High-output syndrome (HOS) is a complication of ileostomy, which can affect quality of life significantly; however, its exact cause remains unknown. The aim of this study was to establish the frequency, as well as the preoperative and intraoperative factors predictive of HOS.The subjects of this study were 164 consecutive patients who underwent colorectal cancer surgery with ileostomy construction at our institute between January, 2011 and August, 2018. Thirteen patients with postoperative complications reported as causes of HOS, including intraperitoneal abscess, paralytic ileus, and outlet obstruction, were excluded. We used a logistic regression analysis to identify the factors predictive of HOS.P = 0.03), but other patient factors such as age, gender, body mass index, and use of daily laxatives were not significantly different between the groups. The HOS group had significantly more cases of total proctocolectomy (P = 0.04), but other surgical factors such as operative time, and blood transfusion were not significantly different between the two groups.HOS developed in 36 of the 151 patients (23.8%). There were significantly more diabetic patients in the HOS group (These results indicate that diabetes and total proctocolectomy are preoperative predictors of HOS, allowing for the possibility of early intervention via post-surgical treatment. Temporary ileostomy is being performed frequently in recent years in line with the increase in anal preservation surgery for lower rectal cancer and to prevent suture failure, resulting in additional opportunities for constructing a small bowel stoma , 2. JapaClostridium difficile [High-output syndrome (HOS) is a complication of small-bowel stoma, which can cause dehydration or renal dysfunction and affect quality of life. The effluent from HOS becomes clinically significant when the daily output exceeds 2000 ml , leadingifficile  or salmoifficile  are thouThe aim of this study was to establish the frequency, as well as the preoperative and intraoperative factors predictive of HOS. The identification of preoperative predictive factors will enable early intervention and assist with the development of novel therapeutic drugs.All of the experimental protocols described in the present study were approved by the Institutional Ethical Review Committee (UMIN 15046-3: comprehensive agreement) and conform to the provisions of the Declaration of Helsinki.n = 2), outlet obstruction [n = 4), and ileus (n = 1). Among the 164 patients, three had anastomotic leakage, four had intra-abdominal abscess, four had outlet obstruction, and two had ileus. After the exclusion of these 13 patients, because their complications are documented postoperative factors for HOS, 151 patients were the subjects of the final analysis. Adult patients were considered to have HOS if they were referred with a stoma output of more than 2000 ml⁄24 h for three or more consecutive days and biochemical disturbances were imminent [n = 36) and non-HOS (n = 115). All procedures were performed by highly experienced staff colorectal surgeons.The subjects of this retrospective case–control study were 164 consecutive patients who underwent elective ileostomy for colorectal cancer between January, 2011 and August, 2018 at Osaka University . Twenty-nine patients 17.7%) suffered postoperative complications, most of which (75.9%) were Grade 1 or 2 according to the Clavien–Dindo classification , 11. Clatruction  (n = 4),.7% suffeWe compared data on patient demographics, body mass index (BMI), American Society of Anesthesiologists physical status (ASA-PS) classification , preoperIleostomy was created approximately 40–50 cm from away the ileocecal valve for all patients, who had the same postoperative care pathway. Stoma sites were based on a retrospective analysis of operation reports. Early mobilization was encouraged and implemented on postoperative day (POD) 1, as was fluid intake if tolerated. Patients began a light diet on POD 2 and progressed to full meals if tolerated. HOS management is based on the attending doctor’s clinical judgment, and antibiotic or anti-diarrhea agents were given when necessary. Patients were considered fit for discharge when stoma management was mastered and symptoms were stable.χ2 test. Continuous variables were expressed as medians and ranges and were compared with a Mann–Whitney U test or Student’s t test. The level of significance was set at P < 0.05. A univariate analysis of the categorical data and each individual risk factor for HOS were also performed and the variables considered relevant in the univariate analysis (P < 0.05) were entered into a multivariate logistic regression model. The ORs and 95% CIs were calculated for all the variables. All statistical analyses were performed using JMP version 13.0 .Categorical variables were compared using a HOS developed in 36 of the 151 patients (23.8%). Figure P = 0.11), sex distribution (P = 0.97), history of prior surgery , and ASA-PS (P = 0.82) were also not significantly different between the groups. The patient characteristics did not differ significantly between the groups (Table The median BMI of the HOS and non-HOS group patients was 22.7 15.0–38.3) and 23.2 (15.0–32.2) kg/m2, respectively, without a significant difference between the groups. In this study, we did not consider body habitus because we thought that it could be substituted by BMI. The age , TNM stage (P = 0.73), and use of daily laxatives (P = 0.21) were not significantly different between the groups (Table P = 0.55), estimated blood loss (P = 0.61), and lateral lymph node dissection (P = 0.38) were not significantly different between the groups (Table P = 0.03) and total proctocolectomy (P = 0.04). HOS was also significantly associated with age (P = 0.048), diabetes mellitus (P = 0.01), and total proctocolectomy  and total proctocolectomy  were independent significant preoperative factors for HOS , diabetes mellitus , and total proctocolectomy  were independent significant factors for HOS .Patients were discharged from hospital after a mean period of 40.7 ± 12.5 and 21.2 ± 7.8 PODs (mean ± SD) in the HOS group and non-HOS groups, respectively. The hospitalization period was significantly longer in the HOS group . However, this requires a clinical trial to establish bile acid dose adjustments before and after surgery. Diabetes is associated with all postoperative complications, but strict preoperative and operative management of blood glucose levels may also reduce the incidence of HOS.None of the patients in this study needed readmission, but Baker et al.  reportedThe limitations of this study were the fact that it was conducted in a single center and the sample size was small, which means that the conclusions cannot be considered definitive. Moreover, the volumes of water intake and infusion were not taken into account.In conclusion, we identified diabetes and total proctocolectomy as preoperative predictors of HOS. Establishing the preoperative predictive factors will enable early intervention by post-surgical treatment and may assist with the development of novel therapeutic drugs.


Lumbar spine  and lateral spine  BMD increased after 6 months of transdermal estrogen. Lumbar spine BMAT decreased significantly after 3 months . Increases in lateral spine BMD were associated with decreases in CTX (p = 0.047). In conclusion, short‐term treatment with transdermal, physiologic estrogen increases spine BMD in women with AN. Future studies are needed to assess the long‐term efficacy of this treatment. © 2019 The Authors. JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.Anorexia nervosa (AN) is a psychiatric disorder characterized by self‐induced starvation, low body weight, and elevated levels of bone marrow adipose tissue (BMAT). BMAT is negatively associated with BMD in AN and more than 85% of women with AN have low bone mass and an increased risk of fracture. Although a majority of women with AN are amenorrheic, which is associated with low BMD, oral contraceptive pills, containing supraphysiologic doses of estrogen, are not effective in increasing bone mass. We performed a 6‐month, open‐label study of transdermal estradiol (0.045 mg/day) + levonorgestrel (0.015 mg/day) in 11 women with AN (mean age ± SEM: 37.2 ± 2.3 years) to investigate the effects of transdermal, physiologic doses of estrogen on BMD and BMAT in women with AN. We measured change in BMD by DXA, change in BMAT at the spine/hip by  Anorexia nervosa, a psychiatric disorder predominantly affecting women, is characterized by an inappropriately low body weight caused by self‐induced starvation. Over 85% of women with this disorder have low bone mass,Functional hypothalamic amenorrhea, a common finding in women with anorexia nervosa, is an adaptive response to chronic starvation, resulting in decreased energy expenditure on the costly process of reproduction during periods of nutritional insufficiency. Duration of amenorrhea is associated with low BMD in women with anorexia nervosaIn contrast, treatment with physiologic, transdermal estrogen for 18 months in adolescents with anorexia nervosa significantly increases BMD,An additional potential determinant of low bone mass in anorexia nervosa is bone marrow adipose tissue (BMAT). In contrast to subcutaneous and visceral fat, levels of BMAT are elevated in anorexia nervosa and inversely associated with BMD.We studied 11 premenopausal women with anorexia nervosa (mean ± SEM: 37.2 ± 2.3 years). Participants were recruited through online advertisements and referrals from local eating disorder providers. Subjects were interviewed by the team psychologist (KE) to ensure that they met DSM‐5n = 11): 1300 mg/day ±188 mg/day (SEM)]. At each study visit, blood was drawn for laboratory studies, radiologic imaging (described below) was performed, and subjects were weighed on an electronic scale while wearing a hospital gown. Height was measured as the average of three readings on a single stadiometer at their first study visit. Frame‐size estimation was performed by caliper measurement of elbow breadth and compared with norms based on US National Health and Nutrition Examination Survey I data; percent ideal body weight was calculated based on 1983 Metropolitan Life Height