Lowering mortality risk in CR-HvKP infection in intestinal immunohistological and microbiota restoration.

Gut damage during carbapenem-resistant and hypervirulent Klebsiella pneumoniae (CR-HvKP) infection is associated with a death risk. Understanding the mechanisms by which CR-HvKP causes intestinal damage and gut microbiota alteration, and the impact on immunity, is crucial for developing therapeutic strategies. This study investigated if gastrointestinal tract damage and disruption of gut microbiota induced by CR-HvKP infection undermined host immunity and facilitated multi-organ invasion of CR-HvKP; whether the therapeutic value of the rifampicin (RIF) and zidovudine (ZDV) combination was attributed to their ability to repair damages and restore host immunity was determined. A sepsis model was utilized to assess the intestinal pathological changes. Metagenomic analysis was performed to characterize the alteration of gut microbiota. The effects of the RIF and ZDV on suppressing inflammatory responses and improving immune functions and gut microbiota were evaluated by immunopathological and transcriptomic analyses. Rapid colonic damage occurred upon activation of the inflammation signaling pathways during lethal infections. Gut inflammation compromised host innate immunity and led to a significant decrease in probiotics abundance, including Bifidobacterium and Lactobacillus. Treatment with combination drugs significantly attenuated the inflammatory response, up-regulated immune cell differentiation signaling pathways, and promoted the abundance of Bifidobacterium (33.40 %). Consistently, supplementation of Bifidobacterium alone delayed the death in sepsis model. Gut inflammation and disrupted microbiota are key disease features of CR-HvKP infection but can be reversed by the RIF and ZDV drug combination. The finding that these drugs can restore host immunity through multiple mechanisms is novel and deserves further investigation of their clinical application potential.

Rural-Urban Differences in Vaccination and Hesitancy Rates and Trust: US COVID-19 Trends and Impact Survey on a Social Media Platform, May 2021-April 2022.

Objectives. To analyze rural-urban differences in COVID-19 vaccination uptake, hesitancy, and trust in information sources in the United States. Methods. We used data from a large survey of Facebook users. We computed the vaccination, hesitancy, and decline rates and the trust proportions among individuals hesitant toward COVID-19 information sources for rural and urban regions in each state from May 2021 to April 2022. Results. In 48 states with adequate data, on average, two thirds of states showed statistically significant differences in monthly vaccination rates between rural and urban regions, with rural regions having a lower vaccination rate at all times. Far fewer states showed statistically significant differences when comparing monthly hesitancy and decline rates for urban versus rural regions. Doctors and health professionals received the highest level of trust. Friends and family were also among the most trusted sources in rural areas where the vaccination uptake was low. Conclusions. Rural-urban difference in hesitancy rates among those still unvaccinated was much smaller than the rural-urban difference in vaccination rates, suggesting that access to vaccines may be another contributor to the lower vaccination rates in rural areas. (Am J Public Health. 2023;113(6):680-688. https://doi.org/10.2105/AJPH.2023.307274).

Modeling COVID-19 Mortality Across 44 Countries: Face Covering May Reduce Deaths.

INTRODUCTION: Despite ongoing efforts to vaccinate communities against COVID-19, the necessity of face mask use in controlling the pandemic remains subject to debate. Several studies have investigated face masks and COVID-19, covering smaller and less diverse populations than this study's sample. This study examines a hypothesized association of face-covering mandates with COVID-19 mortality decline across 44 countries in 2 continents. METHODS: In a retrospective cohort study, changes in COVID-19‒related daily mortality rate per million population from February 15 to May 31, 2020 were compared between 27 countries with and 17 countries without face mask mandates in nearly 1 billion (911,446,220 total) people. Longitudinal mixed effect modeling was applied and adjusted for over 10 relevant demographic, social, clinical, and time-dependent confounders. RESULTS: Average COVID-19 mortality per million was 288.54 in countries without face mask policies and 48.40 in countries with face mask policies. In no mask countries, adjusted average daily increase was 0.1553 - 0.0017 X (days since the first case) log deaths per million, compared with 0.0900 - 0.0009 X (days since the first case) log deaths per million in the countries with a mandate. A total of 60 days into the pandemic, countries without face mask mandates had an average daily increase of 0.0533 deaths per million, compared with the average daily increase of 0.0360 deaths per million for countries with face mask mandates. CONCLUSIONS: This study's significant results show that face mask mandates were associated with lower COVID-19 deaths rates than the rates in countries without mandates. These findings support the use of face masks to prevent excess COVID-19 deaths and should be advised during airborne disease epidemics.

