Lung function in post-poliomyelitis syndrome: a cross-sectional study

OBJECTIVE:

To compare lung function between patients with post-poliomyelitis syndrome and those with sequelae of paralytic poliomyelitis (without any signs or symptoms of post-poliomyelitis syndrome), as well as between patients with post-poliomyelitis syndrome and healthy controls.

METHODS:

Twenty-nine male participants were assigned to one of three groups: control; poliomyelitis (comprising patients who had had paralytic poliomyelitis but had not developed post-poliomyelitis syndrome); and post-poliomyelitis syndrome. Volunteers underwent lung function measurements (spirometry and respiratory muscle strength assessment).

RESULTS:

The results of the spirometric assessment revealed no significant differences among the groups except for an approximately 27% lower mean maximal voluntary ventilation in the post-poliomyelitis syndrome group when compared with the control group (p = 0.0127). Nevertheless, the maximal voluntary ventilation values for the post-poliomyelitis group were compared with those for the Brazilian population and were found to be normal. No significant differences were observed in respiratory muscle strength among the groups.

CONCLUSIONS:

With the exception of lower maximal voluntary ventilation, there was no significant lung function impairment in outpatients diagnosed with post-poliomyelitis syndrome when compared with healthy subjects and with patients with sequelae of poliomyelitis without post-poliomyelitis syndrome. This is an important clinical finding because it shows that patients with post-poliomyelitis syndrome can have preserved lung function.     

Diabetic status and the relation of the three domains of glycemic control to mortality in critically ill patients: an international multicenter cohort study

Introduction

Hyperglycemia, hypoglycemia, and increased glycemic variability have each been independently associated with increased risk of mortality in critically ill patients. The role of diabetic status on modulating the relation of these three domains of glycemic control with mortality remains uncertain. The purpose of this investigation was to determine how diabetic status affects the relation of hyperglycemia, hypoglycemia, and increased glycemic variability with the risk of mortality in critically ill patients.

Methods

This is a retrospective analysis of prospectively collected data involving 44,964 patients admitted to 23 intensive care units (ICUs) from nine countries, between February 2001 and May 2012. We analyzed mean blood glucose concentration (BG), coefficient of variation (CV), and minimal BG and created multivariable models to analyze their independent association with mortality. Patients were stratified according to the diagnosis of diabetes.

Results

Among patients without diabetes, mean BG bands between 80 and 140 mg/dl were independently associated with decreased risk of mortality, and mean BG bands >140 mg/dl, with increased risk of mortality. Among patients with diabetes, mean BG from 80 to 110 mg/dl was associated with increased risk of mortality and mean BG from 110 to 180 mg/dl with decreased risk of mortality. An effect of center was noted on the relation between mean BG and mortality. Hypoglycemia, defined as minimum BG <70 mg/dl, was independently associated with increased risk of mortality among patients with and without diabetes and increased glycemic variability, defined as CV >20%, was independently associated with increased risk of mortality only among patients without diabetes. Derangements of more than one domain of glycemic control had a cumulative association with mortality, especially for patients without diabetes.

Conclusions

Although hyperglycemia, hypoglycemia, and increased glycemic variability is each independently associated with mortality in critically ill patients, diabetic status modulates these relations in clinically important ways. Our findings suggest that patients with diabetes may benefit from higher glucose target ranges than will those without diabetes. Additionally, hypoglycemia is independently associated with increased risk of mortality regardless of the patient''s diabetic status, and increased glycemic variability is independently associated with increased risk of mortality among patients without diabetes.See related commentary by Krinsley, http://ccforum.com/content/17/2/131See related commentary by Finfer and Billot, http://ccforum.com/content/17/2/134     

Public health implications of opening National Football League stadiums during the COVID-19 pandemic

Using attendance data from the 2020 National Football League (NFL) regular season and local COVID-19 case counts, we estimate the public health impact of opening NFL stadiums to fans during the COVID-19 pandemic. Data are analyzed using robust synthetic control, a statistical method that is employed to obtain counterfactual estimates from observational data. Unlike previous studies [J. Kurland et al., SSRN, 2021], which do not consider confounding factors such as evolving policy landscapes in different states, the synthetic control methodology allows us to account for effects that are county specific and may be changing over time. We find it is likely that opening stadiums had no impact on local COVID-19 case counts; this suggests that, for the 2020 NFL season, the benefits of providing a tightly controlled outdoor spectating environment—including masking and distancing requirements—counterbalanced the risks associated with opening. These results are specific to the 2020 NFL season, and care should be taken in generalizing our conclusions. In particular, 1) these data reflect a period during which earlier strains of COVID-19 were dominant prior to the emergence of more-transmissive strains such as the Delta and Omicron variants, and 2) the data are restricted to outdoor environments; hence our results cannot be applied to small indoor spaces where transmission-restricting controls are essential.