and Weight tables.All subjects were seen at the Translational and Clinical Research Center at the Massachusetts General Hospital  for a baseline visit, 3‐month study visit, and final (6‐month) study visit. After the baseline visit, subjects were initiated on a weekly transdermal patch  containing both estradiol (0.045 mg/day) and the progestin levonorgestrel (0.015 mg/day). Although doses of transdermal estradiol as low as 0.014 mg/day have been shown to be effective for the treatment of low BMD in postmenopausal women,The study was approved by the Partners HealthCare Institutional Review Board and complied with the Health Insurance Portability and Accountability Act guidelines. Written informed consent was obtained from all subjects.Subjects underwent DXA to measure areal BMD of the posterior–anterior lumbar spine (L1–L4), lateral spine (L2–L4), left total hip, and left femoral neck using a Hologic Discovery A densitometer  at their baseline and final study visits. At the 3‐month study visit, areal BMD of the lumbar and lateral spine was also measured by DXA. Coefficients of variation of DXA have been reported as ≤1.08% for lumbar spine BMD and <2.2% for femoral neck BMD.1H‐magnetic resonance spectroscopy (1H‐MRS) was performed by the Metabolic Imaging Core of the Nutrition and Obesity Research Center at Harvard , as previously described.1H‐MRS of the L4 vertebral body, the proximal femoral epiphysis, metaphysis, and mid‐diaphysis was performed to determine lipid content . For the L4 vertebra, a voxel measuring 15 × 15 × 15 mm (3.4 mL) was placed within the L4 vertebral body. Single‐voxel 1H‐MRS data were acquired using point‐resolved spatially localized spectroscopy (PRESS) pulse sequence without water suppression with the following parameters: TE of 30 ms, TR of 3000 ms, 8 acquisitions, 1024 data points, and receiver bandwidth of 2000 Hz. For the femur, a voxel measuring 12 × 12 × 12 mm (1.7 mL) was positioned within the proximal femoral epiphysis, and single voxel 1H‐MRS using the same non‐water‐suppressed PRESS pulse sequence was performed. This process was repeated with voxel placement in the metaphysis at the intertrochanteric region and the mid‐diaphysis. Automated procedures for optimization of gradient shimming and transmit and receive gain were used. The coefficient of variation (CV) for marrow fat quantification in this population at the reported sites, determined by scanning five subjects twice, has been reported as 3%.In brief, 1H‐MRS data was performed using LCModel software . Data were transferred from the scanner to a Linux workstation, and metabolite quantification was performed using eddy current correction and water scaling. A customized fitting algorithm for bone marrow analysis provided estimates for all lipid signals combined . LCModel bone marrow lipid estimates were automatically scaled to unsuppressed water peak (4.7 ppm) and expressed as lipid/water ratio.Fitting of the IGF‐1 was measured by a luminescent immunoassay analyzer . The detection limit for IGF‐1 was 4.4 ng/mL, with an intra‐assay CV of 2.2% and an interassay CV of 5.1%. N‐terminal propeptide of type 1 procollagen (P1NP) and osteocalcin, both markers of bone formation, and C‐terminal collagen cross‐links (CTX), a marker of bone resorption, were also measured by a luminescent immunoassay analyzer . For P1NP, the intra‐assay CV was 3.0% and the interassay CV was 5.0%. For osteocalcin the intra‐assay CV was 3.0% and the interassay CV was 6.0%. For CTX, the intra‐assay CV was 3.2% and the interassay CV was 6.2%. Sclerostin was measured by ELISA  with an intra‐assay CV of 2.0% and an interassay CV of 9.5%.t test. If the data were not normally distributed, medians and the interquartile range were reported and compared using the Wilcoxon test. Paired sample t tests or Wilcoxon signed rank test  were used to compare changes in BMD and BMAT parameters between the study visits. To develop new hypotheses, we assessed univariate associations between changes in biologically plausible hormonal parameters and changes in BMD and BMAT in response to transdermal estrogen as part of this exploratory study; given the small sample size (n = 8 study completers), Spearman's coefficients were calculated to assess these univariate relationships. Repeated measures analysis was performed to investigate changes with time for CTX, P1NP, osteocalcin, and sclerostin using the baseline, 3‐month, and 6‐month timepoints. A p value of <0.05 was considered significant.Statistical analysis was performed using JMP Pro 13.0  software. If the data were normally distributed, means and standard error of the mean (SEM) were reported and compared using the Student's n = 3) of subjects reported a history of a stress fracture. Participants reporting a history of stress fracture had significantly lower BMD at the total hip and femoral neck as compared with participants reporting no prior history of a stress fracture . Two additional participants, who did not have a history of a stress fracture, reported a history of a prior traumatic fracture; there were no significant differences in BMD at any site in individuals with a history of any fracture (n = 5) compared with those with no history of fracture (p = 0.315 to 0.927).Baseline characteristics of the study subjects are listed in Table p = 0.033) . The mean change in weight for the group was 0.6% ± 2.3% over the 6‐month study. When we excluded the subject who gained more weight than the rest of the subjects  and the subject who lost more weight than the rest of the subjects , the results remained significant . Total hip BMD  and femoral neck BMD  did not change significantly over the 6‐month study.After 6 months of physiologic, transdermal estrogen, spine BMD increased significantly. Lumbar spine BMD increased by 2.0% ± 0.8% (p = 0.046) . The subject who gained more weight than the other study subjects was also the only subject to report a change in physical activity level during the course of the study (decrease in running); therefore, changes in physical activity level also did not affect the results. In the hip, after 3 months of treatment with transdermal estrogen, BMAT also decreased in the epiphysis , diaphysis , and metaphysis , but the changes were not statistically significant , but the decrease did not achieve statistical significance. There were also nonsignificant changes in BMAT at the hip after 6 months of treatment with transdermal, physiologic estrogen   Fig. . We havep = 0.047) (Table p = 0.003) and 6 months (p = 0.005) of treatment with transdermal estrogen as compared with their baseline values ; compared with baseline levels, CTX levels decreased between −4.9% and − 63.9% after 3 months and between −21.7% and − 70.5% after 6 months of transdermal estrogen. The 6‐month change in CTX was predicted by the 3‐month change in diaphyseal BMAT   or 6 months (p = 0.469) of treatment with transdermal, physiologic estrogen . There was a trend towards a significant positive association between change in P1NP over the 6‐month treatment period and change in lateral spine BMD after 6 months of treatment   or 6 months (p = 0.469) of treatment with transdermal estrogen . Change in osteocalcin after 6 months of treatment was predicted by 3‐month changes in epiphyseal BMAT   . The decrease in sclerostin after 3 months was significantly associated with the increases in lateral spine BMD observed after 3 months of treatment  (Table p = 0.985), as levels of sclerostin increased between the 3‐month and 6‐month timepoints . There were no associations between change in sclerostin and change in BMAT.Levels of sclerostin, a WNT inhibitor and negative regulator of bone formation, decreased significantly after 3 months of treatment with transdermal physiologic estrogen  or 6 months (p = 0.813) of treatment with transdermal estrogen (Table p = 0.033) (Table p = 0.036) . This subject had been on oral contraceptives in the past, but not for over 10 years prior to study participation. The second subject, who was 31 years old at the initiation of the study and reported 180 months of amenorrhea, discontinued participation because of difficulty scheduling visits and because she experienced breast tissue growth, as well as increased breast tenderness. This patient had also been on oral contraceptives for 24 months in the past, but not for 9 years prior to study participation. This patient reported being evaluated by her primary care physician and a breast surgeon, both of whom encouraged her to continue participation in the study, but the multiple visits required for evaluation of the breast tissue growth made it difficult to also continue with the scheduled study visits.We have shown that short‐term treatment with transdermal, physiologic estrogen treatment increases spine BMD in adult women with anorexia nervosa. We have also found that vertebral BMAT, a possible determinant of low bone mass, decreases in response to estrogen replacement in this population. These data suggest that physiologic, transdermal estrogen replacement should be further studied as a possible treatment for low bone mass in adult women with anorexia nervosa. These contrasting modeling/remodeling rates may contribute to important differences observed in response to treatments for low bone mass in adults versus adolescents with anorexia nervosa; therefore, potential therapies must be independently studied in both populations.Anorexia nervosa is a psychiatric disorder with a lifetime prevalence of approximately 2.2% in women,Given the overall higher rate of bone resorption in adult women with anorexia nervosa as compared with adolescents, and the known effects of estrogen on bone turnover,Sclerostin, an inhibitor of bone formation, has been associated with hip fractures in postmenopausal women,Levels of BMAT, a component of the bone marrow microenvironment, are elevated in women with anorexia nervosa, despite low levels of subcutaneous and visceral adipose tissue.Limitations of this exploratory pilot study include our small sample size and lack of a control group. Based on data from prior longitudinal studies, the annual rate of bone loss is estimated to be −2.6% in the spine for women with anorexia nervosaAll authors state that they have no conflicts of interest.


