Despite a range of efforts to increase physical activity participation in Australia, inactivity levels in older adults have remained high over recent decades, contributing to increased rates of chronic health conditions. Lifestyle interventions, including telephone counseling (TC), improve physical activity participation and associated health outcomes over the short term; however, ongoing feedback and support is required to maintain these changes. Newer technologies such as wearable activity trackers (ATs) may offer an alternative method for providing ongoing support.This study aims to investigate whether newer technologies such as wearable ATs assist in providing ongoing support to maintain physical activity levels and health outcomes.Older adults aged >60 years who had just completed a 12-week face-to-face individualized community exercise program in Tasmania, Australia, participated in the study. They were randomized to receive AT, TC, or usual care (UC). All groups received a home exercise program and an optional referral to a community-based exercise program. The AT group also received an AT and text message feedback from an accredited exercise physiologist (AEP). The TC group received phone calls from an AEP throughout the 12-month intervention. The primary outcome was daily steps measured by an ActivPAL (TM) accelerometer at baseline and at 3, 6, and 12 months. Secondary outcome measures included body composition, blood pressure, 10-time sit-to-stand (TTSTS) test, timed up and go test, and cardiorespiratory fitness. This trial was approved by the Tasmanian Health and Medical Human Research Ethics Committee (H0014713).P=.81; and MD −588 steps, 95% CI −1359 to 182 steps; P=.09), and UC showed a reduction in daily steps  during the 12-month period. Diastolic blood pressure was significantly higher after AT than after UC , and TTSTS was significantly slower on TC compared with UC .A total of 117 participants were randomized to the study . At baseline, the participants  completed an average of 6136 steps (SD 2985) per day. Although there were no significant differences between groups, the TC and AT groups maintained daily step counts (mean difference [MD] −79 steps, 95% CI −823 to 663 steps; The use of an AT with AEP support or TC is effective at maintaining daily step count in older adults over a 12-month period, suggesting that wearable ATs are as effective as TC. Further research to investigate which option is more cost-effective would be beneficial.Australian New Zealand Clinical Trial Registry ACTRN12615001104549; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=369118 Appropriate and ongoing support is needed to assist older adults to engage in regular physical activity to minimize the functional decline and loss of independence associated with aging . StructuSeveral systematic reviews have assessed methods of maintaining physical activity participation ,8. TelepActivity trackers (ATs) provide consumers with the ability to objectively monitor and receive feedback relating to daily physical activity and can provide health professionals with an objective measure, allowing for the provision of targeted feedback and ongoing support . FurtherConsequently, this study aimed to examine the effects of a commercially available AT with usual care (UC) and an established method of postintervention follow-up (TC) to assist older adults in maintaining their daily step count over a 12-month period. It was hypothesized that older adults in both the AT and TC groups would maintain physical activity levels over the 12-month intervention compared with those in the UC group.The study was a three-arm, 12-month randomized controlled trial (RCT) investigating the use of an AT, TC, and UC in the maintenance of physical activity levels and health outcomes in older adults. The study was approved by the Tasmanian Health and Medical Human Research Ethics Committee H0014713) and was registered with the Australian New Zealand Clinical Trials Registry (ACTRN12615001104549). This study was reported in accordance with the CONSORT-EHEALTH  checklist [713 and wTo be eligible, participants had to have completed the 12-week Strength2Strength (S2S) Tasmania Exercise Treatment Initiative, be above the age of 60 years, and have or be at risk of developing a chronic medical condition. The S2S program was a community-based 12-week exercise and education program led by accredited exercise physiologists (AEPs) in Tasmania, Australia. The S2S program provided participants with individually tailored exercise programs to suit their health conditions and goals. The primary focus of the exercise program was strength-based exercises; however, a range of cardiovascular, strength, and balance exercises were included. Participants’ exercise prescriptions were reviewed weekly and progressed as required. In addition to attending the S2S program, participants were encouraged to complete an adapted version of their exercises for the home environment at least twice a week.The RCT was a community intervention, with assessments conducted at the University of Tasmania exercise clinic. Participants were excluded from the study if they chose not to participate in the S2S program, had an unstable medical condition that prevented them from participating in regular physical activity, had a neurological condition, or had a limited understanding of English, which prevented them from meeting the self-reporting requirements of the study. All outcome measures were collected at the start and the end of the S2S program and at the 3-, 6-, and 12-month follow-up . Data coAll participants were provided with information regarding the study at the beginning of the S2S program. The lead researcher answered questions and completed the consent process with interested participants. Randomization was performed using computer-generated blocks of 15 by a third person not directly involved in the study and was recorded in sealed opaque envelopes with envelopes opened sequentially at the end of the S2S program to reveal the intervention allocation. Participants from the same household were randomized to the same intervention group to maintain intervention integrity . Participants were asked to avoid using physical activity monitoring devices other than those directly provided by researchers for the duration of the study.The UC group received standard care, which included the provision of an individualized home-based exercise program. The home program included similar exercises to those prescribed during the S2S program but modified for the home environment and for any equipment that was available to the participant. An optional referral to a range of community-based physical activity programs was also offered.In addition to receiving UC, participants randomized to AT were provided with a Jawbone UP24  AT and ZTE (TM) mobile device and data plan. Participants who already had a compatible smartphone could choose to use the mobile device provided or their own device. No reimbursement for data costs was