Excessive birth weight is associated with maternal and neonatal complications. However, ultrasonically estimated large for gestational age  predicts these complications poorly.To determine whether a maternal serum metabolite ratio developed for fetal growth restriction (FGR) is predictive of birth weight across the whole range, including LGA at birth.Metabolites were measured using ultrahigh performance liquid chromatography-tandem mass spectroscopy. The 4-metabolite ratio was previously derived from an analysis of FGR cases and a random subcohort from the Pregnancy Outcome Prediction study. Here, we evaluated its relationship at 36 weeks of gestational age (wkGA) with birth weight in the subcohort (n = 281). External validation in the Born in Bradford (BiB) study (n = 2366) used the metabolite ratio at 24 to 28 wkGA.P = 6.7 × 10−5]. The ratio was also inversely associated with birth weight z score . Analysis in the BiB cohort confirmed that the ratio at 24 to 28 wkGA predicted LGA  and was inversely associated with birth weight z score .The inverse of the metabolite ratio at 36 wkGA predicted LGA at term [the area under the receiver operating characteristic curve (AUROCC) = 0.82, 95% CI 0.73 to 0.91, A metabolite ratio which is strongly predictive of FGR is equally predictive of LGA birth weight and is inversely associated with birth weight across the whole range. A high absolute birth weight, independent of gestational age, is called macrosomia and is usually defined as >4000 g . MacrosoThe current approach to ultrasound scanning to detect LGA in pregnancy is to target women at high risk—for example, those with diabetes mellitus or who have a symphyseal-fundal height above the upper limit of the normal range for gestational age . The observations in the Cochrane review indicate that screening the whole population for LGA and offering earlier induction of labor to screen positive women could decrease maternal and neonatal morbidity. However, low risk women necessarily have a lower prior risk of complications and in many cases where the baby is thought to be LGA by ultrasound, the result is a false positive. Modeling has demonstrated that very large numbers of low-risk women need to have intervention to prevent relatively small numbers of adverse events .It has been suggested that combining ultrasonic EFW with maternal biochemical markers could improve diagnostic effectiveness . We haveThe overall approach of the study was (1) to analyze the metabolite ratio and its components measured at 36 wkGA in relation to LGA and birth weight z score at term in the Pregnancy Outcome Prediction (POP) study, (2) to externally validate the POP study results in 24 to 28 wkGA samples from 2 independent subgroups of the Born in Bradford (BiB) study in relation to subsequent LGA and birth weight z score, and (3) to repeat the POP study analysis using measurements from 28 wkGA and the same definitions to allow a direct comparison with the validation results from the BiB study.The POP study was a prospective cohort study of unselected nulliparous women with a singleton pregnancy attending the Rosie Hospital, Cambridge, UK, between January 2008 and July 2012, and it has been previously described in detail ,7. In brWe previously analyzed serial maternal serum samples  using a case cohort analysis in the POP study ; howeverWe wrote a prospective analysis plan for external validation of the metabolite ratio and its components at 24 to 28 wkGA in relation to subsequent delivery of LGA infant and birth weight as a continuous z score in the BiB study [Supplemental Panel 1 ]. The BiThe primary analysis of the POP study used the 36 wkGA serum sample, as the focus of the study was predicting large babies at term, and 36 wkGA was the latest gestational sampling point in the POP study. The BiB study only had a single gestational time point of blood sampling, which was at 24 to 28 wkGA. Hence, we repeated the POP study analysis using the 28 wkGA metabolite ratio to compare associations between the two cohorts. When we compared data from the POP and BiB studies, the same modified definition of LGA was employed in the POP study to allow a direct comparison between the cohorts (see following discussion).In the POP study, nonfasting maternal serum samples were analyzed, whereas in the BiB study, fasting plasma samples were analyzed. Metabolomic measurements were performed by Metabolon  using ultrahigh performance liquid chromatography-tandem mass spectroscopy as the platform. The details have been described previously ,18. In bThe 4-metabolite ratio [(A × B)/(C × D)], its numerator and denominator, and the 4 individual metabolites were used as exposures in the present analysis. The 4 individual metabolites were (A) 1-(1-enyl-stearoyl)-2-oleoyl-GPC (P-18:0/18:1), (B) 1,5-anhydroglucitol , (C) 5alpha-androstan-3alpha,17alpha-diol disulfate, and (D) N1,N12-diacetylspermine. The main outcomes were (1) birth weight >90th percentile (LGA) and (2) birth weight z score for gestational age and fetal sex using the UK 1990 reference  and the 95% CI. The metabolite ratio was originally developed to be positively associated with FGR. Consequently, the direction of association with LGA should be in the opposite direction; hence, the inverse of the ratio was used to calculate the AUROCC. The associations between each exposure and LGA were also analyzed using logistic regression, and associations were expressed as the odds ratio (OR) and the 95% CI for a 1 SD increase in the given exposure.  Panel 1 ]. StatisThe random sample of the POP study cohort included 313 participants. Of these, 11 had preexisting or gestational diabetes and 21 did not have the metabolite measurements available at 36 wkGA, leaving 281 eligible participants. This group included 15 LGA cases and 266 noncases [Supplemental Figure 1 ]. With tP-value between the ratio measured at 36 wkGA and fetal sex was 0.20 when the outcome was LGA and 0.66 when the outcome was birth weight z score.Despite the small numbers, all 4 metabolites at 36 wkGA were associated with the risk of delivering an LGA infant . As antiP = 1.3 × 10−9). The ratio was also predictive of LGA . Interestingly, when the 2 BiB subgroups were analyzed separately, the association with birth weight was weaker in the first sub-group compared with the second  . However, there was strong evidence for an association between the metabolite ratio and the risk of LGA in BiB subgroup 2   . Similarto 0.72)  and 3.Analysis of 28 wkGA samples in the POP study demonstrated that a 1 SD increase in the log-transformed metabolite ratio was associated with a −0.22 SD (95% CI −0.12 to −0.32) change in birth weight z score. Moreover, using the same modified definition of LGA , the 28 wkGA measurement of the ratio was also associated with LGA in the POP study . Hence, the point estimates for BiB subgroup 2 were within the 95% CI of the POP study values relating to the 28 wkGA blood sample, whereas those of subgroup 1 fell outside this range.P-values, are also unlikely to have occurred by the play of chance. The associations with both birth weight z score and the risk of delivering an LGA infant were also validated in a separate group of cases and controls from the BiB study, another pregnancy cohort. Validation was observed despite the fact that the BiB cohort was highly dissimilar from the POP study  and employed different sample types (fasting plasma vs nonfasting serum). We conclude that there is strong evidence to indicate that the metabolite ratio is predictive of fetal growth across the whole range of fetal size. Hence, it may offer clinically useful prediction of LGA, as well as of FGR.The main finding of the present study was that a metabolite ratio, which we had previously described as a predictor for FGR , was alsThe clinical importance of the current work is that this test has potential for use as a screening test in combination with ultrasound. Screening all pregnant women with ultrasound is not currently recommended due to lack of direct evidence that it is clinically and economically effective . One of The biological plausibility of associations between each of the metabolites and fetal growth has previously been discussed . HoweverA relative weakness of the present study is that the gestational age of collection of blood in BiB was at a much earlier stage in pregnancy (24-28 wkGA) than that for the main analyses in the POP study (36 wkGA). We addressed this by comparing the associations with the POP study sample collected at 28 wkGA. However, we anticipate that the ultimate clinical application relating to screening for LGA will take place near term, as the main intervention is earlier term delivery. Ideally, external validation would also have involved analysis of samples at around 36 wkGA. Analysis of the BiB data confirmed the presence of associations with both birth weight z score and with the risk of delivering an LGA infant. The associations were generally weaker, but external validation results are often weaker than those found in the discovery sample (a phenomenon known as “winner’s curse”). However, a further complexity is that the strength of association differed comparing the 2 subgroups of the BiB study. This inconsistency is difficult to explain, given the similarity between the 2 subgroups and given that we observed consistent associations between the 2 subgroups when we analyzed FGR. It may be explained partly by the method for quantifying metabolites in the samples. The metabolomics platform employed in both studies was a discovery platform and the method did not include isotopically labeled standard but rather calculated relative concentrations. More consistent results might be achieved when all metabolites are quantified by the actual molar concentration, and this is an area for future study, as well as being a necessary step for clinical development of the assay.P-value of 0.062 but an AUROCC of 0.68. The likelihood that these values are consistent with a true association with LGA is supported by the observation that the metabolite was strongly associated with the birth weight z score. We overcame the limitation of small numbers by choosing a larger cohort with a mixed parity for validation. The total number of LGA infants included in the analysis from the BiB study was 86 (the proportion of LGA was 4%), and therefore there was much less uncertainty in the estimation of the associations between LGA and the metabolites. The potential impact of fasting on the metabolite ratio could not be directly assessed in the present analysis, and this could be an area for future study. Another interesting area for further study would be to examine the association between the ratio and birth weight related long-term childhood health conditions.Among the women included in the analysis from the POP study (n = 281), the proportion of LGA was about 5%, and the number of women delivering an LGA infant was low (n = 15). This was partly due to the deliberate exclusion of diabetic women and partly because nulliparous women deliver smaller infants on average. For example, with these numbers, estimation of the association between 5alpha-androstan-3alpha,17alpha-diol disulfate and LGA resulted in a Although our validation cohort consisted of ~50% women of Pakistani ethnicity, there were small proportions of other ethnic groups such as black Africans in both POP and BiB study cohorts. Therefore, further external validation is needed to assess the generalizability of our results in other ethnic groups.The analyses adjusted for maternal characteristics in the POP study demonstrated that the observed associations were robust. Moreover, self-reported smoking had a high sensitivity and very high specificity against maternal serum cotinine N-oxide levels. Hence, we do not believe that the results are likely to be affected by the self-report of maternal variables.We were not able to perform an analysis of fully customized birth weight percentiles in the BiB study. However, the utility of customization is not universally accepted. For example, 1 of the largest studies in fetal growth in the last 10 years (InterGrowth 21st) does not support customization , and theTo conclude, we have shown than a recently described metabolite ratio which is predictive for FGR is also associated with birth weight across the whole range, including LGA. The ratio has the potential to improve the prediction of LGA over the current methods. Further studies using a quantitative version of the assay and tested in late pregnancy in other populations would strengthen the case for the ratio’s potential clinical utility in this context.