Using hierarchical clustering analysis to evaluate COVID-19 pandemic preparedness and performance in 180 countries in 2020.

OBJECTIVE: To rank and score 180 countries according to COVID-19 cases and fatality in 2020 and compare the results to existing pandemic vulnerability prediction models and results generated by standard epidemiological scoring techniques. SETTING: One hundred and eighty countries' patients with COVID-19 and fatality data representing the healthcare system preparedness and performance in combating the pandemic in 2020. DESIGN: Using the retrospective daily COVID-19 data in 2020 broken into 24 half-month periods, we applied unsupervised machine learning techniques, in particular, hierarchical clustering analysis to cluster countries into five groups within each period according to their cumulative COVID-19 fatality per day over the year and cumulative COVID-19 cases per million population per day over the half-month period. We used the average of the period scores to assign countries' final scores for each measure. PRIMARY OUTCOME: The primary outcomes are the COVID-19 cases and fatality grades in 2020. RESULTS: The United Arab Emirates and the USA with F in COVID-19 cases, achieved A or B in the fatality scores. Belgium and Sweden ranked F in both scores. Although no African country ranked F for COVID-19 cases, several African countries such as Gambia and Liberia had F for fatality scores. More developing countries ranked D and F in fatality than in COVID-19 case rankings. The classic epidemiological measures such as averages and rates have a relatively good correlation with our methodology, but past predictions failed to forecast the COVID-19 countries' preparedness. CONCLUSION: COVID-19 fatality can be a good proxy for countries' resources and system's resilience in managing the pandemic. These findings suggest that countries' economic and sociopolitical factors may behave in a more complex way as were believed. To explore these complex epidemiological associations, models can benefit enormously by taking advantage of methods developed in computer science and machine learning.

A three-year multifaceted intervention to prevent obesity in children of Mexican-heritage.

BACKGROUND: Obesity and overweight have increased dramatically in the United States over the last decades. The complexity of interrelated causal factors that result in obesity needs to be addressed within the cultural dynamic of sub-populations. In this study, we sought to estimate the effects of a multifaceted, community-based intervention on body mass index (BMI) among Mexican-heritage children. METHODS: Niños Sanos, Familia Sana (Healthy Children, Healthy Family) was a quasi-experimental intervention study designed to reduce the rate of BMI growth among Mexican-heritage children in California's Central Valley. Two rural communities were matched based on demographic and environmental characteristics and were assigned as the intervention or comparison community. The three-year intervention included parent workshops on nutrition and physical activity; school-based nutrition lessons and enhanced physical education program for children; and a monthly voucher for fruits and vegetables. Eligible children were between 3 and 8 years old at baseline. Intent-to-treat analyses were estimated using linear mixed-effect models with random intercepts. We ran a series of models for each gender where predictors were fixed except interactions between age groups and obesity status at baseline with intervention to determine the magnitude of impact on BMI. RESULTS: At baseline, mean (SD) BMI z-score (zBMI) was 0.97 (0.98) in the intervention group (n = 387) and 0.98 (1.02) in the comparison group (n = 313) (NS). The intervention was significantly associated with log-transformed BMI (ß = 0.04 (0.02), P = 0.03) and zBMI (ß = 0.25 (0.12), P = 0.04) among boys and log-transformed BMI among obese girls (ß = - 0.04 (0.02), P = 0.04). The intervention was significantly and inversely associated with BMI in obese boys and girls across all age groups and normal weight boys in the oldest group (over 6 years) relative to their counterparts in the comparison community. CONCLUSIONS: A community-based, multifaceted intervention was effective at slowing the rate of BMI growth among Mexican-heritage children. Our findings suggest that practitioners should consider strategies that address gender disparities and work with a variety of stakeholders to target childhood obesity. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT01900613 . Registered 16th July 2013.

