Mortality Surveillance for the COVID-19 Pandemic: Review of the Centers for Disease Control and Prevention's Multiple System Strategy.

Mortality surveillance systems can have limitations, including reporting delays, incomplete reporting, missing data, and insufficient detail on important risk or sociodemographic factors that can impact the accuracy of estimates of current trends, disease severity, and related disparities across subpopulations. The Centers for Disease Control and Prevention used multiple data systems during the COVID-19 emergency response-line-level case‒death surveillance, aggregate death surveillance, and the National Vital Statistics System-to collectively provide more comprehensive and timely information on COVID-19‒associated mortality necessary for informed decisions. This article will review in detail the line-level, aggregate, and National Vital Statistics System surveillance systems and the purpose and use of each. This retrospective review of the hybrid surveillance systems strategy may serve as an example for adaptive informational approaches needed over the course of future public health emergencies. (Am J Public Health. Published online ahead of print July 25, 2024:e1-e10. https://doi.org/10.2105/AJPH.2024.307743).

SARS-CoV-2 Infection and Death Rates Among Maintenance Dialysis Patients During Delta and Early Omicron Waves - United States, June 30, 2021-September 27, 2022.

Persons receiving maintenance dialysis are at increased risk for SARS-CoV-2 infection and its severe outcomes, including death. However, rates of SARS-CoV-2 infection and COVID-19-related deaths in this population are not well described. Since November 2020, CDC's National Healthcare Safety Network (NHSN) has collected weekly data monitoring incidence of SARS-CoV-2 infections (defined as a positive SARS-CoV-2 test result) and COVID-19-related deaths (defined as the death of a patient who had not fully recovered from a SARS-CoV-2 infection) among maintenance dialysis patients. This analysis used NHSN dialysis facility COVID-19 data reported during June 30, 2021-September 27, 2022, to describe rates of SARS-CoV-2 infection and COVID-19-related death among maintenance dialysis patients. The overall infection rate was 30.47 per 10,000 patient-weeks (39.64 among unvaccinated patients and 27.24 among patients who had completed a primary COVID-19 vaccination series). The overall death rate was 1.74 per 10,000 patient-weeks. Implementing recommended infection control measures in dialysis facilities and ensuring patients and staff members are up to date with recommended COVID-19 vaccination is critical to limiting COVID-19-associated morbidity and mortality.

COVID-19 Surveillance After Expiration of the Public Health Emergency Declaration - United States, May 11, 2023.

On January 31, 2020, the U.S. Department of Health and Human Services (HHS) declared, under Section 319 of the Public Health Service Act, a U.S. public health emergency because of the emergence of a novel virus, SARS-CoV-2.* After 13 renewals, the public health emergency will expire on May 11, 2023. Authorizations to collect certain public health data will expire on that date as well. Monitoring the impact of COVID-19 and the effectiveness of prevention and control strategies remains a public health priority, and a number of surveillance indicators have been identified to facilitate ongoing monitoring. After expiration of the public health emergency, COVID-19-associated hospital admission levels will be the primary indicator of COVID-19 trends to help guide community and personal decisions related to risk and prevention behaviors; the percentage of COVID-19-associated deaths among all reported deaths, based on provisional death certificate data, will be the primary indicator used to monitor COVID-19 mortality. Emergency department (ED) visits with a COVID-19 diagnosis and the percentage of positive SARS-CoV-2 test results, derived from an established sentinel network, will help detect early changes in trends. National genomic surveillance will continue to be used to estimate SARS-CoV-2 variant proportions; wastewater surveillance and traveler-based genomic surveillance will also continue to be used to monitor SARS-CoV-2 variants. Disease severity and hospitalization-related outcomes are monitored via sentinel surveillance and large health care databases. Monitoring of COVID-19 vaccination coverage, vaccine effectiveness (VE), and vaccine safety will also continue. Integrated strategies for surveillance of COVID-19 and other respiratory viruses can further guide prevention efforts. COVID-19-associated hospitalizations and deaths are largely preventable through receipt of updated vaccines and timely administration of therapeutics (1-4).

RT-CLAD: Artificial Intelligence-Based Real-Time Chironomid Larva Detection in Drinking Water Treatment Plants.