Previously, we demonstrated that Sec17 and Sec18 can bypass the requirement of complete zippering for fusion , but it has been unclear whether this activity of Sec17 and Sec18 is directly coupled to HOPS. HOPS can be replaced for fusion by a synthetic tether when the three Q-SNAREs are pre-assembled. We now report that fusion intermediates with arrested SNARE zippering, formed with a synthetic tether but without HOPS, support Sec17/Sec18-triggered fusion. This zippering-bypass fusion is thus a direct result of Sec17 and Sec18 interactions: with each other, with the platform of partially zippered SNAREs, and with the apposed tethered membranes. As these fusion elements are shared among all exocytic and endocytic traffic, Sec17 and Sec18 may have a general role in directly promoting fusion.Yeast vacuolar membrane fusion has been reconstituted with R, Qa, Qb, and Qc-family SNAREs, Sec17/αSNAP, Sec18/NSF, and the hexameric HOPS complex. HOPS tethers membranes and catalyzes SNARE assembly into RQaQbQc  Each SNARE domain has heptad repeating apolar residues which are buried together in the center of the assembling four-helical coiled coils structure, driving SNARE assembly in an N- to C-direction. This directional assembly, termed ‘zippering’, can provide sufficient energy for fusion  complex (m of SNAREs for SNARE complex assembly (We study membrane fusion mechanisms with the vacuoles (lysosomes) of  complex . HOPS bi complex  and to i complex , activat complex  to initi complex  and for  complex  and Qc S complex  as well.assembly . HOPS caassembly .trans-SNARE complexes  fused to a PX domain. Dimeric GST-PX binds PI3P in each bilayer to tether membranes  was amplified by PCR with CloneAMP HiFi PCR premix . The amplified DNA fragment was cloned into BamHI and SalI digested pGST parallel1 vector  with an For GST-PX: AGGGCGCCATGGATCCGGCAGCTAATTCTGTAGGGAA.FAGTTGAGCTCGTCGACTATGGCTTTGACAACTGCAGGA.R GST-PX was prepared as described .2, to load the Ypt7 with GTP. After separate preincubation for 10 min at 27°C of both proteoliposome preparations and of mixtures of all soluble proteins  empty assay wells received in rapid succession 5 μl of Ypt7/R proteoliposomes, 5 μl of Ypt7/3Q proteoliposomes, and an 8 μl mixture of all soluble components. FRET representing fusion was recorded each minute for 30 min, as described  direct role for Sec17/18 in content mixing. However, in its present form, the work doesn't go far enough to address whether the proposed novel role for Sec17/18 occurs and makes an important contribution to the reaction under normal conditions."Expert #1. "…As a "research advance" the work does answer an important question left by their previous Expert #2. "Here the authors rely on an artificial truncation of one of the SNAREs, as in the previous paper, and eliminate HOPS in favor of an artificial tether. This creates a situation in which Sec17 and 18 appear to be essential for fusion. How specific is the requirement for Sec17 and 18 when their natural binding partners are altered? For this study to be meaningful, the authors would have to show that the pair of native proteins are required for fusion per se under more normal conditions with the full set of SNAREs, HOPS etc."eLife. Your article has been reviewed by 3 peer reviewers, one of whom is a member of our Board of Reviewing Editors, and the evaluation has been overseen by Suzanne Pfeffer as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Alexey J Merz (Reviewer #3).Thank you for submitting your article "Fusion of Tethered Membranes can be Driven by Sec18/NSF and Sec17/αSNAP without HOPS" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:This brief study has now been reviewed by three experts. There was only one technical issue raised (titration of components). However, all three reviewers were concerned about the "incremental" nature of the advance. The study shows that their Sec17/18-dependent vacuole fusion reaction employing arrested SNAREs and without ATP hydrolysis proceeds without a specific contribution from HOPS. The potential significance of this to a wider audience is that Sec17/18 may contribute directly to the fusion step of many or all SNARE-mediated fusion reactions. But this aspect is not sufficiently tested and needs to be addressed. This and the other reviewer issues are compiled below. Feasibility is clearly a concern regarding an in vivo test (point#4) but the issue of evidence that it normally occurs (e.g. rate acceleration for wildtype components as in point#1) merits particular attention.1. A main issue is the increment of advance. It succeeds in filling in a missing piece of their 2021 paper but on its own does not substantially advance understanding of the critical issue of whether Sec17/18 normally plays this role. If it did, one would expect fusion by normal SNAREs to be significantly accelerated by Sec17/18. From Figure 1B, 1F, this does not appear to be the case.2. One would expect Sec17/18 association with the normal SNAREs to occur prior to fusion, but there is no data to address this.3. The concentration of Sec17/18, the most critical component, is not indicated in either the figure legend or methods.4. Can this unexpected role for Sec17/18 be demonstrated/confirmed for intact cells? Is it essential for wildtype rates of fusion? Or is it strictly redundant in the face of the assembly force of SNARE pairing as a driving force for fusion?5. Does it actually act on other SNAREs? This is important because the clearest argument for this brief study being viewed as a highly significant research advance (over their previous work) is the implication that the unexpected direct role of Sec17/18 can now be considered likely to take place in other, if not all, SNARE-mediated fusion events.6. No concentrations for any soluble component are given. The final concentrations of Vam7/Qc, Sec17, Sec18, HOPS, ATPgS, etc., must be specified. Readers should not have to look at prior publications to locate this information. A possible criticism of this and some similar prior work is that the experiments are conducted at only single concentrations of each reagent, rather than using concentration titrations for the protein components in the reaction. Nevertheless, the results support the authors' conclusions and are broadly consistent with prior work.7. The last paragraph of the introduction could probably be omitted without loss of clarity.8. Line 46: extensive mutational analyses (both in vitro and in vivo) of the Sec17 membrane-interaction loop is present in Schwartz et al. 2017. This work should be cited.9. Lines 62-65: the pioneering paper by Hanson, Jahn et al. was a key step toward the consensus that SNARE complexes are oriented in a parallel coiled coil, and that Sec17/18 may act after, rather than before, fusion. It should be cited.10. Lines 72-73: again, Schwartz et al., 2017 should be cited. [Editors’ note: the authors resubmitted a revised version of the paper for consideration. What follows is the authors’ response to the first round of review.]Expert #1. "…As a "research advance" the work does answer an important question left by their previous eLife paper: whether HOPS is needed for Sec17 and Sec18 to engage partially zippered SNAREs. An artificial tether substitutes so HOPS is not specifically needed and this unexpected role for Sec17/18 could extend to other fusion reactions. It is careful biochemistry that extends an eLife paper and provides data supporting a  direct role for Sec17/18 in content mixing. However, in its present form, the work doesn't go far enough to address whether the proposed novel role for Sec17/18 occurs and makes an important contribution to the reaction under normal conditions."Expert #2. "Here the authors rely on an artificial truncation of one of the SNAREs, as in the previous paper, and eliminate HOPS in favor of an artificial tether. This creates a situation in which Sec17 and 18 appear to be essential for fusion. How specific is the requirement for Sec17 and 18 when their natural binding partners are altered? For this study to be meaningful, the authors would have to show that the pair of native proteins are required for fusion per se under more normal conditions with the full set of SNAREs, HOPS etc."We now state clearly, with reference, that Sec17/Sec18 stimulation has been reported with all wild-type components, i.e., SNAREs. Our new finding establishes that the role of Sec17/Sec18 in promoting fusion is not limited to HOPS-catalyzed reactions, and thus merits serious considerations for other endocytic and exocytic trafficking steps[Editors’ note: what follows is the authors’ response to the second round of review.]Essential revisions:This brief study has now been reviewed by three experts. There was only one technical issue raised (titration of components). However, all three reviewers were concerned about the "incremental" nature of the advance. The study shows that their Sec17/18-dependent vacuole fusion reaction employing arrested SNAREs and without ATP hydrolysis proceeds without a specific contribution from HOPS. The potential significance of this to a wider audience is that Sec17/18 may contribute directly to the fusion step of many or all SNARE-mediated fusion reactions. But this aspect is not sufficiently tested and needs to be addressed. This and the other reviewer issues are compiled below. Feasibility is clearly a concern regarding an in vivo test (point#4) but the issue of evidence that it normally occurs (e.g. rate acceleration for wildtype components as in point#1) merits particular attention.1. A main issue is the increment of advance. It succeeds in filling in a missing piece of their 2021 paper but on its own does not substantially advance understanding of the critical issue of whether Sec17/18 normally plays this role. If it did, one would expect fusion by normal SNAREs to be significantly accelerated by Sec17/18. From Figure 1B, 1F, this does not appear to be the case.This point is now directly addressed in the next-to-last paragraph of the Introduction:"Sec17 and Sec18 enhance the rate of HOPS-dependent vacuole membrane fusion with all wild-type fusion components  without requiring ATP hydrolysis . […] Our current work, substituting a synthetic protein tether for HOPS, shows that Sec17/Sec18 can also drive HOPS-independent fusion."A similar analysis in non-vacuolar fusion reactions will require their reconstitution with all purified components, not only SNAREs, NSF, and SNAP but also