Stent edge dissection (SED) is a well-known predictor of worse clinical outcomes. However, impact of SED after current-generation drug-eluting stent (DES) implantation remains unknown since there was no study using only current-generation DES to assess impact of SED. This study aimed to investigate a relationship between SED detected by optical coherence tomography (OCT) and clinical outcomes after current-generation DES implantation.This study enrolled 175 patients receiving OCT after current-generation DES implantation. The SED group was compared with the non-SED group in terms of the primary study endpoints which was the cumulative incidence of major adverse cardiac event (MACE) composed of cardiac death, target vessel myocardial infarction (TV-MI), and clinically-driven target lesion revascularization (CD-TLR).Of 175 patients, SED detected by OCT was observed in 32 patients, while 143 patients did not show SED. In the crude population, the SED group showed a significantly higher incidence of CD-TLR, definite stent thrombosis, TV-MI and cardiac death relative to the non-SED group. After adjustment by an inverse probability weighted methods, the SED group showed a significantly higher incidence of MACE compared with the non-SED group . Fibrocalcific or lipidic plaques, greater lumen eccentricity, and stent-oversizing were the predictors of SED.SED detected by OCT after the current-generation DES implantation led to unfavorable outcomes. Aggressive post-dilatation around the stent edge might worse clinical outcomes due to SED, although achievement of optimal stent expansion is strongly encouraged to improve clinical outcomes. Stent implantation can result in vessel wall injury between the edge of a stent and the adjacent vessel wall , 2. It hRegardless of imaging modalities such as IVUS or OCT, there was no study that evaluated clinical outcomes of SED after only current-generation DES implantation. The current-generation DES has provided a different performance from bare metal stent (BMS) and first-generation DES since it improved safety and feasibility compared with BMS or first-generation DES , 16. It This study was a single-center, retrospective observational study at Saitama Medical Center, Jichi Medical University. Between April 2010 and March 2020, the consecutive patients undergoing PCI were reviewed. The inclusion criteria were as follows: 1) OCT or optical frequency domain imaging (OFDI) were performed, and 2) current-generation DESs were implanted. The exclusion criteria were as follows: 1) stent was not required during the procedure, 2) BMS or first-generation DES were implanted, 3) final OCT or OFDI images after the procedures were not available, and 4) quality of images was poor to analyze. When a patient received OCT procedures more than once during the study period, only the initial procedure was included. Those patients were divided into the SED and non-SED groups according to the presence of SED detected by OCT. SED detected by OCT was defined as a disruption of the vessel luminal surface with flap at an adjacent site to the stent edge (< 5mm). The strategy of procedures was dependent on operators using OCT assessment. If necessary, the lesion preparation such as pre-dilatation, aspiration and rotational atherectomy was performed before stenting. Pre-dilatation was considered when stent underexpansion and the difficulty of device derivability were expected, or when pre-dilatation makes it easy to perform the next procedure for any reasons. Aspiration was performed when an obvious thrombus by angiography was observed, or when the effectiveness of aspiration was expected. Rotational atherectomy was required when heavily calcified lesions by angiography or intra-coronary imaging such as IVUS or OCT was observed, or unsuccessful balloon dilatation or unsuccessful balloon delivery occurred due to calcification. This study was approved by the institutional review board of Saitama Medical Center, Jichi Medical University (S20-124), and written informed consent was waived because of the retrospective study design. Follow-up data until August 2020 were obtained from a review of hospital records based on clinic visits. Saitama Medical Center, Jichi Medical University is a local core hospital. The annual average number of PCI was approximately 400 to 800 cases a year during this study-period. There are two catheter rooms and at least several interventional cardiologists performed the PCI-procedures in this hospital, although they were not consistent for this study-period from 2010 to 2020.The primary study endpoints were the cumulative incidence of MACE which were composed of cardiac death, target vessel myocardial infarction (TV-MI), and clinically-driven target lesion revascularization (CD-TLR). All-cause of death, stent thrombosis, and target vessel revascularization (TVR) were also evaluated in this study. Definitions of clinical endpoints were based on the Academic Research Consortium (ARC) . CardiacWe performed OCT using one of the following systems: M2 OCT system , ILUMIEN , OPTIS , and LUNAWAVE . Motorized pullback OCT imaging was performed at a rate of 1.0 mm/s through the stent. Images were acquired at 15.6 frames/s and digitally archived. C7XR, ILUMIEN, and OPTIS system were acquired automatically at a pullback rate of 20 mm/s (100 frames/s) or 36 mm/s (180 frames/s) and that of OFDI at a pullback rate of 20 mm/s (160 frames/s). All images were stored digitally and analyzed offline by the LightLab OCT imaging proprietary software (LightLab Imaging), ILUMIEN/OPTIS software , or Terumo software. Procedure details using each modality have been previously described .All OCT images were analyzed based on conventional definitions reported in expert consensus OCT documents , 21. UsiStent border was defined as the first and last cross-sections of the stented segment where struts could be seen in all 4 quadrants . Stent bData are presented as values and percentages, mean±SD, or median (interquartile range). Categorical variables were compared between the 2 groups with Fisher’s exact test. Continuous variables were compared between groups using the unpaired t-test or the Mann-Whitney U test, based on the data distribution. For edge-level analysis, the generalized estimated equation (GEE) methods was used. Continuous variables were tested by the GEE method with gamma with the log-link based on the data distribution. Categorical data were tested by the GEE method with an ordinal logistic model. For each endpoint, the cumulative incidence probability through the study-period was estimated using the Kaplan-Meier method. The statistical analysis to adjust the background is necessary since this is a retrospective study. In this study including the small number, the inverse probability treatment weighting (IPTW) using the propensity score is chosen to retain all the patient data since propensity score matching reduces the population after matching patients . Hazard During this study period, 302 patients underwent PCI with OCT. Finally, of these patients, 175 patients who were treated with current-generation DESs and underwent post-procedure imaging by OCT were enrolled in this study . Those pProximal, distal and mean reference areas, minimal stent area, and percent expansion were not significantly different between the 2 groups . Table 3Morphological and quantitative data of SED was shown in In the analysis of stent edges, 5 edges were excluded since proximal edges were not observed by OCT due to ostium stenting. The independent risk factor for SED was lumen eccentricity , stent-oversizing index  and progressive atherosclerotic lesion  .The main findings in this study were as follows: 1) SED detected by OCT was observed in 18.3% of cases, 2) SED detected by OCT after current-generation DES implantation was significantly associated with MACE, and 3) Risk factors for SED detected by OCT were lumen eccentricity, stent-oversizing, and progressive atherosclerotic lesion .Each imaging modality such as angiography, IVUS and OCT leads to different incidences of SED due to its resolution. Previous studies showed that SED was observed more often with IVUS 7.8–19.0%) than with angiography 0–4.8%) 9.0% than, 24. OCT.8% 9.0% Previous studies using IVUS and angiography showed SED resulted in a higher rate of stent thrombosis and TLR, although SED had a high possibility of natural healing , 14, 27.Our question was whether these results can be simply applied to the current-generation DES. The current-generation DES equipped with biocompatibility improved safety and efficacy as compared to first-generation DES and BMS , 16. A bIn this study, progressive atherosclerotic plaques, i.e, lipidic and fibrocalcific plaques, and lumen eccentricity were the risk for SED after current-generation DES implantation, which was concordant with previous reports. Several previous studies showed the factors of SED after stent implantation such as excessive stent expansion, calcified or lipidic plaques, residual plaque eccentricity, stent length, and ST-elevation MI presentation , 13, 24.The previous studies have consistently demonstrated that small minimal stent area (MSA) significantly showed worse TLR and MACE . TherefoThis study showed that progressive atherosclerotic plaques  by OCT and lumen eccentricity were associated with SED. Therefore, operators have to avoid locations with these features as stent landing zones. However, even though using OCT, SED related to stent-oversizing could not be completely avoided. One of the potential reasons for SED related to stent-oversizing is that the actual stent landing zones were different from those initially planned. Those unplanned stent landing zones can be led by heart-beat, longer or shorter length of the stents than planned, or any technical mistakes. Therefore, the possibility of stent landing at unplanned zones should be considered when stent length is selected. A choice of a downsized stent is also an option to avoid SED. When it is difficult to expect the landing zone, a downsized stent can be a choice for safety. The actual landing zone would be checked by an intra-coronary imaging device after an implantation of a downsized stent, and then, the appropriate size of post-balloon should be decided to correct stent malapposition if present.This study has the following limitations. First, this study was a retrospective and observational study at a single center. Second, the patient background might not be completely adjusted. The prevalence of ACS, which significantly differed between the SED and non-SED groups, was not included as the covariant set for IPTW, because the definition of AHA/ACC lesion classification includes the information of thrombus which is more frequently found in ACS than in stable angina. In the process of variables selection, if clinically similar variables remained, we selected the variable that we considered to be more clinically relevant in order to avoid multicollinearity. Therefore, AHA/ACC type classification was used as the covariant set rather than ACS or not. Third, the population of this study is small. Finally, this study covered the long study-period from 2010 to 2020, which was long enough to change the strategy of the procedure in a single center. In fact, changes in the PCI-strategy and -procedure might affect the occurrence of SED. However, those changes would not affect so much on the main conclusions of this study, since the aim of this study was to investigate the factors of SED and compare clinical outcomes between the SED and non-SED groups. However, in order to exclude possible confounding factors, further prospective and large-scale trials which include enough population during a short period are needed to assess the significance of SEDs.SED detected by OCT after current-generation DES implantation was associated with worse clinical outcomes. Atherosclerotic lesions, lumen eccentricity, and stent-oversizing were predictors of SED. The operators need to plan strategies to avoid SED, although it is important to achieve optimal stent expansion in order to improve clinical outcomes.S1 Dataset(XLSX)Click here for additional data file.