This study investigated the effects of users’ familiarity with the objects depicted in icons on the cognitive performance of icon identification. First, without knowing the specific semantic information of icons, 20 participants were required to search for target icons among visually similar distractors for 3-hour-long training sessions across 1 week, during which their familiarity with different icons was manipulated by differential exposure frequencies. Half of the icons were presented 10 times more often than the other half. Subsequently, participants’ abilities to recall corresponding semantic information when cued with associated target icons were tested after they had learned all the icons. The results showed that, in both the visual search task and the semantic information recall task, participants performed significantly better when the icons were more familiar. Importantly, the effects of icon complexity in the visual search task diminished as participants became familiar with the icons, and the beneficial effects of familiarity in the semantic information recall task were larger when the icons were complex. These findings have practical implications for icon design. When creating new icons for time critical user interfaces, icons should be kept as simple as possible and employ familiar, commonly used, graphics. With the improvement of informationization as well as the development of human–computer interactions, icons have become an important component of digital user interfaces . CompareIn general, when designers create an icon, three icon characteristics need to be taken into consideration: visual complexity, concreteness, and semantic distance . To datehome page and setting. However, we can still consider these two icons as high familiarity icons because the objects depicted in them, a house and a gear, often appear in our daily life.However, there are two forms of users’ familiarity with icons . The firUnfortunately, previous studies seem to overlook the effects of the second form of familiarity. Depending on the user’s experiences in daily life, the familiarity with the objects depicted in icons might also have effects on icon identification. For example, when we see a button that we have never used before, we might understand it better and recall its function more accurately if it includes a familiar symbol. Therefore, the present study aims to investigate the role of users’ familiarity with the objects depicted in icons during icon identification.Although only a few studies have explored the effects of familiarity on icon identification, numerous researchers have conducted extensive studies in other related areas, such as picture naming e.g., , LanguagTo explore our prediction, in the current study, we experimentally manipulated the exposure frequency of the icons that were unknown to our participants by asking them to perform a series of visual search tasks for similar icons. As a result, participants should be more familiar with the high-frequency icons. Then, we employed a semantic information recall task to investigate the effects of familiarity with the objects depicted in icons after participants had learned all the icons. We expect that people identify familiar icons and recall corresponding semantic information more successfully. Moreover, in addition to familiarity, two more independent variables (icon complexity and concreteness) were also employed in this study; we will explore whether there are interactions between them as well.The procedure in this study was approved by the International Review Board of Southeast University. Participants read and signed a consent form before participating in the experiment.Twenty college students, 12 men and 8 women (ages ranging from 22 to 29 years) from Southeast University participated in this study. All participants were volunteers and had never participated in similar experiments before.definitely simple, 5 = definitely complex); for concreteness ratings, icons were to be regarded as concrete if they depicted real objects ; for familiarity ratings, icons were to be regarded as familiar if they often appeared in daily life . Based on the rating results, 80 icons from four sets were used in the experiment . Subsequently, one-way analyses of variance (ANOVAs), followed by Newman-Keuls comparisons, were conducted to ensure the ratings differed in accordance with the requirements of each experimental condition  = 734.67, p < .001, and between the concreteness ratings of the concrete group and the abstract group, F = 1,553.59, p < .001. However, there was no significant difference among the familiarity ratings of icons, F = 1.19, p = .19, or icons groups, F = .74, p = .44. Three hundred and eighty icons were chosen from the symbols used on industrial machineries and household goods as well as from the icons used in the systems of computers and smart phones. Before the formal experiments, we recruited 50 volunteers to rate all the three factors  of each icon on a 5-point scale. Instructions were similar to those adopted in previous studies . For comondition . The resnot closely related, 5 = very strongly related). Semantic distance is a measure of the closeness of the relationship between the icon and what it is intended to represent  = 1.68, p = .25.Subsequently, each icon was assigned a Chinese word as a piece of semantic information which offered a relevant description of the paired icon. The assigning of semantic information to icons was based on the agreement between five experts (icon designers). To ensure that the icon/semantic information pairs used in our experiment were at the same level, we asked 20 volunteers to rate the semantic distance of each icon/semantic information pair  and created four distractors for each icon , with half of the icons exposed more frequently, each participant learned the relationships between icons and semantic information one by one in random order. Subsequently, participants’ abilities to recall the semantic information were tested see . In the We analyzed response time (RT) by linear mixed-effects regression, and we analyzed accuracy data via logistic mixed-effects regressions . For the2(1) = 1,822.312, p < .001, and more accurate, ΔAIC = −270, LLR χ2(1) = 272.719, p < .001, in identifying whether the target icon was present or absent from the search display. Importantly, participants identified high-frequency icons more quickly, ΔAIC = −229, LLR χ2(1) = 230.798, p < .001, and more accurately, ΔAIC = −118, LLR χ2(1) = 119.593, p < .001. Participants’ accuracy and response times on the visual search task improved significantly for both frequency conditions as all the icons became more familiar. In addition, performance was always better for the high-frequency icons.Over the 3 days of training, participants performed better on the visual search task see , becomin2(1) =1,092.390, p < .001, and more accurate, ΔAIC = −194, LLR χ2(1) = 195.959, p < .001, when they were searching for simple target icons. In addition, a strong interaction was observed between the effects of icon complexity and blocks of trials on search accuracy, ΔAIC = −11, LLR χ2(1) = 13.032, p < .001. However, no interaction was found between complexity and blocks of trials on response times, ΔAIC = 2, LLR χ2(1) = 0.068, p = .795.2(1) = 0.152, p = .697. However, participants responded more quickly to concrete icons, ΔAIC = −42, LLR χ2(1) = 43.932, p < .001. In addition, there was no significant interaction between icon concreteness and blocks of trials on accuracy, ΔAIC = 2, LLR χ2(1) = 0.118, p = .731, or response times, ΔAIC = 2, LLR χ2(1) = 0.008, p = .931.2(1) = 11.651, p < .001, and when the icons were simple, ΔAIC =−10.7, LLR χ2(1) = 12.690, p < .001. However, there was no significant effect of icon concreteness on recall accuracy, ΔAIC = −1, LLR χ2(1) = 2.099, p = .147.2(1) = 12.768, p < .001; abstract icons, ΔAIC = −5, LLR χ2(1) =7.361, p < .01; and concrete icons, ΔAIC = −2, LLR χ2(1) = 4.375, p < .05. However, there was no significant effect of familiarity for simple icons, ΔAIC = 1, LLR χ2(1) = 1.397, p = .237. In addition, a strong interaction was observed between the effects of icon complexity and familiarity, ΔAIC = −10, LLR χ2(1) = 12.690, p < .001. However, there was no significant interaction between icon concreteness and familiarity, ΔAIC = 2, LLR χ2(1) = 2.239, p = .326.2(1) = 10.485, p < .001, and for complex and concrete icons, ΔAIC = −4, LLR χ2(1) = 3.366, p < .05. However, there was no significant effect of familiarity for simple and abstract icons, ΔAIC = 2, LLR χ2(1) = 0.462, p = .497, or for simple and concrete icons, ΔAIC = 1, LLR χ2(1) = 0.981, p = .322.The key question of this study is how to experimentally simulate users’ different levels of familiarity with the objects depicted in icons. According to the familiarity encoding methods used in related areas , we maniMost results of the visual search task were consistent with the results of previous studies , 2001, sMoreover, In addition, our results showed that there was a significant interaction on the accuracy of the visual search task. The differences in accuracy between simple and complex icons diminished greatly as participants became familiar with the icons. The reason for this, we contend, is that as the exposure times of the complex icons increased, participants became more familiar with the features of each complex icon. As a result, it became less difficult for them to identify the target icons, and the error rate declined significantly. Besides, since the simple icons contained fewer features, search performance for simple icons was still better than that for complex icons and tended to stabilize with time. However, there was no interaction effect in response time of the visual search task. The differences in response time between simple and complex icons were relatively constant throughout the training session. It is most likely due to that the training session was not enough. The left panel of Finally, our results also showed that although there was no effect of icon concreteness on visual search accuracy, people responded more quickly to concrete icons than to abstract icons see . One posAfter numerous trials of familiarity training with icons that differed in terms of their familiarity, the effects of icon familiarity in the semantic information recall task were expected to be observed more directly. Our results revealed that users’ familiarity with the objects depicted in icons indeed influenced icon identification. As predicted, participants performed significantly better when the icons were more familiar. According to the theory of Numerous researchers have suggested that the currently active information in an individual’s working memory will decay gradually with time, and it will become unavailable to subsequent processing if it is not reactivated . From thIn addition, the results also showed that icon complexity was another important factor that influenced participants’ semantic information recall performance. The explanation might be similar to that concerning the effects of familiarity, as discussed earlier: The information of complex icons was more difficult to reactivate and decayed faster as a result, or the relationships between complex icons and semantic information were not encoded successfully during the learning stage, and thus, they were more susceptible to interference. However, it should be noted that we did not observe a significant effect of concreteness, which Moreover, our results also showed that there was a significant interaction between complexity and familiarity see . When thThese findings have important implications for the design of icons. (a) If visual search activity is the key task of the digital interface, and the operator’s response time seriously affects the quality of the entire mission, for example, in aircraft operations, nuclear power control, and weapons operation, then reducing icon complexity should be a top priority. The reason is that the effect of icon complexity on reaction time does not diminish as users receive extra training. Thus, in this case, designers should reduce the amount of details in icons to lessen the visual complexity. (b) If the task is not time-critical and the context in which the digital interface used is relatively relaxed, the designer may increase the level of details in icons to make the whole interface more attractive to users. Although the improvement of icon complexity might influence the identification of the icons at the beginning, that effect will quickly diminish as a result of user experience. (c) Designers should not merely consider the user's familiarity with the relationship between an icon and its meaning; the effect of the icon’s familiarity should also be taken into consideration when designers create new icons. Icons containing more familiar symbols are easier to remember and identify. Thus, unless the design of a new icon is very simple, in most cases, it is better for designers to use common graphics to create new icons.Users’ familiarity with icons takes two forms: familiarity with the relationships between icons and semantic information, and familiarity with the objects depicted in icons. Although numerous studies have demonstrated that the former is one of the primary determinants of icon identification, it is still not known whether the latter affects the cognitive process. In this article, we investigated the effects of users’ familiarity with the objects depicted in icons on the cognitive performance of icon identification. The results showed that, in both visual search task and the semantic information recall task, participants performed worse when the objects depicted in icons were less familiar. Importantly, the detrimental effects of low familiarity on recall performance increased as the complexity of icon increased. These findings extend our understanding of how familiarity affects icon identification. However, this study is limited in several aspects. (a) Due to the limitation of experimental equipment, we only analyzed the accuracy data of the semantic information recall task. More data such as eye movement data and electroencephalograms (EEGs) can be collected to provide more convincing evidence. (b) Participants in this study are all college students who have rich experience of using human-computer interfaces. However, the user groups should be diverse in reality, including children, elder, etc. In the future, we will employ more advanced experimental equipment and expand our study to more participants and more participant diversity.