provided to those who chose to use their own smartphone. The device was worn on the nondominant wrist for the duration of the 12-month intervention. Participants were provided with an individual information session on how to use, pair, and charge the device at the time of randomization to the AT group. Written instructions and telephone support were provided to troubleshoot any technical issues. A separate Jawbone account was created for each participant, allowing the AEP to remotely access each participant’s daily step data. The Jawbone UP24 (TM) was paired to synchronize with the Jawbone UP (TM) app on the mobile device. A daily step goal was individually prescribed for each participant based on their physical function and current level of physical activity. This daily step goal was programmed into the UP (TM) app at the time of randomization, with automated feedback provided by the tracker and app based on this daily step goal. Participants were asked to synchronize the tracker with the UP (TM) app at the end of each day but could check the progress toward their daily step goal as desired. In addition to daily feedback available through the UP (TM) app, participants received weekly, personalized text messages from an AEP. The text message contained feedback related to average daily steps and a comparison of their daily step goal with that of the previous week. The daily step goal was slightly adjusted (±200-500 steps) during the weekly text message feedback from the AEP based on the previous week’s step data. The daily step goal in the UP (TM) app however remained the same, as this could not be adjusted remotely. If participants continued to significantly underachieve or exceed their initial daily step goal, the UP (TM) app was adjusted during their 3-, 6-, or 12-month assessment. An example of the text messages sent to participants by the AEP is provided in Participants randomized to the TC group received UC and a physical activity counseling phone call once a fortnight for the first 3 months and once a month for the remaining 9 months of the intervention. Phone calls were delivered by an AEP experienced in motivational interviewing techniques , followiThe primary outcome measure was physical activity participation in the form of a daily step count measured by an ActivPAL (TM) accelerometer (PAL Technologies Ltd), which has been shown to be a valid and reliable device for monitoring physical activity in older adults and individuals with an altered walking gait. The ActivPAL (TM) was enclosed in a small flexible sleeve to cover the monitor and fitted to the front of the thigh using a Tegaderm (TM) film to allow participants to perform their usual daily activities (including showering or bathing) with the device in place. The ActivPAL (TM) was worn day and night for a 7-day period with a minimum of 5 days’ worth of valid data required for inclusion at each assessment time. Data files from the ActivPAL (TM) were downloaded from the devices, and event files were created using a proprietary software . Event files were analyzed using a custom software  to determine daily step counts and total nonstepping time. Only days with full recordings were considered. Daily step counts were averaged for each assessment time point. Nonstepping time was calculated by summing the time spent lying, sitting, and standing over a 24-hour period. Self-reported physical activity  were obtained using the Active Australia Survey (AAS) . The AASDuring the 12-week S2S intervention, participants reported a mean increase of 450 MET minutes of physical activity per week, with an SD of 1.5 times the change. The sample size was calculated on a predicted maintenance of 100% of additional physical activity in the AT and TC groups and a decrease of 50% of additional physical activity in the UC group over the 12-month intervention. STATA 12 (Stata Corp) was used to calculate the sample size on the basis of a mean difference (MD) of 225 MET minutes per week with an SD of the change of 350 MET minutes per week, a power of 80%, and an α level of .05. This indicated a required sample size of 38 participants per group. To allow for withdrawals, 50 participants per group were recruited.P values for comparison between groups were adjusted with Holm test for multiple comparisons. Statistical analysis was first conducted by completing the missing values through the last observation carried forward (LOCF) technique [P values obtained from logistic regression analyses adjusted for multiple comparisons.All data were analyzed using STATA version 13.1 (StataCorp LLC) and graphically represented using GraphPad Prism . Comparisons between the 3 interventions (as change from baseline) were made using mixed effects, repeated measures linear regression and replicated with ordered logistic regression adjusted for repeated measures because the assumptions of linear regression were not met for most variables. echnique . In addiBetween September 2014 and June 2016, 152 people consented to participate when starting the S2S program, with 117 randomized to one of the study intervention groups on completion of the S2S program. The average age of the participants was 72.4 years . Participants in the AT group wore the AT for an average of 84% of the available days (306 out of 365 days). The number of nonwear days ranged from 1 to 164 days. The reasons for not wearing the band included forgetting to put the band on , technical issues (n=20), illness (n=8), and holidays (n=5). Participants in the TC group received an average of 12 (SD 2) phone calls. Phone calls lasted an average of 11.1 min. The primary reason for missed phone calls was due to participants being away on holidays (n=5).P=.005). In contrast, step counts for the TC and AT groups did not change . There were no significant differences in changes in daily step counts between any of the groups over the 12-month intervention . For LM, no change was observed between baseline and 12 months in the AT and TC groups, whereas the UC group showed a significant reduction in LM over the 12-month period .No changes in body weight were observed over the 12-month period; however, there was a mean increase in BF% observed in both the TC and AT groups , whereas no changes were observed in the TC and AT groups. SBP remained unchanged during the 12-month intervention period, and no differences were observed between the 3 groups. Therefore, there was a significant reduction in DBP in the UC group when compared with the AT group . No other between-group differences for DBP were observed between baseline and 12 months.DBP was significantly reduced over the 12-month period in the UC group . The UC group performed the TTSTS significantly faster than the TC group between baseline and 12 months . The time taken to perform the TUAG did not change over the 12-month period for any of the intervention groups nor did the groups differ over the 12-month follow-up.The UC group was the only group to demonstrate an improvement in TTSTS performance during the 