An inability to bear self-weight is one of the unfavorable results in geriatric hip fracture, which needs to be prevented. This study determines pre-operative, intra-operative, and post-operative prognostic factors of the inability to bear self-weight at discharge in patients with fragility femoral neck fracture. This retrospective study was conducted at Chiang Mai University (CMU) hospital with an observational cohort design. Electronic medical records of patients aged ≥ 50 years old with fragility femoral neck fractures between 1 January 2015 and 31 December 2019 were reviewed. Pre-, intra-, and post-operative factors were collected. Ambulation status at discharge time was classified into either ability or inability to bear self-weight. Analysis of prognostic factors was done using multivariable risk ratio regression. In total, 269 patients were recruited in this study. Significantly prognostic factors of inability to bear self-weight at discharge were end-stage renal disease (ESRD), cirrhosis, cerebrovascular disease, pre-fracture ambulatory status, having associated fractures, increasing intra-operative blood loss, and having pressure sore. These prognostic factors could be used to predict patients’ outcomes at discharge. Proper management could then be offered to the patients by the multidisciplinary care team to enhance surgical outcomes. During the past decades, Thailand has been continuously growing into a complete aging society . An incrThe primary treatment goal was to enable the patients to return to their pre-fracture state with optimal ambulation and the ability to self-care. Achieving the patients’ ability to ambulate independently can maximize their full functional potential and prevent serious complications, such as venous thromboembolism, pressure sores/ulcers, pneumonia, or urinary tract infections ,5. MoreoTo properly deal with patient and their families’ concerns regarding ambulation and functional outcomes after surgery, the orthopedic surgeons or general physicians should understand the natural progression of the disease and prognostic factors that affect patients’ status. This study aimed to explore accurate pre-, intra-, and postoperative prognostic factors that affect the inability to bear self-weight at discharge in patients with fragility femoral neck fracture in the Thai clinical context.This prognostic factor research was conducted with a retrospective cohort design. Electronic medical records of patients aged ≥ 50 years old diagnosed with a fragility femoral neck fracture between 1 January 2015 and 31 December 2019 were reviewed and retrieved. Patients with pathological fractures due to bone tumors or bone metastasis or patients who were referred to other hospitals were excluded from this study.The data on potential prognostic factors were collected in three separate categories: pre-operative factors, intra-operative factors, and post-operative factors. For pre-operative factors, we collected the data on sex ,10, age For intra-operative factors, the data from operative notes and anesthetic records were extracted, such as the amount of time from admission to surgery, total anesthetic time, and the volume of intra-operative blood loss . Post-operative factors included post-operative intensive care unit (ICU) admission or ventilator use, major post-operative complications  , other oThe endpoint of interest is the ambulation status at discharge for each patient. All patients were classified as either unable or able to bear self-weight at discharge. Patients who were unable to bear self-weight were defined as patients who could only ambulate in a wheelchair or patients who could not ambulate (non-ambulatory status). In contrast, patients who were able to bear self-weight were defined as patients who had independent ambulation or patients who could ambulate with gait aids. p-value < 0.05 was considered statistically significant. Categorical variables were described by frequency and percentage. Based on data distribution, continuous variables were presented with mean and standard deviation or median and interquartile range (IQR). Fisher’s exact probability test was used to compare the differences in proportion across the two study groups . Fisher’s exact was chosen over the chi-square test due to the relatively small number of sample size. By contrast, continuous variables were compared using Student’s t-test (parametric) or the Mann–Whitney U (non-parametric) test, depending on the data distribution. Statistical analysis was performed using Stata Statistical Package version 16.1 . A two-sided Univariable and multivariable risk ratio regression under Poisson distribution, or Poisson working model, were used to explore and identify associations between prognostic factors and an inability to bear self-weight at discharge. In performing statistical modeling, we executed three separate models: pre-operative factors model, intra-operative factors model, and post-operative factors model. Each model would be adjusted by the confounder summary score derived from other models. For instance, all pre-operative factors would be conditioned on intra-operative and post-operative confounder summary scores . The multiple imputation with chained equation method (MICE) with a total of 20 imputed datasets was used to impute the missing values of factors with less than 50% missing. Sex, age, pre-fracture ambulation status, comorbidity, types of surgical treatments, and ambulation status at discharge were selected as independent variables to predict the mean of those missing values by using linear regression. Any variables with more than 50% missing data were not considered potential prognostic factors and, thus, were not included in the analysis.p < 0.001). Missing data on the following prognostic variables was observed: BMI, serum albumin level, and median pain score on rehabilitation day. However, the proportion of missing data was less than 20%  were unable to bear their self-weight at discharge after surgery . Most ofthan 20% .For intra-operative factors, patients whose total time from admission to surgery was higher than 48 h and patients with longer anesthetic times were more likely to be unable to bear self-weight at discharge . Howeverp = 0.042), cirrhosis , cerebrovascular diseases , pre-fracture ambulation with gait aids , non-ambulatory before the fracture event , having associated fractures , every 100 mL of intra-operative blood loss , and having pressure sores after surgery . The multivariable risk ratio regression results are shown separately for pre-operative, intra-operative, and post-operative factors in The geriatric- or fragility hip fracture is one of the health problems in every country. This study focuses on femoral neck fracture because the treatment of choices differs significantly from those of pertrochanteric fractures. In treating patients with fragility femoral neck fracture, especially for displaced fracture, arthroplasty is generally preferred over fixation ,29,30. BThe ambulatory status at discharge is one of the issues to which the patients and their caregivers were concerned , as the In this study, we have identified seven significant prognostic factors for the inability to bear self-weight at discharge in patients with fragility femoral neck fractures who underwent surgical operations: ESRD, cirrhosis, cerebrovascular disease, ambulation with gait aids or non-ambulatory status before the fracture event, having associated fractures, high intra-operative blood loss, and the presence of pressure sore after surgery. For clinical implementation, these factors could be classified into two groups, modifiable and non-modifiable factors. Patients’ comorbidities  and pre-fracture ambulation status are non-modifiable. However, patients with these conditions should not be overlooked. Proper pre-operative management should be performed to ensure that the patients are fit for surgery. The presence of associated fractures, higher volume of intra-operative blood loss, and the presence of pressure sore after surgery are, in contrast, practically modifiable factors. p < 0.01). Rehabilitation of patients diagnosed with hip fractures and associated fractures can be difficult and challenging due to pain, balance, and gait issues [Associated fractures occur in approximately 4 to 6.5% of patients diagnosed with femoral neck fractures . Most ast issues . Orthopet issues .More intra-operative blood loss causes anemia, which is related to many unfavorable postoperative outcomes, especially delirium and cardiovascular event triggering. These events are significant barriers to the successful implementation of early rehabilitation programs among geriatric patients . The proNon-modifiable factors should be controlled and corrected as much as possible. For example, adequate intake of calories could improve nutritional status among patients with chronic diseases ,45. Nonep-values, which were sample size-dependent. Finally, the results of our study might be generalizable to health care settings with similar clinical contexts and patient or healthcare characteristics. External validation of our results in other countries, or different settings, should be warranted.There were some limitations to this study. Firstly, this study is conducted using a retrospective cohort study design. The presence of missing data on prognostic variables that were not routinely collected has the potential to bias our results. Fortunately, less than 20% of missing data were identified. Moreover, standard imputation methods were used to account for this issue properly. Secondly, only relatively small samples were available to be included compared to the number of potential predictors pre-specified to be analyzed. According to power back calculation, our current sample size was inadequate to identify statistically significant differences of several predictors within the model . HoweverThe ability to bear self-weight is one of the expectations of physicians, patients with fragility hip fractures, and their families. Significant prognostic factors that affect the inability to bear self-weight at discharge were ESRD, cirrhosis, cerebrovascular diseases, pre-fracture ambulatory status, associated fractures, increased intra-operative blood loss, and the occurrence of pressure sores after surgery. Prognostic factors are not used to judge the patients’ status at discharge nor to decide whether surgical operations should be performed but are used as essential prognostic information to communicate with the patients and their families during shared decision-making. Multidisciplinary collaboration with holistic care during the perioperative period is key to improving patients’ outcomes and quality of life.


