptomics profiling efforts to understand AML's heterogeneity, studies focused on the proteomic profiles associated with pediatric AML cytogenetic features remain limited. Furthermore, the majority of biological functions within cells are operated by proteins (i.e., enzymes) and most drugs target the proteome rather than the genome or transcriptome, thus, highlighting the significance of studying proteomics. Here, we present our results from a pilot study investigating global proteomic profiles of leukemic cells obtained at diagnosis from 16 pediatric AML patients using a robust TMT-LC/LC-MS/MS platform. The proteome profiles were compared among patients with or without core binding factor (CBF) translocation indicated by a t(8;21) or inv(16) cytogenetic abnormality, minimal residual disease status at the end of the first cycle of chemotherapy (MRD1), and in vitro chemosensitivity of leukemic cells to cytarabine (Ara-C LC50). Our results established proteomic differences between CBF and non-CBF AML subtypes, providing insights to AML subtypes physiology, and identified potential druggable proteome targets such as THY1 (CD90), NEBL, CTSF, COL2A1, CAT, MGLL (MAGL), MACROH2A2, CLIP2 (isoform 1 and 2), ANPEP (CD13), MMP14, and AK5.

Carbonization and H3PO4 activation of fern Dicranopteris linearis and electrochemical properties for electric double layer capacitor electrode.

Today, the world's climate change is a growing problem, plant carbon sequestration is one of the effective ways to mitigate climate change by reducing greenhouse gases, mostly carbon gases. Dicranopteris linearis (D. linearis), a common fern species in the tropic or subtropic ecoregions, has been recently recognized as a potential feedstock to produce highly porous biochar. This study aims to enhance the specific surface area (SSA) and pore volumes of biochars derived from the D. linearis by H3PO4 activation and examine electrical properties of the activated biochars and their possible usage for the electric double-layer capacitor (EDLC) electrode. The treated raw fern was activated with H3PO4 85% by the three different mixing ratios 1:0, 1:1, and 1:3 (w/w) and then pyrolysis under N2 flow maintained at 500 °C for 1 h. The performance as the electrode for an EDLC was evaluated in 1 mol L-1 H2SO4 solution for the H3PO4-activated samples. The SSA and pore volumes were drastically increased after activation. The maximum SSA and pore volume were 1212 m2 g-1 and 1.43 cm3 g-1, respectively for the biochar activated at 400 °C with a weight mixing ratio 1:3 (w/w) between the fern and H3PO4 acid while these values of the biochar at 400 °C were 12 m2 g-1 and 0.02 cm3 g-1, respectively. The biochar activated at 600 °C with the mixing ratio 1:1 (w/w) showed the maximum capacitance value, ca. 108 F g-1 at 1 mV s-1. The activation using H3PO4 showed a positive tendency to enhance electrochemical properties and it could be a premise toward a higher performance of EDLC from the D. linearis derived activated biochar.

Colloidal interactions of micro-sized biochar and a kaolinitic soil clay.

Fine-sized biochars and clay minerals co-present in various circumstances, e.g., agricultural land and water treatment. Because both of these materials are scavengers for nutrients, agrochemicals and other toxicants, their dispersibility and transportability have received much attention. However, little is documented about their colloidal interactions and to what extent biochar particles can stimulate the dispersion of clay minerals. Here, the effect of engineered micro-sized biochar amendment on the surface charge (SC) and colloidal dynamics of the clay fraction of a kaolinite-rich soil was determined. The engineered biochars showed distinctive SC and colloidal properties depending on their pyrolysis conditions (e.g., oxygen level and temperature) and solution chemistry (i.e., pH and cation type). Two types of biochars prepared under non-biochar-oriented pyrolysis (open heating, 'O-biochar') and biochar-oriented pyrolysis (N2-supported heating, 'N2-biochar') showed contrasting effects on the colloidal dynamics of clay. The O-biochars provoked aggregation due to their higher content of soluble salts, which increased ionic strength and provided multivalent cations, inducing bridging between negatively charged colloids. In contrast, the N2 biochars low in soluble salts and rich in negatively charged burned organic matter compounds favoured the dispersion of clay. The adjustment of biochar production methods can therefore be highlighted as the way to customize biochar for specific uses or to reduce the risk of clay loss from soils in the short term. In the long term, when soluble salts are removed by leaching, it is likely that dispersion is facilitated and the risk for erosion increases.

Benchmarking Neural Networks For Quantum Computations.

The power of quantum computers is still somewhat speculative. Although they are certainly faster than classical ones at some tasks, the class of problems they can efficiently solve has not been mapped definitively onto known classical complexity theory. This means that we do not know for which calculations there will be a "quantum advantage," once an algorithm is found. One way to answer the question is to find those algorithms, but finding truly quantum algorithms turns out to be very difficult. In previous work, over the past three decades, we have pursued the idea of using techniques of machine learning to develop algorithms for quantum computing. Here, we compare the performance of standard real- and complex-valued classical neural networks with that of one of our models for a quantum neural network, on both classical problems and on an archetypal quantum problem: the computation of an entanglement witness. The quantum network is shown to need far fewer epochs and a much smaller network to achieve comparable or better results.