12-month intervention period  completed the MSWT, whereas 59  completed the 6MWT. No differences in the distance walked during either the MSWT or 6MWT were observed between the 3 intervention groups or between baseline and 12 months within any of the intervention groups.Self-reported physical function or mental health scores as measured by the SF-36 did not change during the 12-month intervention period for the 3 intervention groups, and there was no difference between groups at 12 months.P=.05) between baseline and 12 months, whereas the AT group showed a significant decrease in self-reported minutes of physical activity per week between baseline and 12 months (P=.04). This was also observed in the per-protocol analysis . The previously observed significant decrease in LM and time taken to perform the TTSTS between baseline and 12 months for the UC group was found to be no longer significant following the LOCF analysis . Following the per-protocol analysis, a significant decrease in body weight was observed in the UC group (P=.05), and DBP in the AT group was found to differ significantly from the TC group (P=.05) in addition to the UC group, which was observed in the intention-to-treat analysis.The results from the LOCF and per-protocol analyses showed similar results with a few key exceptions. For the LOCF analysis, the UC group showed a significant increase in nonstepping time (This study aimed to investigate how wearable ATs may assist in providing ongoing support to maintain physical activity levels and health outcomes in older adults compared with TC and UC over a 12-month intervention. We found that both ATs and TC were similarly effective at successfully maintaining daily step count over the 12-month intervention. The UC group maintained daily step count throughout the 9 months; however, a significant reduction in daily steps was observed at 12 months. Although previous research has suggested that the use of ATs either as part of a broader intervention or as a stand-alone intervention can significantly improve daily step counts , this isMethods section, the LOCF analysis was completed as it was specified in the published protocol for this study [Interestingly, the results of the LOCF analysis showed a significant reduction in self-reported physical activity during the 12-month intervention in the AT group, reporting a mean decrease of 100 min of activity per week. The AT group was the only intervention group to consistently self-report performing at least 150 min of activity throughout the 12-month intervention and was performing between 70 and 87 min more of physical activity than both the UC and TC groups at 12 months. As this is approximately 50% of the recommended levels of physical activity, the AT group exceeded the minimally important clinical difference when compared with the TC and UC groups . This suAlthough overall body weight did not change for any of the included intervention groups, some changes in body composition were observed. An increase in BF% was observed in all intervention groups but was significant only in the TC and AT groups. Although elevated BF increases the risk of cardiovascular disease  and is aAlthough unexpected, a significant reduction in DBP and an improvement in physical function as measured by TTSTS was observed in the UC group, with significant differences also observed between the UC and AT groups for DBP and the UC and TC groups for TTSTS. The reason for the decrease in DBP observed in the UC group and the observed difference between the UC and AT groups are potentially related to the 6.8 kg weight loss in the UC group. Although this finding was not statistically significant, it did exceed the minimum clinically important difference for weight loss . As the check-in with BCTs used as required to assist the maintenance of activity levels rather than as an intervention aimed at increasing physical activity participation. Currently, as there are limited options available that allow the sharing of AT data with health care professionals, it is acknowledged that providing feedback based on an individual’s AT data can also be time intensive and resource intensive because of the data mining and interpretation required. Through improvements in the interoperability of proprietary and third-party mobile apps, the level of data mining would be reduced [As ATs and TC were similarly effective in this study, the advantages and disadvantages of both methods should be considered to help determine if one is more suitable than the other and has more potential to encourage increases or maintenance in physical activity. ATs have several potential benefits compared with TC. An AT allows individuals to self-monitor physical activity, which can improve physical activity participation . Further reduced . In addiA key strength of this study was the long-term intervention period, as most previous research included interventions of no longer than 6 months. The study design also allowed for direct comparison between feedback provided by an AT with TC, an established method of providing ongoing support. Evaluating the effect of newer technologies such as ATs is important in understanding how they can be incorporated into standard clinical practice. In addition, participants were recruited from a clinical exercise program, offering a good representation of the chronic conditions present in community-dwelling older adults.A limitation of this study potentially affecting the ability to interpret results was that the a priori sample size was not met. A total of 150 participants were recruited at the start of the S2S program; however, a number of participants did not complete the S2S program and were not randomized. Owing to the cessation of the S2S program, additional participants could not be recruited to account for the withdrawals before the intervention. Further dropouts occurred during the 12-month intervention. The primary reason for withdrawal was ill health, which is not unexpected, given that more than 70% of the study population had 2 or more chronic conditions. Overall, 5 participants withdrew from the AT group because of feeling uncomfortable using the device. Although ATs are well accepted in older adult populations , this hiBoth TC- and AT-based interventions are effective at maintaining physical activity levels in older adults following a structured lifestyle intervention. As connected health technologies improve, ATs may provide an alternative to TC to assist older adults to remain active. The costs associated with delivering each intervention and the effects of each intervention on health utility should also be considered and investigated further.The following practical implications arose from the study findings:A consumer-based wearable AT is an effective alternative to traditional TC support to assist older adults in maintaining daily step counts.Clinicians should consider the individual needs of patients to determine whether TC or an AT is better suited to provide ongoing feedback and support.