There is emerging evidence that frail individuals present a decreased physiological reserve, decreased ability to maintain homeostasis, and increased vulnerability to stressors. The concept of frailty has become increasingly recognized as a valuable measure in oncological surgical patients, including those with head and neck cancer. Preoperative screening for frailty may provide an individualized risk assessment that can be used by an interdisciplinary team for preoperative counseling and to improve outcomes. The aim of this meta-analysis was to evaluate the relationship between frailty and the risk of major postoperative complications in frail individuals submitted to head and neck oncologic surgery. PubMed, SCOPUS, Web of Science, Google Scholar and OpenThesis were systematically searched to identify studies that evaluated the risk of major postoperative complications in frail individuals undergoing head and neck oncologic surgery. The search was performed on August 31, 2020, without language or date restrictions. Two independent investigators screened the searched studies based on each paper’s title and abstract. Relevant studies were read in full and selected according to the eligibility criteria. Frailty was assessed by modified Frailty Index (mFI-11) and major postoperative complications were measured by the Clavien-Dindo classification. We performed a categorical and dose-response meta-analysis using a random-effects model to evaluate the association between frailty and the risk of major postoperative complications in patients submitted to head and neck oncologic surgery. The results of the meta-analysis were expressed as relative risk (RR) and 95% confidence interval (95% CI). The risk of bias was assessed using the Newcastle-Ottawa Scale (NOS). Four studies  were included in this systematic review and meta-analysis. Frail patients presented an increased risk of life-threatening complications requiring intensive care unit (ICU) admission  and 30-day mortality  compared to non-frail patients. We found evidence of dose-response trend between mFI-11 and major postoperative complications. Higher frailty scores are associated with a significant increase in ICU-level complications and 30-day mortality after head and neck oncologic surgery. Key words:Frailty, head and neck neoplasms, postoperative complications, mortality. Frailty has been characterized as a multidimensional syndrome resulting from cumulative cellular damage over the life course and is identified as a major predictor of adverse outcomes in older subjects . Frail iThe heterogeneity of patients with cancer and the multidimensional effects of both malignancy and surgery underscore the importance of incorporating more comprehensive preoperative assessments in oncology surgery . Preoperet al., which evaluates frailty through five criteria: unintentional weight loss, subjective exhaustion, low grip strength, reduced walking speed, and low levels of physical activity  system, which includes five categories (I-V) of increasing severity based on the clinical treatment of complications. CD grade IV (CD-IV) and CD grade V (CD-V) are considered major postoperative complications and include any life-threatening complication requiring intensive care unit (ICU) admission and death, respectively . The knoThis study was conducted following the Meta-Analysis of Observational Studies in Epidemiology (MOOSE) statement . Institu- Research question and eligibility criteriaThe present study focused on the following question: Is there a dose-response relationship between frailty and the risk of postoperative complications in head and neck oncologic surgery? Studies were considered eligible if they satisfied the following criteria: (i) they were retrospective or prospective studies; (ii) patients submitted to head and neck oncologic surgery, including upper aerodigestive tract (UADT) , thyroid, parathyroid, associated lymph nodes, soft tissues, and bone ,14;  screened the searched studies based on each paper’s title and abstract. Relevant studies were read in full and selected according to the eligibility criteria. Disagreements between the two reviewers were resolved by consensus or by a third reviewer (P.R.S.M.-F.).- Frailty and major postoperative complications assessmentFrailty was assessed using the mFI-11, which includes 10 items related to comorbid conditions and one item related to the patient’s functional status . To calculate the mFI-11, the presence of each variable equals one point, and the total points for each patient is divided by 11 to obtain the patient’s mFI score . Patients were classified as non-frail (mFI = 0), pre-frail (mFI = 0.09-0.18), and frail (mFI ≥ 0.27) .Major postoperative complications included CD-IV  and CD-V (death).- Data extractionTwo independent investigators (A.C.A.C. and M.L.T.M.) extracted data from the published reports using a predefined protocol. Information about the study design, eligible population, age and gender distribution, inclusion and exclusion criteria, frailty assessment, and postoperative complications were checked. To evaluate the relationship between mFI-11 and postoperative complications, we extracted from primary studies the number of CD-IV and CD-V complications for each cutoff value for frailty from the mFI-11.- Risk of bias assessmentThe quality of the studies was assessed by two independent reviewers (A.C.A.C. and M.L.T.M.) using the Newcastle-Ottawa Scale (NOS), and included evaluations of the representativeness of the sample size, selection of a comparison group , ascertainment of exposure by secure records, outcome of interest not present at the start of the study, outcomes adjusted for age and other possible confounding variables, assessment of postoperative complications by independent blind assessment or record linkage, adequate follow-up period for outcome of interest (30 days), and loss to follow-up unlikely to introduce bias. These items were not merged into a quality score. Instead, the relevant information for each domain was tabulated to allow for greater transparency.- Data analysisWe performed a categorical and dose-response meta-analysis using a random-effects model to evaluate the association between frailty and the risk of major postoperative complications in patients submitted to head and neck oncologic surgery. The categorical meta-analysis was conducted by pooling the relative risk (RR) for frail patients (mFI ≥ 0.27) compared to non-frail individuals (mFI = 0). Forest plots were used to graphically present the pooled RR and the 95% confidence intervals (CI). P-values lower than 0.05 were statistically significant. Heterogeneity was investigated using the Cochran Q test with a cutoff of 10% for significance and quantified using the I2 index . PublicaThe multivariate dose-response meta-analysis evaluated the relationship between mFI-11 scores and the risk for major postoperative complications. This analysis was performed using the dosresmeta R package and modelled with both a linear and quadratic curve . We usedWe used the Akaike Information Criterion (AIC) to compare the fittings of dose−response curves with linear and quadratic models. The AIC value represents how well a model fits the data set, where a relatively low AIC means a better fit than a higher AIC.- Study selectionThe initial search located 427 records through electronic databases, of which 162 were collected from PubMed, 46 from SCOPUS, 116 from Web of Science, 100 from Google Scholar and three via a hand-searching. No studies were found in the OpenThesis database. Seventeen studies were potentially relevant and were analyzed in full. After reading the full-texts, 13 studies were excluded due to the frailty or postoperative complications measurements, outcome data, or reconstructive surgical procedure. Finally, four studies satisfied the eligibility criteria and were included in the present systematic review ,20-22. A- Study characteristics and risk of bias assessmentThe studies included in this systematic review reported data from 10,358 patients submitted to head and neck oncologic surgery. However, due to the potential overlapping population, data from 394 patients undergoing total laryngectomy and 17 patients submitted to esophagectomy were excluded. Finally, data from 9,947 patients were analyzed in this study.All studies had a retrospective design and included data from the American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP). The ACS-NSQIP is a national risk- and case-mix-adjusted database that collects deidentified information to measure and improve the quality of surgical care. This database includes data on preoperative risk factors, intraoperative variables, and 30-day postoperative mortality and morbidity outcomes for patients undergoing major surgical procedures in both the inpatient and outpatient setting.et al. frail patients (mFI-11 ≥ 0.27) and 203 (4.4%) non-frail patients (mFI-11 = 0) had CD-IV complications. Frail patients presented an increased risk of CD-IV complications compared to non-frail patients  . We tested the best-fitting model using the AIC, and the linear function model was more appropriate to predict the CD-IV complications and 30-day mortality. Dose-specific RR with the corresponding 95% CI are reported in Head and neck cancer accounts for more than 900,000 cases and 370,000 deaths worldwide annually . SuccessOur meta-analysis showed an increased risk of CD-IV complications and 30-day mortality in frail patients after head and neck oncologic surgery. The ability to predict relevant postoperative outcomes is one of the most important characteristics of any risk stratification variable or system. Risk prediction plays an important role in recognizing patients at high risk of complications and in implementing appropriate treatments, thereby preventing failure to rescue (FTR). FTR is the loss of life among hospitalized patients resulting from the inadequate recognition and treatment of potentially fatal complications, and can be used as an important indicator of hospital care quality . DespiteThe mFI-11 has been validated in various surgical fields. In degenerative spine disease  and orthAlthough there are other approaches used to predict postoperative complications after head and neck surgery including the American Society of Anesthesiologists (ASA) classification and the Johns Hopkins Adjusted Clinical Groups (ACG) system, which are based on the evaluation of physical health and Fried physical phenotype model of frailty, respectively, the mFI-11 seems to be a more practical predictive tool because variables are easily assessed in the clinical setting and require less time in assessing frailty ,10,19. SThe mFI-11 score can be applied using medical record databases  and can Recently, a categorical meta-analysis showed an association between frailty phenotype and postoperative complications among patients aged 60 years and over submitted to cardiac, gastrointestinal, and orthopedic surgeries . HoweverThis study has some major limitations. We included a heterogeneous group of tumors arising from the head and neck region, patients undergoing concomitant chemotherapy and radiotherapy, and a variety of surgical approaches. In addition, cancer surgeries may require more surgery time, which may be associated with an increased risk of complications. These characteristics may have a potential role in the between-study heterogeneity despite the choice of the random-effects model. Clinical or methodological diversity may be important sources of heterogeneity. In this sense, mFI-11 can be improved to capture these differences in head and neck cancer patients to mitigate heterogeneity and confer greater predictive value.Although studies included in this systematic review had a low risk of bias, we recognize the potential for missing data in the medical records, the lack of comparisons with a physical frailty phenotype due to the limitations of the dataset, and the lack of long-term register-based follow-up. Moreover, further studies are needed to compare mFI-11 with other measures of comorbidities.Higher frailty scores are associated with a significant increase in ICU-level complications and 30-day mortality after head and neck oncologic surgery.


