Incidence and Risk Factors for Wound Infections after Trimeresurus stejnegeri Snakebites in Taiwan.

Snakebite envenomation is a neglected tropical disease. Taiwan, with its subtropical and Southeast Asian environment, provides suitable habitat for several venomous snake species. Trimeresurus stejnegeri, an arboreal pit viper, is the most common cause of venomous snakebite in Taiwan. Trimeresurus stejnegeri envenomation can cause local swelling, occasional ecchymosis, and wound infection. The primary treatment of T. stejnegeri envenomation is the binary antivenom, vacuum freeze-dried F(ab')2 fragments of equine antibodies, against T. stejnegeri and Protobothrops mucrosquamatus. This study aimed to analyze the incidence of post-envenomation wound infections caused by T. stejnegeri based on data collected over a decade from institutions affiliated with the Chang Gung Memorial Hospital in Taiwan. A total of 254 patients were enrolled in this study. Clinical and laboratory data, treatment information, and patient outcomes were extracted from electronic medical records. Wound infection was associated with delay in antivenom initiation (adjusted odds ratio: 3.987; 95% CI: 1.406-11.302). The infection rates were 20.5%, 12.5%, 31.3%, and 48.1% for antivenom administration within 2 hours, 2-4 hours, 4-6 hours, and > 6 hours, respectively. Therefore, early initiation of antivenom treatment (within 6 hours) is recommended. Morganella morganii was cultured from wounds of the patients, whereas Enterobacter cloacae and Enterococcus faecalis were cultured from both the oral cavity of snakes and the wounds of the patients. For post-envenomation patients who develop a local infection, empiric antibiotics such as third-generation cephalosporins, quinolones, and piperacillin/tazobactam are recommended because snakebite wound infections are often polymicrobial in nature.

Impact of COVID-19 Outbreak on Influenza and Pneumococcal Vaccination Uptake: A Multi-Center Retrospective Study.

During the coronavirus disease 2019 (COVID-19) pandemic, global vaccination efforts declined due to the burden on health systems and community resistance to epidemic control measures. Influenza and pneumococcal vaccines have been recommended for vulnerable populations to prevent severe pneumonia. We investigated community response towards influenza and pneumococcal vaccines (pneumococcal conjugate vaccine and pneumococcal polysaccharide vaccine) after the COVID-19 outbreak in Taiwan. We retrospectively included adults who visited Chang Gung Memorial Hospital (CGMH) institutions for influenza or pneumococcal vaccination from January 2018 to December 2021. The first case of COVID-19 in Taiwan was detected in January 2020; therefore, in this study, hospitalized cases from January 2018 to December 2019 were defined as "before COVID-19 outbreak," and hospitalized cases from January 2020 to December 2021 were defined as "after COVID-19 outbreak". A total of 105,386 adults were enrolled in the study. An increase in influenza vaccination (n = 33,139 vs. n = 62,634) and pneumococcal vaccination (n = 3035 vs. n = 4260) were observed after the COVID-19 outbreak. In addition, there was an increased willingness to receive both influenza and pneumococcal vaccinations among women, adults without underlying disease and younger adults. The COVID-19 pandemic may have increased awareness of the importance of vaccination in Taiwan.

Experience of carbon monoxide poisoning and the outcome predicting score: A multicenter retrospective study.

BACKGROUND: Carbon monoxide poisoning (COP), resulting from accidental and intentional exposure, is a leading cause of fatal poisoning worldwide. Except for early death, neurological sequelae are common and impose a large burden on patients, caregivers, and the society. MATERIALS AND METHODS: This retrospective study included patients who visited the emergency departments (EDs) of the medical institutes of Chang Gung Memorial Hospital after COP with a carboxyhemoglobin level > 10% between January 2009 and October 2018. Patients who experienced out-of-hospital cardiac arrest (OHCA) were excluded. Poor outcome was defined as mortality or a Glasgow coma scale (GCS) <13 at discharge. Stepwise regression analysis was performed, and a receiver operating characteristic (ROC) curve was applied to analyze our newly created scoring system for prognosis prediction. RESULTS: This study enrolled 1171 patients. Fire scene (F) (aOR, 20.635; 95% CI, 8.345-51.023), intentional CO exposure (I) (aOR, 2.634; 95% CI, 1.335-5.196), respiratory failure (R) (aOR, 9.944; 95% CI, 5.533-17.873), every point of reduced GCS (E) (aOR, 1.253; 95% CI, 1.186-1.323), and diabetes mellitus (D) (aOR, 2.749; 95% CI, 1.201-6.292) were identified as predictors of poor outcomes. The FIRED score was created. CONCLUSION: The FIRED score could predict the outcomes of non-OHCA patients with a carboxyhemoglobin level > 10% after COP using five factors that can be obtained by history taking and basic examination. An FIRED score ≥ 10 was associated with a poor outcome (sensitivity, 89.6%; specificity, 82.4%; AUC0.930).

Chronic kidney disease as an important risk factor for tumor recurrences, progression and overall survival in primary non-muscle-invasive bladder cancer.