A recent article by Wang et al. elegantly synthesized new nanoparticles consisting of cyclic guanosine monophosphate–adenosine monophosphate (cGAMP), which were administered alongside vaccines, to successfully deliver into the lungs of animals leading to protection against different subtypes of influenza infection without inflammatory side effects.1 However, certain infectious agents can cause widespread health and societal burdens.2 A common problem with current vaccine treatments is the inability to protect against the infection when it has mutated owing to antigenic drift.3 Hence, there has always been a long quest in the field of infection to discover treatment options that will produce cross-protection (heterosubtypic immunity) to infectious stereotypes. A method of providing this type of protection is to activate the immune system in the early phase of the infection without a chronic pathogenic inflammatory response to the foreign particles . Past attempts to target STING have failed owing to the inability to deliver STING agonists into the cytosol of AECs without breaching the integrity of the pulmonary surfactant (PS). Wang et al. hypothesized that nanoparticles consisting of cGAMP, an agonist of STING alongside an adjuvanted H1N1 vaccine, will induce an immune response by CD8+ T cells, which can yield heterosubtypic immunity.5Type I interferons (IFN-Is) are produced by alveolar epithelial cells (AECs) and immune cells to protect against infection.The authors at first successfully synthesized nanoparticles with the correct size, function, and safety. They tested the effects of the nanoparticles in vitro (bone marrow-derived macrophages) and in an in vivo model of infection. These results imply that the nanoparticles may be metabolically stable and have efficacy in humans, but more clinical trials are required. The authors also robustly showed that the protective effects of the nanoparticles did not occur in STING-deficient mice confirming that cGAMP, rather than any other constituents, was responsible for PS-GAMP adjuvanticity. The authors then went on to demonstrate that the nanoparticles uptake was specific to the lung, which suggested that there is a natural and molecule-specific mechanism of particle clearance in the lung that would sustain the integrity of the PS and alveolar epithelial barriers.The next set of experiments demonstrated that the uptake of nanoparticles was only confined to the lungs of mice. In addition, there was no adverse inflammatory response in any body part and serum cytokines -6, IL-10, and tumor necrosis factor-α) levels did not change alongside mouse body weight and temperature. Moreover, the inclusion of PS-GAMP nanoparticles in the vaccination fully protected mice and ferrets from homologous viral challenges as early as 2 days after immunization in models of infection. Wang et al. went on to use gap junction blocking drugs to show that there is a gap junction–mediated flux of cGAMP to AECs from alveolar macrophages (AMs). These results empathised that the AECs rather than AMs appear to be essential in orchestrating innate and adaptive immune responses in the respiratory system during viral infection and determining the potency of PS-GAMP.+ TRM cells, rather than circulating memory CD8+ T cells. Most importantly, these in vivo results suggested that the nanoparticles plus vaccine approach was similarly effective for both influenza A and B viral vaccines. Finally, the authors successfully repeated the long-term heterosubtypic protection induced by the dual PS-GAMP and vaccine treatment in a ferret model of infection.The final set of experiments in the article were conducted to determine whether the PS-GAMP nanoparticles alongside the influenza vaccination can protect against a number of different subtypes of the influenza viruses . Mice receiving vaccines together with PS-GAMP were protected from challenges of different subtypes of the influenza viruses by CD8+ T cell immune responses. This early activation did not elicit a pathogenic inflammatory response but in fact protected against the H1N1 and heterosubtypic H3N2, H5N1, and H7N9 viruses for at least 6 months while maintaining lung-resident memory CD8+ T cells. The authors further showed that, when AECs lacked Sting or gap junctions were blocked, PS-GAMP–mediated adjuvanticity did not function in vivo. Overall, the studies by Wang et al. will be central to how new vaccines and augmented therapies will be designed and administered in the future. The ferret models used throughout the paper suggest the potential for the approach in humans, but clinical trials are required. Nonetheless, these fundamental key findings can possibly help create a new generation of treatments that can protect against viral mutations in early infection to stop the spread of different respiratory viruses, not only influenza viruses, and cease global pandemics.In summary, Wang et al. elegantly demonstrated nanoparticles consisting of cGAMP, an agonist of STING, helped augment adjuvanted H1N1 vaccine–induced humoral and CD8