SM protiens, tethering complexes, Rabs, and other factors. This is nearly achieved now for synaptic fusion, but the multiple proteins special to the Ca-responsivity of the synapse  distinguish its fusion from that of general endocytic and exocytic traffic. Endosome homotypic fusion was brilliantly reconstituted by Zerial and colleagues in a Nature study, but there has been no follow-through of mechanistic studies in the ensuing years. Thus other systems to test the generality of our findings with vacuolar fusion are still lacking.2. One would expect Sec17/18 association with the normal SNAREs to occur prior to fusion, but there is no data to address this.This was addressed most directly in Harner and Wickner (2018), and we've added this at the start of the Discussion in the revised paper.3. The concentration of Sec17/18, the most critical component, is not indicated in either the figure legend or methods.Sorry for this awful omission. We've now added these concentrations to the figure legend: 500nM Sec17, 250nM Sec18, and 1mM ATPgS.4. Can this unexpected role for Sec17/18 be demonstrated/confirmed for intact cells? Is it essential for wildtype rates of fusion? Or is it strictly redundant in the face of the assembly force of SNARE pairing as a driving force for fusion?eLife paper, which the current submission is built upon, shows that " Slow fusion can be driven either by zippering or by the apolar loops of Sec17 which had assembled on a platform of at least partially zippered trans-SNARE complex, but rapid HOPS-dependent fusion requires both SNARE zippering and Sec17 with its apolar loops." (as stated in the current Introduction).We now cite this more clearly in the introduction: "Rapid HOPS-dependent fusion requires both SNARE zippering and Sec17 with its apolar loops, as analyzed in vitro and in vivo, where overexpression of Sec17 with an intact N-terminal apolar loop substantially restored the fusion defect caused by Qc-SNARE domain truncation ." Is it essential for wildtype rates of fusion? Or is it strictly redundant in the face of the assembly force of SNARE pairing as a driving force for fusion? I'm not sure how we'd measure the wild-type (in vivo) rate of fusion, but we certainly haven't in the past. I'm a bit unclear about just what's being asked here. Zippering and Sec17-induced fusion are redundant, but (perhaps more important) they're complementary. The Song et al. 2021 5. Does it actually act on other SNAREs? This is important because the clearest argument for this brief study being viewed as a highly significant research advance (over their previous work) is the implication that the unexpected direct role of Sec17/18 can now be considered likely to take place in other, if not all, SNARE-mediated fusion events.We now address this important question directly at the end of the Discussion: "Our current findings suggest that Sec17 and Sec18 support of zippering-bypass fusion may not be restricted to the vacuole/lysosome, but may contribute to SNARE-mediated fusion at other steps in endocytic and exocytic vesicular trafficking. Testing this hypothesis will necessitate the reconstitution of fusion at other organelles with their SNAREs, Rab, Rab-effector tethering complex, and SM protein." This is far beyond our current scope.6. No concentrations for any soluble component are given. The final concentrations of Vam7/Qc, Sec17, Sec18, HOPS, ATPgS, etc., must be specified. Readers should not have to look at prior publications to locate this information. A possible criticism of this and some similar prior work is that the experiments are conducted at only single concentrations of each reagent, rather than using concentration titrations for the protein components in the reaction. Nevertheless, the results support the authors' conclusions and are broadly consistent with prior work.I apologize for this awful omission. These concentrations are now explicit in the last paragraph of the Methods. All the assays are with 3Q-proteoliposome, and thus there is no concentration given for Qc .7. The last paragraph of the introduction could probably be omitted without loss of clarity.We have rearranged, and rewritten, the Introduction with a goal of added clarity. We beg to be allowed to keep the last paragraph, as we feel it provides a useful overview of the strategy we've followed.8. Line 46: extensive mutational analyses (both in vitro and in vivo) of the Sec17 membrane-interaction loop is present in Schwartz et al. 2017. This work should be cited.Thank you, we do now.9. Lines 62-65: the pioneering paper by Hanson, Jahn et al. was a key step toward the consensus that SNARE complexes are oriented in a parallel coiled coil, and that Sec17/18 may act after, rather than before, fusion. It should be cited.Thank you, we've added two citations to their pioneering work.10. Lines 72-73: again, Schwartz et al., 2017 should be cited.Thank you, now done.


Pyogenic facet joint infection (PFJI) is a relatively rare spinal infection. Clinical suspicion of this condition is a key for diagnosis. We report a case of PFJI which required decompression surgery for severe neurological dysfunction. The patient was a 44-year-old woman who had a previous history of orthotic therapy for idiopathic scoliosis. The patient was admitted to our hospital with a history of two days of high fever and severe low back pain. There was no neurologic deficit, and blood tests revealed high levels of inflammatory markers. There was a slight amount of fluid that had collected at L4/5 facet joint in lumbar MRI. She was admitted for examination and treatment of fever of unknown origin and low back pain. Antibiotic treatment started the day after hospitalization since the first report of the blood culture taken upon admission tested positive to gram-positive cocci. As low back pain and fever persisted, an MRI was taken again on the fifth day of hospitalization. Repeated MRI showed fluid extension from the left facet joint to paravertebral muscles and epidural space. She was diagnosed with PFJI, and facet joint puncture was performed. At this time, it became clear that she had foot drop on the right, the contralateral side of the PFJI. She underwent irrigation, debridement, and partial laminectomy. Methicillin-sensitive Staphylococcus aureus (MSSA) was detected in blood cultures at the time of hospitalization, in the puncture fluid and tissue collected during surgery. The patient recovered completely from foot drop after the operation and a three-month course of antibiotics. As the imaging findings may be inadequate in the early stages of onset and PFJI potentially causes neurologic deficit such as foot drop, neurological findings need to be carefully observed even after hospitalization and one should reexamine the MRI if symptoms or clinical findings did not improve or were aggravated. Pyogenic facet joint infection (PFJI) is a relatively rare spinal infection , which iμL, and C-reactive protein (CRP) was elevated to 13 mg/dL. Although scoliosis was observed on X-ray, no clear bone or joint destruction was confirmed. MRI showed a slight amount of fluid had collected at the left L4/5 facet joint; these findings are considered to be associated with degenerative changes  count was 9,250 cells/ changes . She wasThe first report of the blood culture taken upon admission tested positive to gram-positive cocci, and the patient was started on an intravenous course of sulbactam ampicillin (3 g every 6 hours) and vancomycin (1 g every 8 hours) as the empiric treatment of bacteremia on the second day of hospitalization. Methicillin-sensitive Staphylococcus aureus (MSSA) was isolated in the blood culture on the fourth day after admission, which was sensitive to cephazolin, levofloxacin, and many other antibiotic drugs. And the patient was put on an intravenous antibiotic course of cephazolin (2 g every 8 hours) and oral rifampicin (450 mg every day) for the MSSA bacteremia. On the fifth day after admission, the CRP level was 11 mg/dL, low back pain persisted, and fever was still approximately 38 degrees. Therefore, we took another MRI. The repeated MRI revealed continuous fluid from the facet joint to paravertebral muscles and epidural space . Other fWe performed a diagnostic facet joint puncture on the left L4/5  on the 8In response to the antibiotic treatment, the CRP level decreased and pain tended to improve, but we observed the right foot drop and MMT3 level muscle weakness in the iliopsoas and quadriceps the day after the puncture. As the atypical findings of lumbar radiculopathy coincided in this case; weakness in the contralateral side of the affected facet joint, and no leg sensory dysfunction including pain and numbness, we searched for other causes of paralysis such as cerebral infarction. However, none were clear, so we performed L4/5 and 5/S1 partial laminectomy and left L4/5 facet joint capsulotomy and irrigation debridement on the 11th day of admission. The epidural space and the left L4/5 facet joint were filled with inflammatory granulomatous tissue.After the operation, the patient was continued on a course of antibiotics, CRP gradually decreased, and CRP became negative a month after hospitalization. Two weeks after surgery, we took a follow-up MRI, which showed signal changes at the left L5 pedicle and L4 and 5 vertebral bodies around the pedicle, which suggested osteomyelitis . TherefoPyogenic arthritis of the facet joint was first reported in 1987 by Halpin and Gibson . It is cSymptoms are mainly severe low back pain not relieved by rest and fever. About 40% of patients have some kind of radiculopathy, but severe cases may involve paralysis or cauda equina syndrome, which account for approximately 10% of all cases . The patMRI is most effective in confirming the diagnosis of PFJI at an early stage , and a cParaspinal and/or epidural extension is seen in 81% of cases , and in The focus of the treatment is antibiotic administration and drainage, and surgical treatment is mainly performed on severe neurologic dysfunction, like in this case . The patWe experienced a case of PFJI that required surgery due to foot drop. We need to take PFJI into account when we suspect spinal infection disease. Since the imaging findings may be inadequate in the early stages of onset and PFJI potentially causes neurologic deficit such as foot drop, it was considered necessary to follow-up carefully and reexamine the MRI if symptoms or clinical findings did not improve or became aggravated.