In coordination with the Florida Department of Health, the VA Sunshine Healthcare Network (VISN 8) established Long-Term Care Strike Teams to provide services to the LTC facilities most affected by the COVID-19 pandemic across the state of Florida. Between April 2020 through September 2020, the Strike Teams provided direct patient care to community residents, infection control/ prevention education, and patient/staff COVID-19 swabbing. We encountered facilities with large numbers of staff infected with COVID-19 and agency staff that were refusing to come to “COVID-infected” facilities. Remaining staff, including the administrators, were under much psychological distress. However, our experience supporting the long-term care facilities also had a major impact on our own perceptions of nursing home care. The bravery, dedication, and caring that we witnessed reinforced that the health care workers in long-term care facilities are true heroes.


In the pursuit of understanding life, model membranes made of phospholipids were envisaged decades ago as a platform for the bottom-up study of biological processes. Micron-sized lipid vesicles have gained great acceptance as their bilayer membrane resembles the natural cell membrane. Important biological events involving membranes, such as membrane protein insertion, membrane fusion, and intercellular communication, will be highlighted in this review with recent research updates. We will first review different lipid bilayer platforms used for incorporation of integral membrane proteins and challenges associated with their functional reconstitution. We next discuss different methods for reconstitution of membrane fusion and compare their fusion efficiency. Lastly, we will highlight the importance and challenges of intercellular communication between synthetic cells and synthetic cells-to-natural cells. We will summarize the review by highlighting the challenges and opportunities associated with studying membrane–membrane interactions and possible future research directions. Nature is the prime source of inspiration for humans to understand life and create something new. Many of today’s technologies have been inspired from our surroundings, for example, the invention of flight (birds), submarine , Shinkansen bullet train (kingfisher), sonar (dolphin and bats), and many more. The scientific community has not been untouched by this inventiveness that has led to many novel research branches, for example biomimicry, synthetic cell research, and in vitro protein synthesis as the frontiers in the field of synthetic biology ,3,4,5,6.A natural cell was thought to have a simple construction, which later turned out to be a highly complex unit of life hosting many reactions with spatiotemporal precision . Cell meResearchers have been trying to mimic cellular functions to enhance their understanding about vital biological processes. To achieve this, bilayer membranes made with amphiphilic phospholipids are used. Many platforms are available where lipid bilayers are created, including standing bilayer membrane, supported bilayer, and unilamellar lipid vesicles see . StandinSignificant efforts have been made in recapitulating different aspects of membrane–membrane interactions, such as vesicle–vesicle fusion. We will highlight the conditions required to achieve membrane fusion and challenges associated with it. We will primarily discuss DNA-mediated and coiled-coil peptide-mediated fusion and their potential future direction. Lastly, inter/intra-cellular communication will be discussed, where we will highlight some recent research on synthetic cell-to-synthetic cell and synthetic-to-natural cell communications.‘Black’ lipid membrane (BLM) received its name because of its appearance by optical microscopy. BLM was discovered some 60 years ago when Mueller et al. ,14 reconThe main limitation of this method, apart from the fact that the bilayer formed by this method is fragile, is the presence of extra solvent in the bilayer membrane that compromises the measurements. Subsequently, a solvent-free bilayer assembly method was introduced by Montal and Mueller . In thisSupported lipid bilayer (SLB) is a planar bilayer formed as a result of self-assembly of phospholipids on a hydrophilic  surface ,21. The 2, indium-tin-oxide, gold, silver, and platinum ..−7 M . . 137]. UMembrane-less liquid–liquid phase-separated droplets can also be used as models of synthetic cells. Interactive behaviors such as prey and predator, for example, have been reconstructed between proteinosomes and coacervates . InteracE. coli. Later, Lentini et al. designed synthetic cells that can sense the presence of V. fischeri through N-3-(oxohexanoyl)homoserine lactone (3OC6 HSL) and communicate with E. coli by synthesis of another homoserine lactone 3OC12 HSL, or participate in the V. fischeri quorum-sensing mechanism by synthesis and release of 3OC6 HSL , and cell-to-cell communications. We discussed different platforms of generating synthetic lipid bilayer membranes in the context of studying different ion channels and membrane proteins. There has been significant progress in generating giant vesicles with maximum encapsulation and minimum-to-no leakage , especiaAlthough DNA-mediated and coiled-coil peptide-mediated membrane fusion have gained popularity, they suffer from issues of controllability and stability of hemi-fusion or fusion intermediates. Recently, inter-cellular communications among synthetic cells and between synthetic cells and natural cells have received great attention in synthetic cell research . We beli


Traditionally, opioid-related disease burden was primarily due to heroin use. However, increases in extra-medical  use has precipitated the current overdose epidemic in North America. We aim to examine the state-level prevalence of heroin and NMPO dependence and their associations with opioid-related mortality and state-level socio-demographic profiles. Data were pooled from the 2005–2014 National Survey on Drug Use and Health (NSDUH). We examine opioid-related mortality from CDC WONDER (Cause of Death database) by the past year prevalence of DSM-IV heroin and NMPO dependence, by age and sex, and their associations with state-level socio-demographic characteristics from census data. State-level rates of heroin dependence were associated with opioid-related death rates in young and mid-aged adults, while rates of NMPO dependence were associated with opioid-related death rates across all ages. The prevalence of heroin dependence was positively associated with state-level GDP/capita and urbanity. State-level NMPO dependence prevalence was associated with higher unemployment, lower GDP/capita, and a lower high-school completion rate. The prevalence of heroin and NMPO dependence are associated with a broad range of geographical and socio-demographic groups. Taking a wider view of populations affected by the opioid epidemic, inclusive interventions for all are needed to reduce opioid-related disease burden. Opioid dependence and related harms in the United States (U.S.) represents a substantial public health issue . There hIn the late 1990s and early 2000s, NMPO-associated mortality rose with the wave of increased opioid prescribing . PharmacThe rates of opioid mortality vary by location, with the Northeast and Appalachian regions being the most heavily impacted ,15. The There has been a number of previous state-specific studies on the epidemiology of opioid use in the U.S., including studies in Ohio , ConnectThis is a cross-sectional analysis of state-level prevalence, mortality, and socio-demographic variables between 2005–2014. Three data sources were used for this study with information presented in the corresponding sections below. The state-level prevalence estimates were sourced from the National Survey on Drug Use and Health .The NSDUH is a nationally representative study that collects data on substance use and dependence. The NSDUH interviews approximately 70,000 persons aged 12 years and above every year. This study used survey data available from 2004–2014 in the analysis. It samples residents of households, non-institutional group quarters, and civilians living on military bases. Respondents were selected through a stratified multistage area probability sample, representing each of the 50 states and the District of Columbia. Household addresses were randomly selected, then 1–2 members of the household may be selected to complete a full interview. Participation is voluntary and the participants are reimbursed with USD 30 for their