Nanocrystalline and amorphous nanostructured tellurium (Te) thin films were grown and their gas-sensing properties were investigated at different operating temperatures with respect to scanning electron microscopy and X-ray diffraction analyses. It was shown that both types of films interacted with nitrogen dioxide, which resulted in a decrease of electrical conductivity. The gas sensitivity, as well as the response and recovery times, differed between these two nanostructured films. It is worth mentioning that these properties also depend on the operating temperature and the applied gas concentration on the films. An increase in the operating temperature decreased not only the response and recovery times but also the gas sensitivity of the nanocrystalline films. This shortcoming could be solved by using the amorphous nanostructured Te films which, even at 22 °C, exhibited higher gas sensitivity and shorter response and recovery times by more than one order of magnitude in comparison to the nanocrystalline Te films. These results were interpreted in terms of an increase in disorder (amorphization), leading to an increase in the surface chemical activity of chalcogenides, as well as an increase in the active surface area due to substrate porosity. Subsequent studies showed that it was possible to increase the concentration range sensitivity to more than 300 ppm NO2 by growing single-crystalline microtubes. In order to do that, Te metal was evaporated onto quartz substrates under an inert argon gas at ambient pressure [2TeO3, in aqueous solution, via hydrothermal treatment, whereas Ma and colleagues [Tellurium (Te) is a multifunctional chemical element used for the development of many devices, such as diodes with high 1006 rectifers , oxidized silicon or sapphire substrates via thermal vacuum evaporation of pure Te [2) and reducing (H2S) toxic gases at temperatures between 77 and 423 K. Depending on the experimental conditions in which the films were prepared , their sensing characteristics were found to vary. For instance, the best response and recovery time values toward NO2 were around 30 s and 7 min, respectively, at 40% sensitivity (defined as the relative variation of the resistance). Such sensing parameters did not differ much from the similar parameters obtained earlier for microcrystalline Te films. Further investigations have been extended to Te nanotubes grown on quartz or Si(111) substrates through a catalyst-free growing process in a furnace filled with argon [2, the Te nanotube-based sensors showed similar (or sometimes lower) numbers regarding sensitivity and response/recovery times in comparison to Te single-crystalline microtube-based gas sensors [3 gas sensors based on such nanocomponents was 5–18 s but the recovery time ranged between 170–720 s. From comparison with state-of-the-art devices, it can be observed that the physically nanostructured Te thin films exhibit great potential for applications in development in advanced gas sensors and, so far, are the only Te-based nanostructured sensors tested with this purpose. Besides, it can also be observed that nanostructuring is mostly performed via phase transformations, such as hydrothermal recrystallization and growth of Te nanocrystals, nanotubes or nanowires from the gas phase under vacuum or argon atmosphere. On the other hand, nanostructuring can be performed mechanically as indicated by the possibility of growth of nanocrystalline gas sensors via rf sputtering (13.6 MHz) of Te in an ultra-high-purity argon atmosphere [The present work is related to investigations of the interaction between nanostructured Te films and toxic gases. According to the literature, such investigations firstly have been provided utilizing the nanocrystalline Te films grown onto Pyrex glass, alumina  was evaporated under 10−4 Pa vacuum conditions. The evaporation was carried out using VUP-5 equipment  from a tantalum boat, keeping the same distance (20 cm) between the evaporation boat and substrate, without any cooling or heating of the latter. To grow films with a nanocrystalline structure, a growth rate of about 10 nm/s was used whereas, for amorphous thin films, the growth rate was increased to 30 nm/s. The deposition rate was increased by raising the temperature of the evaporator. The calibration was performed via measuring the final thickness of the grown film versus the time of deposition at given temperature of the evaporator. Under these conditions, the amorphous Te films were grown on either continuous (Pyrex glass) or porous (Al2O3) nanostructured substrates. Then, rectangular 70 mm2 samples were cut from nanocrystalline and nanostructured amorphous Te films, which were either prepared for further morphological and structural analysis or for fabrication of gas-sensitive devices. After preparation, the thickness and shape of the films were studied using a SIS SCAN Control C  atomic force microscope. The surface morphology of the films was investigated using either a TESLA BS 340 or a VEGA TESCAN TS 5130 MM  scanning electron microscope (SEM). To investigate the structural features of the grown films, X-ray analysis was performed using a DRONE–YM1  diffractometer with Fe Kα radiation. The rotational velocity of the scintillation counter was set to be either 2 or 4°/min. For the electrical and gas-sensing characterization, the samples were supplied with symmetrical gold or platinum electrodes, identified in our preliminary works as ohmics [2O3. Pure amorphous films grown on Pyrex glass substrates were uniform and did not exhibit a striking morphological structure; therefore, their SEM image was omitted.Two methods were used to nanostructure Te-based films: growth of Te nanocrystals on Pyrex glass substrates or deposition of amorphous Te films onto nanostructured (porous) Als ohmics –26, whic2O3 substrate . bstrates .2O3 substrates with a higher deposition rate ≈30 nm/s , which was introduced into the experimental setup. Ambient air was used as both the carrier and the reference gas. Nanostructured Te-based gas-sensing devices were put into a 10 mL test cell and the NO2 diluted in ambient air was injected at a 100 mL/min flow rate parallel to the film surface. To perform both the heating and the annealing of the samples, the test cell was mounted inside a furnace. A platinum temperature detector  was placed close to the film and was used to assist with the temperature control.NOus paper . Gaseous2 concentrations at different temperatures. The switching between the mixture of NO2 vapor and reference gases was computer-controlled. The time delay between measurements was 2 s, which was, simultaneously, much smaller than the sensor response time and much higher than the assessed dielectric relaxation time value. In order to transform