The segmentation of power lines (PLs) from aerial images is a crucial task for the safe navigation of unmanned aerial vehicles (UAVs) operating at low altitudes. Despite the advances in deep learning-based approaches for PL segmentation, these models are still vulnerable to the class imbalance present in the data. The PLs occupy only a minimal portion (1–5%) of the aerial images as compared to the background region (95–99%). Generally, this class imbalance problem is addressed via the use of PL-specific detectors in conjunction with the popular class balanced cross entropy (BBCE) loss function. However, these PL-specific detectors do not work outside their application areas and a BBCE loss requires hyperparameter tuning for class-wise weights, which is not trivial. Moreover, the BBCE loss results in low dice scores and precision values and thus, fails to achieve an optimal trade-off between dice scores, model accuracy, and precision–recall values. In this work, we propose a generalized focal loss function based on the Matthews correlation coefficient (MCC) or the Phi coefficient to address the class imbalance problem in PL segmentation while utilizing a generic deep segmentation architecture. We evaluate our loss function by improving the vanilla U-Net model with an additional convolutional auxiliary classifier head (ACU-Net) for better learning and faster model convergence. The evaluation of two PL datasets, namely the Mendeley Power Line Dataset and the Power Line Dataset of Urban Scenes (PLDU), where PLs occupy around 1% and 2% of the aerial images area, respectively, reveal that our proposed loss function outperforms the popular BBCE loss by 16% in PL dice scores on both the datasets, 19% in precision and false detection rate (FDR) values for the Mendeley PL dataset and 15% in precision and FDR values for the PLDU with a minor degradation in the accuracy and recall values. Moreover, our proposed ACU-Net outperforms the baseline vanilla U-Net for the characteristic evaluation parameters in the range of 1–10% for both the PL datasets. Thus, our proposed loss function with ACU-Net achieves an optimal trade-off for the characteristic evaluation parameters without any bells and whistles. Our code is available at Github. Unmanned aerial vehicles (UAVs) with vision systems are usually employed for the automated inspection and maintenance of electrical power lines from aerial images. As compared to the manual inspection approaches, automated inspection and maintenance is more efficient because it is effortless, transient, easily accessible in remote areas and yields consistent inspection results ,2. At thPLs are regarded as one of the most dangerous obstacles for UAVs due to their weak visual appearances and pervasive existence . Hence, The recent deep learning-based semantic segmentation networks have been applied successfully to the PL detection task to detect the PLs at the pixel level ,6,7,8. TAmongst the available methods to tackle the class imbalance problem in deep segmentation networks, algorithm-level methods, particularly the novel loss functions, are most commonly used because they do not affect the training times by altering the data distributions and require little to no tuning. For a detailed survey on the methods to handle class imbalance in deep learning classifiers, we direct the interested readers to Johnson, J.M. and Khoshgoftaar, T.M. . The losIn this research, we empirically investigate various loss functions to tackle the class imbalance and the subsequent thin RoIs identification problem in PL detection. We propose a novel focal loss function based on the Phi coefficient  to optimize the deep segmentation network for PL detection. Our proposed loss function outperforms the existing baselines on two benchmark PL detection datasets by achieving an optimal trade-off for the evaluation parameters. We employ a U-Net -based arA novel focal Phi loss (FPL) function for highly imbalanced data and thin RoI segmentation, where we modulate the Phi coefficient   to achieValidation of the proposed novel FPL function in terms of characteristic evaluation parameters , class-wise dice scores and precision) on two PL detection benchmark datasets with varying levels of class imbalance.Empirical analysis of the choice of loss function for PL detection and evaluation of the proposed novel FPL function with state-of-the-art loss functions for handling class imbalance.A deeply supervised U-Net, named the auxiliary classifier U-Net (ACU-Net), improved with the simple addition of a convolutional auxiliary classifier for faster model convergence and better feature representations.The major contributions of this research are four-fold, namely:The remainder of this paper is organized as follows. A deep network tends to learn its desired objective, such as pixel classification, via the minimization of an appropriate loss function during training. The loss function is a mathematical formulation of the error between the expected results and the results predicted by the deep network. Several loss functions have been proposed in the literature to handle the class imbalance problem in deep semantic segmentation networks. These loss functions can be fairly divided into three categories: namely, the distribution-based loss functions, the region-based loss functions and the compound loss functions .Distribution-based loss functions stem from the distribution of class labels and tend to minimize the difference between the predicted and actual probabilities of image pixels during model training . These iHere, y and ŷ specify the actual and predicted probabilities, respectively. In the case of class imbalance, the BCE loss yields sub-optimal results which are biased towards the majority class because it weighs all pixels equally during the loss calculation. Hence, the deep network always receives a low loss, because the background pixels are classified easily, and the network does not learn the actual objective of classifying the minority class pixels accurately. This class imbalance is alleviated by assigning weights to either only the foreground class, i.e., the PLs in our study, as in the case of weighted BCE (WBCE) loss, or to both the classes in BBCE loss . The WBCThe class weights (β and (1 − β)) are inversely proportional to the number of samples in the class. Another variant of the BCE loss that addresses the class imbalance problem is the focal loss (FL)  defined t is the predicted probability, α is the class weighting parameter while γ is the modulating factor that shifts the model’s focus on learning hard negatives during training by down weighting the easy (majority class) examples. Here, pTo the best of authors’ knowledge, all the studies on PL detection utilize the BCE loss ,14,15,16Region-based or overlap metric-based loss functions directly measure the degree of overlap between the predicted and the actual image regions of the foreground class of interest only. Hence, region-based losses are usually preferred over BCE losses for handling skewed data because these losses reflect the segmentation quality in a better manner as compared to the BCE losses . DL 19]19] basedTP, FN, and FP signify the true positive, false negative and false positive predictions, respectively, and ε is the smoothing factor to prevent infinite values. α and β are the hyperparameters in TL (Equation (6)) which allow the FNs and FPs to be weighted differently as opposed to DL, which weighs both quantities equally (Equation (5)). TL simplifies to DL when α = β = 0.5. Although these weights improve the recall rate for skewed datasets, they require tuning to perform optimally. It should also be noted that none of these losses penalize the misclassifications of the background information, making it difficult to optimize them for accurate background predictions . MoreoveCompound loss functions combine the distribution and overlap-based loss functions to tackle the class imbalance in semantic segmentation problems. These loss functions also introduce additional weighting parameters to weight the constituent losses and thus, require tuning to achieve optimal performance. Furthermore, these losses have also been applied to medical segmentation tasks only ,33 whereAs far as PL detection is concerned, the work in  utilizesIn summary, PL detection suffers from a severe class imbalance  problem due to thin RoIs, which adversely affects the detection performance. The current literature on PL detection largely utilizes the distribution-based BCE loss and its variants for tackling the class imbalance issue without an investigation of alternate loss functions. Empirical investigation of the popular BBCE loss  for PL dThe Matthews correlation coefficient (MCC) or the Phi coefficient is widely used in machine learning and bioinformatics for assessing the quality of binary classifications. It measures the degree of correlation between the actual and the predicted values and returns a value in the range of , where −1 and 1 indicate a complete disjoint and a perfect prediction, respectively . The PhiMCC loss  can be dMCC by introducing an exponent γ. In order to differentiate our proposed loss function from LMCC, we name it the focal Phi loss (FPL) instead of the focal Matthews correlation coefficient loss. We define our Focal Phi Loss (FPL) as:Similar to DL and TL, MCC loss has also been applied to medical image segmentation with small RoIs and less severe class imbalance . An expeMCC, we incorporate numerical stability to the MCC metric calculation in Equation (10) by introducing a smoothing factor ε to prevent undefined values. The focal parameter γ in our FPL (Equation (9)) allows the loss to focus more on the hard classes that are detected with lower probability. The idea of applying a focal exponent γ to the loss function is inspired from the works [It should be noted that unlike Lhe works ,30, whicFPL handles it automatically by assigning the class sample size ratios of √B/A and √A/B to TN and TP, respectively, during MCC calculation, where A and B specify the majority and minority classes, respectively [As far as skewness is concerned, our Lectively . That isHence, there is no need to assign additional weighting parameters for addressing the class imbalance issue as is the case in distribution-based losses (Equations (2)–(4)).To achieve an optimal trade-off for the characteristic evaluation parameters and to better optimize the learning process, we propose an auxiliary classifier U-Net (ACU-Net) as the baseline segmentation network and optimize it using our proposed FPL  according to the formula [i, and maxi(|Ci|) and mini(|Ci|) return the maximum and minimum class size over all i classes, respectively. The details of the PL benchmark datasets discussed are summarized in We have utilized two benchmark PL datasets in this study, namely the Mendeley PL Dataset  and the  formula  defined Some of the images from these datasets and their corresponding ground truth annotations are illustrated in The PL detection task is framed as a binary semantic segmentation problem with the aim of accurately classifying and localizing the minority class PL pixels against the majority class background pixels. Since the size of the datasets is rather small for training a deep segmentation network, the following on the fly data augmentations are applied to the images in both the