A year and a half into the global COVID-19 pandemic, we have an opportunity to analyze and reflect upon the policies and decisions enacted over the past 18 mo. Given the distributed nature of policy decisions in the United States, we find ourselves in a unique position in which states and municipalities have explored different strategies to combat the virus, and the efficacy of those policies has been imprinted in the local case counts, hospitalizations, and death records. In particular, these data contain a wealth of information about which policies have proven to be effective in preserving the health and safety of our communities.One activity that one may wish to consider is the opening of outdoor sporting events to spectators. This question has recently generated quite a bit of interest as ballparks across the nation open for summer and events such as the 2021 Summer Olympics in Japan take place.^* On the one hand, governing bodies are naturally wary of opening stadiums given the well-documented importance of avoiding large gatherings. On the other hand, sporting events are often held outdoors, where airflow is largely unobstructed (1), and in venues where crowd density can be carefully controlled if the event is properly managed. In the absence of a detailed analysis, it is not immediately obvious which of these effects dominates.Data from the National Football League (NFL) may provide an answer to this question. During the 2020 regular season, teams in the NFL collaborated with local communities to determine whether or not to allow fans in the stadiums during the pandemic. In general, stadiums that opened their doors to fans adopted pandemic requirements for all in attendance (1), which typically include some combination of staggered entry, required masking, health questionnaires, temperature checks for staff, deployment of compliance officers, modified concessions, social distancing in seating and lines, mobile ticketing, enhanced cleaning protocols, amplified health and safety communications, and capacity limitations. The highest capacity that any NFL stadium allowed during the 2020 regular season was 30% (Dallas), with most other stadiums considerably below that limit (2). These policy decisions were made based on local guidelines, local prevalence, community risk tolerance, and other localized considerations; some stadiums ultimately decided to allow fans at the games, while others remained closed, providing perhaps the first set of natural experiments that can be analyzed to investigate the impact of opening stadiums on COVID-19 case rates. In the words of Kurland et al. (3), who recently provided a first look at this data, “Scant evidence has been gathered in the extant literature on the impact of sport venues on local public health, influenza-related mortality rates, or disease contagion more generally. There is a complete absence of any evidence related to the impact of fans gathering at sporting events, or mass gatherings more generally, on incidence of COVID-19 at the local-level.” The natural experiments from the 2020 NFL season and other sports leagues present a golden opportunity to address these questions in the context of the original 2020 COVID-19 strain (4, 5).In the Kurland et al. (3) study, the authors compared COVID-19 case data from NFL stadium counties that allowed fans in the stadium to counties that did not allow fans, and looked for spikes in the data in the weeks following a game; the authors concluded, from this analysis, that the presence of large numbers of fans at NFL games led to “tangible increases” in the local incidence of COVID-19 cases. However, this type of analysis may be problematic: In this context, the control stadiums (i.e., those without fans) tend to be embedded in states with stricter COVID-19 policies—rather than a random control—so the sample of control counties is strongly biased. New York and Dallas, for example, are immersed in very different environments with different pandemic policies, and it is not at all obvious that one can attribute the differences in case spikes to the stadiums, given the enormous number of confounding factors.Fortunately, there exists a rich literature of techniques—longitudinal methods, hierarchical methods, factor model methods, synthetic control, etc.—that we can draw upon to account for these confounding factors. In this particular analysis, we turn to synthetic control (6–9), which has been applied in a diversity of fields—criminology (10), healthcare (11), sports (12), and political science and policy evaluation (13–15), to name a few. At its heart, synthetic control is a method for estimating a counterfactual in the absence of an intervention, in this case, what would have happened if stadiums had not opened. The method provides a systematic way to choose relevant comparison units when randomized controls are not available.To illustrate the power of synthetic control, imagine the ideal experiment one would like to run in order to quantify the impact of opening the Dallas stadium to fans. In principle, we would like to have COVID-19 case counts from Dallas County throughout the season with the stadium open to fans and case counts from a Dallas twin—with identical people and policies to the first Dallas—in which the stadium did not open for comparison. The first set of data (Dallas open to fans) is readily available. The second set of data can be constructed from information from other counties in Texas—hereafter referred to as donor counties—which have policies and characteristics similar to Dallas. Synthetic control provides a methodology to build a weighted combination of these Dallas-like counties, which can then be used as a control group, that is, a “synthetic” Dallas twin. In particular, we seek the linear combination of case counts from other Texas counties that most closely mirrors the Dallas case counts prior to the stadium opening. Given that none of these non-Dallas counties have a stadium, this linear combination can be extended postintervention (i.e., after opening the stadium) to estimate what would have happened in the synthetic Dallas in which no stadium opened. Once it has been established that the stadium county and the synthetically generated county have similar behavior over extended periods of time prior to the intervention, a discrepancy in the number of COVID-19 cases following the intervention may be interpreted as a result of allowing fans in the stadium. One of the advantages of this method is that it can account for the effects of confounding factors that are county specific and may be changing over time, which is crucial in the ever-evolving policy landscape of a pandemic (16). In particular, our methodology allows for correlation between the decision to open the stadium and characteristics that define the county (cultural or political leaning, population density, demographics, etc.), but cannot account for correlations between the decision and exogenous noise.At this point, it is reasonable to speculate whether one should expect linear combinations of donor counties to accurately represent stadium counties (both observed and counterfactual). In general, assuming linearity is appropriate provided there exists an underlying low-dimensional structure to the case count data, that is, if the matrix containing discretized time series of donor county case counts is approximately low rank. Under a such a setting, linearity between counties is an almost immediate consequence (see Materials and Methods for details). This low-rank assumption is common in the matrix completion literature; notably, low-rank matrices have also been shown to naturally arise in modern datasets and emerge from “well-behaved” generative models (e.g., Lipschitz functions) (17–20). This point will be revisited in Results, where we test for low rankedness empirically in the context of our dataset.Finally, the selection of donor units is a critical step in the successful implementation of creating a synthetic control. In particular, donor units (in our case, counties) should have the following characteristics:

• 1)Counties affected by the intervention or by events of a similar nature should be excluded from the donor pool.
• 2)Counties that may have suffered large “idiosyncratic shocks” (7, 21) during the preintervention period should be excluded.
• 3)The donor pool should be restricted to counties with characteristics similar to the stadium county; in this case, we restrict our pool to counties from the same state to maintain some consistency in COVID-19 policies.
• 4)Case counts that cover an extended period of time prior to the intervention are required for both stadium counties and donor counties.

In order to establish which counties satisfy these constraints, the NFL provided us with aggregate attendance data indicating the percentage of fans from each county in each state (2). In general, 10% or more of the fans come from the county in which the stadium is located. Hence, we designate counties that provided more than 10% of the fan base as stadium counties. In addition, there are a number of counties that are home to many fans but not to the same extent as that of the stadium counties. Since there is some ambiguity as to whether these counties should be counted as stadium counties or donor counties, we designate counties that supply between 1% and 10% of the fan base as buffer counties and, in light of the first criterion above, do not include them as either stadium or donor counties. Second, to address criterion 3, we only include counties in the donor pool that come from the same state as the stadium county. Although there is variation at the county level, overarching COVID-19 guidance, in general, comes from the states; hence, we assume that policies are relatively consistent within states and allow that they may vary dramatically from state to state. In addition, we only retain counties in which at least 200 cases have been recorded, in order to eliminate donor counties that are either markedly underreporting or undertesting. Finally, we are fortunate that football season starts in September, which allows us to address criterion 4; given that relatively reliable COVID-19 case count data have been available since approximately April 2020, we have 4 mo of training data at our disposal to learn the weights for the synthetic counties. Criterion 2 is trickier, given that we do not necessarily know, a priori, all events that could cause a shock to the system; however, a posteriori, we can investigate the outcomes and look for signs of such a shock.^†     

镉相关翻译启动因子TIF3致Ras癌基因蛋白的异常表达

目的 研究镉相关翻译启动因子TIF3对多种细胞肿瘤相关基因表达的影响,探索镉的分子致癌机制。方法应用TIF3基因真核细胞稳定表达系统和Western blot检测技术,用各种单克隆抗体检测细胞肿瘤相关基因蛋白的表达情况。结果 相对于载体转染中国仓鼠卵巢(CHO)对照细胞,在4株具有高效稳定表达TIF3编码蛋白质的CHO细胞系中均有pan—ras癌基因蛋白异常表达,其编码蛋白(21kDa)含量均明显高于对照组;其余肿瘤相关基因蛋白如c—myc．c-jun,MDM2,ODC,P16,p53,Cyclin D1的表达蛋白在TIF3转基因细胞与对照细胞之间未见明显差别。结论 镉相关翻译启动因子TIF3是一种镉应答原癌基因,TIF3的超额表达可导致Ras癌基因蛋白异常表达,这可能是镉应答原癌基因TIF3的分子致癌机制。     