the resistance signal into a voltage signal, the sample was connected in series to a load resistance using a dc voltage supplier. In all measurements, the load resistance was chosen to be approximately one order of magnitude lower than the sample resistance.The data was processed using a PC equipped with a data acquisition board . Characteristic transient current response curves were collected at a constant applied voltage (5 V), using different NO2 at room temperature (22 °C). As a comparison, and under the same conditions, the response of a microcrystalline film grown on a Pyrex glass substrate at a deposition rate of 1 nm/s was also added. The morphological and structural features of microcrystalline films were previously described in detail [n detail –6. It ca2 gas mixture at a constant bias voltage and 22 °C operating temperature. Square pulses of NO2 vapor at concentrations of 0 ppm, 0.5 ppm, and 1.0 ppm were applied. The dashed green line shows the switching profile. It is seen that the current follows the same pattern but the baseline strongly increases with temperature, as depicted in 2, as a relative percent increase in current (in %/ppm) according to Ia and Ig are the currents flowing through the specimen in air and in the presence of NO2, respectively, and C is the gas concentration.where trv) is longer than the response time (trs). These parameters, listed in Ig.trs and trv decrease with the temperature increase; however, the sensitivity of the films diminishes. In the following subsection, interesting results will be explored in terms of solving this sensitivity issue by nanostructuring amorphous Te-based films.According to 2O3 substrate. The NO2 exposure profile curve is shown by the green continuous line at the bottom. The operating temperature was maintained at 22 °C. There is no noticeable baseline drift in either the nanocrystalline or in the amorphous nanostructured films; however, a dramatic change in the response kinetics can be clearly observed. These changes consist of a decrease in both the response and recovery times for amorphous Te films grown on preliminary nanostructured Al2O3 substrates. As shown in Another peculiarity of the transient characteristics of amorphous nanostructured films is a spontaneous reduction (or increase) in the gas-induced current in terms of step change concentration of the target gas.2 when in contact with microcrystalline, nanocrystalline or amorphous nanostructured Te-based films. Since that review was dedicated to exploring only the fabrication and investigation of gas-sensing properties of nanocrystalline Te films, it was only superficially mentioned that the amorphous films could improve the response kinetic parameters in detriment of sensitivity. One way to enhance both the response time and sensitivity to NO2 seems to be the mechanical nanostructuring of amorphous Te films, which is investigated and discussed in this paper. So far, nanocrystalline Te films have been grown using either the thermal vacuum evaporation of pure Te [2O3 or sapphire substrates. It was shown that the film morphology as well as the gas sensitivity is controlled by several factors, such as the nature and temperature of the substrate, the type and concentration of the target gas molecules, film thickness, post-preparation thermal treatment and operating temperature. It is more or less generally accepted that the films grown on glass substrates at room temperature exhibit a maximum sensitivity to gases such as NO2, H2S or ammonia. This is the main reason why this method was used in this work to manufacture and examine Te films. According to the SEM image shown in 2, the surface and grain boundaries are the most affected by the gas reaction. Although the gas sensing occurs due to the variation in hole density at the enriched region (surface and grain boundary), in the presence of gaseous media, the bulk is responsible for the observed increase in the baseline current when the temperature increases (2) sensitivity, as shown in r). As τr = εε0ρ , it is clear that τr decreases since there is a reduction in the resistivity when the temperature increase and the system reaches steady state in less time. Another suitable explanation for the reduction in the response and recovery times with the increase in temperature might be the shift in the adsorption–desorption equilibrium, pointed out in Langmuir's theory [This paper presents a study regarding two different types of nanostructured Te films physically built either in the form of nanocrystals, grown onto flat substrates, or vitreous Te, deposited onto nanostructured (porous) dielectric templates. It was expected that the physical properties, including the adsorptive ones, of these films would differ from each other and also from those observed in the microcrystalline Te-based films. In fact, this assumption, previously mentioned in our review paper , was con pure Te –20 or it pure Te  onto glancreases . Elevates theory .2O3 substrates . The reason for such behavior seems to be due to the increase in the chemical activity of chalcogenides at the surface when disorder (amorphization) increases [The nanostructuring of Te-based films by growing amorphous Te on nanoporous Albstrates  allows fncreases –33 and tncreases . It is sThe nanostructuring of Te-based films by growing vitreous Te on a pre-nanostructured (porous) dielectric template significantly improves their gas sensing capabilities. At room temperature (22 °C), the response and recovery times decrease by approximately 10 and 20 times, respectively, in comparison with nanocrystalline Te films. In addition, there is an increase in the gas sensitivity by 15%/ppm. These achievements can be attributed to two main factors: the increase in chemical activity of chalcogenides at the surface due to increase in disordering (amorphization) and an increase in the active surface area due to increased substrate porosity.