Economic burden estimates are essential to guide policy‐making for influenza vaccination, especially in resource‐limited settings.We estimated the cost, absenteeism, and years of life lost (YLL) of medically and non‐medically attended influenza‐associated mild and severe respiratory, circulatory and non‐respiratory/non‐circulatory illness in South Africa during 2013‐2015 using a modified version of the World Health Organization (WHO) worksheet based tool for estimating the economic burden of seasonal influenza. Additionally, we restricted the analysis to influenza‐associated severe acute respiratory illness (SARI) and influenza‐like illness  as suggested in the WHO manual.The estimated mean annual cost of influenza‐associated illness was $270.5 million, of which $111.3 million (41%) were government‐incurred costs, 40.7 million (15%) were out‐of‐pocket expenses, and $118.4 million (44%) were indirect costs. The cost of influenza‐associated medically attended mild illness ($107.9 million) was 2.3 times higher than that of severe illness ($47.1 million). Influenza‐associated respiratory illness costs ($251.4 million) accounted for 93% of the total cost. Estimated absenteeism and YLL were 13.2 million days and 304 867 years, respectively. Among patients with influenza‐associated WHO‐defined ILI or SARI, the costs ($95.3 million), absenteeism (4.5 million days), and YLL (65 697) were 35%, 34%, and 21% of the total economic and health burden of influenza.The economic burden of influenza‐associated illness was substantial from both a government and a societal perspective. Models that limit estimates to those obtained from patients with WHO‐defined ILI or SARI substantially underestimated the total economic and health burden of influenza‐associated illness. South Africa has a policy of introducing new vaccines or increasing vaccination coverage only after careful consideration of the evidence of disease burden and the potential costs and benefits from such vaccines. However, economic burden estimates of influenza‐associated illness in South Africa are lacking. In this study, we aimed to estimate the mean annual direct and indirect costs, absenteeism, and years of life lost (YLL) from influenza‐associated illness in South Africa from 2013 through 2015.22.1We estimated the economic burden from a societal perspective. Costs in South African Rands were converted to 2015 US dollar using average monthly South African Rand to US dollar exchange rate in 2015. All costs were expressed in 2015 prices using the South Africa all‐items Consumer Price Index.We aggregated influenza‐associated illness within four syndromic endpoints according to published literature from South Africa,We considered the cost of influenza‐associated illness in three categories as follows: (a) direct medical costs: costs related to treatment incurred both within and outside health facilities ; (b) direct non‐medical costs: illness‐related expenditures that do not relate directly to medical treatment ; and (c) indirect costs: the value of lost productivity because of reduced working time (for both patient and caregiver) during the illness episode or while receiving care. Medical and medication costs not covered by healthcare facilities, consultation with traditional healers, and direct non‐medical costs were considered out‐of‐pocket expenditures.2.2For estimation of the economic burden and YLL from influenza‐associated illness, we used a combination of published or publically available data2.3To estimate the national economic burden of influenza‐associated illness, we used the WHO manual and toolkit, a worksheet based tool, for estimating the economic burden of seasonal influenzaWe reported total costs summed across the three main endpoints as well as cost estimates disaggregated by the three main syndromic endpoints considered in this study . Estimates were parametrized and generated within six age categories  and subsequently aggregated.In addition, we implemented a sensitivity analysis using the WHO toolkit without the abovementioned modifications and restricted to influenza‐associated ILI and SARI  as suggested in the WHO manual.2.3.1In the main analysis for medically attended severe illness, data on quantities of resources used during hospitalization were obtained from influenza‐positive patients hospitalized with SARI  among patients with influenza‐associated circulatory and non‐respiratory/non‐circulatory illness compared to those with influenza‐associated SARI or ILI. Hence, we implemented a sensitivity analysis by replacing the estimated cost of hospitalization and outpatient consultation among influenza‐positive patients with SARI or ILI by the cost per patient day equivalent  and any outpatient consultation cost to obtain an estimate that would account for differential treatment costs across different clinical presentations  was approved by the University of the Witwatersrand Human Research Ethics Committee (HREC) and the University of KwaZulu‐Natal Human Biomedical Research Ethics Committee (BREC) protocol numbers M081042 and BF157/08, respectively. The ILI protocol (DS2) was approved by HREC and BREC protocol numbers M120133 and BF080/12, respectively. This surveillance was deemed non‐research by the US Centers for Disease Control and Prevention (non‐research determination number: 2012‐6197). The costing protocol (DS6) was approved by the HREC protocol number M121195. All other data sources were publically available.33.1P = 0.356) (DS3).In 2013‐2015, we enrolled and tested 13 912 patients at selected sentinel sites, of which 5525 (39.7%) presented with ILI and 8387 (60.3%) presented with SARI. Among these, influenza viruses were detected in 7.9% (1106/13 912) of specimens; 12.4% (683/5525) in patients presenting with ILI; and 5.0% (423/8387) in patients presenting with SARI. The mean length of hospitalization was 5.5 days among any patient with SARI and 5.3 days among laboratory influenza‐positive patients with SARI  with respiratory, circulatory, or non‐respiratory/non‐circulatory illness, respectively (DS4).3.2In 2014, we enrolled 1788 patients into a costing study at selected influenza surveillance sentinel sites, of which 527 (29.5%) presented with ILI and 1261 (70.5%) presented with SARI. Of these, 15.2% (80/527) and 5.4% (68/1261) tested positive for influenza viruses, respectively. The mean monthly household income, including government support grants, was $559 . Average direct out‐of‐pocket costs and number of days absent from school or work are provided in Table 3.3The mean direct and indirect cost per illness episode was $25 overall; $770 for medically attended severe illness; $40 for medically attended mild illness; and $15 for non‐medically attended mild or severe illness. Mean annual cost of influenza‐associated illness was estimated at $270.5 million , 3% ($8.4 million), and 4% ($10.6 million) of the total cost, respectively Table . This coThe cost of influenza‐associated illness using the PDE was estimated at $292.4 million Table . The cos3.4The mean annual absenteeism associated with influenza‐associated illness was estimated at 13.2 million days , 1% 74 959 days), and 1% 117 312 days) of the total absenteeism, respectively  with direct and indirect costs contributing almost equally to the total cost and the cost of non‐medically attended illness making up 43% of the total economic burden. The economic burden incurred by the healthcare system to treat medically attended illness accounted for 41% ($111.3 million) of the total cost. The estimated mean annual length of absenteeism from school or work due to influenza‐associated illness was also substantial (13.2 million days) with absenteeism from non‐medically attended illness making up 70% of total absenteeism. Estimates obtained among individuals meeting the WHO SARI or ILI case definitions  was similar to those estimated from studies conducted in high‐ or middle‐income countries in North America, Europe, Asia, and Australia (range: 0.01%‐0.14%).In our setting, the indirect cost of influenza‐associated illness accounted for 44% of the total cost. This was similar to studies conducted in the United States and Asia (range: 13%‐56%), whereas in Europe, the indirect cost accounted for a higher proportion of the total cost (range: 88%‐92%).Whereas the cost per illness episode of non‐medically attended illness ($15) was lower than that for medically attended illness, the cost of non‐medically attended illness accounted for 43% of the total cost. This is because a large proportion of influenza‐associated mild (~74%) and severe ~56%) illness is not medically attended in South Africa.% illnessIrrespective of syndromes, the cost of influenza‐associated medically attended mild illness accounted for 70% of the cost of any medically attended illness (severe and mild). Whereas the cost of and absenteeism from influenza‐associated severe illness per episode are higher than those of mild illness, the number of severe illness episodes is substantially lower  than the number of mild illness episodes.We found a substantial underestimation of the total cost, absenteeism, and YLL when restricting the analysis to individuals with influenza‐associated SARI and ILI, a subset of all‐respiratory severe and mild illness, respectively. This is because, in our setting, influenza‐associated ILI, influenza‐associated non‐fatal SARI, and influenza‐associated SARI deaths represent only 34%, 40%, and 20% of the total number of influenza‐associated mild, severe‐non‐fatal, and fatal illness, respectively.Our study has some limitations that warrant discussion. First, we did not include the indirect cost associated with deaths including lost productivity because such estimates are not available for South Africa. Second, because we did not have specific estimates of costs for influenza‐associated all‐circulatory and non‐respiratory/non‐circulatory illness, we assumed the same costs as for influenza‐associated respiratory illness. However, given the proportionally small number of influenza‐associated non‐respiratory illness , this category minimally influenced the total cost estimates. In addition, the economic burden estimates using the PDE costs  and the influenza‐associated illness specific cost  were similar. Last, because of the high disparity of wages in South Africa, we used the minimum wages to estimate indirect cost; hence, this represents a minimum estimate.In conclusion, we found a substantial economic burden, absenteeism, and YLL associated with influenza disease in South Africa, with influenza‐associated mild and non‐medically attended illness accounting for a large proportion of total cost and absenteeism in our setting. The economic burden was high from both a government and a societal perspective. Estimates obtained from individuals meeting the SARI and ILI case definition as suggested in the WHO manual for estimating the economic burden of seasonal influenzaThe SARI protocol (DS2 in Table All authors declare that they have no commercial or other associations that may pose a conflict of interest.All authors had full access to the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Tempia S, Moyes J, Cohen AL, and Cohen C conceived and designed the study. Tempia S, Moyes J, Cohen AL, Walaza S, Teurnicht FK, Hellferscee O, Wolter N, von Gottberg A, Nguweneza A, McAnerney JM, Dawood H, Variava E, and Cohen C acquired, analyzed, or interpreted the data. Tempia S drafted the manuscript. Tempia S, Moyes J, Cohen AL, Walaza S, Edoka I, McMorrow ML, Treurnicht FK, Hellferscee O, Wolter N, von Gottberg A, Nguweneza A, McAnerney JM, Dawood H, Variava E, and Cohen C critically revised the manuscript for important intellectual content.The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the US Centers for Disease Control and Prevention, USA, or the National Institute for Communicable Diseases, South Africa. Click here for additional data file.