Facteurs associés aux perdus de vue des patients sous traitement antirétroviral dans un centre de traitement ambulatoire du VIH à Conakry,Guinée

Background

Late or inadequate therapeutic management increases the risk of mortality associated with HIV/AIDS. The aim of this study was to analyze the proportion and factors associated with loss of follow-up in HIV patients who receiving antiretroviral therapy at Conakry.

Successful Treatment of Bone Marrow Failure in Gaucher's Disease with Low-dose Modified Glucocerebrosidase

We report the beneficial effects of enzyme replacement therapywith mannose-terminated human glucocerebrosidase (‘Ceredase’)in a patient suffering from transfusion-dependent bone marrowfailure due to Gaucher's disease. Treatment with low-dose enzymeinfusions, given twice weekly, rapidly reversed the haematopoieticfailure and incapacitating skeletal disease. It appears likelythat prior splenectomy favourably influenced the response tothis therapy.     

Beta2 integrin/ICAM-1 adhesion molecule interactions in cutaneous inflammation and tumor promotion

The beta2 integrin (CD 18/CD 11 a, b, c) family of proteins mediate adherence of leukocytes to vascular endothelium and the associated ligand, intercellular adhesion molecule-1 (ICAM-1; CD 54), interacts with beta2 integrin proteins to allow transendothelial migration of leukocytes into sites of inflammation. The present study examines the function of these proteins in a murine model of acute cutaneous inflammation induced following topical application of 12-O- tetradecanoylphorbol-13-acetate (TPA) to the dorsal epidermis of SENCAR mice and in a model of skin multistage carcinogenesis. At 24 h following topical application of TPA to the dorsal epidermis of mice, dermal leukocytes expressed higher levels of beta2 integrin protein compared with the lower levels of beta2 integrin protein expression by peripheral blood leukocytes. ICAM-1 protein was localized to epidermal keratinocytes and vascular endothelium in TPA-treated skin and to proliferating papilloma cells. Intravenous (i.v.) injection of either 50 microg anti-beta2 integrin antibody alone or in combination with anti-ICAM-1 antibody significantly inhibited both TPA-stimulated neutrophil infiltration into the dermis (P < 0.001) and myeloperoxidase (MPO) activity (P < 0.03 anti-beta2 integrin antibody; P < 0.01 anti- beta2 integrin + ICAM-1 adhesion molecule antibodies), but had no effect on TPA-induced epidermal hyperplasia. In addition, injection of either anti-ICAM-1 adhesion molecule antibody alone (P < 0.004) or in combination with anti-beta2 integrin antibody (P < 0.001) significantly inhibited TPA-induced production of 7,8-dihydroxy-2'-deoxyguanosine (8- OHdG) immunoreactive proteins by epidermal keratinocytes. Beta2 integrin/ICAM-1 adhesion molecules work in concert to regulate migration, retention and functional activation of leukocytes within the dermis during TPA-induced skin inflammation and within stromal tissue of papillomas that form during multi-stage carcinogenesis. Agents that inhibit these receptor/ligand interactions may be useful in defining the roles of specific cell populations in cutaneous inflammation and multistage carcinogenesis and may also have potential as anti-promoting and anti-progression agents.      

Postappendectomy fluid collections in children: incidence, nature, and evolution evaluated using US

At the authors' medical center, most patients with postappendectomy fluid collections are treated conservatively. Thirty-two (15%) of 216 children underwent postoperative sonography following appendectomies. In ten patients (31%), a total of 16 fluid collections were found on the initial postoperative sonogram. In the seven patients (70%) whose fluid collections were confined to the pelvis, the condition was treated conservatively and it resolved in 2-9 weeks. In three patients, fluid collections required surgical drainage and proved to be abscesses. In two of the three patients, abscesses were multiple and widely distributed in the abdomen, and the patients were clinically ill. The authors conclude that clinically symptomatic fluid collections develop postoperatively in approximately 5% of children who have undergone appendectomy for acute appendicitis and that the size and course of the fluid collection can be objectively monitored using sonography. Such fluid collections confined to the pelvis ultimately resolve with conservative, nonoperative therapy, although resolution may take up to 2 months.