Analysis of SEER Adenosquamous Carcinoma Data to Identify Cause Specific Survival Predictors and Socioeconomic Disparities.

BACKGROUND: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) adenosquamous carcinoma data to identify predictive models and potential disparities in outcome. MATERIALS AND METHODS: This study analyzed socio-economic, staging and treatment factors available in the SEER database for adenosquamous carcinoma. For the risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. An area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. RESULTS: A total of 20,712 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 54.2 (78.4) months. Some 2/3 of the patients were female. The mean (S.D.) age was 63 (13.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.71). 13.9% of the patients were un-staged and had risk of cause specific death of 61.3% that was higher than the 45.3% risk for the regional disease and lower than the 70.3% for metastatic disease. Sex, site, radiotherapy, and surgery had ROC areas of about 0.55-0.65. Rural residence and race contributed to socioeconomic disparity for treatment outcome. Radiotherapy was underused even with localized and regional stages when the intent was curative. This under use was most pronounced in older patients. CONCLUSIONS: Anatomic stage was predictive and useful in treatment selection. Under-staging may have contributed to poor outcome.

Analysis of SEER Glassy Cell Carcinoma Data: Underuse of Radiotherapy and Predicators of Cause Specific Survival.

BACKGROUND: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) for glassy cell carcinoma data to identify predictive models and potential disparities in outcome. MATERIALS AND METHODS: This study analyzed socio-economic, staging and treatment factors. For risk modeling, each factor was fitted by a generalized linear model to predict the cause specific survival. Area under the receiver operating characteristic curves (ROCs) were computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate modeling errors. Risk of glassy cell carcinoma death was computed for the predictors for comparison. RESULTS: There were 79 patients included in this study. The mean follow up time (S.D.) was 37 (32.8) months. Female patients outnumbered males 4:1. The mean (S.D.) age was 54.4 (19.8) years. SEER stage was the most predictive factor of outcome (ROC area of 0.69). The risks of cause specific death were, respectively, 9.4% for localized, 16.7% for regional, 35% for the un-staged/others category, and 60% for distant disease. After optimization, separation between the regional and unstaged/others category was removed with a higher ROC area of 0.72. Several socio-economic factors had small but measurable effects on outcome. Radiotherapy had not been used in 90% of patients with regional disease. CONCLUSIONS: Optimized SEER stage was predictive and useful in treatment selection. Underuse of radiotherapy may have contributed to poor outcome.

Signed-gradient adaptive step size LMS algorithm for biomedical applications.

Gradient adaptive step size adaptive filters have been widely used to adapt different biomedical application environments and obtain useful life signals from serious ambient noise and interferences. In order to further improve the signal-to-noise ratio (SNR) of the life signals, this paper presents a class of signed-gradient adaptive step size least mean square (LMS) adaptive filters. The proposed algorithms introduce a sign function to replace the gradient of squared error in the step size updating process of the gradient adaptive step size LMS adaptive filters. The performance of both gradient and signed-gradient algorithms with dual adaptive filters is compared by extracting heartbeat signals from ambient noise in stethoscopes. Simulation results demonstrate that though the signed-gradient adaptive step size LMS algorithm converges at a slower rate at the early stage of iteration, it has a smaller mean squared error (MSE) at the stage of convergence, thus achieves a higher SNR.

A novel gradient adaptive step size LMS algorithm with dual adaptive filters.