An enzyme-based electrochemical biosensor probe with sensitivity to detect astrocytic versus glioma uptake of glutamate in real time in vitro.

Glutamate, a major excitatory neurotransmitter in the central nervous system, is essential for regulation of thought, movement, memory, and other higher functions controlled by the brain. Dysregulation of glutamate signaling is associated with severe neuropathological conditions, such as epilepsy, and glioma, a form of brain cancer. Glutamate signals are currently detected by several types of neurochemical probes ranging from microdialysis-based to enzyme-based carbon fiber microsensors. However, an important technology gap exists in the ability to measure glutamate dynamics continuously, and in real time, and from multiple locations in the brain, which limits our ability to further understand the involved spatiotemporal mechanisms of underlying neuropathologies. To overcome this limitation, we developed an enzymatic glutamate microbiosensor, in the form of a ceramic-substrate enabled platinum microelectrode array, that continuously, in real time, measures changes in glutamate concentration from multiple recording sites. In addition, the developed microbiosensor is almost four-fold more sensitive to glutamate than enzymatic sensors previously reported in the literature. Further analysis of glutamate dynamics recorded by our microbiosensor in cultured astrocytes (control condition) and glioma cells (pathological condition) clearly distinguished normal versus impaired glutamate uptake, respectively. These results confirm that the developed glutamate microbiosensor array can become a useful tool in monitoring and understanding glutamate signaling and its regulation in normal and pathological conditions. Furthermore, the developed microbiosensor can be used to measure the effects of potential therapeutic drugs to treat a range of neurological diseases.

The Influence of Assay Design, Blinding, and Gymnema sylvestre on Sucrose Detection by Humans.

The detection and grading of tastes corresponding to different taste modalities can be tested in engaging laboratory sessions using students themselves as test subjects. This article describes a series of experiments in which data pertaining to the detection of salty and sweet tastes are obtained, and the ability of the herb Gymnema sylvestre to disrupt the detection of sucrose is quantified. The effects of blinding and different assay designs on EC50 estimation are also investigated. The data obtained allow for substantial data analysis, including non-linear regression using fixed and free parameters to quantify dose-response relationships, and the use of often under-utilized permutation tests to determine significant differences when the underlying data display heteroscedasticity.

Silencing a key gene of the common symbiosis pathway in Nicotiana attenuata specifically impairs arbuscular mycorrhizal infection without influencing the root-associated microbiome or plant growth.

While the biochemical function of calcium and calmodulin-dependent protein kinase (CCaMK) is well studied, and plants impaired in the expression of CCaMK are known not to be infected by arbuscular mycorrhizal fungi (AMF) in glasshouse studies, the whole-plant and ecological consequences of CCaMK silencing are not well understood. Here we show that three independently transformed lines of Nicotiana attenuata plants silenced in CCaMK (irCCaMK) are neither infected by Rhizophagus irregularis in the glasshouse nor by native fungal inoculum in the field. The overall fungal community of field-grown roots did not differ significantly among empty vector (EV) and the transgenic lines, and the bacterial communities only showed minor differences, as revealed by the alpha-diversity parameters of bacterial OTUs, which were higher in EV plants compared with two of the three transformed lines, while beta-diversity parameters did not differ. Furthermore, growth and fitness parameters were similar in the glasshouse and field. Herbivory-inducible and basal levels of salicylic acid, jasmonic acid and abscisic acid did not differ among the genotypes, suggesting that activation of the classical defence pathways are not affected by CCaMK silencing. Based on these results, we conclude that silencing of CCaMK has few, if any, non-target effects.

Single-incision laparoscopic appendectomy using conventional instruments: an initial experience using a novel technique.

BACKGROUND: Laparoscopic appendectomy has gained wide acceptance. This study aimed to evaluate the feasibility, safety, and cosmetic results of a novel technique: single incision laparoscopic (SIL) appendectomy. METHODS: The study enrolled consecutive patients undergoing appendectomy for acute appendicitis. Appendectomy was performed using three trocars and conventional laparoscopic instruments through a single small umbilical incision (length, ~3 cm). The patients received standard pre- and postoperative care and presented for a follow-up visit after a mean of 27 days. RESULTS: The study cohort consisted of 26 patients (10 women and 16 men) with an average age of 44 years (range, 13-83 years). Of 26 appendectomies, 22 (85%) were achieved through a single surgical site. The mean SIL appendectomy operative time was 58 min (range, 33-107 min). No operative complications occurred. The average postoperative hospital stay was 1.2 days for nonperforated appendicitis and 2.7 days for perforated appendicitis. At the follow-up visit, no patient showed any evidence of incisional hernia. The operative incisions were minimally visible, and all the individuals reported a highly favorable cosmetic outcome. CONCLUSIONS: The results of the study demonstrated that laparoscopic appendectomy can be achieved through a single umbilical incision using conventional instruments and that this approach is successful, safe, and aesthetic.