PURPOSE: To evaluate the impact of chronic kidney disease (CKD) on oncologic outcomes among non-muscle-invasive bladder cancer (NMIBC) patients who underwent transurethral resection of bladder cancer. METHODS: We followed up the medical data of 158 newly diagnosed NMIBC patients enrolled in this study for more than 4 years. The ten putative risk factors included patient age, gender, white blood cells, neutrophil to lymphocyte ratio, tumor count, size, grade, stage, CKD (estimated glomerular filtration rate, eGFR < 60) and histological differentiation. RESULTS: Total recurrent bladder and upper urinary tract (UUT) tumors were observed in 51 patients (32 %) and 5 patients (3 %), respectively. Cancer progression to the high pT(≥pT2) stage was found in 9 patients (6 %). Cancer-specific and overall survival rates were 91 % (144/158 patients) and 78 % (123/158 patients), respectively. In univariate analysis, significant predictive determinants were tumor count, size, grade, stage, CKD and squamous differentiation for bladder tumor recurrence; CKD and squamous differentiation for UUT tumor recurrence; and tumor count, grade, stage and CKD for cancer progression. On the other hand, old age (>70 years), high grade, T1 stage, and CKD were poor prognostic factors for overall survival. In multivariate analysis, CKD was an independent risk factor for bladder/UUT tumor recurrences and the overall survival rate. CONCLUSIONS: NMIBC patients with CKD had worse prognosis and higher tumor recurrence and progression rates than other patients. These patients should be intensively monitored at upper and lower urinary tracts and be aggressively treated for comorbidities of CKD.

An up-down bit pattern approach to coregulated and negative-coregulated gene clustering of microarray data.

Biclustering, which performs simultaneous clustering of rows (e.g., genes) and columns (e.g., conditions), has been shown to be important for analyzing microarray data. To find biclusters, there have been many methods proposed. Most of these methods can find only clusters with coregulated patterns, which means that the expression levels of genes in a found cluster rise and fall simultaneously. However, for real microarray data, there exist negative-correlated patterns, which means that the tendencies of expression levels of some genes may be completely inverse to those of the other genes under some conditions. Although one method called Co-gclustering was proposed to simultaneously find clusters with correlated and negative-correlated patterns, its time complexity is exponential to the number of conditions, which may not be efficient. Therefore, in this article, we propose a new method, Up-Down Bit pattern (UDB), to efficiently find clusters with correlated and negative-correlated patterns. First, we utilize up-down bit patterns to record those condition pairs where one gene is upregulated or downregulated. One gene is upregulated (or downregulated) under condition pair a and b if its expression level shows an upward (or downward) tendency from condition a to condition b. Then, we apply a heuristic idea on these up-down bit patterns to efficiently find clusters, which will reduce the time complexity from exponential time to polynomial time. From the experimental results, we show that the UDB method is more efficient than the Co-gclustering method.

Mining subspace clusters from DNA microarray data using large itemset techniques.

Mining subspace clusters from the DNA microarrays could help researchers identify those genes which commonly contribute to a disease, where a subspace cluster indicates a subset of genes whose expression levels are similar under a subset of conditions. Since in a DNA microarray, the number of genes is far larger than the number of conditions, those previous proposed algorithms which compute the maximum dimension sets (MDSs) for any two genes will take a long time to mine subspace clusters. In this article, we propose the Large Itemset-Based Clustering (LISC) algorithm for mining subspace clusters. Instead of constructing MDSs for any two genes, we construct only MDSs for any two conditions. Then, we transform the task of finding the maximal possible gene sets into the problem of mining large itemsets from the condition-pair MDSs. Since we are only interested in those subspace clusters with gene sets as large as possible, it is desirable to pay attention to those gene sets which have reasonable large support values in the condition-pair MDSs. From our simulation results, we show that the proposed algorithm needs shorter processing time than those previous proposed algorithms which need to construct gene-pair MDSs.

A Condition-Enumeration Tree method for mining biclusters from DNA microarray data sets.

Biclustering, which performs simultaneous clustering of rows (e.g., genes) and columns (e.g., conditions), has proved of great value for finding interesting patterns from microarray data. To find biclusters, a model called pCluster was proposed. A pCluster consists of a set of genes and a set of conditions, where the expression levels of these genes have a similar variation under these conditions. Based on this model, most of the previous methods need to compute MDSs (maximum dimension sets) for every two genes in the microarray data. Since the number of genes is far larger than the number of conditions, this step is inefficient. Another method called MicroCluster was proposed. This method does not compute MDSs for every two genes, and transforms the problem into a graph problem. However, it needs to solve the Maximal Clique problem, which is NP-Complete. To avoid the above disadvantages, in this paper, we propose a new method, CE-Tree (Condition-Enumeration Tree), for finding pClusters. Instead of generating MDSs for every two genes, we generate only MDSs for every two conditions. Then, based only on these MDSs, we expand the CE-Tree in a special local breadth-first within global depth-first manner to efficiently find all pClusters. We also utilize the idea of the traditional hash join approach to efficiently support the CE-Tree. From the simulation results, we show that the CE-Tree method could find pClusters more efficiently than those previous methods.