Multivariate Cox analysis of each lncRNA with clinical characteristics as covariates identified a total of 46 prognostic lncRNA probes. Robust likelihood‐based survival and least absolute shrinkage and selection operator (LASSO) models were used to establish a 6‐lncRNA signature with prognostic value. Receiver operating characteristic (ROC) curve analyses were employed to compare survival prediction in terms of specificity and sensitivity. Patients with high‐risk scores exhibited a significantly worse overall survival (OS) than patients with low‐risk scores , and the area under the ROC curve (AUC) for 5‐year survival was 0.77. A nomogram and forest plot were constructed to compare the clinical characteristics and risk scores by a multivariable Cox regression analysis, which suggested that the 6‐lncRNA signature can independently make the prognosis evaluation of patients. Single‐sample GSEA (ssGSEA) was used to determine the relationships between the 6‐lncRNA signature and biological functions. The internal validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 (n = 75) and the external validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57303 (n = 70) were successfully used to validate the robustness of our 6‐lncRNA signature. In conclusion, based on the above results, the 6‐lncRNA signature can effectively make the prognosis evaluation of GC patients.Gastric cancer (GC) remains an important malignancy worldwide with poor prognosis. Long noncoding RNAs (lncRNAs) can markedly affect cancer progression. Moreover, lncRNAs have been proposed as diagnostic or prognostic biomarkers of GC. Therefore, the current study aimed to explore lncRNA‐based prognostic biomarkers for GC. LncRNA expression profiles from the Gene Expression Omnibus (GEO) database were first downloaded. After re‐annotation of lncRNAs, a univariate Cox analysis identified 177 prognostic lncRNA probes in the training set  Identification of a 6‐lncRNA prognostic signature based on microarray re‐annotation in gastric cancer GC is a common cancer (ranking as the fifth) and the third common cause of cancer death.Long noncoding RNA (lncRNAs) are transcripts with more than 200 nucleotides, and dysregulated lncRNAs are associated with various human diseases.Recently, based on microarray and RNA‐seq methods, together with available open databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), people can easily obtain expression data for human cancers. In addition, using bioinformatics analysis, lncRNA signatures with prognostic value were established. For example, a two‐lncRNA signature, a potential biomarker for the prognosis of cervical cancer, was identified using a public database.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254, we used robust likelihood‐based survival and LASSO models to establish a 6‐lncRNA signature with prognostic value. Patients with high‐risk scores had markedly worse overall survival (OS) than patients with low‐risk scores , and the AUC for 5‐year survival was 0.77. In addition, the 6‐lncRNA signature could be used to independently make the prognosis evaluation of GC patients. The internal validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 and the external validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57303 were successfully used to validate the robustness of our 6‐lncRNA signature. In conclusion, based on the above results, the 6‐lncRNA signature displays a strong power for prognostic evaluation in GC patients.In this study, we performed a multistep re‐annotation analysis of lncRNA expression in GC. Based on lncRNA expression profiles in 22.1https://www.ncbi.nlm.nih.gov/geo/.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57303,http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 dataset contains 300 GC samples with clinical information . The NetAffx files are the direct platform for probes including the probe set ID, gene symbol, gene title, Ensembl gene ID, Refseq transcript ID, and other information for the specific probe set.We first mapped the Affymetrix Human Genome U133 Plus 2.0 Array probe set ID to NetAffx Annotation Files . The short noncoding RNAs such as pseudogenes, microRNAs, and other short RNAs were removed.For the probe sets with Ensembl IDs, we only retained those annotated with “lincRNA,” “processed transcripts,” “non‐coding,” or “misc_RNA” in Ensembl annotations.Finally, corresponding Affymetrix probe IDs were used to generate annotated lncRNA transcript profiles Table .To evaluate lncRNA expression based on probe ID, we used the methods described by Zhang et al.2.3http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 into two sets, namely, (a) a training set and (b) an internal validation set, in a random manner according to a ratio of 3:1. Next, we used the expression profiles of the above re‐annotated lncRNA probes and patients in the training set (n = 225). Using the R package survival, we performed a univariate Cox proportional hazards analysis using the coxph function. The statistical significance cutoff of the P‐value was considered at <.01. Then, we assessed other clinical features, including age, pT, pN, pM, and pStage, by Kaplan‐Meier analyses. The statistical significance cutoff of the P‐value was the same as above. Finally, we considered the above clinical factors as covariates. We performed a multivariate Cox proportional hazards analysis using prognostic lncRNA probes combined with clinical factors.To select key lncRNA probes with prognostic values, we divided all patients in 2.4rbsurv. This model uses the partial likelihood of the Cox model as the underlying method. For robustness, this package selects survival‐associated genes by separating the two sets as a cross‐validation technology with large variability. It employs forward selection, generating some gene models, and selecting an optimal model by Akaike information criteria (AIC). According to the study by Wang et al,Robust likelihood‐based survival modeling was carried out for selecting prognosis‐related lncRNAs using the R package 2.5glmnet.Based on the above identification of prognosis‐related lncRNA probes for GC, we further needed to narrow the gene range and establish a prognostic signature. Thus, we performed LASSO analysis, which constructs a more refined model using a penalty function. This method can compress some coefficients to zero; therefore, some unimportant indicators are reduced to 0, leaving a small number of indicators, for which the weight is not 0. LASSO analysis was implemented with the R package 2.6P‐value <.05 was considered significant.Each lncRNA probe was accompanied by a formula of risk score, and the estimated regression coefficients were used to weight the formula in LASSO analysis. GC patients in the training and validation sets were divided into two groups, a group with high risk and a group with low risk, by taking the corresponding median risk score as the cutoff point. Kaplan‐Meier curves were used to compare the two groups regarding their survival outcomes with the assistance of the log‐rank test. Receiver operating characteristic (ROC) curve analysis was employed to compare survival prediction with regard to the specificity and sensitivity according to lncRNA risk scores. A 2.7rms and forestplot were used to construct the nomogram and forest plot, respectively.To examine the relationship between the prediction accuracy of prognostic signature and clinical characteristics such as age, sex, pStage, pT, pN, pM, and risk scores, we employed a univariate Cox proportional hazards model. Meanwhile, the differential patterns of various clinical characteristics were analyzed. A nomogram and forest plot were used to display the results of the multivariable Cox analysis including all of the above variables. The R packages 2.8To observe the relationship between risk scores and biological functions, we used ssGSEA2.9http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 (n = 75), the entire set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 (n = 300), and the external validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57303 (n = 70). The Kaplan‐Meier curves for OS were used to compare the two groups regarding survival outcomes with the log‐rank test.The same risk formula was used to validate the internal validation set 33.1In this study, we performed a multistep re‐annotation analysis of lncRNA expression in GC Figure . Based ohttp://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 dataset were first obtained. The samples in http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 were divided into two sets based on the ratio of 3:1 in a random manner: the first is a training set, and the other is an internal validation set. The two sets contained 225 and 75 GC samples, respectively. http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57303 (n = 70) was used as an external validation set. We included the patient's age, survival status, sex, T stage, N stage, M stage, and tumor stage . We further performed multivariate Cox proportional hazards modeling of each significant lncRNA probe (n = 177) combined with clinical characteristics as covariates. Thus, we performed 177 multivariate Cox analyses and finally a total of 46 lncRNA probes had a significance threshold < 0.01 , lymph node (pN), metastasis (pM), and tumor stage (pStage) using Kaplan‐Meier analysis. As shown in Figure 01 Table .3.3rbsurv to construct a robust likelihood‐based survival model. It used the partial likelihood of the Cox model, which has been the basic method. The method implemented robust likelihood‐based survival analysis, repeated 1000 times. We calculated the standard deviation of all lncRNA probes . The median cutoff point of risk scores was used to divide all patients into groups with a high risk (n = 112) and a low risk (n = 113). Figure Additionally, ROC curve analyses were implemented to compare whether the survival prediction was sensitive and specific among the 6‐lncRNA signature‐based risk scores Figure C. The AU3.5P‐value <.0001) and prognosis of GC . In addition, the 6‐lncRNA signature was with an AUC of 0.75 for 5‐year survival and different survival outcomes in Stage IV .Furthermore, we analyzed the risk score distribution among different clinical stages, degrees of tumor invasion, degrees of lymph node involvement, and metastasis Figure A. The reP‐value <.001 , we classified patients into groups with a high risk (n = 37) and a low risk (n = 38) taking the median score as the cutoff point. The distributions of risk scores, survival durations of patients, and lncRNA expression levels are shown in Figure P‐value = .005, Figure To determine the robustness of this model, we used the same coefficients in the validation sets. First, using the same risk formula in the internal validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 (n = 300) was evaluated . We also validated the robustness of this 6‐lncRNA signature with an AUC of 0.73 for 5‐year survival . Moreover, the survival outcomes of patients in the high‐risk group were worse than that of patients in the low‐risk group . In agreement with the abovementioned findings, the OS times of patients in the high‐risk group were markedly shorter than that of patients in the low‐risk group from the external validation set  in GC. In addition, based on the TCGA database and LASSO models, Cheng identified a 3‐lncRNA prognostic signature including RP11‐108 M12.3, CYP4A22‐AS1, and AP000695.6.http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE79973, including 10 paired GC and normal tissues, was first used to identify the differentially expressed lncRNAs. Based on LASSO Cox regression model, 12‐lncRNA signature was finally identified with AUC of 0.869. Although there have been a series of methods for lncRNA signature establishment, the power of prognostic evaluation was different for each study. The ROC values of some models proposed in other literatures were higher than the model proposed in our study; however, the number of lncRNAs in these prognostic signatures was much higher than that in our 6‐lncRNA signature (5‐year AUC = 0.77 in our study). Considering clinical application values, the number of lncRNAs in the model should be as small as possible with high prognostic evaluation value. Here, we compared the predictive power using the AUC value among recent reports on lncRNA signatures in GC .In our study, using the robust likelihood‐based survival and LASSO models, we established a 6‐lncRNA signature with prognostic value in GC. These six lncRNAs were IPW, NCRNA00086, RP11‐38P22.2, ERVH48‐1, LOC158572, and AC004080.17. The human homologue IPW of the lncRNA IPW is located on chromosome 15, which is deleted in over 70% of patients with Prader‐Willi syndrome (PWS).Regarding the results of ssGSEA, we found that several pathways, such as adherens junction, gap junction, calcium signaling, actin cytoskeleton regulation, extracellular matrix receptor interaction, Wnt signaling, and mammalian target of rapamycin (mTOR) signaling, were positively associated with risk scores. The mTOR signaling pathway participates in different human cancers.Accurate prognosis is the basis for planning appropriate treatments for cancer patients. Due to the heterogeneity, the stage of greater change in survival rate will encounter greater uncertainty. Patient outcomes vary widely even within similarly staged cohorts.Although the 6‐lncRNA prognostic signature identified in our study was robust, there were several limitations. First, we need to validate this 6‐lncRNA signature in large‐scale clinical GC samples. Second, this signature should be subjected to prospective validation prior to its clinical applications. Finally, whether the 6‐lncRNA combined with other clinical characteristics can increase the predictive power of based on AUC values remains an interesting question for us.In conclusion, we performed multistep prognostic analyses of lncRNAs in GC. Robust likelihood‐based survival and LASSO models were used to successfully establish a 6‐lncRNA prognostic signature. The robustness of our 6‐lncRNA signature was also validated. In conclusion, the 6‐lncRNA signature can effectively make GC patient prognosis evaluation.The authors declare that there was no conflict of interest.BM conceived and designed the study. BM and YML performed this work. BM drafted the manuscript. YPR reviewed and revised the paper. All authors read and approved the manuscript. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file. Click here for additional data file.