contributions to the survey. The participants conduct the interviews in their own home. Participants answer the questions in private and the interview takes, on average, one hour to complete. All the information collected is confidential and used only for statistical purposes.Statistical imputation is used to replace missing or ambiguous values after editing for key variables. The procedure applies probabilistic statistical methods to identify another similar respondent whose data were used to replace the missing values or ambiguous responses in the recipient record. Methodological documentations of the NSDUH are published online by SAMHSA. Analysis weights are created so that estimates are representative of the target population. Response rates for each year of the study were ranged from 82–91% .Geographic identifiers are collected in the NSDUH, but micro-data on opioid dependence by states are not distributed in the public domain. State-level data on past-year opioid dependence were obtained from the Substance Abuse and Mental Health Services Administration (SAMHSA) through a special data request that approved the use of and provided data from 2005–2014. Year-by-year data by state was not provided by SAMHSA due to small numbers after stratification by age and sex to protect the confidentiality of the participants. Therefore, the state-level was provided as the weighted mean of the 10-year sample. That is, analysis weights were applied to the estimates to be representative of the target population. Then, data over the 10 years were pooled by  SAMHSA across the years, to provide state-level prevalence estimate for our state-level associates analyses.Past-year opioid dependence, stratified by age, sex, and state, was defined according to the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV). Included in the interview were questions based on the six criteria in the DSM-IV for diagnosing substance-use dependence. For heroin and pain relievers, a seventh withdrawal criterion was added and was defined by a respondent reporting having experienced a number of withdrawal symptoms . A respondent was defined as having dependence if they met three or more of the seven dependence criteria for heroin or opioid use. Three categories of opioid dependence were examined: (1) heroin dependence, (2) NMPO dependence, and (3) any opioid dependence (including those who met either of the first two categories). Categories 1 and 2 are not mutually exclusive; they include people who met criteria for both heroin and NMPOs.Mortality caused by overdose  due to opioids  was retrieved from the CDC Wonder Cause of Death database. These codes were selected because they represent opioid-related deaths. A description of the codes can be found in The state-level socio-demographic characteristics examined included: GDP/capita, mean personal income, high-school completion rate, rurality, and unemployment rate. GDP/capita was calculated as each state’s annual Gross Domestic Product divided by their resident population . PersonaFirstly, we examined the state-level variations in opioid dependence by presenting the prevalence estimates by the states. All analyses were stratified by the three opioid dependence categories. We presented the weighted prevalence overall, and by age and sex , and NMPO dependence ranged from 0.2–1.2% . The rates were lowest in Nebraska (3.2), Hawaii (3.9), and Mississippi (3.9), and highest in West Virginia (31.6), New Hampshire (23.4), and New Mexico see .p < 0.001; see https://public.tableau.com/app/profile/janni.leung/viz/Opioid-relateddeathratesbystate-levelprevalenceofopioidheroinandnon-medicalprescriptionopioiddependenceintheUSA-LeungJ2022/Dashboard (accessed on 15 December 2021)). Opioid-related mortality was more strongly associated with NMPO  than with heroin dependence  and NMPO-dependence  rates were significantly correlated with opioid death rates in the 18–25 years age group. In the 26+ years age groups, NMUPO-dependence prevalence was significantly associated with opioid death rates, but heroin dependence was not.There was a positive association between state-level opioid dependence rates and state-level opioid-related death rates  and lower high school completion  rates were significantly associated with a higher prevalence of state-level opioid dependence (see p = 0.005), lower personal income , and lower high school completion rate  were associated with NMPO dependence prevalence. In contrast, higher GDP/capita , higher personal income , and lower proportion living in rural areas , were associated with a prevalence of heroin dependence in the states.Descriptive statistics for the socio-demographic factors across the states are available in ence see . At a stThis study examined the associations between NMPO and heroin dependence with state-level opioid-related deaths and socio-demographic characteristics in the United States. The prevalence of heroin dependence was associated with opioid-related deaths in young-to-middle-aged groups, possibly because of the low prevalence in adults over 50 years of age. Prevalence of NMPO dependence was correlated with opioid-related deaths in adults from all age groups. We found that indicators of higher socioeconomic advantage were associated with a higher prevalence of heroin dependence, but negatively associated with NMPO dependence. This finding may be explained by the greater accessibility of heroin in bigger cities, which are primarily located in higher-income states . In contThe finding that the prevalence of opioid dependence was significantly correlated with opioid-related mortality, corroborated with the literature that determined that individuals with long-term opioid dependence are at a higher risk of overdose ,14. Our The finding that NMPO dependence was correlated with opioid-related deaths in middle-aged and older adults (aged 26+ years) is relevant for public health strategies to address the opioid epidemic. Compared to younger people, middle-aged and older adults are more likely to develop dependence initiated by prescribed pain relievers , often oPrevious studies observed that the factors that influence the association between opioid use and state-level socio-demographics include the relatively expensive  street price of heroin , as wellOur findings imply that public health interventions for the opioid epidemic need to consider the broad variety of socio-demographic groups affected. There is strong evidence that opioid agonist treatment (OAT), for which coverage is currently low, is highly effective in reducing opioid use and opioid-related deaths, including overdose, suicide, HIV, hepatitis C virus, and injuries . A recenThis study has a few limitations. The NSDUH findings cannot be generalized to special and high-risk populations, such as incarcerated individuals or intravenous drug users, among whom mortality risk due to overdose is much higher.Another limitation is that our state-level measures were pooled across multiple years, precluding an analysis of temporal trends by state. We cannot assume that our variables have not changed across time, so our findings cannot be used to inform population-level changes over time. The validity of year-by-year variations in the prevalence of heroin use in general population surveys has been under debate . Under-rWe did not have data that explicitly distinguishes synthetic opioids, nor illicitly manufactured synthetic opioids, which drove opioid mortality in the U.S.A. . An analFuture studies should examine the concurrent use of opioids with other substances to inform interventions, because there are differences in the use of other substances among people who use NMPOs . A recenIn addition to understanding the associates of an opioid-related burden, our findings have other public health implications,  . Future The prevalence of opioid dependence was not only associated with mortality in young adults, but also associated with opioid-related mortality in older adults by extra-medical use of pharmaceutical opioids. Prevalence rates of opioid dependence were associated with a range of geographical and socio-demographic groups, including populations with high income and education levels. Public health interventions and public policies should take a broader view of the variety of populations affected by opioid misuse, inclusive of people affected by both heroin and prescription opioid use, the age ranges, and genders affected. We need to be increasing the access to effective interventions for all, to reduce opioid-related disease burden in the U.S.A.