The presence of chironomid larvae in tap water has sparked public concern regarding the water supply system in South Korea. Despite ongoing efforts to establish a safe water supply system, entirely preventing larval occurrences remains a significant challenge. Therefore, we developed a real-time chironomid larva detection system (RT-CLAD) based on deep learning technology, which was implemented in drinking water treatment plants. The acquisition of larval images was facilitated by a multi-spectral camera with a wide spectral range, enabling the capture of unique wavelet bands associated with larvae. Three state-of-the-art deep learning algorithms, namely the convolutional neural network (CNN), you only look once (YOLO), and residual neural network (ResNet), renowned for their exceptional performance in object detection tasks, were employed. Following a comparative analysis of these algorithms, the most accurate and rapid model was selected for RT-CLAD. To achieve the efficient and accurate detection of larvae, the original images were transformed into a specific wavelet format, followed by preprocessing to minimize data size. Consequently, the CNN, YOLO, and ResNet algorithms successfully detected larvae with 100% accuracy. In comparison to YOLO and ResNet, the CNN algorithm demonstrated greater efficiency because of its faster processing and simpler architecture. We anticipate that our RT-CLAD will address larva detection challenges in water treatment plants, thereby enhancing water supply security.

Impact of pitavastatin on new-onset diabetes mellitus compared to atorvastatin and rosuvastatin: a distributed network analysis of 10 real-world databases.

BACKGROUND: Statin treatment increases the risk of new-onset diabetes mellitus (NODM); however, data directly comparing the risk of NODM among individual statins is limited. We compared the risk of NODM between patients using pitavastatin and atorvastatin or rosuvastatin using reliable, large-scale data. METHODS: Data of electronic health records from ten hospitals converted to the Observational Medical Outcomes Partnership Common Data Model (n = 14,605,368 patients) were used to identify new users of pitavastatin, atorvastatin, or rosuvastatin (atorvastatin + rosuvastatin) for ≥ 180 days without a previous history of diabetes or HbA1c level ≥ 5.7%. We conducted a cohort study using Cox regression analysis to examine the hazard ratio (HR) of NODM after propensity score matching (PSM) and then performed an aggregate meta-analysis of the HR. RESULTS: After 1:2 PSM, 10,238 new pitavastatin users (15,998 person-years of follow-up) and 18,605 atorvastatin + rosuvastatin users (33,477 person-years of follow-up) were pooled from 10 databases. The meta-analysis of the HRs demonstrated that pitavastatin resulted in a significantly reduced risk of NODM than atorvastatin + rosuvastatin (HR 0.72; 95% CI 0.59-0.87). In sub-analysis, pitavastatin was associated with a lower risk of NODM than atorvastatin or rosuvastatin after 1:1 PSM (HR 0.69; CI 0.54-0.88 and HR 0.74; CI 0.55-0.99, respectively). A consistently low risk of NODM in pitavastatin users was observed when compared with low-to-moderate-intensity atorvastatin + rosuvastatin users (HR 0.78; CI 0.62-0.98). CONCLUSIONS: In this retrospective, multicenter active-comparator, new-user, cohort study, pitavastatin reduced the risk of NODM compared with atorvastatin or rosuvastatin.

Gene expression profiles of diabetic kidney disease and neuropathy in eNOS knockout mice: Predictors of pathology and RAS blockade effects.

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are two common diabetic complications. However, their pathogenesis remains elusive and current therapies are only modestly effective. We evaluated genome-wide expression to identify pathways involved in DKD and DPN progression in db/db eNOS-/- mice receiving renin-angiotensin-aldosterone system (RAS)-blocking drugs to mimic the current standard of care for DKD patients. Diabetes and eNOS deletion worsened DKD, which improved with RAS treatment. Diabetes also induced DPN, which was not affected by eNOS deletion or RAS blockade. Given the multiple factors affecting DKD and the graded differences in disease severity across mouse groups, an automatic data analysis method, SOM, or self-organizing map was used to elucidate glomerular transcriptional changes associated with DKD, whereas pairwise bioinformatic analysis was used for DPN. These analyses revealed that enhanced gene expression in several pro-inflammatory networks and reduced expression of development genes correlated with worsening DKD. Although RAS treatment ameliorated the nephropathy phenotype, it did not alter the more abnormal gene expression changes in kidney. Moreover, RAS exacerbated expression of genes related to inflammation and oxidant generation in peripheral nerves. The graded increase in inflammatory gene expression and decrease in development gene expression with DKD progression underline the potentially important role of these pathways in DKD pathogenesis. Since RAS blockers worsened this gene expression pattern in both DKD and DPN, it may partly explain the inadequate therapeutic efficacy of such blockers.