Least mean square (LMS) adaptive filter has been used to extract life signals from serious ambient noises and interferences in biomedical applications. However, a LMS adaptive filter with a fixed step size always suffers from slow convergence rate or large signal distortion due to the diversity of the application environments. An ideal adaptive filtering system should be able to adapt different environments and obtain the useful signals with low distortion. Adaptive filter with gradient adaptive step size is therefore more desirable in order to meet the demands of adaptation and convergence rate, which adjusts the step-size parameter automatically by using gradient descent technique. In this paper, a novel gradient adaptive step size LMS adaptive filter is presented. The proposed algorithm utilizes two adaptive filters to estimate gradients accurately, thus achieves good adaptation and performance. Though it uses two LMS adaptive filters, it has a low computational complexity. An active noise cancellation (ANC) system with two applications for extracting heartbeat and lung sound signals from noises is used to simulate the performance of the proposed algorithm.

Racial and social economic factors impact on the cause specific survival of pancreatic cancer: a SEER survey.

BACKGROUND: This study used Surveillance, Epidemiology and End Results (SEER) pancreatic cancer data to identify predictive models and potential socio-economic disparities in pancreatic cancer outcome. MATERIALS AND METHODS: For risk modeling, Kaplan Meier method was used for cause specific survival analysis. The Kolmogorov-Smirnov's test was used to compare survival curves. The Cox proportional hazard method was applied for multivariate analysis. The area under the ROC curve was computed for predictors of absolute risk of death, optimized to improve efficiency. RESULTS: This study included 58,747 patients. The mean follow up time (S.D.) was 7.6 (10.6) months. SEER stage and grade were strongly predictive univariates. Sex, race, and three socio-economic factors (county level family income, rural-urban residence status, and county level education attainment) were independent multivariate predictors. Racial and socio-economic factors were associated with about 2% difference in absolute cause specific survival. CONCLUSIONS: This study s found significant effects of socio-economic factors on pancreas cancer outcome. These data may generate hypotheses for trials to eliminate these outcome disparities.

Racial and socioeconomic disparities in malignant carcinoid cancer cause specific survival: analysis of the surveillance, epidemiology and end results national cancer registry.

BACKGROUND: This study hypothesized living in a poor neighborhood decreased the cause specific survival in individuals suffering from carcinoid carcinomas. Surveillance, Epidemiology and End Results (SEER) carcinoid carcinoma data were used to identify potential socioeconomic disparities in outcome. MATERIALS AND METHODS: This study analyzed socioeconomic, staging and treatment factors available in the SEER database for carcinoid carcinomas. The Kaplan-Meier method was used to analyze time to events and the Kolmogorov-Smirnov test to compare survival curves. The Cox proportional hazard method was employed for multivariate analysis. Areas under the receiver operating characteristic curves (ROCs) were computed to screen the predictors for further analysis. RESULTS: There were 38,546 patients diagnosed from 1973 to 2009 included in this study. The mean follow up time (S.D.) was 68.1 (70.7) months. SEER stage was the most predictive factor of outcome (ROC area of 0.79). 16.4% of patients were un-staged. Race/ethnicity, rural urban residence and county level family income were significant predictors of cause specific survival on multivariate analysis, these accounting for about 5% of the difference in actuarial cause specific survival at 20 years of follow up. CONCLUSIONS: This study found poorer cause specific survival of carcinoid carcinomas of individuals living in poor and rural neighborhoods.

Impact of socioeconomic disparities on cause-specific survival of retinoblastoma.