Objective: Developing an integrative approach to early treatment response classification using survival modeling and bioinformatics with various biomarkers for early assessment of filgrastim (granulocyte colony stimulating factor) treatment effects in amyotrophic lateral sclerosis (ALS) patients. Filgrastim, a hematopoietic growth factor with excellent safety, routinely applied in oncology and stem cell mobilization, had shown preliminary efficacy in ALS.Methods: We conducted individualized long-term filgrastim treatment in 36 ALS patients. The PRO-ACT database, with outcome data from 23 international clinical ALS trials, served as historical control and mathematical reference for survival modeling. Imaging data as well as cytokine and cellular data from stem cell analysis were processed as biomarkers in a non-linear principal component analysis (NLPCA) to identify individual response.Results: Cox proportional hazard and matched-pair analyses revealed a significant survival benefit for filgrastim-treated patients over PRO-ACT comparators. We generated a model for survival estimation based on patients in the PRO-ACT database and then applied the model to filgrastim-treated patients. Model-identified filgrastim responders displayed less functional decline and impressively longer survival than non-responders. Multimodal biomarkers were then analyzed by PCA in the context of model-defined treatment response, allowing identification of subsequent treatment response as early as within 3 months of therapy. Strong treatment response with a median survival of 3.8 years after start of therapy was associated with younger age, increased hematopoietic stem cell mobilization, less aggressive inflammatory cytokine plasma profiles, and preserved pattern of fractional anisotropy as determined by magnetic resonance diffusion tensor imaging (DTI-MRI).Conclusion: Long-term filgrastim is safe, is well-tolerated, and has significant positive effects on disease progression and survival in a small cohort of ALS patients. Developing and applying a model-based biomarker response classification allows use of multimodal biomarker patterns in full potential. This can identify strong individual treatment responders (here: filgrastim) at a very early stage of therapy and may pave the way to an effective individualized treatment option. Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disorder with a median survival time of 15.8 months from diagnosis .Filgrastim  is a safe and widely used FDA-approved drug to mobilize hematopoietic stem cells. Filgrastim is also a neuronal growth factor with neuroprotective and regenerative properties , 4, enhaWe provided individual filgrastim treatment to ALS patients in our ALS-outpatient clinic. Filgrastim was applied subcutaneously in a cyclic fashion with individually adjusted dosing. Monthly safety monitoring included assessment of functional decline and survival, as well as gathering of serum retain samples. MRI scans were performed every 3 months. Patient safety and biomarker data from our population have been analyzed earlier –10, 13, Subsequently, we compared survival and functional decline of filgrastim-treated ALS patients to the largest publicly available collection of ALS patient data from international clinical trials, the PRO-ACT database Biomarkers for disease progression and treatment response are essential in ALS. In view of the heterogeneity concerning disease etiology, pathophysiology, and clinical phenotype, it seems unlikely that single biomarkers could provide this information in total. Rather, an integrated observation of panel biomarkers within different biological systems over time could be useful in the understanding of such a complex disease—and why ALS patients respond so differently to a given treatment. We generated models for survival prediction to identify ALS patients, who might benefit most from this new long-term treatment approach, and assessed development in different biomarker domains  in responding and non-responding patients over time. Non-linear principal component analysis (NLPCA) has been successfully applied in CNS disorders such as traumatic brain injury . NLPCA aTreatment with subcutaneous filgrastim was offered since January 2010 to 36 patients with definite or probable sporadic ALS according to the revised El Escorial criteria  after wrThe survival and functional decline control dataset was based on the latest version of PRO-ACT (release January 2016)Survival analyses were conducted on a final PRO-ACT dataset of 4,617 patients as defined in the database*month (representing the interaction effect), ALSFRS-R score and age at treatment initiation, treatment latency (onset delta), and sex; the indicator variable for ALSFRS-R score imputation at baseline (ALS BL Imp Ind) was included where it had significant influence. The interaction term “database*month” quantified the difference in steepness of ALSFRS-R decline between groups. The analyses were focused mainly on the first 6 months of treatment. The estimated mean ALSFRS-R course was compared in patient subgroups. An extended model, including three-way interactions (database*month*onset delta), tested dependency on treatment latency , time of ALSFRS-R measurement modeled as three-knot spline, To allow estimation of survival times in individual patients, a general linear model with exponential distribution and a reciprocal link function was built on deceased rp-PRO-ACT patients. Variables taken were ALSFRS-R slope (robust estimate), age, and treatment latency. The model-predicted individual survival was correlated and compared to the observed survival within this subgroup. Next, the model was applied to all PRO-ACT and filgrastim-treated patients (including censored patients). Subsequently, hypothetical survival was compared to observed survival. Filgrastim treatment response was derived from the difference between individual model-predicted and observed survival, a Kaplan–Meyer analysis for both groups was conducted. To address the question, if ALSFRS-R time courses differ dependent on filgrastim response, treatment response was added in the mixed-effect model for estimation of functional decline.+ and CD34+CD38− hematopoietic stem and progenitor cells (HSPC) were analyzed in peripheral blood by flow cytometry as previously described  as a robust descriptive tool to reduce the dimensionality of large numbers of variables, handling various data types  and missing values . A non-lA linear mixed model was conducted, with the previously determined responder categories serving as independent variables  received filgrastim in addition to riluzole . Mean agp < 0.0001 by log-rank test) and the subgroup of rp-PRO-ACT patients  from the first year onward compared to all PRO-ACT patients , the Cox proportional-hazard ratio was 1.93 and 0.52, respectively   . In a “M.6-fold) , the estdatabase*month (p < 0.0001) indicates a treatment difference in functional decline evolving linearly over time . The interaction term ver time . Using ap < 0.0001) with a difference near zero. This remained when applying the model to all PRO-ACT patients. The difference between observed and model-predicted survival was significantly greater in filgrastim patients than in all PRO-ACT patients and in the rp-PRO-ACT subgroup  derived from 447 deceased patients in the rp-PRO-ACT subgroup. The resulting model-predicted patient survival correlated with the observed survival within this subgroup (reatment . When coreatment : youngerWe then clustered patients by treatment response (15 patients in each group) to analyze peripheral cytokines by an area under the curve (AUC) approach. Assessing timepoints at 3, 6, and 9 months of treatment revealed significantly higher serum levels of IL 6, MCP 1, eotaxin, and MCP 4, as well as TNF beta and IL 7 as a trend, in non-responding patients . We thenp < 0.01; p < 0.01; p < 0.01; p < 0.01; p < 0.01].We first investigated the neuroimaging data at 12 months. PC3 was driven mainly by areas from motor system and neurogenesis and accounts for 9.5% of variance. Responding patients had significantly higher PC3 scores, indicative of more sustained motor system/neurogenesis than non-responders (p < 0.01) and stem cell markers  as well as markers for inflammatory cytokines  explained the variance in responding and non-responding patients . For cytokines, two significant PCs separated the patient groups: responders had lower scores in PC1  and higher scores in PC3  (both p < 0.01). The meta-PC was significantly predictive  of treatment response (AUC = 0.677, p = 0.026; Imaging data could not be analyzed at 6 and 3 months due to missing data. At 6 months of treatment, again hematology (PC1, 24.7% of the variance, patients . This reWe retrospectively analyzed a novel therapeutic approach in ALS with long-term filgrastim application in 36 patients. We chose the well-known PRO-ACT database for reference and modeling. The treatment of a small number of ALS patients and the use of historical controls are obvious limitations one has to keep in mind when generalizing our results. To ensure comparability at baseline, we characterized the cohorts of filgrastim-treated and PRO-ACT patients, addressed the main outcome covariates, and adjusted for baseline difference whenever necessary. The comparability was quite adequate; however, filgrastim patients were on average 6 years younger than PRO-ACT patients, which was compensated for whenever needed . The rilA main achievement by our statistical approach was the generation of a model for survival estimation that was based on deceased patients in the PRO-ACT database. When this survival model was applied to filgrastim-treated patients, it classified them as responders or non-responders due to individual differences between model-estimated and observed survival time . The cliAs we did not perform genetic testing, lack of information on genetic background is a limitation when analyzing the data. We found that younger filgrastim patients responded better to treatment; however, on top of their age-related prognostic advantage, they survived considerably longer than their age-matched PRO-ACT counterparts . With fiFilgrastim may exert beneficial effects in ALS by multiple possible mechanisms of action. It is a neuronal growth factor within the CNS that exerts neuroprotective properties , 4 and iThe impact of a biomarker-guided individualized ALS therapy was highlighted by modeling. We addressed the complexity of individual ALS patients by modeling the different pathophysiologic impacts of inflammation, stem cells, and brain structure, as well as biomarker response to treatment, in a comprehensive way, not paralleled before in ALS. We thereby detected approximately 40% of strong filgrastim responders. We need to keep in mind that in other patient populations response rates may be higher or lower, depending on individual compositions, treatment latency, and duration. Indeed, biomarker evaluation allowed assignment of patient response to filgrastim as early as within 3 months of treatment. Thus, treatment time windows for response known from oncology may also apply for neurodegeneration; this strongly highlights the imperative necessity of early therapeutic intervention.Filgrastim is a widely used, well-tolerated drug, with an excellent safety profile in ALS that has been highlighted by Wallner et al.  and our In this diligent and in-depth comparison of survival and functional decline in filgrastim-treated patients, we considered all PRO-ACT patients as reference and then narrowed the comparison down to the most suitable cohort of rp-PRO-ACT patients, which was also used for further modeling. We paid particular attention to relevant methodical bias, made adjustments for demographic and clinical differences between the two groups, and conducted analysis in carefully matched patients. Comparable results in different statistical approaches confirmed the stability of the difference in functional decline and survival between filgrastim and PRO-ACT. We developed a PRO-ACT-based model for individual survival prediction and applied this model to filgrastim patients to detect treatment response. Individuals most responsive to treatment were then identified at the early stage by principal component biomarker analysis for stem cells, neuroinflammation, and structural imaging. Although the treatment effect is impressively associated with age—young patients benefited most from treatment—also some of the elder patients were clearly treatment responders. Subcutaneous filgrastim has a favorable safety profile: thus, the benefit risk ratio is positive, as our data give a clear signal of efficacy with both slower clinical progression and increased survival in ALS patients.