Decreases in COVID-19 Cases, Emergency Department Visits, Hospital Admissions, and Deaths Among Older Adults Following the Introduction of COVID-19 Vaccine - United States, September 6, 2020-May 1, 2021.

Throughout the COVID-19 pandemic, older U.S. adults have been at increased risk for severe COVID-19-associated illness and death (1). On December 14, 2020, the United States began a nationwide vaccination campaign after the Food and Drug Administration's Emergency Use Authorization of Pfizer-BioNTech COVID-19 vaccine. The Advisory Committee on Immunization Practices (ACIP) recommended prioritizing health care personnel and residents of long-term care facilities, followed by essential workers and persons at risk for severe illness, including adults aged ≥65 years, in the early phases of the vaccination program (2). By May 1, 2021, 82%, 63%, and 42% of persons aged ≥65, 50-64, and 18-49 years, respectively, had received ≥1 COVID-19 vaccine dose. CDC calculated the rates of COVID-19 cases, emergency department (ED) visits, hospital admissions, and deaths by age group during November 29-December 12, 2020 (prevaccine) and April 18-May 1, 2021. The rate ratios comparing the oldest age groups (≥70 years for hospital admissions; ≥65 years for other measures) with adults aged 18-49 years were 40%, 59%, 65%, and 66% lower, respectively, in the latter period. These differential declines are likely due, in part, to higher COVID-19 vaccination coverage among older adults, highlighting the potential benefits of rapidly increasing vaccination coverage.

Semaphorin-3C Is Upregulated in Polycystic Kidney Epithelial Cells and Inhibits Angiogenesis of Glomerular Endothelial Cells.

BACKGROUND: Polycystic kidney disease (PKD) is a hereditary disease characterized by cyst formation in the kidneys bilaterally. It has been observed that semaphorin-3C (SEMA3C) is overexpressed in polycystic kidney epithelial cells. It is hypothesized that upregulated SEMA3C would contribute to survival of polycystic kidney epithelial cells. Furthermore, as the kidney is a highly vascularized organ, the secreted SEMA3C from PKD epithelial cells will affect glomerular endothelial cells (GECs) in a paracrine manner. METHODS: To evaluate the effect of SEMA3C on renal cells, siSEMA3C-treated PKD epithelial cells were used for further analysis, and GECs were exposed to recombinant SEMA3C (rSEMA3C). Also, co-culture and treatment of conditioned media were employed to confirm whether PKD epithelial cells could influence on GECs via SEMA3C secretion. RESULTS: SEMA3C knockdown reduced proliferation of PKD epithelial cells. In case of GECs, exposure to rSEMA3C decreased angiogenesis, which resulted from suppressed migratory ability not cell proliferation. CONCLUSIONS: This study indicates that SEMA3C is the aggravating factor in PKD. Thus, it is proposed that targeting SEMA3C can be effective to mitigate PKD.

RNA-Seq identifies condition-specific biological signatures of ischemia-reperfusion injury in the human kidney.

BACKGROUND: Acute kidney injury (AKI) is defined as a sudden event of kidney failure or kidney damage within a short period. Ischemia-reperfusion injury (IRI) is a critical factor associated with severe AKI and end-stage kidney disease (ESKD). However, the biological mechanisms underlying ischemia and reperfusion are incompletely understood, owing to the complexity of these pathophysiological processes. We aimed to investigate the key biological pathways individually affected by ischemia and reperfusion at the transcriptome level. RESULTS: We analyzed the steady-state gene expression pattern of human kidney tissues from normal (pre-ischemia), ischemia, and reperfusion conditions using RNA-sequencing. Conventional differential expression and self-organizing map (SOM) clustering analyses followed by pathway analysis were performed. Differential expression analysis revealed the metabolic pathways dysregulated in ischemia. Cellular assembly, development and migration, and immune response-related pathways were dysregulated in reperfusion. SOM clustering analysis highlighted the ischemia-mediated significant dysregulation in metabolism, apoptosis, and fibrosis-related pathways, while cell growth, migration, and immune response-related pathways were highly dysregulated by reperfusion after ischemia. The expression of pro-apoptotic genes and death receptors was downregulated during ischemia, indicating the existence of a protective mechanism against ischemic injury. Reperfusion induced alterations in the expression of the genes associated with immune response such as inflammasome and antigen representing genes. Further, the genes related to cell growth and migration, such as AKT, KRAS, and those related to Rho signaling, were downregulated, suggestive of injury responses during reperfusion. Semaphorin 4D and plexin B1 levels were also downregulated. CONCLUSIONS: We show that specific biological pathways were distinctively involved in ischemia and reperfusion during IRI, indicating that condition-specific therapeutic strategies may be imperative to prevent severe kidney damage after IRI in the clinical setting.