Introducing deep learning approach to medical images has rendered a large amount of un-decoded information into usage in clinical research. But mostly, it has been focusing on the performance of the prediction modeling for disease-related entity, but not on the clinical implication of the feature itself. Here we analyzed liver imaging features of abdominal CT images collected from 2019 patients with stage I – III colorectal cancer (CRC) using convolutional neural network (CNN) to elucidate its clinical implication in oncological perspectives.st PC (PC1) + clinical information” had the highest performance (mean AUC = 0.747) to predict 5YLM, compared to the model with clinical features alone (mean AUC = 0.709). The PC1 was independently associated with 5YLM in multivariate analysis . For the 5-year mortality rate, PC1 did not contribute to an improvement to the model with clinical features alone. For the PC1, Kaplan-Meier plots showed a significant difference between PC1 low vs. high group. The 5YLM-free survival of low PC1 was 89.6% and the high PC1 was 95.9%. In addition, PC1 had a significant correlation with sex, body mass index, alcohol consumption, and fatty liver status.CNN generated imaging features from the liver parenchyma. Dimension reduction was done for the features by principal component analysis. We designed multiple prediction models for 5-year metachronous liver metastasis (5YLM) using combinations of clinical variables  and top principal components (PCs), with logistic regression classification. The model using “1The imaging features combined with clinical information improved the performance compared to the standardized prediction model using only clinical information. The liver imaging features generated by CNN may have the potential to predict liver metastasis. These results suggest that even though there were no liver metastasis during the primary colectomy, the features of liver imaging can impose characteristics that could be predictive for metachronous liver metastasis. In colorectal cancer (CRC) patients, liver is the most common site of metastasis due to its anatomical connection with portal circulation . In advaRadiomics is an innovative technique that uses the large volume of imaging features to predict oncological features . It consNonetheless, there are challenges that to evaluate the values of each imaging feature in the prediction model for an oncological outcome is difficult . In tradHere we propose a framework to extract the liver imaging features from CT scan using CNNs in CRC patients and analyze the imaging features using machine learning approaches to predict the metachronous liver metastasis. Additionally, we tried to elucidate its clinical implication in oncological perspectives using statistical analysis. As a proof of concept study, abdominal CT images were collected from 2019 patients who had colectomy for stage I–III CRC, preoperatively.We performed a retrospective, cross-sectional study in patients who underwent colectomy for CRC from January 2008 to September 2013 at Seoul National University Hospital. Stage I-III CRC patients who had curative resection were included in the analyses. The exclusion criteria consisted of patients who had surgery less than 5 years; recurrence in or distant metastasis to other than liver after in less than 5 years after surgery; preoperative neoadjuvant chemotherapy; a history of liver resection; have liver lesion; and had a poor quality of preoperative CT scan. A total of 2019 patients were eligible, and their electronic medical records (EMR) and CT images were collected. The CRC staging description was based on the AJCC staging system, seventh edition, which is a classification system provided by the American Joint Committee on Cancer for describing the extent of cancer progression . We usedP < 0.01) were used for further analyses. Lastly, principal component analysis (PCA) was performed for reducing the feature dimensionality, and this generated new sets of features, such as 1st principal component (PC1) to 10th principal component (PC10), sequentially. At the input stage to the machine learning methods, we further standardized either the clinical features or the PC-transformed image features, by z normalization for each feature to have a mean of zero and unit variance.Figure We trained two machine learning algorithms, including logistic regression classification (LR) and random forest classification (RFC) to predict 5YLM and compared the performances of each model. The models were designed by respective combination of features consisting of clinical features, which includes age, gender, T stage and N stage, and imaging features, which are the sequential summation of PC1 to PC10. The whole data set was divided into a training set (70%) and a test set (30%), and a five-fold cross validation scheme was used on the training set for the parameter tuning of the classification models. Given our highly imbalanced data set, we adopted five-fold, considering that five can be reasonable to allow enough number of positive data samples in each fold. Due to the highly imbalanced data set, we dealt with the imbalance problem of the training set first by oversampling on the negative minority class to meet negative/positive ratio 0.2, followed by down-sampling on the positive majority class to meet negative/positive ratio 0.4 finally. For up-sampling, we used SMOTE (Synthetic Minority Over-Sampling Technique) , and forWe also applied the model with the highest AUC for 5YLM to predict 5-year mortality and observed whether the liver imaging feature contribute to a prediction of 5-year mortality. The AUC performance was compared with the model using clinical feature only for the prediction of 5-year