Ten healthy young men and ten healthy young women walked on a level (0°) gradient a range of speeds (0.67–1.67 m s−1). During walking, there were no significant differences in reductions in arterial oxygen saturation (SpO2) between the sexes. The hypoxia-induced increase in EE, heart rate (HR [bpm]) and ventilation (−1]) were calculated. Using a multivariate model that combined EE, 2 (hypoxia-induced reduction), a very strong fit model both for men  and for women was obtained . The contributions of EE, VE, and HR to ΔSpO2 were markedly different between men and women. 2 in women (Energy expenditure (EE) during treadmill walking under normal conditions (normobaric normoxia, 21% O Maintaining SpO2 at high-altitude may present an important challenge for populations living at altitude. Cardiovascular adaptation to altitude has been primarily studied at rest3, but comparatively little is known about metabolic responses during walking under hypoxia, particularly with regard to anthropometric and sex effects. Previous studies have suggested that the cost of breathing may differ between men and women. Women have smaller lungs and a decreased capacity for lung diffusion compared to age- and height-matched men6, as well as airways with smaller diameters when matched for lung volume8. These anatomical and functional sex differences may increase the work required for women to maintain a given rate of pulmonary ventilation , was significantly greater in women than in men at >55 L min−112.Humans moved into high-altitude regions over the past 20,000 years. At high altitude, a reduction of alveolar PO20. However, breathing mechanics and cost might not be the primary factors determining exercise-induced arterial hypoxemia. Circulatory dynamics could also affect oxygen saturation because oxygen saturation is the fraction of oxygen-saturated hemoglobin relative to the total hemoglobin in the blood. Thus, investigations of hypoxemia and its effects should consider both breathing and circulatory responses  on exercise-induced arterial hypoxemia.If breathing is more mechanically challenging for women, exercise-induced arterial hypoxemia may occur more readily in women than in men21. An important limitation of that study21 was that it included only men. It remains uncertain whether ventilation and circulation costs differ in women.Recently, to evaluate the energy cost of ventilation and circulation during hypoxic walking, an experimental model was developed and tested in healthy, young male lowlanders2 was employed. Men and women walked at a range of speeds under normobaric hypoxic conditions while measuring energy expenditure (EE), 2 would be different between the sexes.In this study, a hypoxemia model to investigate sex differences in ventilation and circulation costs and in SpO1.0 in men were significantly greater than in women. Predicted values (% predicted) adjusted by sex, age, and height were calculated based on the formula by Kubota et al. for Japanese populations22, and no significant differences in these variables between the sexes were observed. Resting La was similar across sexes in all oxygen conditions . There were no significant differences in peak La at 5 min after walking between the sexes within each oxygen condition.Table 2 in both sexes and oxygen conditions are shown  = 55.08, P < 0.001], but there was no main effect for sex in normoxia  and hypoxia  and no interaction between sex and oxygen condition in normoxia  and hypoxia . In normoxia, SpO2 decreased slightly ~3% from the resting baselines in both sexes. In contrast, SpO2 in hypoxia decreased greater compared to normoxia (~10% in men and ~14% in women) from the resting baselines. Results of bivariate analyses of ΔSpO2 (SpO2 hypo − SpO2 norm) revealed significant correlations with ΔEE, ΔThe time course changes in SpOown Fig. . A signiely Fig. .Figure 110 transformed values of ΔEE, Δ2 as the dependent variable, ΔHR was found to be a significant factor in both men (t (8) = −6.54, P = 0.001) and women (t (8) = −4.04, P = 0.010). However, neither ΔEE nor ΔP > 0.05). The model fit was very strong for men  and for women   Fig. .Figure 22 was calculated by using standard partial regression coefficients for men and women, separately. The relative contributions of these variables to ΔSpO2 was markedly different between men and women. In women, the effect of EE and Next, the contribution of ΔEE, Δen) Fig. .E in both men and women increased with speed; in particular, those in men were higher than in women at the fastest walking speed  in men, and ΔEE = 1.248 ± 0.111Δ2 = 0.656, P < 0.05) in women, which is approximately 1 Watt for every 1 L min−1 increment in 2 − 0.061 ± 0.101ΔHR  in men and ΔEE = 0.010 ± 0.006ΔHR2 − 0.083 ± 0.159ΔHR  in women, which is approximately 1 Watt for every 8.8 beats per minute increased in HR for men and for every 15 beats per minute for women.The mean values (±SD) of EE, EE, mentclass2pt{minimeed Fig. . Moreoveeed Fig. . Signifises Fig. . With th2 = 0.718 in men; r2 = 0.615 in women, Fig. P < 0.001 in men and t (8) = 7.16, P < 0.001 in women) and ΔHR (t (8) = 7.22, P < 0.001 in men and t (8) = 8.11, P < 0.001 in women) achieved statistical significance. The model fitted in very strongly . When both HR and −1 increment (men) and 1.6 L min−1 increment (women) in As ΔWe estimated the energy expenditure on cardiopulmonary work during normoxic and moderate hypoxia walking trials by imputing entclass1pt{minima−1 to 1.67 m s−1  and hypoxic (~3%) conditions and. However, in both men and women the percentage of net EE attributable to cardiopulmonary expenditure increased by ~3% as walking speed increased 0.67 m s% and hypFinally, using the multivariate model for cardiopulmonary costs, we calculated the relative contribution of ventilation and circulation to total cardiopulmonary expenditure. The estimated energy cost of circulation (HR) exceeded that of respiration  has been attributed to the mechanical ventilatory limits of the respiratory system23. This result run counter to expectations from womens’ smaller respiratory anatomy and greater mechanical ventilatory constraints4 which would predict more severe or frequent EIAH women than in men20. Given that other studies have also shown no differences in EIAH between the sexes25, the role of mechanical constraints in EIAH should be reassessed.Although significant reductions in SpO20 or hypoxia25, while the present study settings were performed with relatively lower exercise intensity  in hypoxia. In addition, subjects’ training status should also be considered, e.g., trained women20, untrained men vs. trained women25, trained vs. untrained women19, and untrained men vs. women in the present study. Given the very different experimental designs, these controversial findings may not be surprising.One possible explanation to account for the variability in the detection of EIAH across studies is the different study settings . In these previous studies, subjects performed leg cycling exercise until exhaustion in normoxia2 was approximately twice that found in men based on modified analysis model21. It has been reported that oxygen cost of breathing for a given 14, probably, due to smaller lung size6 and airway diameters8. Additionally, a recent study demonstrated that electromyography of the extra-diaphragmatic inspiratory muscles  was significantly greater in women than in men at all submaximal time points and at maximal exercise during incremental leg cycling despite lower 26. The effects of 2 levels probably, induced by improvement in ventilatory efficiency27. This has been suggested to be caused by a reduction in dead space ventilation and an increase in alveolar ventilation28. Together, these results support the idea that 2 changes for women.In women, the contribution of ventilatory work and EE on SpO30; resting 32 and resting 34. To minimize menstrual cycle effects, all women included in the present study were measured during the early follicular phase of their menstrual cycle.It should be noted that experimental design of the present study controlled for the effects of menstrual cycle on 2 changes in women is twice that of men. It has been reported that at ~55 L min−12) of the respiratory muscle was significantly greater in women than in men12. Since experimental protocol of the present study only reached ~55 L min−114, by contrast, at faster gait speed during hypoxic walking, greater breathing frequencies and lower tidal volume might induce greater respiratory It was also found that the contribution of EE to SpO−1 increment in men and women) but, different costs between the sexes for HR (1 Watt for every 9 beats per minute increment for men and 15 beats per minute for women). In our previous study in men21, we found greater ventilatory costs (1 Watt per 2.3 L min−1) and lower circulatory costs (1 Watt per 3 bpm). This discrepancy may simply reflect the limits of precision in our multivariate approach. Another possible explanation to account for these differences may be the different study settings [FiO2 = 15 and 11%O2 in the previous study21 vs. 13%O2 in the present study] and subject characteristics. In the previous study, the subjects were high-fit athletes21, while the subjects in the present study were sedentary and not athletes.In the present study, we found that men and women had similar energy costs for 8 and should be treated with caution. Similarly, although a reduction in SpO2 has been thought to be related pulmonary O2 diffusion capacity2, a previous study found greater inter-individual differences in the alveolar-capillary membrane diffusing capacity and the pulmonary capillary blood volume, which are subcomponents of total lung diffusion capacity with an inspiration of different concentration of O2 (20-40-60% O2)35. Similar results  were found under hypobaric hypoxia at rest36 and during sub-maximal exercise37. In light of these previous studies, it is evident that lung diffusion capacity (which may affect SpO2) causes inter-individual differences under different barometric pressure condition and/or fraction of inspired oxygen. Including these measures in future work will advance our understanding of the sex differences in respiration and circulation costs as well as EIAH during hypoxic walking.There are several limitations in the present study. First, peak aerobic capacity was not directly evaluated, as it is often done. Thus, there is a possibility that relative exercise intensity may be different between the sexes and/or within subjects. To minimize this effect, sex-specific models were used, and resulted in similar changes in EE, see Fig. . Second,2 (hypoxia-induced reduction), a very strong fit for both men  and women  was obtained. The relative contributions of EE, 2 were markedly different between the sexes. Specifically, the contribution of EE and In conclusion, using a multivariate model that used EE, 38. Women subjects, who had irregular menstrual cycles or were taking birth control medication, were excluded from the study. After a detailed description and explanation of all study procedures, possible risks and benefits of participation, each subject signed an informed consent form.The participants were 10 young men and 10 young women, and their physical characteristics are shown in Table All procedures in the present study were approved by the ethical committee of Mount Fuji Research (No: ECMFRI-03-2014) in Japan and were performed in accordance with the guidelines of the Declaration of Helsinki.2) was set at normobaric hypoxia  and moderate hypoxia . The hypoxic gas was continuously blended using a hypoxic gas generator system  and delivered from a 200 litre Douglas bag. Each trial  was performed on different days in random order, and a single blind method was used. First, each participant sat in a chair for 10 min and then stood for 5 min on the treadmill for a baseline standing measurement. Then, the participants started to walk on the treadmill. Following preliminary tests in our laboratory and the modified protocol in the previous studies36, seven walking speeds used in ascending order . Each speed was maintained for four minutes.A motor-driven treadmill  was used to conduct all experiments. Under all experimental conditions, participants walked on