Comparative RNA-Seq transcriptome analyses reveal distinct metabolic pathways in diabetic nerve and kidney disease.

Treating insulin resistance with pioglitazone normalizes renal function and improves small nerve fibre function and architecture; however, it does not affect large myelinated nerve fibre function in mouse models of type 2 diabetes (T2DM), indicating that pioglitazone affects the body in a tissue-specific manner. To identify distinct molecular pathways regulating diabetic peripheral neuropathy (DPN) and nephropathy (DN), as well those affected by pioglitazone, we assessed DPN and DN gene transcript expression in control and diabetic mice with or without pioglitazone treatment. Differential expression analysis and self-organizing maps were then used in parallel to analyse transcriptome data. Differential expression analysis showed that gene expression promoting cell death and the inflammatory response was reversed in the kidney glomeruli but unchanged or exacerbated in sciatic nerve by pioglitazone. Self-organizing map analysis revealed that mitochondrial dysfunction was normalized in kidney and nerve by treatment; however, conserved pathways were opposite in their directionality of regulation. Collectively, our data suggest inflammation may drive large fibre dysfunction, while mitochondrial dysfunction may drive small fibre dysfunction in T2DM. Moreover, targeting both of these pathways is likely to improve DN. This study supports growing evidence that systemic metabolic changes in T2DM are associated with distinct tissue-specific metabolic reprogramming in kidney and nerve and that these changes play a critical role in DN and small fibre DPN pathogenesis. These data also highlight the potential dangers of a 'one size fits all' approach to T2DM therapeutics, as the same drug may simultaneously alleviate one complication while exacerbating another.

Multiple roles of the SO4(2-)/Cl-/OH- exchanger protein Slc26a2 in chondrocyte functions.

Mutations in the SO4(2-)/Cl(-)/OH(-) exchanger Slc26a2 cause the disease diastrophic dysplasia (DTD), resulting in aberrant bone development and, therefore, skeletal deformities. DTD is commonly attributed to a lack of chondrocyte SO4(2-) uptake and proteoglycan sulfation. However, the skeletal phenotype of patients with DTD is typified by reduction in cartilage and osteoporosis of the long bones. Chondrocytes of patients with DTD are irregular in size and have a reduced capacity for proliferation and terminal differentiation. This raises the possibility of additional roles for Slc26a2 in chondrocyte function. Here, we examined the roles of Slc26a2 in chondrocyte biology using two distinct systems: mouse progenitor mesenchymal cells differentiated to chondrocytes and freshly isolated mouse articular chondrocytes differentiated into hypertrophic chondrocytes. Slc26a2 expression was manipulated acutely by delivery of Slc26a2 or shSlc26a2 with lentiviral vectors. We demonstrate that slc26a2 is essential for chondrocyte proliferation and differentiation and for proteoglycan synthesis. Slc26a2 also regulates the terminal stage of chondrocyte cell size expansion. These findings reveal multiple roles for Slc26a2 in chondrocyte biology and emphasize the importance of Slc26a2-mediated protein sulfation in cell signaling, which may account for the complex phenotype of DTD.

Solute carrier family 26 member a2 (Slc26a2) protein functions as an electroneutral SOFormula/OH-/Cl- exchanger regulated by extracellular Cl-.