training datasets.Scaling the images for a ratio range of ;Random rotation in the range of  with a probability of 50%;Horizontal flipping with a probability of 50%;Resizing the images to 256 × 256;Random brightness with a delta of 32;Random contrast in the range of ;Random saturation in the range of ;Random hue with a delta of 0.5;Image padding for the resized images with size 256 × 256.mmsegmentation framework [PL class pixels with a color of ;Bg class pixels with a color of .As far as the validation datasets are concerned, only the scaling and flipping data transforms are applied during the validation phase. We utilize the ramework , a Pytor−3 and a weight decay of 0.0001 for all the loss functions with the exception of region-based losses . For region-based losses, lower LRs are required for model convergence, that is, 5 × 10−5 for the Mendeley PL dataset and 1 × 10−7 for PLDU. The LR of the decode and auxiliary heads is set to 10 times to that of the backbone to achieve faster convergence. Validation and model checkpointing is performed after every 200 iterations during the model training. These parameters are identified via a grid search method. No transfer learning is applied for training ACU-Net in order to present a fair evaluation of the proposed ACU-Net and the proposed FPL function against the vanilla U-Net and other loss functions, respectively. All the experiments are performed using Tesla K80 GPU compute from Google Colab.The network parameters are initialized with zero mean and unit standard deviation Gaussian distribution. The proposed ACU-Net  is trainTo the best of authors’ knowledge, almost all the studies regarding PL detection use the dice scores (DSC) , precision, true positive rate (TPR) , false discovery rate (FDR) and accuracy ,12,13,14ificity)  and the PL (Equation (13)) and DSCBg (Equation (19)) to distinguish the foreground and background PL and Bg classes, respectively. After the training of all the algorithms, the results of PL detection are compared to the GT pixel annotations in the light of above evaluation parameters for performance evaluation.We denote the class-wise DSC by DSCWe divide the results section into four different parts, that is, (1) proposed FPL function results with different values of its constituent parameter γ using the proposed ACU-Net, (2) investigation of various state-of-the-art loss functions for class imbalance to the PL detection task with the proposed ACU-Net and its comparative analysis with our proposed FPL, (3) ablation study results of the proposed ACU-Net, and, (4) analysis and deductions of the obtained results. The best values for each evaluation parameter are written in bold in the tabular results. The code implementation of all the conducted experiments is available at the link mentioned in the The results of training the ACU-Net with the proposed FPL for the segmentation of PLs in the Mendeley PL dataset and PLDU are summarized in α is applied to smooth the graphs and highlight the trend in the data points. Sα is set to 0.6 and 0.9 for PLDU and Mendeley PL datasets, respectively.A plot of the characteristic evaluation parameters against the training iterations (20K) with model validation at every 200 iterations is presented graphically in In this section, we examine five other state-of-the-art loss functions, commonly used for class imbalance problems, on the PL benchmark datasets and compare the results with our proposed FPL function. The five different loss functions evaluated in this study are: BBCE loss, MCC loss, DL, TL and FTL. The parameters for these loss functions are obtained after a grid search. To the best of our knowledge, no research work has investigated the application of different loss functions for the highly imbalanced PL detection task with thin RoIs. All the studies on PL detection mainly rely on BCE loss ,14 and iA qualitative assessment of our proposed FPL in comparison to the baseline BBCE loss and the original MCC loss is depicted via the results in We can observe that for both the sample images, the (a) baseline BBCE loss function results in a high number of FP PL predictions which do not correspond to the actual PL pixels in the GT annotations. The (b) MCC loss addresses these FPs effectively but has a lower recall rate and therefore detects PLs with disconnected pixels. The superiority of our (c) proposed FPL function to other methods can clearly be depicted in these results as the PL predictions are strongly connected and comprise of little to no FPs. Hence, the PLs detected with our proposed FPL are closest to the (b) GT annotations.α = 0.6 is applied to the plot results for both the datasets.Supplementary plots of the characteristic evaluation parameters against the training iterations (20K) for the mentioned loss functions are presented graphically in The novel contributions of this study can be divided into two parts: (a) focal Phi loss (FPL) function for class imbalanced PL detection and thin RoIs segmentation; and (b) U-Net with auxiliary classifier (ACU-Net) for better feature learning and faster model convergence. The proposed FPL function results have already been depicted in PL and DSCBg, respectively), highest precision (~7%), highest TNR values (~0.2%) and lowest FDR values (~7% lower) with a minor degradation of ~1% in the accuracy and TPR values. For PLDU, our proposed FPL (with γ = 1.5) outperforms the MCC loss (γ = 1.0) with the highest DSCPL (~1% higher), highest TPR (~5%) and highest accuracy values (~3%), while our FPL (with γ = 3.0) outperforms MCC loss with the highest DSCBg (~1% higher), highest TNR (~1%), highest precision (~5%) and lowest FDR values (~5% lower). For PLDU, γ = 1.5 is chosen as the optimal value for FPL as it yields the highest results for the PL class parameters as opposed to γ = 3.0 which yields the best results for the Bg class parameters. PL and TPR) and overall model performance  are better when γ < 1 while the results of the characteristic evaluation parameters for the Bg class (DSCBg and TNR) are better with γ > 1. It is also noteworthy that the overall results of all the evaluation parameters are higher for the Mendeley PL dataset as compared to PLDU. This is because the thin RoIs in PLDU are harder to segment (The results in  segment . as compPL and (b) TPR for the thin PL class while maintaining acceptable values for the Bg class, with metrics (b) DSCBg and (f) TNR, for both the datasets. Apart from these per class results, our proposed FPL also yields an overall better model performance for both the datasets with higher (c) accuracy, higher (g) precision and lower (c) FDR values as compared to the MCC loss (γ = 1.0).Similar deductions can be made by visualizing the plots of the characteristic evaluation parameters in PL, ~0.4% for DSCBg, ~1% for TNR and ~19% for precision and FDR values with a minor degradation in the TPR (~3%) and accuracy (~1%) values. For PLDU in PL, ~2% on DSCBg, 4% on TNR and ~15% for precision and FDR values. Although, our proposed FPL lags behind the BBCE loss in terms of accuracy (~7%) and especially TPR values (~18%) for PLDU, it still achieves a better trade-off amongst all the evaluation parameters as compared to the BBCE loss. Furthermore, the region-based DL and TL achieve the highest DSCPL (~70%) and DSCBg (~99%) values with lower FDR (~20%) and the best precision–recall trade-off for the Mendeley PL dataset in PL, TPR) and the overall model performance  while being biased towards the major Bg class with higher DSCBg and TNR values. The comparison of the proposed FPL with the MCC loss has already been discussed in first part of The TL and FTL region-based losses, as well as the BBCE loss, require the tuning of multiple hyperparameters  for model training. Although DL does not require the tuning of additional hyperparameters (α and β are fixed to 0.5), it tends to ignore the TNs in its calculation and thus results in low specificity or TNR values. As a result, our proposed FPL is more promising for class imbalance and thin RoIs segmentation, as it addresses these issues effectively. Similar to the FPL results in PL values decrease for both the datasets. We deduce that this is due to a higher number of FPs as FDR values for the BBCE loss are also higher than for other losses. Thus, there is an inverse relationship between the FDR and DSCPL values and an optimal model must therefore have a lower FDR in order to perform efficiently on the minority PL class.Similar deductions can be made by visualizing the plots in The results in PL), precision and FDR values. Compared to other loss functions, our proposed FPL is simple and effective as it requires tuning for only a single focal parameter (γ). Since FPL yields remarkable results for thin RoIs segmentation with class imbalance, its application can be extended to various research domains where thin RoIs segmentation is of the utmost importance. These domains include but are not limited to mooring lines monitoring and maintenance in the marine vessels, road-lane line detection in autonomous vehicles, sperm and bacterial flagella detection in medicine, automated cracks detection to ensure architectural sustainability, etc. Furthermore, FPL can also pave the way for the design of a generic thin RoIs detector which can save the cost of acquiring a separate application-specific detector for various application areas.In this study, we examined the PL detection task, with thin RoIs, from the class imbalance perspective. We proposed a novel focal Phi loss (FPL) function for the thin RoIs segmentation of PL pixels against the background in two highly imbalanced PL datasets, i.e., the Mendeley PL dataset and the Power Line Dataset of Urban Scenes (PLDU). The proposed FPL introduced a focal component to the MCC loss for achieving an optimal trade-off for the characteristic evaluation parameters. We also provided an in-depth comparative analysis of our proposed FPL against the established loss function baselines with a standardized deep network architecture. Our experiments demonstrate the significance of the choice of loss function when dealing with class imbalance in the PL detection task. Moreover, we also improved the vanilla U-Net by incorporating an additional auxiliary classifier head (ACU-Net) for learning the features of thin PLs effectively and for faster model convergence. Our proposed FPL and ACU-Net outperformed the baseline BBCE loss function for class imbalance in PL detection and the vanilla U-Net architecture, respectively, by achieving an optimal trade-off for the characteristic evaluation parameters, with particularly higher values for the foreground PL class dice scores (DSCCurrently, the proposed FPL lags behind the baseline BBCE loss in accuracy values. We believe that this problem can be alleviated, and the accuracy of FPL can be improved by combining it with the BCE loss as BCE loss is designed to maximize the accuracy values. Apart from the proposed improvement of combining the FPL with BCE loss, an investigation of the application of FPL for application-specific deep PL detectors, instead of a standardized generic U-Net architecture, is also to be carried out as part of potential future work. We also anticipate investigating the choice of alternate auxiliary classifier head architectures for the proposed ACU-Net and observe their performances for different application domains.