the same treadmill; they were free to choose their step frequency at each speed. All participants wore lightweight training shoes, socks, shirts, and underwear. All participants underwent a familiarization session to allow them to become accustomed to the treadmill walking while wearing a gas collection mask for at least three times at several walking speeds. A fraction of inspiratory oxygen  and room air were used to calibrate the gas analyzer before each test. HR was measured with a commercial HR monitor  throughout this study. SpO2 was continuously monitored by the pulse oximeter on the left middle finger, and it recorded every 1 min throughout the study . Fingertip blood samples (0.3 μL) were collected to determine the La at rest and after 5 min of walking. La was measured using a lactate analyzer . The resting respiratory function was assessed with a spirometer . The measurement was performed when the subjects were in a sitting position. Before the test, they practiced at least three times and were familiar with a spirometer . We measured FVC and forced expiratory volume in one second (FEV1.0). Predicted FEV1.0, predicted FVC, and the ratio of these variables (FEV1.0/FVC) were calculated using the equation developed by the Japanese Respiratory Society22.Respiratory data were measured by a breath-by-breath gas analyzer . Inspired and expired gas volumes were measured using a hot-wire respiratory flow system. Flow signals were electrically integrated for the duration of each breath to calculate minute ventilation. The expired fractions of O2 and carbon dioxide output (2) with the following equation:EE at rest and each walking speed was calculated using 2, and HR were averaged for the last 2 min of standing prior to walking. During walking, mean values of final 1 min at each walking speed of physiological data were also obtained40.Gas exchange variables, SpO21. Briefly, total walking energy expenditure at each speed (including rest) during normoxic trials (EEnorm) were subtracted from the values during hypoxic trials at the same walking speed (EEhyp) to calculate a ΔEE value. We used a similar approach to calculate hypoxia-induced changes in hyp − HRnorm). These calculations were conducted separately for men and women. Then, ordinary least squares linear regression was used to compare ΔEE to ΔRespiratory and circulatory cost was evaluated using the same method as in a recent study2 with hypoxia (SpO2 hypo − SpO2 norm) were calculated in men and women separately. Then, multiple regression analysis was used to test for the independent, additive effects of EE, 2 after logarithmic transformation. This design yielded n = 8 comparisons  for men and women, respectively.Similarly, changes in SpO2). Body surface area (BSA) was also calculated with the following equation:Body mass index was calculated by body mass (kg) divided by the square values of height  was performed. Ventilatory or circulatory costs was calculated using a bivariate regression model as described. All statistical analyses were performed using commercial statistical software . The statistical significance was set as a P value of < 0.0541.All data are expressed as mean ± standard deviation (SD). An unpaired t-test was performed in order to compare the physical characteristics, respiratory function, and La between men and women. To compare EE, Supplemental Table 1 and 2


Survival of extremely low birth weight  infants has improved significantly since the 1990s. Consequently, growth monitoring in ELBW infants has gained more relevance.We conducted this study to describe the growth of ELBW infants at a tertiary hospital, to audit macronutrient intake and explore the association of prematurity complications with growth. This was a retrospective study on 92 ELBW infants born at Charlotte Maxeke Johannesburg Academic Hospital. The association between good growth (regaining birth weight in 21 days or less and subsequent growth velocity > 15 g/kg/day) and complications of prematurity was explored.Only 11infants (13%) had a discharge weight above the 10th centile when the Fenton growth chart was used compared to 20 infants (22.4%) when the Intergrowth 21st Project growth standard was used. The mean weight velocity was 13.5 g/kg/day and the mean number of days to regain birth weight was 18.2 days. Factors associated with poor growth were late-onset sepsis, persistent patent ductus arteriosus, continuous positive airway pressure for more than 2 days, invasive ventilation, oxygen on day 28 and being kept nil per os. Protein and caloric intake correlate positively with growth velocity. Unlike the Fenton Growth Charts, use of the Intergrowth 21st Project growth standards revealed the association between neonatal factors and poor growth.Growth outcome in infants is poor at 36 weeks postmenstrual age at our institution. Intergrowth 21st Project growth standards were superior to Fenton Growth Charts, however a multicentre study is required before adoption. Survival of extremely low birth weight (ELBW) infants has improved significantly since the 1990s owing to advances in obstetric and neonatal care . AlthougNutrition in ELBW infants is surrounded by several challenges. In a low resource setting, ITN  may not be readily available due to cost limitations . FurtherThere is little information in South Africa on the growth of ELBW infants and their caloric intake while in hospital. A previous prospective study on very low birth weight (VLBW) infants admitted at Charlotte Maxeke Johannesburg Academic Hospital (CMJAH) showed that infants had initial poor growth followed by catch-up growth but with persistent deficits at 20 months . AnotherThis was a pilot study to review the growth of ELBW infants at CMJAH from birth to 36 weeks postmenstrual age, audit their macronutrient intake and explore the association of prematurity complications with growth.The study was a retrospective longitudinal description of growth and macronutrient intake in ELBW infants at CMJAH.All ELBW infants admitted at CMJAH within 72 h of birth between 01 January 2015 and 31 March 2017 were included in the study.Infants who were transferred to other hospitals or demised before discharge, those with chromosomal abnormalities and major congenital abnormalities were excluded.The study was a secondary analysis of an existing database managed using Research Electronic Data Capture (REDCAP), hosted by the University of Witwatersrand  and a reThe Fenton’s Growth Chart  and InteFor the purpose of computation of macronutrients, the nutritional content of the various types of feeds, IV fluids and ITN was derived from product inserts and previous studies Table  19]. Da. Da19]. Mean weekly caloric intake for each week and mean caloric intake over 28 days was calculated. A mean caloric intake of at least 110 kcal/kg/day was classified as good caloric intake. Daily protein and lipid intake were calculated using a similar formula to the daily caloric intake.www.vtoxford.org). The need for ventilation (invasive and non-invasive), as well as complications of prematurity including NEC, retinopathy of prematurity (ROP), late onset sepsis (> 72 h after birth), oxygen on day 28 of life and patent ductus arteriosus (PDA) were recorded. Necrotizing enterocolitis and PDA were defined in this study as follows;NEC was defined as NEC grade 2 or 3 according to modified Bells’ staging ROP was defined as stage 2 or more PDA was defined as a hemodynamically significant PDA on echocardiography Definitions of complications of prematurity were based on those used in the Vermont Oxford Network  was provided for those ELBW infants whose mothers were unwilling or unable to breastfeed. Newly born infants were started on intravenous fluids  at 80 to 100mls/kg/day on the first day of life. Feeds were introduced on the second day of life starting at 20mls/kg/day. Feeds were gradually increased by 20 to 30mls/kg/day replacing intravenous fluids until 160 to180mls/kg/day of full enteral feeds was reached. Upon reaching full enteral feeds, each feed of breastmilk was fortified with 1 g FM85 (Nestle). Enteral feeds were discontinued if there was evidence of feeding intolerance. Infants who were not on full feeds for more than 48 h due to any reason had ITN prescribed for them provided no contraindications to ITN administration were present. Infants were weighed twice a week on an electronic scale . In the absence of ongoing complications requiring hospital care, infants were discharged upon attaining 1600 g. Infants with respiratory distress syndrome (RDS) were offered nasal continuous positive airway pressure (NCPAP) and surfactant. Upon delivery, infants with RDS were transferred to a Transitional Unit where they were offered NCPAP if available. This was done while the infant awaited transfer to their final destination, the Premature Unit, where NCPAP was usually available.Data was entered into a Microsoft Excel Spreadsheet for data cleaning. The final dataset was exported into IBM SPSS 25 for analysis. The distribution of continuous variables was explored. Those variables with a normal distribution were described using mean and standard deviation (SD) while those with a skewed distribution were described using median and interquartile range (IQR). Categorical variables were described by frequencies and percentages.Comparison of occurrence of prematurity complications between infants with good and those with poor growth.Comparison of the occurrence of prematurity complications between infants who attained a weight above 10th centile at 36 weeks and those who failed to attain such a weight.Delta Z-scores from birth to 36 weeks were analyzed as a continuous variable.An infant with good growth was defined as that infant who regained birth weight in 21 days or less and had a weight velocity of at least 15 g/kg/day , 24. An For continuous variables, student t-test was used for variables with normal distribution and Mann Whitney U-test for ranked data or those variables with a skewed distribution. The Chi-squared test of independence was used for categorical variables.There were 2829 infants with birth weight less than 1500 g infants captured on the database. Of these, 886 were ELBW infants. After excluding those outside the study period, 325 infants remained. Ten infants were transferred to other hospitals and 181 infants died in hospital. Therefore, 134 infants were eligible for the study. Forty-two infants had missing records leaving a final sample of 92. For derivation of weight Z-scores and centiles, 89 were included because 3 infants had a gestational age less than 24 weeks and therefore could not be plotted on the Intergrowth 21st Project newborn charts. Characteristics of the sampled infants are displayed in Table p < 0.001. This was true for both reference growth charts. Therefore, infants were significantly lighter for postmenstrual age at 36 weeks compared to the time of birth. Table The mean birth weight was 867 g SD = 81.4) with a mean birth weight Z-score of −0.45 (SD = 1.01) and − 0.72 (SD = 1.20) as per Fenton Growth Chart and Intergrowth 21st Project newborn weight charts respectively. Table  with a mTable Figures The mean number of days to reach full enteral feeds was 15.9 days (SD = 6.3) and by the seventh day of life, enteral feeds contributed more than 50% of caloric intake. A plateau of 90–92% mean enteral caloric contribution was reached from the twentieth day. During the first 28 days of life, the mean number of days in which infants received at least 160mls/kg/day of enteral feeds was 12.1 days (SD = 6.3). Thirty-four infants had an adequate caloric intake . Mean caloric intake during the first 28 days was 97.0 kcal/kg/day (SD = 14.4). A mean caloric intake above 80 kcal/kg/day was reached on day 8, by day 14 the mean caloric intake was above 110 kcal/kg. There was a steady increase in mean weekly caloric intake from 65,7 kcal/kg/day (SD = 18.7) in the first, 101 kcal/kg/day (SD = 8.6) in the second, 118 kcal/kg/day (SD = 5.0) in the third to 123 kcal/kg/day (SD = 2.9) in the fourth week. The mean protein intake was 2.5 g/kg/day (SD = 0.7) with a mean daily intake above 3.0 g/kg/day being reached on the twentieth day of life. The mean lipid intake was 4.4 g/kg/day (SD = 1.1) with a mean daily intake above 