Slc26a2 is a ubiquitously expressed SO(4)(2-) transporter with high expression levels in cartilage and several epithelia. Mutations in SLC26A2 are associated with diastrophic dysplasia. The mechanism by which Slc26a2 transports SO(4)(2-) and the ion gradients that mediate SO(4)(2-) uptake are poorly understood. We report here that Slc26a2 functions as an SO(4)(2-)/2OH(-), SO(4)(2-)/2Cl(-), and SO(4)(2-)/OH(-)/Cl(-) exchanger, depending on the Cl(-) and OH(-) gradients. At inward Cl(-) and outward pH gradients (high Cl(-)(o) and low pH(o)) Slc26a2 functions primarily as an SO(4)(2-)(o)/2OH(-)(i) exchanger. At low Cl(-)(o) and high pH(o) Slc26a2 functions increasingly as an SO(4)(2-)(o)/2Cl(-)(i) exchanger. The reverse is observed for SO(4)(2-)(i)/2OH(-)(o) and SO(4)(2-)(i)/2Cl(-)(o) exchange. Slc26a2 also exchanges Cl(-) for I(-), Br(-), and NO(3)(-) and Cl(-)(o) competes with SO(4)(2-) on the transport site. Interestingly, Slc26a2 is regulated by an extracellular anion site, required to activate SO(4)(2-)(i)/2OH(-)(o) exchange. Slc26a2 can transport oxalate in exchange for OH(-) and/or Cl(-) with properties similar to SO(4)(2-) transport. Modeling of the Slc26a2 transmembrane domain (TMD) structure identified a conserved extracellular sequence (367)GFXXP(371) between TMD7 and TMD8 close to the conserved Glu(417) in the permeation pathway. Mutation of Glu(417) eliminated transport by Slc26a2, whereas mutation of Phe(368) increased the affinity for SO(4)(2-)(o) 8-fold while reducing the affinity for Cl(-)(o) 2 fold, but without affecting regulation by Cl(-)(o). These findings clarify the mechanism of net SO(4)(2-) transport and describe a novel regulation of Slc26a2 by an extracellular anion binding site and should help in further understanding aberrant SLC26A2 function in diastrophic dysplasia.

Nephrology consultation improves the clinical outcomes of patients with acute kidney injury.

Background: Acute kidney injury (AKI) is prevalent in critically ill patients and is associated with an increased risk of in-hospital mortality. Nephrology consultation may be protective, but this has rarely been evaluated in South Korea. Methods: This multicenter retrospective study was based on the electronic medical records (EMRs) of two third-affiliated hospitals. We extracted the records of patients admitted to intensive care units (ICUs) between 2011 and 2020, and retrospectively detected AKI using the modified serum creatinine criteria of the Kidney Disease: Improving Global Outcomes (KDIGO) guidelines. The AKI diagnosis date was defined as the first day of a significant change in serum creatinine level (≥0.3 mg/dL) within 48 hours. Nephrology consultation status was retrieved from the EMRs. Results: In total, 2,461 AKI patients were included; the median age was 65 years (interquartile range [IQR], 56-75 years), 1,459 (59.3%) were male, and 1,065 (43.3%) were of AKI stage 3. During a median of 5 days (IQR, 3-11 days) of ICU admission, nephrology consultations were provided to 512 patients (20.8%). Patients who received such consultations were older, had more comorbidities, and more commonly required dialysis. In a multivariable model, nephrology consultation reduced the risk of in-hospital mortality by 30% (hazard ratio, 0.71; 95% confidence interval, 0.57-0.88). Other factors significant for in-hospital mortality were older age, a higher sequential organ failure assessment (SOFA) score, sepsis, diabetes, hypertension, heart disease, and cancer. Conclusion: For AKI patients in ICUs, nephrology consultation reduced the risk of in-hospital mortality, particularly among those with multiple comorbidities. Therefore, nephrology consultation should not be omitted during ICU care.

Tracking COVID-19 in the United States With Surveillance of Aggregate Cases and Deaths.

Early during the COVID-19 pandemic, the Centers for Disease Control and Prevention (CDC) leveraged an existing surveillance system infrastructure to monitor COVID-19 cases and deaths in the United States. Given the time needed to report individual-level (also called line-level) COVID-19 case and death data containing detailed information from individual case reports, CDC designed and implemented a new aggregate case surveillance system to inform emergency response decisions more efficiently, with timelier indicators of emerging areas of concern. We describe the processes implemented by CDC to operationalize this novel, multifaceted aggregate surveillance system for collecting COVID-19 case and death data to track the spread and impact of the SARS-CoV-2 virus at national, state, and county levels. We also review the processes established to acquire, process, and validate the aggregate number of cases and deaths due to COVID-19 in the United States at the county and jurisdiction levels during the pandemic. These processes include time-saving tools and strategies implemented to collect and validate authoritative COVID-19 case and death data from jurisdictions, such as web scraping to automate data collection and algorithms to identify and correct data anomalies. This topical review highlights the need to prepare for future emergencies, such as novel disease outbreaks, by having an event-agnostic aggregate surveillance system infrastructure in place to supplement line-level case reporting for near-real-time situational awareness and timely data.