While chemical Ca2+ indicators served to visualize and measure changes in glial cell cytosolic Ca2+ concentration for several decades, genetically encoded Ca2+ indicators (GECIs) have become state of the art in recent years. Great improvements have been made since the development of the first GECI and a large number of GECIs with different physical properties exist, rendering it difficult to select the optimal Ca2+ indicator. This review discusses some of the most frequently used GECIs and their suitability for glial cell research.Ca GECIs have been employed to study glial Ca2+ signaling and proven superior compared to chemical Ca2+ indicators  from 2+ sensing domain and a single fluorescent protein, whose fluorescence intensity changes when shifting between Ca2+-free and Ca2+-bound states, whereas FRET (Förster or fluorescence resonance energy transfer) GECIs consist of two fluorescent proteins linked by the Ca2+ binding domain  of donor fluorescence is largely devoid of these artifacts but image acquisition is much slower and costly hybrid detectors and photon-counting boards are required. For quantification of Ca2+ signals with FRET sensors, the Ca2+ binding domain used to construct the GECI must also be considered. The Ca2+ binding domain of calmodulin, e.g., binds four calcium ions with positive cooperativity with a Hill coefficient in the range of 2, leading to a non-linear representation of the ambient Ca2+ concentration by the FRET ratio  and multiple Ca2+ binding sites (four for most of the GECIs) and a key reason why expression levels must be kept low.Not only the properties of the fluorescent proteins determine the usefulness of the Cacurrently used for glial cell research or related fields, their key properties, and for which applications they are best suited. As outlined above, there are many factors to consider when selecting the appropriate GECI. There are numerous available GECIs. Alone the derivatives descending from the first GCaMP developed by Nakai et al.  comprise2+ indicators. These must be matched to the light source, detectors, and filter sets available or new optical elements must be purchased. The excitation and emission maxima are listed for the various indicators. Since only the donor fluorophore is excited when using dual fluorophore (FRET) sensors, the excitation spectrum and maximum of the acceptor fluorophore are not quoted but it is highly advisable to also have the ability to directly excite the acceptor to visualize the cells, e.g., in order to select the optimal field of view.The first considerations are the excitation and emission spectra of the Cad reflects the Ca2+ concentration at which half of the Ca2+ indicator molecules are bound by Ca2+, while the other half is dissociated . This is much less important for FRET sensors as one can always excite the acceptor directly to visualize and focus before switching to excite the donor for the actual ratiometric measurements.The GECIs with the highest dynamic range often achieve this by extremely low fluorescence in the apo state, i.e., when no Ca2+-saturated (Fmax) and the fluorescence intensity in Ca2+-free conditions (Fmin) divided by the fluorescence value in Ca2+-free conditions: (Fmax − Fmin)/Fmin. For FRET sensors, it is similarly calculated by using the FRET ratio values instead of the fluorescence values. As mentioned above, while a high dynamic range is always desirable for single-wavelength GECIs it often reflects very low baseline fluorescence rather than very bright Ca2+-bound fluorescence. It is therefore important to consider not only the dynamic range but also how bright the sensor is at baseline. Whereas the best single wavelength sensors now have dynamic ranges of several hundred percent, the FRET sensors always appear inferior in this regard at first glance. In fact, a dynamic range of 40% is excellent for a FRET sensor.For single-fluorophore indicators, the dynamic range is calculated by the difference between the fluorescence intensity when Ca2+ by the indicator molecule is reflected by values such as the half rise time (t1/2 rise) and half decay time (t1/2 decay). If binding and unbinding of Ca2+ occurs much slower than the actual increase and decrease in the Ca2+ concentration, the time course of the Ca2+ signal is distorted. Although it was believed for many years that Ca2+ transients in astrocytes rise and decay at slow rates and hence can readily be recorded with Ca2+ indicators with slow on and off kinetics, more recent studies showed that fast Ca2+ signals in astrocyte microdomains can occur at a time scale of tens of ms , a 543-nm or 568-nm laser (green excitation), deep blue lasers , which are necessary for optimal excitation of cyan fluorophores such as CFP, cerulean or turquoise, are much less common. Therefore, we list lasers suitable for ideal (one-photon) excitation in the figurative summaries. In addition to confocal microscopy, an increasing number of laboratories use two-photon microscopy, in particular, to study glial Ca2+ signaling in vivo  are better options  to 334 nM , and the slowest of these is as fast as GCaMP6f. None rival the apo brightness of jGCaMP7b, although resting brightness will also depend on resting Ca2+ concentration. Also of interest are jYCaMP1 and jYCaMP1s (Kd 79 and 70 nM) and XCaMP-Y, which are particularly suitable for 2-photon excitation using inexpensive pulsed lasers with a fixed wavelength around 1,030 nm (Inoue et al., 2+ indicators designed to retain the excellent responsiveness of the R-GECO series and reduce photoswitching, which limits the usefulness of i.e., jRGECO1a (Shen et al., While we have limited the indicators presented in 2+, FRET sensors are the method of choice, however, transgenic mouse models and ready-to-use viruses are not commercially available. Among the FRET sensors, Twitch-2B is a good choice and has been reported to work in non-excitable cells such as microglial cells (Brawek et al., 2+ concentrations, hence GCaMP6f is favored as it allows visualization of astrocytes at rest. We have recently found that the single-wavelength sensor jGCaMP7b possesses an excellent combination of properties for use in astrocytes, including large dynamic range, fast kinetics, and high resting fluorescence and therefore included it in There is not a single GECI that provides optimal characteristics for all applications. For quantification of changes in cytosolic CaAll authors contributed to the research that is the base for this review as well as to the writing and editing of the manuscript. All authors contributed to the article and approved the submitted version.The 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 primary aim of this study was to assess the outcome of elderly intensive care unit (ICU) patients treated during the spring and autumn COVID-19 surges in Europe.This was a prospective European observational study (the COVIP study) in ICU patients aged 70 years and older admitted with COVID-19 disease from March to December 2020 to 159 ICUs in 14 European countries. An electronic database was used to register a number of parameters including: SOFA score, Clinical Frailty Scale, co-morbidities, usual ICU procedures and survival at 90 days. The study was registered at ClinicalTrials.gov (NCT04321265).p < 0.0001). The PaO2/FiO2 ratio at admission was higher during surge 1, and more patients received invasive mechanical ventilation . During the first 15 days of treatment, survival was similar during the first and the second surge. Survival was lower in the second surge after day 15 and differed after 30 days (57% vs 50%) as well as after 90 days (51% vs 40%).In total, 2625 patients were included, 1327 from the first and 1298 from the second surge. Median age was 74 and 75 years in surge 1 and 2, respectively. SOFA score was higher in the first surge (median 6 versus 5, An unexpected, but significant, decrease in 30-day and 90-day survival was observed during the second surge in our cohort of elderly ICU patients. The reason for this is unclear. Our main concern is whether the widespread changes in practice and treatment of COVID-19 between the two surges have contributed to this increased mortality in elderly patients. Further studies are urgently warranted to provide more evidence for current practice in elderly patients.NCT04321265, registered March 19th, 2020.The online version contains supplementary material available at 10.1186/s13054-021-03739-7. The first surge of the COVID-19 pandemic between March and May 2020 affected the elderly population disproportionally. Elderly patients were over-represented both among ICU admissions and among non-survivors .Thus, a robust prognostic stratification of elderly patients, many of whom had multiple co-morbidities, posed significant challenges in a disease which had not previously been encountered. In addition to established and new disease severity scores, geriatric characteristics notably frailty, co-morbidity and functional status were soon confirmed to be important prognostic factors in elderly COVID-19 patients –6. CliniDuring the summer months of 2020, the spread of the virus declined. However, it became evident by early autumn that a second surge of the SARS-Cov-2 pandemic was imminent . In contThe aim of this study was to assess a possible outcome difference in the first and the second surge of the pandemic in critically ill elderly ICU patients. The hypothesis was that outcome of elderly ICU patients with COVID-19 improved during the second surge of the pandemic due to implemented changes in practice, based on experience and evidence available.www.vipstudy.org). National coordinators were responsible for the recruitment of ICUs and for obtaining national and local ethical permission. In addition, national coordinators supervised patient recruitment. Due to variations in requirement for ethical consent, some countries could recruit patients without upfront informed consent, while others had to obtain it. The study deliberately allowed for co-enrolment of study patients to other COVID-19 studies. The study was registered on ClinicalTrials.gov (ID: NCT04321265) and adhered to the European Union General Data Privacy Regulation (GDPR) directive.The study was a prospective observational multi-centre study of COVID-19 patients aged 70 years and older admitted to 159 ICUs in 14 European countries, called the COVIP study. Recruitment took place from 19 March to 31 December 2020. A list of collaborators is shown in Additional file Study preparation started during the first phase of the pandemic, and recruitment commenced on 19 March 2020. Throughout the pandemic, recruitment to the COVIP study was monitored by weekly virtual steering group meetings. The first recruitment period, representing the first surge of the pandemic, was defined as from 19 March until 26 May 2020, and the second recruitment period, reflecting the second surge, from 1 September to 31 December 2020. This was also reflected by the number of ICU patients published by international registries. Each participating ICU included consecutive patients. To limit workload during the pandemic, centres were not asked to protocol a screening log. A diagnosis of COVID-19 was made based on a positive polymerase chain reaction (PCR) test.Eligible patients were 70 years or older with a proven diagnosis of COVID-19 and admitted to an intensive care unit (ICU). Pre-ICU triage was not part of this study. To avoid duplication caused by the transfer of a patient from one ICU to another, each patient could only be entered once into the database regardless of readmission, transfer or other reason. This resulted in a unique electronic database record for each patient. The reference date was day 1 of the first admission to an ICU. All consecutive days were numbered sequentially from the day of admission. To limit bias in the comparison of the two surges, only patients admitted to an ICU in a European country that recruited patients during both surges were included in the analysis  on admission was calculated using the arterial PO2 [mmHg] and the FiO2 [fraction of 1] from the first arterial blood gas. ICU length of stay (LOS) was recorded in hours. As described previously, [Centres collected data using the online case record form (CRF). Day one sequential organ-failure assessment (SOFA) score on admission was calculated either manually or using an online calculator in the electronic CRF as described previously , 12. Theviously,  the elecThe frailty level prior to the acute illness and hospital admission was assessed using the Clinical Frailty Scale (CFS) as published previously . A detaiPatients were followed up until 90 days or death. The primary endpoint was survival at 30 days, the secondary endpoint survival at 90 days. Data could be retrieved either directly, from the hospital administration system or after discharge using telephone follow-up. Limitation of life-sustaining therapies such as withholding or withdrawing organ support was documented .Baseline characteristics of patients were analysed as frequencies and percentages for categorical variables and as medians and interquartile ranges (IQRs) for continuous variables. Comparisons between the two periods were evaluated using the Kruskal–Wallis test (ANOVA) for continuous variables and the χ2 or Fisher exact test for categorical variables as appropriate. Comparisons between surges were evaluated using the Wilcoxon test for continuous variables and the χ2 or Fisher exact test for categorical variables as appropriate.Incidence of organ support and treatment limitations were estimated using cumulative incidence analysis considering ICU death and ICU discharge as competing risks. Univariate comparisons were performed using Gray’s test.The crude overall survival up to 90 days after ICU admission was estimated by the Kaplan–Meier method and compared between groups using a log-rank test.In order to compare survival between the two surges adjusting for