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Ethics committee of the University of Regensburg, Regensburg, Germany. The patients/participants provided their written informed consent to participate in this study.SJ: care for ALS patients, conception of intervention, acquisition, analysis, interpretation of data, and wrote the manuscript. JH, WK, and AF: substantial contribution to the conception of the study analysis, interpretation of data, and revision of the manuscript. BB and AS: substantial contribution to the conception of the study, analysis, interpretation of data, and revision of the manuscript. SP, EW, and SK: performed cytokine experiments and revision of the manuscript. AW: acquisition and analysis of MRI data and revision of the manuscript. SI and JG: performed and analyzed HSPC experiments and revision of the manuscript. TK: organization of care and disposition of patient material. IK: assistance in maintenance of the database. MT and GS: acquisition of the MRI data and revision of the manuscript. TP: revision of the manuscript. LA: discussion, critical review of intervention and analysis, and revision of the manuscript. WS-M: care for ALS patients, conception of intervention and analysis, and revision of the manuscript. T-HB: conception of intervention and analysis, and revision of the manuscript. UB: care for ALS patients, conception of intervention and analysis, revision of the manuscript, and clinical responsibility for intervention. All authors contributed to the article and approved the submitted version.UB and LA hold patents for clinical application of G-CSF in ALS. Orphan Drug Status is granted for EU and US by EMA and FDA—all within NeuroVision Pharma GmbH, Murnau, Germany. UB, LA, and T-HB are owners of the company Velvio GmbH. WK is the owner of the company BDS Koch. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


The goal of PDBe-KB is to place macromolecular structure data in their biological context by developing standardised data exchange formats and integrating functional annotations from the contributing partner resources into a knowledge graph that can provide valuable biological insights. Since we described PDBe-KB in 2019, there have been significant improvements in the variety of available annotation data sets and user functionality. Here, we provide an overview of the consortium, highlighting the addition of annotations such as predicted covalent binders, phosphorylation sites, effects of mutations on the protein structure and energetic local frustration. In addition, we describe a library of reusable web-based visualisation components and introduce new features such as a bulk download data service and a novel superposition service that generates clusters of superposed protein chains weekly for the whole PDB archive.The Protein Data Bank in Europe – Knowledge Base (PDBe-KB,  The structure of biological macromolecules and their complexes is invaluable for understanding their functions ,2. TheseFor 50 years, the Protein Data Bank (PDB), managed by the worldwide Protein Data Bank consortium (wwPDB) , has serWhile macromolecular structures are invaluable, they often need to be interpreted using additional structural and functional annotation layers to answer specific biological questions . For exaMany specialist data resources, and scientific software provide such annotations, and their number keeps growing . HoweverWe established PDBe-KB in 2018 to make these annotations FAIR  through a global collaboration between PDBe and leading specialist data providers and scientific software developers . This cohttps://pdbe-kb.org/guidelines). Contributing data resources are requested to provide their PDB residue or PDB chain annotations in a data format defined and maintained by the consortium (https://github.com/PDBe-KB/funpdbe-schema). This data exchange format evolves according to the partner resources' requirements, and the consortium reviews the specification during annual PDBe-KB workshops. In addition, PDBe-KB makes the integrated annotations openly accessible to the scientific community through file transfer protocol (ftp://ftp.ebi.ac.uk/pub/databases/pdbe-kb), programmatic access (https://pdbe-kb.org/graph-api) and web pages (https://pdbe-kb.org/proteins). As a result, the consortium grew from 18 to 30 collaborating data resources from 11 different countries in the past two years (Table PDBe-KB is an open consortium transparently governed by a collaboration guideline (The infrastructure of PDBe-KB consists of four main components. These are (i) a deposition system for annotations; (ii) a graph database that integrates annotations with the core PDB data; (iii) a rich set of application programming interface (API) endpoints that provide access to the data; (iv) a set of reusable web components that are combined to create the PDBe-KB aggregated views Figure .https://github.com/PDBe-KB/funpdbe-schema. Collaborators then copy their JSON files to private FTP areas provided by PDBe-KB, hosted at EMBL-EBI in Hinxton. A weekly running data processing pipeline parses, validates and integrates the data from these JSON files into the PDBe graph database. When displaying or providing access to annotations from any PDBe-KB partner resources, we provide direct links the users can follow to find the original data set from the corresponding database or scientific software.The data deposition system changed significantly to ensure scalability as more partner data resources joined the consortium. Data providers are required to convert their annotations to JavaScript Object Notation (JSON) files, according to the data exchange format specification, which is available at https://pdbe-kb.org/graph-download, and users can install it in-house to use it as a research tool for data mining. It requires ∼0.5TB of local storage space, preferably on an SSD drive with a recommended 6 GB RAM and eight cores.The PDBe graph database is an up-to-date knowledge graph that contains the latest PDB data, linked to the corresponding UniProt accessions and integrated with structural, functional and biophysical annotations. It is implemented in Neo4j v3.5 and has over 1 billion nodes and 1.5 billion edges. The database is openly accessible at https://pdbe-kb.org/graph-api.The PDBe aggregated API provides programmatic access to all the aspects of the data contained within the graph database. As we integrated new annotations into PDBe-KB, we have expanded the API and currently provide over 90 different API endpoints. We have described these endpoints and provided use case examples elsewhere . The APIhttps://github.com/PDBe-KB?q=component.PDBe-KB web pages use modular web components which can be reused and customised easily. We have created an open-source library for these components so that data service developers can use them as plugins for visualising structural data. In addition, they provide built-in support for the PDBe aggregated API, allowing developers to display data from PDBe-KB conveniently. We implemented the web components using the AngularJS framework. They are available and freely reusable from GitHub at We continuously develop the PDBe-KB pages, displaying all the available structural information for a protein, keyed on a UniProt accession. We call these pages aggregated views of proteins. In addition, we have added several features, in particular: (i) a superposition service to visualise protein chains clustered by structural similarity; (ii) a bulk download service that provides easy access to all the coordinate files, validation reports, sequences for a protein of interest; (iii) a section dedicated to processed proteins and (iv) annotations for small molecules and macromolecular interaction partners.https://github.com/PDBe-KB/pdbe-kb-manual/wiki/Superposition. The superposed coordinates are made available on the aggregated views of proteins, where clustered, superposed PDB chains can be displayed using the interactive 3D molecular viewer, Mol* , sequences (FASTA format) and validation data Figure . The updhttps://www.ebi.ac.uk/pdbe/pdbe-kb/proteins/PRO_0000449633 is the dedicated page of the 2'-O-methyltransferase nsp16 of SARS-CoV-2, which is a processed protein from Replicase polyprotein 1ab (UniProt accession P0DTD1). These pages are available for all the processed/mature proteins with known structures, not only viral proteins.In response to the COVID-19 pandemic, we developed a unique set of web pages focused on the proteins of SARS-CoV-2 in early 2020. However, it became apparent that we could improve the display of polyproteins. In particular, the aggregated views were not highlighting the mature, processed proteins, and users could not zoom in on these proteins. To address this, we have integrated information on processed proteins from UniProt and have added a new section that highlights the segments they occupy on the full-length polyprotein sequences Figure . We now While experimentally determining structures remains a costly and labour-intensive endeavour, there have been significant advances in the field of structural predictions. Researchers increasingly deploy Artificial Intelligence (AI) techniques to predict a protein's structure computationally from its amino-acid sequence alone ,13,48. WMost of the annotations provided by the PDBe-KB partner resources focus on amino acid residues and their functions or biophysical characteristics, yet PDBe-KB has information also on molecular entities such as small molecules or macromolecular interaction partners Figure . For exahttps://pdbeurope.github.io/api-webinars/index.html.Working together with the Training team of EMBL-EBI, we actively participated in training courses and continued to create training materials and tutorials that describe the new functionalities and changes to PDBe-KB web services and web pages. Recently, we created a set of tutorials that encompass programmatic access to PDBe data, data processing packages and web components that visualise the structures and annotations. These tutorials are available at PDBe-KB expands the structural, functional, and biophysical annotations of molecular structure data according to its long-term goals. By allowing integrated and FAIR access to these annotations, researchers in academia and industry can take advantage of the rich ecosystem of specialist data resources and scientific software and efficiently collate data to answer specific biological questions. Since we established PDBe-KB in 2018, the collaboration grew, integrating data from 30 partner resources across 11 countries, providing over 1.2 billion residue-level annotations. Furthermore, PDBe-KB continues to be one of the main activities of the ELIXIR 3D-BioInfo community, which brings together researchers, structural bioinformatics developers and data providers to discuss and strive for data FAIRness, benefitting the broader scientific community .While we plan to further improve the aggregated views of proteins, we are also developing novel aggregated web pages for ligands, providing comprehensive structural and functional information on all the observed small molecules in the PDB archive.Finally, we would like to extend an invitation to all the data providers and scientific software developers to join the consortium and increase user exposure through this community-driven data-sharing platform and knowledge base.In conclusion, PDBe-KB keeps evolving and makes structural data and their structural, functional, and biophysical annotations more accessible to the scientific community, reaching over 340 000 users annually either through their usage of the rich set of programmatic access endpoints or by their visits to the PDBe-KB aggregated views pages.https://pdbe-kb.org. Individual, protein-focused pages per UniProt accessions are available at https://pdbe-kb.org/proteins/P0DTD1. Documentation of the consortium members is available at https://github.com/PDBe-KB/pdbe-kb-manual/wiki. Users can download the graph database from https://pdbe-kb.org/graph-download, and users can find the aggregated API at https://pdbe-kb.org/graph-api. The PDBe-KB web component library is public at https://github.com/PDBe-KB?q=component. Finally, we make all the annotations available in JSON format from ftp://ftp.ebi.ac.uk/pub/databases/pdbe-kb.PDBe-KB is available at