(Glyco)sphingolipidology: an amazing challenge and opportunity for systems biology.

Sphingolipids are found in essentially all eukaryotes and in some prokaryotes and viruses, where they influence cell structure, signaling and interactions with the extracellular environment. Because of the combinatorial nature of their biosynthesis, the sphingolipidome comprises untold thousands of species that encompass bioactive backbones and complex phospho- and glycolipids. Mass spectrometry is able to analyze a growing fraction of the sphingolipidome and is beginning to provide information about localization. Use of these structure specific, quantitative methods is producing insights, and surprises, regarding sphingolipid structure, metabolism, function and disease. Dealing with such large data sets poses special challenges for systems biology, but the intrinsic and elegant interrelationships among these compounds might provide a key to dealing with the complexity of the sphingolipidome.

Improving efficiency of COVID-19 aggregate case and death surveillance data transmission for jurisdictions: current and future role of application programming interfaces (APIs).

During the coronavirus disease-2019 (COVID-19) pandemic, the Centers for Disease Control and Prevention (CDC) supplemented traditional COVID-19 case and death reporting with COVID-19 aggregate case and death surveillance (ACS) to track daily cumulative numbers. Later, as public health jurisdictions (PHJs) revised the historical COVID-19 case and death data due to data reconciliation and updates, CDC devised a manual process to update these records in the ACS dataset for improving the accuracy of COVID-19 case and death data. Automatic data transfer via an application programming interface (API), an intermediary that enables software applications to communicate, reduces the time and effort in transferring data from PHJs to CDC. However, APIs must meet specific content requirements for use by CDC. As of March 2022, CDC has integrated APIs from 3 jurisdictions for COVID-19 ACS. Expanded use of APIs may provide efficiencies for COVID-19 and other emergency response planning efforts as evidenced by this proof-of-concept. In this article, we share the utility of APIs in COVID-19 ACS.

Findings from the Council of State and Territorial Epidemiologists' 2008 assessment of state reportable and nationally notifiable conditions in the United States and considerations for the future.

CONTEXT: The State Reportable Conditions Assessment (SRCA) is an annual assessment of reporting requirements for reportable public health conditions. The Council of State and Territorial Epidemiologists (CSTE) and the Centers for Disease Control and Prevention have gained valuable experience in developing a centralized repository of information about reportable conditions across US states and territories. OBJECTIVE: This study examines the reporting status in states of nationally notifiable conditions used to inform public health and national surveillance initiatives. DESIGN: Conditions included in SRCA are updated annually by using a Web-based tool created by the CSTE. SETTING: SRCA information for 2008 was reported from all US states, 2 cities, and 4 territories. PARTICIPANTS: Respondents included state or territorial epidemiologists (or designees) for reporting jurisdictions. MAIN OUTCOME MEASURE: Conditions were classified as explicitly reportable, implicitly reportable, or not reportable. RESULTS were tabulated to determine reporting statistics for the conditions nationwide. RESULTS: The SRCA included 101 conditions recommended for national notification: 93 (92%) were infectious conditions, and 8 (8%) were other (noninfectious or crosscutting) conditions. Of nationally notifiable infectious conditions, 61 (66%) were explicitly reportable in 90% or more jurisdictions; only 2 (25%) noninfectious or crosscutting nationally notifiable conditions were explicitly reportable in 90% or more jurisdictions. Furthermore, 3 nationally notifiable infectious conditions were explicitly reportable in less than 70% of jurisdictions. CONCLUSIONS: Although most nationally notifiable conditions were explicitly reportable, we found that many of these conditions have implicit reporting authority in states. As notifiable condition surveillance moves toward an informatics-driven approach, automated electronic case-detection systems will need explicit information about what conditions are reportable. Future work should address the feasibility of standardizing the format of reportable disease lists and nomenclature used to facilitate data aggregation and interpretation across states.

Whole-Exome Sequencing in Papillary Microcarcinoma: Potential Early Biomarkers of Lateral Lymph Node Metastasis.