patients characteristics, a Cox model was fitted including the following variables: age, sex, PaO2/FiO2 ratio, other SOFA components, frailty, BMI, habitat and comorbidities  was studied separately. This was done in order to identify factors relevant for outcome earlier and later during the treatment course.We first estimated our models on the complete data set and then used multiple imputation for participants with missing data, using predictive mean matching for continuous variables, logistic regression for binary data, and polytomous regression for (unordered) categorical data. The cumulative baseline hazard was approximated by the Nelson–Aalen estimator and included in the imputation model . HundredIncidence of organ support and treatment limitations were estimated using cumulative incidence analysis considering ICU death and ICU discharge as competing risks. Univariate comparisons were performed using Gray’s test.The general statistical analysis was conceptualised during the set-up of the study at the beginning of the pandemic but has been revised during the analysis of the data obtained.p < 0.05 was considered statistically significant. Statistical analyses were performed with R 3.2.3 software packages .All p values were two-sided, and 2/FiO2 ratio and SOFA score were lower in patients during the second surge of the pandemic and there was an increased prevalence of diabetes mellitus and ischemic heart disease.As illustrated in Table During the second surge of the pandemic, management had changed when compared with the first surge of the pandemic Table . Patientp < 0.001). Survival before 15 days was found to be similar for patients during the two surges; the unadjusted hazard ratio (HR) for survival before 15 days for the second versus first surge was 1.06 . Survival after day 15 was found to be worse for patients admitted to ICU during the second surge than for patients admitted during the first surge .We observed a lower rate of survival in the second surge of the pandemic  and an increased use of corticosteroids during the second surge. Although management of patients changed, we cannot clearly attribute the change in outcome to a specific change in practice.These findings are surprising, as the ICU community had gained experience in treating these patients during the first surge. Here, the initial very high reported  mortalitTo date, there are only very few reports comparing the two surges in COVID-19 hospitalised patients. In a study from 955 US hospitals, researchers compared a trend analysis for the first surge: from 1 January to 30 April 2020 and 1 May to 30 June 2020. The overall hospital event rate for 30 day mortality or referral to hospice within 30 days fell from 16.56 to 9.29%, indicating improved outcome in the last period of the first surge suggesting a steep learning curve . This ha2/FiO2 ratio at admission.Our patients were all treated in the ICU during both surges, and therefore, a comparison with these initial experiences from other patient groups is difficult. However, reports from Intensive Care National Audit and Research Centre (ICNARC) in the UK reveal valuable information in this respect. This national registry compared patients admitted before and after 1 September, 2020, and found a small increase in mortality in patients ventilated within the first 24 h from 46.6% in the first to 48.7% in the second cohort and a similar reduction in all patients discharged alive from the ICU . Their rThere are several possible reasons for the increased mortality seen in our study although we can only describe associations in this kind of study setting. While our data do not give a satisfactory explanation, it allows for several potential contributing factors to be discussed and to guide specific attention to differences occurring during the disease course, such as the increasing relevance of kidney failure and potential gender differences after day 15.2/FiO2 ratio seen at ICU admission in the second period, possibly suggesting more severe respiratory failure. This combined with a trend towards a reduction in the use of mechanical ventilation may not have been beneficial in this group of elderly critically ill patients, although this remains speculative. Although several studies and a meta-analysis suggested that timing of intubation may have no effect on mortality and morbidity in COVID-19 [A worse outcome might have been caused by the increased length of time spent in other departments before ICU admission, resulting in patients deteriorating prior to eventual admission. This is supported by a decreased PaOCOVID-19 , this reCOVID-19 .A similar rate of limitation of life-sustaining therapy was seen in the two cohorts, so this is unlikely to account for the difference in mortality. Two additional differences are a slight increase in age and frailty score in the second cohort, which could explain an increase in mortality; however, the difference in mortality remained even after adjustment for these factors.Another possible explanation for the increased mortality, which cannot be ruled out, could be a reduction in quality of care, despite all dedicated efforts on the part of the staff, in the second compared to the first surge. When the second surge started, many hospitals and in particular ICUs had already been overstretched for half a year and were running well above their usual capacity. This had consequences for both the permanent staff who had been working increased hours over a long period of time, and also the continuous dilution of expertise as non-ICU personnel, both physicians and nurses were being brought in to work in ICUs. There has been great concern about the burden of work on the health of ICU workers , leadingAnother important factor may be related to use of corticosteroids. In the second surge, 93% of our patients received corticosteroids, which is more than twice that found in the first cohort. It is well documented that steroids have potentially serious side effects in ICU patients. Steroids increase infection rate and hence mortality in patients admitted with influenza pneumonia  and thusFinally, development of COVID-19 mutations may change virulence and hence potentially lead to worse outcomes. It is well known that during the pandemic a new mutant virus emerged in Europe . The cliAnother possible explanation could be selection bias creating differences between the cohort in the first and second surge. That patients are already selected before ICU admission is common, and many undergo formal and informal triage. Criteria for triage were extensively discussed from the beginning of the pandemic when some ICUs experienced a rapid overflow of patients. We do not have detailed insight in what happened before ICU admission in our study, as this was not a research question. There are, however, some casemix differences between the first and second surge, mainly connected to longer time in hospital before ICU admission during the second surge, and a decreased oxygen ratio at ICU admission. This indicates potential differences in initial treatment leading to changes in selection maybe connected with increased knowledge of the feasibility to treat some severe covid-19 patients outside the ICU .Our study has further weaknesses mainly concerning the absence of details of variables that might account for our differences in outcome. There was no information about how steroids were administered, no control group of younger COVID-19 patients for comparison and there was no information about quality of care and the nurse-to-patient ratios as well as a measure of stress for personnel. Also, admission policies or local guideline changes were not recorded. The recording of treatment limitations is not without difficulties but important in ICU studies in elderly patients . The recThis is the first study in critically ill elderly ICU patients with COVID-19 infection that compares mortality data between the first and second surges of the pandemic. We have found an unexpected but significant rise in mortality in elderly COVID-19 patients treated in the ICU during the second surge. The cause of this rise is unknown but possible explanations have been discussed. Our main concern is whether the widespread changes in practice and treatment of COVID-19 between the two surges have contributed to this increased mortality in elderly patients. Further studies are urgently warranted to provide more evidence for current practice in elderly patients.Additional file 1. List of collaborators: COVIP-study; Description: List of COVIP study collaborators with affiliations.Additional file 2. COVIP Country map; Distribution of study sites and included patients per country. The first number is the number of ICUs per country, the second the total number of included patients per country.Additional file 3. Number of included patients: n (% within the wave) for the first ((until 26 May 2020) and second wave (1 September–31 December 2020) per country.Additional file 4. Recruitment within the individual countries in relation to the start of the study.Additional file 5. Consort flow chart illustrating screening and inclusion into the COVIP studyAdditional file 6. A detailed definition of comorbidities and organ support.Additional file 7. All statistical details on this analysis with and without imputation for missing data for the overall survival probability analysis until day 90 areAdditional file 8. Kaplan–Meier for different subgroups divided into the first and second wave with several sensitivity analyses for survival analysis and the adjusted survival models.Additional file 9. Subgroup analysis for different subgroups with a sensitivity analysis excluding the first four weeks of each surge


How can past studies of the volume-outcomes association in surgery be interpreted and how might future research on these questions generate valid results?In this cohort study that emulated 4 hypothetical target trials among 9136 Medicare beneficiaries undergoing pancreatectomy for malignancy, mortality was higher for higher volumes only when target trials with poorly defined interventions were emulated.The target trial framework demonstrated in this study may be useful for volume-outcomes researchers who are not willing to make unrealistic assumptions in their studies. This cohort study analyzes 4 hypothetical target trials of Medicare beneficiaries with pancreatic malignant neoplasms to analyze postoperative mortality differences across surgical and hospital operative volumes. The association of surgeons’ and hospitals’ operative volumes with postoperative patient outcomes has been studied for decades and holds important policy implications; however, in many volume-outcome analyses, this association is described without the envisioning of a clear intervention, which often introduces unintentional bias. Acting on such results may lead to unintended consequences from policy interventions or patient recommendations.To specify how  target trials would be designed to estimate the association between postoperative mortality of patients undergoing operations and a range of surgeon and hospital volume conditions and then to emulate these trials by using observational data.This observational data analysis emulated 4 hypothetical target trials of increasing complexity, ranging from a poorly defined trial that would randomly assign participants only to surgeon volume to one that would randomly assign participants to surgeon volume, hospital volume, and specific surgeon and hospital. This population-based cohort study included 9136 Medicare beneficiaries with a first diagnosis of pancreatic malignant neoplasm who did not require neoadjuvant therapy and underwent pancreatectomy between January 1, 2012, and September 30, 2016. Data analysis was performed between September 1, 2019, and October 8, 2021.Number of pancreatectomies performed by surgeon and hospital during the prior year.Ninety-day mortality.The analyses included 9136 Medicare beneficiaries treated by 1358 surgeons at 697 hospitals; median age was 73.3 years , and 4642 were men (51%). When trials with poorly defined interventions on surgeon volume were emulated, the estimated 90-day mortality was 7.9%  for lower-volume surgeons and 5.2%  for higher-volume surgeons. When trials with better-defined interventions were emulated, the difference was reduced: 7.8%  for lower-volume surgeons and 7.2%  for higher-volume surgeons.In this cohort study that emulated 4 different target trials with data from Medicare beneficiaries undergoing pancreatectomy, mortality differences across surgical volume levels were attenuated when the interventions were well defined. The application of the hypothetical target trial framework to this specific volume-outcomes scenario revealed the complexities of this research question and the unintentional biases introduced in prior studies, which emulated poorly defined trials whose results are therefore difficult to interpret. The target trial framework may be of value to outcomes researchers asking questions that correspond to well-defined interventions for the real world. Previous studies have suggested that higher hospital and surgeon volumes have a beneficial association with postoperative outcomes. A correlation between the “number of surgical procedures done in a hospital and the in-hospital mortality rate for those patients” was first reported in 1979.7 which have since been adopted by several major academic hospital networks.8 However, the findings that support these rules are based on simplistic and relatively unrealistic comparisons of high- vs low-volume surgeons and institutions.In response to these reports, patient safety organizations, such as the Leapfrog Group, recommended minimum hospital and surgeon volume standards for credentialing certain procedures,Among patients planning to undergo elective resection of a pancreatic malignant neoplasm, we specified 4 hypothetical randomized trials—target