5 g/kg/day being reached at the fourteenth day of life.r = −0.38) and the longer the infant remained on feeds of at least 160mls/kg/day (r = 0.44). Increases in protein (r = 0.38), lipid (r = 0.42) and caloric intake (r = 0.38) were associated with an increase in weight velocity during the first 28 days of life.Pearson correlational analysis showed that during the first 28 days of life, weight velocity increased with a shorter duration to attain full feeds (n = 25) and that classified as “poor growth” (n = 67). Complications of prematurity which showed a significant association with poor growth were CPAP for more than 2 days, invasive ventilation, oxygen on day 28 of life, late-onset sepsis, PDA and being kept nil per os (excluding the first day of life) (Table p = 0.024), those with PDA (p = 0.001) and sepsis after day 3 (p = 0.050) compared to those who did not have such complications. Being kept NPO (p = 0.123), receiving for CPAP more than 2 days (p = 0.069), antenatal steroids (p = 0.219) and invasive ventilation (p = 0.254) did not show a significant difference in delta Z-scores compared to those who did not have such complications. A Chi squared test showed a significant association between infants having a weight at 36 weeks less than the 10th centile and those who had sepsis after day 3 of life when the Intergrowth 21st Project growth standards were used (p = 0.021).A comparison was done between the group classified as “good growth” . In a South African study by Mudahemuka et al., the growth of 69 VLBW infants who survived to discharge from the neonatal unit was reviewed. In the study, a growth velocity < 14 g/kg/day was associated with antenatal steroids and the number of days nil per os without ITN correlated negatively with the discharge weight Z-score . In the In the present study, a greater portion of infants were considered small for gestation age using the Intergrowth 21st Project growth standards compared to the Fenton Growth Chart. The increase in number of infants plotting above the 10th centile at 36 weeks using the Intergrowth 21st standards approaches statistical significance. These findings are consistent with a study conducted by Funda et al. on 248 infants in Turkey comparing the Fenton Growth Chart to Intergrowth 21st Project standards . Funda eThis was a retrospective study and infants were managed as per attending physicians’ discretion. The method by which gestational age was determined was not standardized. A larger sample would have allowed a better description of rare complications of prematurity and infants who attended follow-up clinic at 12 months corrected age as only a few infants were picked up. Thirty-six infants (40.4%) were discharged before 36 weeks had their discharge weight Z-score analyzed as the one at 36 weeks, however, the resulting error was assumed to be small since the mean postmenstrual age at discharge was 36.7 weeks. Infants discharged before 36 weeks had a mean postmenstrual age of 34.5 weeks (SD = 1.1) at the time of discharge.However, this study provides a detailed description of macronutrient intake, such detail has rarely been described in South Africa and other middle and low-income countries. The present study also explores the usefulness of the Intergrowth 21st Project growth standards compared with the traditionally used Fenton Growth Charts.The growth of ELBW infants at CMJAH is suboptimum. Multiple complications of prematurity are associated with poor growth and nutritional intake correlates positively with weight velocity. There is need for a multifaceted approach to address characteristics associated with poor growth. Respiratory complications of prematurity can be reduced by increased coverage of antenatal steroids. Infants are likely receiving NCPAP after a significant delay at CMJAH neonatal unit, there is a need to avail enough NCPAP units in the Transitional Unit. A protocol addressing both volume and nutritional requirements during initiation of enteral feeds is needed. Consideration should be given to start ITN by 24 h rather than the current 48 h in infants not on full enteral feeds. Small enteral feeds can also be introduced on the first day of life. Chorioamnionitis is likely underreported, more vigilant surveillance and treatment in pregnant mothers could prevent premature delivery. Strong consideration should be given for the use of Intergrowth 21st Project growth standards as reference for normal postnatal growth in premature infants instead of the Fenton Growth Chart currently in use. Unlike the Fentons Growth Charts, use of the Intergrowth 21st Project growth standards revealed the association between neonatal factors and poor growth. A large multicenter prospective study is needed to assess the extent to which changes brought about by the Intergrowth 21st Project growth standards correlate with long term outcomes.


The incidence of gestational diabetes has been reported to have risen over the first decade of this century. Some studies have also found it to vary with seasons of the year. We investigated temporal and seasonal trends on gestational diabetes incidence in a single-centre cohort study from Cambridge, UK, and attempted to explain trends using associated risk factors.Using a cosinor model, we tested both temporal and seasonal trends in gestational diabetes incidence in 1074 women recruited to the Cambridge Baby Growth Study in 2001–2009 who underwent oral glucose tolerance tests around week 28 of pregnancy.p = 2.1 × 10−3], but no seasonal effect (p = 0.7). HOMA B  and the insulin disposition index , unlike HOMA S, showed negative temporal trends. Risk factor analyses showed a concomitant temporal slight increase in the index of multiple deprivation . This index was positively associated with HOMA B (p = 6.1 × 10−5) but not directly with gestational diabetes (p = 0.6), HOMA S (p = 0.2) or the insulin disposition index (p = 0.4).There was a temporal increase in gestational diabetes incidence over the course of recruitment to this study [0.014  proportional increase per year, In this cohort, there were temporal, but not seasonal, increases in gestational diabetes incidence between the years 2001 and 2009, which appeared to be related more to reductions in insulin secretion than sensitivity. Possible mediators of this link include confounding factors related to deprivation. Gestational diabetes (GDM) is traditionally defined as carbohydrate intolerance with its onset or the first recognition in pregnancy , althougWith the apparent worldwide rise in the prevalence of GDM , paralleThe prospective, longitudinal Cambridge Baby Growth Study (CBGS) was established as an observational cohort initially covering pregnancy, birth and infancy . 2229 moThe Cambridge Baby Growth Study was approved by the Cambridge Local Research Ethics Committee, Addenbrooke’s Hospital, Cambridge, UK (00/325). All procedures followed were in accordance with the institutional guidelines. Written informed consent was obtained from all the study participants.At a median (inter-quartile range) of 28.4 (28.1–28.7) weeks gestation, 1074 of the CBGS mothers underwent a 75-g oral glucose tolerance test (OGTT) after fasting overnight . Venous All biochemical kit-based assays were run according to the manufacturer’s instructions. Glucose concentrations were measured using a routine glucose oxidase-based method. OGTT plasma insulin concentrations were measured by ELISA . Intra-assay imprecision (CV) was 4.3% at 14 mU/L (82 pmol/L), 3.0% at 67 mU/L (402 pmol/L) and 5.7% at 151 mU/L (907 pmol/L). Equivalent inter-assay imprecision was 4.3, 5.1 and 5.4%, respectively. C-peptide concentrations were also measured by ELISA . Intra-assay imprecision was 2.8% at 1.3 ng/mL (0.43 nmol/L) and at 4.4 ng/mL (1.47 nmol/L) and 3.2% at 8.4 ng/mL (2.80 nmol/L). Equivalent inter-assay imprecision was 15.7, 7.8 and 10.3%, respectively.https://www.dtu.ox.ac.uk/homacalculator/) , with a trend for the incidence of GDM increasing through the years 2001–2009  per year, p = 3.3 × 10−3, n = 1040]. There was, however, no significant association of GDM with seasonality  , p = 0.4; acrophase − 0.4 , p = 0.7]. Conversely there was a temporal trend with the OGTT 60-min glucose concentrations  , without a seasonal effect .The overall prevalence of GDM in this population was 10.1% (108/1071). Cosinor analysis showed a significant temporal effect associated with the year of analysis  but not with the season of testing , a finding which was confirmed by linear regression . This association was still evident if adjusting for GDM  or by only including women who were classified as not having GDM . However, the association disappeared if it were tested using c-peptide-derived HOMA S, in a smaller number of women .Insulin-derived HOMA S was positively associated with the year of testing , in contrast to the findings with HOMA S, insulin-derived HOMA B was negatively associated with year of testing . This association was confirmed by linear regression . Again, it was still evident if adjusting for GDM  or if only including women without GDM . This association was still present if it were tested using c-peptide-derived HOMA B, in a smaller number of women .Whilst still having a lack of association with seasonality , a finding which was confirmed by linear regression . This association was still evident if adjusting for GDM  or if only including women without GDM . The insulin disposition index was not, however, associated with season of testing . The c-peptide disposition index was also negatively associated with year of testing .Similar to the results for HOMA B, the insulin disposition index was negatively associated with year of testing . One potential confounder that year of analysis was modestly albeit highly significantly associated with, and the index of multiple deprivation   units per year, p = 5.2 × 10−8]. In these women, the index of multiple deprivation was significantly positively associated with HOMA B  or the insulin disposition index .The year of analysis was not associated with either the maternal BMI (In this analysis, there was a strong trend for the incidence of GDM increasing as recruitment to the cohort progressed between 2001 and 2009. Trends over a similar period of time have also been observed in populations in Canada , the USAThe temporal increase in GDM incidence was clearly environmentally mediated as its tempo was too fast for a genetic change. In investigating its potential causes and the reduced insulin secretion, we could not find parallel increases in BMI (itself strongly associated with GDM and partially driving the link between factors such as early age at menarche and increased GDM risk  as has pAlthough we observed a temporal trend of increasing GDM incidence as the decade progressed in this analysis, we could not find a seasonal trend. This is despite seasonal trends in GDM previously have been observed in populations in Sweden , AustralThe strengths of our prospective study include the fact that we used cosinor analysis to adjust linear temporal trends for separate potential seasonal effects, unlike some of the other studies in this area. In addition, we had third trimester OGTT data from all the study participants and so we were able to investigate whether temporal trends in GDM incidence were related to changes in insulin sensitivity or secretion, a level of detail that is rarely available in larger surveys. We calculated HOMAs using both insulin and c-peptide concentrations so that detectable temporal trends were less likely to have resulted from drift in assay performance. In addition to its strengths, the study does have a number of limitations, however. Firstly, this Cambridge cohort may not fully reflect the UK population as a whole particularly in relation to ethnic mix and smoking prevalence , althougIn conclusion, we observed a temporal but not seasonal trend for an increasing incidence of GDM in Cambridge Baby Growth Study pregnancies from the years 2001–2009. This was associated with reductions in indices of insulin secretion rather than insulin sensitivity. Although we do not know what caused these changes, it does not appear to relate to changes in maternal obesity or age. Factors relating to deprivation offer potential explanations.