BACKGROUND: Early identification of patients with high-risk papillary thyroid microcarcinoma (PTMC) that is likely to progress has become a critical challenge. We aimed to identify somatic mutations associated with lateral neck lymph node (LN) metastasis (N1b) in patients with PTMC. METHODS: Whole-exome sequencing (WES) of 14 PTMCs with no LN metastasis (N0) and 13 N1b PTMCs was performed using primary tumors and matched normal thyroid tissues. RESULTS: The mutational burden was comparable in N0 and N1b tumors, as the median number of mutations was 23 (range, 12 to 46) in N0 and 24 (range, 12 to 50) in N1b PTMC (P=0.918). The most frequent mutations were detected in PGS1, SLC4A8, DAAM2, and HELZ in N1b PTMCs alone, and the K158Q mutation in PGS1 (four patients, Fisher's exact test P=0.041) was significantly enriched in N1b PTMCs. Based on pathway analysis, somatic mutations belonging to the receptor tyrosine kinase-RAS and NOTCH pathways were most frequently affected in N1b PTMCs. We identified four mutations that are predicted to be pathogenic in four genes based on Clinvar and Combined Annotation-Dependent Depletion score: BRAF, USH2A, CFTR, and PHIP. A missense mutation in CFTR and a nonsense mutation in PHIP were detected in N1b PTMCs only, although in one case each. BRAF mutation was detected in both N0 and N1b PTMCs. CONCLUSION: This first comprehensive WES analysis of the mutational landscape of N0 and N1b PTMCs identified pathogenic genes that affect biological functions associated with the aggressive phenotype of PTMC.

A Markov blanket-based method for detecting causal SNPs in GWAS.

BACKGROUND: Detecting epistatic interactions associated with complex and common diseases can help to improve prevention, diagnosis and treatment of these diseases. With the development of genome-wide association studies (GWAS), designing powerful and robust computational method for identifying epistatic interactions associated with common diseases becomes a great challenge to bioinformatics society, because the study of epistatic interactions often deals with the large size of the genotyped data and the huge amount of combinations of all the possible genetic factors. Most existing computational detection methods are based on the classification capacity of SNP sets, which may fail to identify SNP sets that are strongly associated with the diseases and introduce a lot of false positives. In addition, most methods are not suitable for genome-wide scale studies due to their computational complexity. RESULTS: We propose a new Markov Blanket-based method, DASSO-MB (Detection of ASSOciations using Markov Blanket) to detect epistatic interactions in case-control GWAS. Markov blanket of a target variable T can completely shield T from all other variables. Thus, we can guarantee that the SNP set detected by DASSO-MB has a strong association with diseases and contains fewest false positives. Furthermore, DASSO-MB uses a heuristic search strategy by calculating the association between variables to avoid the time-consuming training process as in other machine-learning methods. We apply our algorithm to simulated datasets and a real case-control dataset. We compare DASSO-MB to other commonly-used methods and show that our method significantly outperforms other methods and is capable of finding SNPs strongly associated with diseases. CONCLUSIONS: Our study shows that DASSO-MB can identify a minimal set of causal SNPs associated with diseases, which contains less false positives compared to other existing methods. Given the huge size of genomic dataset produced by GWAS, this is critical in saving the potential costs of biological experiments and being an efficient guideline for pathogenesis research.

Matrix metalloproteinase (MMP)-12 regulates MMP-9 expression in interleukin-1beta-treated articular chondrocytes.

Limited information is available on the expression and role of matrix metalloproteinase (MMP)-12 in chondrocytes. We characterized the expression mechanism of MMP-12 and possible function in chondrocytes. Interleukin (IL)-1beta induced the expression and activation of MMP-12 in primary culture chondrocytes and cartilage explants via mitogen-activated protein (MAP) kinase signaling pathways. Among MAP kinases, extracellular signal-regulated kinase and p38 kinase are necessary for MMP-12 expression, whereas c-jun N-terminal kinase is required for the activation of MMP-12. The possibility that MMP-12 acts as a modulator of other MMP was examined. MMP-12 alone did not affect other MMP expressions. However, MMP-12 enhanced expression and activation of MMP-9 in the presence of IL-1beta. Our results indicate that IL-1beta in chondrocytes induces the expression and activation of MMP-12, which, in turn, augments MMP-9 expression and activation.