Retinoblastoma (RB) is a rare disease of infancy and early childhood. This study investigated the effects of socioeconomic factors on the cause-specific survival of RB. Data from patients diagnosed with RB between 1973 and 2009 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. The study included 1,456 patients with a the mean follow-up time (SD) of 128.75 (113.74) months and a mean age (SD) of 1.4 (2.6) years. This study analyzed socioeconomic, staging and treatment factors available in the SEER database for RB. Kaplan-Meier analysis was used to analyze time-to-failure data. The two-sample Kolmogorov-Smirnov test was used for univariate analysis and the Cox proportional hazards model was used for multivariate analysis. The area under the receiver operating characteristic (ROC) curve was computed for predictors. SEER stage was the most significant predictive pretreatment factor. The identified socioeconomic barriers included ethnicity and rural-urban residence status that led to a 3% decrease in RB cause-specific survival. Thus, eliminating barriers to treatment is crucial for reducing the outcome disparities.

Poor treatment outcome of neuroblastoma and other peripheral nerve cell tumors may be related to under usage of radiotherapy and socio-economic disparity: a US SEER data analysis.

PURPOSE: This study used receiver operating characteristic curve to analyze Surveillance, Epidemiology and End Results (SEER) neuroblastoma (NB) and other peripheral nerve cell tumors (PNCT) outcome data. This study found under usage of radiotherapy in these patients. MATERIALS AND METHODS: This study analyzed socio-economic, staging and treatment factors available in the SEER database for NB and other PNCT. For the risk modeling, each factor was fitted by a generalized jinear model to predict the outcome (soft tissue specific death, yes/no). The area under the receiver operating characteristic curve (ROC) was computed. Similar strata were combined to construct the most parsimonious models. A random sampling algorithm was used to estimate the modeling errors. Risk of neuroendocrine (other endocrine including thymus as coded in SEER) death was computed for the predictors. RESULTS: There were 5261 patients diagnosed from 1973 to 2009 were included in this study. The mean follow up time (S.D.) was 83.8 (97.6) months. The mean (SD) age was 18 (25) years. About 30.45% of patients were un-staged. The SEER staging has high ROC (SD) area of 0.58 (0.01) among the factors tested. We simplified the 4-layered risk levels (local, regional, distant, un-staged/others) to a simpler 3-tiered model with comparable ROC area of 0.59 (0.01). Less than 50% of PNCT patients received radiotherapy (RT) including the ones with localized disease. This avoidance of RT use occurred in adults and children. CONCLUSION: The high under-staging rate may have prevented patients from selecting definitive radiotherapy (RT) after surgery. Using RT for, especially, adult PNCT patients is a potential way to improve outcome.

Modified Log-LMS adaptive filter with low signal distortion for biomedical applications.

Life signals from human body, e.g. heartbeat or electrocardiography (ECG), are usually weak and susceptible to external noise and interference. Adaptive filter is a good tool to reduce the influence of ambient noise/interference on the life signals. Least mean squares (LMS) algorithm, as one of most popular adaptive algorithms for active noise cancellation (ANC) by adaptive filtering, has the advantage of easy implementation. In order to further decrease the complexity of LMS algorithm based adaptive filter, a Log-LMS algorithm was proposed, which quantized signals by the function of log2. The algorithm can replace multipliers by simple shifting. However, both LMS algorithm and Log-LMS algorithm have the disadvantage of serious signal distortion in biomedical applications. In this paper, a modified Log-LMS algorithm is presented, which divides the convergence process into two different stages, and utilizes different quantization method in each stage. Two scenarios of biomedical applications are used for analysis, 1) using stethoscope in emergence medical helicopter and 2) measuring ECG under power line interference. The simulated results show that the modified algorithm can achieve fast convergence and low signal distortion in processing periodic life signals.

A volumetric trend analysis of the prostate and seminal vesicles during a course of intensity-modulated radiation therapy.