Providing caregiving support to people with Duchenne muscular dystrophy (DMD) is challenging, beginning in early childhood, and continuing through the progression of multidimensional disability. This study addressed the interplay between caregiver impact, out-of-pocket expenditures, and DMD disability. To examine these interconnections, we investigated the association between caregiver impact domains and out-of-pocket expenditures; and the presence of clusters in caregivers on the basis of DMD-related disability domains in the patients for whom they provided caregiving support.This web-based study recruited 566 DMD caregivers , examining caregiver impact using the DMD Caregiver Impact Measure, PROMIS-derived parent-proxy (PPP) measures of their child’s disability, and items tapping out-of-pocket expenditures related to home and vehicle accommodations and assistive devices. T-tests compared caregiver impact scores by out-of-pocket expenditures incurred. Latent Profile Analyses (LPA) were conducted to generate impact profiles related to child’s disability as reported by caregiver proxies.Higher out-of-pocket expenditures were generally associated with worse impact on the subscales, but several expenditures  were associated with lower impact. LPA indicated that the four-group solution provided the best relative fit and yielded good profile separation (entropy = 0.91). Caregivers with lowest impact reported the highest mobility, cognitive, and upper extremity functioning of their DMD care recipients, whereas the highest caregiver impact was driven by their care recipient’s negative affect and fatigue. The upper-middle impact group showed great variability in proxy-disability domains, whereas the lower-middle group had similar levels of disability across domains. Profiles were represented across all child ages.Out-of-pocket expenditures were often associated with worse caregiver impact, but some associated with milder impact . While their son’s level of disability and age were related to impact on the DMD caregiver, the domains giving rise to highest caregiver impact were not the most visible aspects of disability, such as mobility, but rather negative affect and fatigue. Other contextual attributes are likely implicated, and will be addressed in the companion paper.The online version contains supplementary material available at 10.1186/s41687-022-00425-2. Providing caregiving support to chronically-ill children is challenging, particularly in the context of a diagnosis and disease onset in the early-childhood and a progressive disease trajectory. The long-term effects of caregiving are well-documented, and can include poorer health, quality of life (QOL), and well-being –5. Treat12, although medical advances [A particularly challenging diagnosis for caregiving is Duchenne muscular dystrophy (DMD). DMD is a progressive, rare, and irreversible neuromuscular disorder occurring primarily in males—1 in 5050 live births –9. Usualadvances  have ledadvances .Research on DMD caregiving suggests that impact increases with disease progression  and thatThe present study sought to address how best to characterize the interplay between caregiver impact, DMD disability, and DMD-necessitated out-of-pocket expenditures. We addressed questions such as “How do out-of-pocket expenditures link with caregiver-impact domains?” and “How do caregiver-impact domain levels and DMD disability domain levels cluster together?” By describing these interconnections, we hope to better understand the impact of DMD on caregivers. To examine these interconnections, we investigated the association between caregiver-impact domains and out-of-pocket expenditures; and the presence of clusters in caregivers on the basis of DMD-related disability domains in the patients for whom they provided caregiving support.www.alchemer.com) from June to November 2020. Dillman’s Tailored Design Method [This study recruited participants via Rare Patient Voice, LLC; patient-advocacy groups; and word of mouth . Eligible participants were age 18 or older, able to complete an online questionnaire, and were providing caregiving support to a family-member with DMD at least two years old, usually their son. Ineligibility criteria included not being of age 18, not being able to complete an online questionnaire, not providing caregiving support to a family-member with DMD who was at least two years old, and not being able to provide informed consent. Caregiver-participants with motor, visual, and/or other problems that made it difficult for them to complete the web-based survey instrument, enlisted the assistance of a household member to enter their survey answers. This survey was administered through the HIPAA-compliant, secure Alchemer engine , transitional (up to age 12), and non-ambulatory (age >  = 13), with increasing dependence and involvement of other systems as the person ages into adulthood (age >  = 18). If caregivers had more than one person with DMD for whom they were providing caregiving support, they were asked to report on the eldest or most disabled person (the index patient). Caregivers were paid $75 honoraria for their time completing the survey. The protocol was reviewed and approved by the New England Independent Review Board (NEIRB #20201623), and all participants provided informed consent prior to beginning the survey.Person-reported outcomes (PROs) were used to characterize and describe caregiver-impact groups. Characterizing the groups focused on caregiver impact, DMD care recipient’s disability, and out-of-pocket expenditures.Caregiver Impact was assessed using a DMD adaptation of the Hemophilia Caregiver Impact measure [ measure , 28. Ite measure . The resCare recipient’s disability was measured using the Patient-Reported Outcome Measurement Information System (PROMIS) Parent Proxy (PPP) item banks [em banks . These wem banks . The PPPOut-of-pocket expenditures assessed included binary variables reflecting the following 11 home and vehicle modifications that might have been implemented to accommodate their child’s DMD: entrance, bathroom, doorways, van, bedroom, kitchen, elevator, new home, ceiling lift, scooter, and other (open text option to specify). We refer to these modifications as “expenditures” because they involve financial costs, in contrast to other accommodations that might not be directly related to financial costs, such as choosing clothing items that do not involve buttons.d of 0.2 to 0.49 is considered a small effect size, 0.5 to 0.79 is medium, and 0.8 or greater is large[Initial analyses sought to use Rasch modeling to summarize the out-of-pocket expenditures on a single unidimensional scale, but these analyses did not result in good item- or person-fit or strong correlations with the DCI subscales. Accordingly, we used a simpler approach, computing independent-sample t-tests and related effect sizes for group differences on each out-of-pocket category and DCI subscale. Using Cohen’s criteria, a  is large.To investigate the interplay of DMD caregiver impact and patient disability domains, two multivariate analyses were implemented and compared. Hierarchical cluster analysis  was usedThe second multivariate approach utilized Latent Profile Analysis (LPA) . LPA is We aimed to recruit a minimum of 130 caregivers per age stratum, allowing for 20% attrition while still yielding 80% power, α = 0.05, to detect a medium effect size in a multiple regression model with seven covariates . A mediuIBM SPSS version 27  and MpluTable Caregivers reported providing support to one to five people with DMD  Table , represeTable d statistics of small or larger ES are shown. Results suggest that some out-of-pocket expenditures were related to multiple aspects of caregiver impact, whereas others showed no association across domains. Specifically, the expenditures linked with the highest negative impact were new home, ceiling lift, van, and entrance modifications. The expenditures that had notable associations with negative impact, albeit on fewer domains, were bedroom and bathroom modifications and purchasing a medical scooter. Expenditures involving doorways and elevators were not associated with any DCI impact. Stated differently, out-of-pocket accommodations were associated with caregiver Physical impact for six categories; with Symptom impact for five; with Lifestyle, Social, Emotional, and Financial impact for four; and with Practical and Positive Emotions impact for two.Table lower caregiver impact. For example, kitchen modifications were associated only with better caregiver outcomes: lower physical impact and improved caregiver positive emotions. Bathroom modifications were associated with lower physical and financial impact, though with higher symptom impact. Ceiling lift was associated with a lower practical impact, despite higher symptom, lifestyle, social, physical, and emotional impacts. Medical scooter was associated with lower practical and financial impacts despite the higher symptom impact.A second notable pattern shown in Table 2 > 0.30). The four-cluster solution showed good discrimination between clusters on 2- and 3-D scatterplots, and multinomial logistic regression using cluster membership as the dependent variable showed good ability of the 16 characteristics  to predict cluster membership.Results of each multivariate analysis yielded a similar solution (4 groupings) with a strong association between groupings (Cramer’s V = 0.55). Each hierarchically-formed cluster had a reasonably large sample, and there was large between-cluster variance relative to within-cluster variance , a reasonably large sample within each profile, and good values for the other model-fit statistics  . The LPA also has the advantage of providing more formal metrics of model fit. We thus worked with the four LPA profiles for subsequent descriptive analyses.A comparison of the cluster and LPA solutions suggested that the latter explained more variance on average in the 16 DCI and PPP scores on which they were based  of disability and age, but also as a function of the necessary accommodations and assistive devices to which they have access. In our companion paper , we examAdditional file 1. Table S1: DMD caregiver impact measure descriptive statistics. Table S2: Summary of latent profile analysis model fit results.