trials—with increasingly better-defined interventions on operative volumes. Each of these trials was then emulated with observational data from Medicare claims. In sequentially considering these 4 scenarios, we demonstrated how the hypothetical target trial framework may be of value to outcomes researchers asking research questions that correspond to well-defined interventions for the real world.A randomized trial to estimate the effect of operative volume may never be implemented because of logistic challenges , enrollment difficulties , financial problems , and other challenges. Because of these impediments in conducting a randomized trial, observational data analyses are the best chance to inform decisions about surgeon and hospital volume.9—and then try to emulate it by using observational data. This section outlines the design of 4 hypothetical target trials in which patients with a first diagnosis of pancreatic malignant neoplasm are randomly assigned to select a surgeon with a particular operative volume in the past year.A helpful tool to strengthen the validity of observational analyses is to specify the hypothetical randomized trial that one would like to conduct—the target trialFor each of the hypothetical trials, patients  will be eligible if they are older than 65 years, do not have evidence of metastatic disease, and are not expected to receive chemotherapy preoperatively. All patients will be followed up for 3 months or until death, whichever occurs earlier, and the risk of all-cause mortality will be compared between groups.10 and the New York Department of Health.11Each of the 4 trials will randomly assign trial participants to selecting a surgeon who performed a particular number of operations in the past year. The method to select the surgeon will vary across trials, as described later. For all trials, we assume that we will have knowledge of the number of pancreatic resections for malignant neoplasms that each surgeon in the United States performed during the previous year. Similar information has been made available by organizations such as ProPublicaIf these hypothetical trials were conducted, intention-to-treat estimates of 90-day mortality risk could be calculated nonparametrically or with standard logistic models , but must be ready to travel, sometimes long distances, to reach the chosen surgeon. The directed acyclic graph in eFigure 1A in the This trial would be of little interest to policy makers and patients because the interventions do not consider the hospital context in which surgeons operate. If, for example, surgeons’ operative volumes were associated with the operative volume of the hospital where they work and hospital volume had a direct effect on the outcome , this trial would not provide useful information for decision-making. A more useful trial would take into account the hospital volume in addition to the surgeon volume.Eligible patients are randomly assigned to a surgeon volume ranging from 0 to 25 pancreatic resections per year and to a hospital volume ranging from 0 to 45 pancreatic resections per year; that is, the trial has 1196 arms. This trial is represented by the directed acyclic graph in eFigure 1B in the Although this trial defines the interventions by both surgeon volume and hospital volume, patients may still be required to travel, sometimes long distances, to reach a surgeon and hospital fulfilling their assignment criteria. Therefore, implementing this trial would be impractical. A more useful trial would take into account the distance between a patient and the surgeon and hospital with the requisite operative volumes.Eligible patients are randomly assigned to 1 of the strategies specified in trial 2, but if the nearest available surgeon and hospital are at least 1.5 hours’ driving distance away, then the patient is assigned to undergo an operation by the surgeon and hospital with the volumes closest to those assigned within 1.5 hours’ driving distance. If no surgeon or hospital is available within 1.5 hours, patients may select their surgeon and hospital by criteria of their choosing. This trial is represented by the directed acyclic graph in eFigure 1C in the This trial is better defined than the previous ones, but its results would be hard to interpret if there existed certain savvy patients who knew how to select the best available surgeon or hospital (among those available to them under their assigned strategy) and if these patients were also healthier. A noncausal association between the specific surgeon  selected by a patient and that patient’s probability of mortality would follow.A more useful trial would prevent patients from using their knowledge about characteristics of surgeons or hospitals to select them. Otherwise, the trial estimates would not be transportable to a future population (or another country’s population) with a different distribution of surgeon or hospital characteristics.9) A directed acyclic graph for trial 4 is shown in eFigure 1D in the Eligible patients are first randomly assigned to one of the strategies specified in trial 3, and then randomly assigned to a particular surgeon and hospital among available ones. In this way, the effect of assignment to a specific operative volume of a surgeon and hospital is decoupled from the distribution of surgeon and hospital characteristics.  reporting guideline. Participant race and ethnicity were derived from the Medicare claims database. Patient consent was not required or obtained for this database study. Because the study met criteria for exemption, the need for informed consent was waived by the institutional review board.The aforementioned protocols may never be conducted as randomized trials because of the difficulty of enrolling many thousands of patients willing to adhere to an intervention potentially requiring them to trust a surgeon they have never met from an unfamiliar hospital far from home. However, researchers can attempt to emulate these trials by using observational data from Medicare, a federal health insurance program that provides coverage for approximately 96% of US citizens aged 65 years or older. This section describes how to emulate key aspects of the target trials; numeric results are presented in the next section. This study was approved by the Harvard T.H. Chan School of Public Health institutional review board, and its reporting follows the Strengthening the Reporting of Observational Studies in Epidemiology , short life expectancy, or extent of malignancy ; or the patient may progress to unresectable disease or develop severe comorbidities in the period between surgeon selection and tumor resection. In both cases, the operation may be canceled.Eligible patients were assigned to the strategy that was consistent with their observed data. For example, for the emulation of trial 1, a patient operated on by a surgeon who conducted 10 pancreatic resections per year was assigned to the group “surgeon volume 10.” For the emulation of trials 3 and 4, patients at least a 1.5-hour drive from any surgeon were assigned to their observed surgeon and hospital volume . The proportion of patients within the travel time thresholds by surgeon and hospital volume is summarized in eFigure 2 in the To attempt to emulate a randomized assignment of surgeon and hospital volume, the analysis adjusted for baseline covariates expected to be prognostic for postoperative mortality and associated with surgeon  operative volume as described in the Statistical Analysis section  from a standard logistic model , only 3 underwent pancreatectomy by a surgeon with a volume of at least 15 operations per year. This quasi-nonpositivity is not a problem for the emulation of trial 3 because driving time is explicitly incorporated into the strategies and thus no adjustment for it is required.12 Additional details are available in the eMethods in the To emulate the second randomized assignment in trial 4, the aforementioned procedure was modified to add further adjustment for surgeon- and hospital-level covariates , in addiWe identified 9136 patients who met the aforementioned eligibility criteria between January 1, 2012, and September 30, 2016. Median age was 73.3 years , 4642 were men (51%), 4494 were women (49%), 600 were Black (7%), 8130 were White (89%), and 406 (4%) were Asian (134 [1%]), Hispanic (63 [1%]), North American Native (25 [0.3%]), or other race and ethnicity unspecified in the Medicare database (184 [2%]). Their characteristics are summarized in At median-volume hospitals , risk of 90-day mortality with higher- and lower-volume surgeons ranged from 5.2%  to 7.9%  in the emulation of trial 1, from 4.5%  to 6.8%  in the emulation of trial 2, from 7.2%  to 7.8%  in the emulation of trial 3, and from 7.1%  to 7.9%  in the emulation of trial 4. Estimates for each of the 4 target trial emulations are further summarized in The corresponding risk differences in 90-day postoperative mortality are presented in For trial 1, the estimated risk difference in 90-day mortality was −2.6 percentage points  for an operation by surgeons who performed 2 pancreatectomies (10th percentile) in the past year (without changing the distribution of available surgeons with these volumes) compared with an operation by surgeons who performed 25 pancreatectomies (95th percentile) in the past year. For trial 2, the corresponding risk difference estimate was −2.3 percentage points  at median-volume hospitals and −2.3 percentage points  at higher-volume hospitals. For trial 3, the corresponding risk difference estimate was −0.5 percentage points  at middle-volume hospitals and −0.6 percentage points  at higher-volume hospitals. For trial 4, the risk difference estimate was −0.8 percentage points  at median-volume hospitals and −0.8 percentage points  at higher-volume hospitals.Mortality estimates stratified by surgeon operative volume rather than hospital operative volume are presented in eFigure 3 in the In this cohort study, we demonstrated how to specify and emulate trials for the association between mortality and different surgeon and hospital operative volumes, using the example of patients undergoing pancreatectomy. This approach may be used to interpret prior volume-outcome studies and to characterize potential pitfalls for future research.11 can be viewed as attempts to emulate these designs, and hence the relevance of their estimates for decision-making is questionable. Moreover, most prior studies have been cross-sectional rather than longitudinal, which makes their estimates even harder to evaluate.Trial 1 is unrealistic because it considers interventions on surgeon volume only. Trial 2 is an improvement because it considers joint interventions on surgeon volume and hospital volume, but it is still unrealistic because it disregards travel time. Ignoring travel time may result in confounding bias that cannot be adjusted for because of the zero probability (nonpositivity) of traveling long distances to reach lower-volume surgeons. Despite the multiple shortcomings of trials 1 and 2, previous observational analysesTrials 3 and 4 considered better-defined interventions and explicitly incorporated travel time. Trial 3 did not distinguish between the effect of the surgeon’s  volume and the effect of confounders on the association between a particular surgeon and mortality . But thi10 and there is no reason to suspect that surgeons would operate only on patients with or without Medicare. Therefore, approximate rank preservation of surgeons and hospitals by their volume between these data and a nationally representative sample of patients (not limited to Medicare beneficiaries) can be expected. Fourth, in our emulations, patients could be assigned to a strategy only by using the observed data at surgery. Thus, only trials in which all patients assigned to a surgeon ultimately undergo an operation were emulated. If a high proportion of patients were to become inoperable or die in the preoperative period, estimates would differ from those obtained in truly randomized trials (even in the absence of other biases). This proportion could not be calculated because, in the Medicare data, recording of the time of diagnosis was inconsistent. Fifth, these target trials and their emulations are intended to estimate the effect sizes of interventions on patients selecting their surgeons and hospitals according to operative volume. However, emulating these trials would not help to estimate the effect size of surgeons increasing or decreasing their number of operations over time. Emulation of such a trial requires consideration of time-varying confounding and is described elsewhere.13This study has several limitations. First, residual confounding is always a concern when observational data are analyzed. We attempted to eliminate this bias through adjustment for multiple potential confounders. Second, we directly incorporated driving distance into the specified interventions to avoid losing generalizability; researchers interested only in a subpopulation of patients who live near higher-volume surgeons and hospitals might instead restrict their study to these eligible patients. Third, because operations performed for patients who were not Medicare beneficiaries were not included in the data, the measurement of operative volume may have underestimated the total (Medicare and non-Medicare) operative volume of a surgeon or hospital. However, exocrine pancreatic cancer is a disease of the elderly (incident diagnosis is highest among men aged 65-69 years and women aged 75-79 years)In summary, for volume-outcomes research, it is important to specify a well-defined intervention. Through this cohort study that emulated 4 hypothetical trial designs, we illustrated that the results and their interpretations differ according to the hypothetical target trial of interest. Introducing bias with analytic choices that deviate from the intended trial may lead to erroneous decision making.