PURPOSE: The purpose of this study was to evaluate the volumetric changes of the prostate and seminal vesicles (SV) during a definitive course of intensity-modulated radiation therapy using a combined computed tomography (CT)-linear accelerator system. METHODS AND MATERIALS: Fifteen patients were enrolled in this IRB-approved prospective study. The treatment plan was designed to deliver a total dose of 75.6 Gy over 42 fractions to 98% of the planning target volume. In-room CT scans were acquired using a CT-on-rails system 3 times weekly just before daily treatment throughout the course of radiation therapy. Rates of volume change over time for the prostate and SV were calculated using linear regression analysis. RESULTS: Fifteen patients had a total of 369 CT scans during the study. For 53% of patients (N = 8), there was a statistically significant decrease in prostate volume over time (range, 0.05-0.47 mL/d or 0.7%-3.5% per week). The median volume reduction was -11.5% (4.9 mL) at the end of the treatment. There was no significant change in the volume of the SV during the course of treatment. The decrease in prostate volume was significantly correlated with the initial volume of the prostate, with larger glands shrinking more during treatment (P = 0.001). CONCLUSION: Patients with enlarged prostates may experience volumetric reduction in their gland size during a protracted course of radiation therapy. However, the magnitude of the volume reduction is relatively small. In the current era of dose escalation, future studies with adaptive radiation therapy strategy may be appropriate to minimize radiation exposure to normal tissues.

Intensity-modulated radiation therapy with noncoplanar beams for treatment of prostate cancer in patients with bilateral hip prosthesis-a case study.

Megavoltage photon intensity-modulated radiation therapy (IMRT) is typically used in the treatment of prostate cancer at our institution. Approximately 1% to 2% of patients with prostate cancer have hip prostheses. The presence of the prosthesis usually complicates the planning process because of dose perturbation around the prosthesis, radiation attenuation through the prosthesis, and the introduction of computed tomography artifacts in the planning volume. In addition, hip prostheses are typically made of materials of high atomic number, which add uncertainty to the dosimetry of the prostate and critical organs in the planning volume. When the prosthesis is bilateral, treatment planning is further complicated because only a limited number of beam angles can be used to avoid the prostheses. In this case study, we will report the observed advantages of using noncoplanar beams in the delivery of IMRT to a prostate cancer patient with bilateral hip prostheses. The treatment was planned for 75.6 Gy using a 7-field coplanar approach and a noncoplanar arrangement, with all fields avoiding entrance though the prostheses. Our results indicate that, compared with the coplanar plan, the noncoplanar plan delivers the prescribed dose to the target with a slightly better conformality and sparing of rectal tissue versus the coplanar plan.

Daily bone alignment with limited repeat CT correction rivals daily ultrasound alignment for prostate radiotherapy.

PURPOSE: To compare the effectiveness of daily ultrasound (US)- and computed tomography (CT)-guided alignments with an off-line correction protocol using daily bone alignment plus a correction factor for systematic internal prostate displacement (CF(ID)). METHODS AND MATERIALS: Ten prostate cancer patients underwent CT scans three times weekly using an integrated CT-linear accelerator system, followed by alignment using US for daily radiotherapy. Intensity-modulated radiotherapy plans were designed with our current clinical margins. The treatment plan was copied onto the repeat CT images and aligned using several methods: (1) bone alignment plus CF(ID) after three off-line CT scans (bone+3CT), (2) bone alignment plus CF(ID) after six off-line CT scans (bone+6CT), (3) US alignment, and (4) CT alignment. The accuracy of the repeated US and CT measurements to determine the CF(ID) was compared. The target dosimetric effect was quantified. RESULTS: The CF(ID) for internal systematic prostate displacements was more accurately measured with limited repeat CT scans than with US (residual error, 0.0 +/- 0.7 mm vs. 2.0 +/- 3.2 mm). Bone+3CT, bone+6CT, and US provided equivalent prostate and seminal vesicle dose coverage, but bone+3CT and bone+6CT produced more precise daily alignments. Daily CT alignment provided the greatest target dose coverage. CONCLUSION: Daily bone alignment plus CF(ID) for internal systematic prostate displacement provided better daily alignment precision and equivalent dose coverage compared with daily US alignment. The CF(ID) should be based on at least three repeat CT scans, which could be collected before the start of treatment or during the first 3 treatment days. Daily bone alignment plus CF(ID) provides another option for accurate prostate cancer patient positioning.