mortality.P < 0.05). For survival analysis, the patients excluded by the previous exclusion criteria, such as recurrence in or distant metastasis to other than liver after in less than 5 years after surgery, were included. The optimal cut-off points for those PCs to divide patients into two groups were determined by MaxStat packages in R . MaxStat uses the maximally selected rank statistics to recommend the optimal cut-off point for the survival plot [Among the models to predict 5YLM, we used the model with the highest AUC to perform multivariate logistic regression to identify associations between the imaging features and 5YLM. Then, Kaplan-Meier analysis was conducted based on the principal components (PCs) that were significantly associated with metachronous liver metastasis-free survival and overall survival  and Python software (version 3.6.2). Associations between clinical information and 5YLM rate were assessed by Chi-square test, Student’s t-test, and analysis of variance (ANOVA) for independent groups in Table The Institutional Review Board of Seoul National University Hospital approved the study protocol (IRB number 1902–088-1010), and the study was conducted in accordance with the Declaration of Helsinki. Informed consent was waived by the board.Our study sample comprised 2019 patients  who had colectomy for stage I-III colorectal cancer. The mean patient age was 62.32 +/− 9.21 years. There were 100 cases (4.96%) of metachronous liver metastasis during the follow. Patient characteristics are shown in Table P < 0.01). From the 12 features, using the PCA, we generated new sets of features notated as PC1 to PC10.We preprocessed the extracted imaging features from CNN based on the significance of association with 5YLM rate by performing univariate logistic regression analysis. Twelve features passed the suggestive significance level , but also adopted various combinations  in order to validate which combination of features more contribute to the prediction performance. Two supervised machine learning methods, LR and RFC, were used for the performance evaluations.The performances of each model are shown in Table P < 0.001) , which is the best performed model, an association study was done to investigate the association between the features and 5YLM. By multivariate logistic regression analysis, PC1 showed independent association with 5YLM, significantly   Fig. b.Fig. 2In the correlation analysis between the significant PC1 and the clinical variable in Table We applied the 5YLM prediction model with the highest AUC model, which combined clinical features with PC1, with respect to the prediction of 5-year mortality. In the 5-year mortality prediction model, the liver imaging features did not have an additional predictive power (mean AUC =0.690), when compared with clinical only models (mean AUC = 0.700), in both models, trained with LR and RFC.Currently, clinical features, such as age, sex, T stage, and N stage are most commonly used to predict the prognosis of colorectal cancer survival . Comparip-values.This is quite interesting result because adding the 50 × 50 pixel liver imaging features would contain extremely limited information, but still, we demonstrated the additional imaging features when combined with clinical features improved prediction performance of liver metastasis. The underlying mechanism would be that, though at the time of surgery the liver does not seem to impose liver metastasis on gross finding of CT images, there could be underlying molecular changes in liver that could be predictive of liver metastasis. For 5-year mortality, the liver feature did not have additive predictive power in prediction, but there was a significant association with overall survival in K-M plot. In our study, we applied both statistical association analysis and computational predictive analysis for the outcome. Association studies would focus on understanding a relationship between the variables and outcomes, while prediction studies train the model with the training data to obtain the variables to investigate their predictive power for the corresponding outcome. Association studies might provide explanation for the relationship but might not have predictive power. On the contrary, prediction studies might have high performance but hard to interpret it. This might imply that the liver image features alone are not enough to precisely predict the outcome of 5-year mortality, however they are associated with the 5-year mortality based on the results of Kaplan-Meier survival analysis with significant Metachronous liver metastasis significantly influence the prognosis of CRC patients who had curative colectomy , 23, andIn the study design, we additionally preprocessed the 4096 imaging features extracted by training the convolutional neural network, VGG16, based on the significance of association with 5YLM rate. VGG16 is a convolutional neural network model which achieved 92,7% accuracy in ImageNet, a dataset of over 14 million images belonging to 1000 classes, and it used RGB images for training . This moWith the preprocess features, principal component analysis was performed for reducing the feature dimensionality, and new sets of features, such as 1st principal component (PC1) to 10th principal component (PC10) as an imaging features for the prediction model and showed that PC1 had improved the performance of prediction model by clinical information to predict 5YLM. We used the PC transformed as imaging features rather than the ones after preprocessing, which is primarily generated by CNN, because these did not have promising results in the prediction model (data not shown). This might reflect the fact that the respective imaging feature by itself do not have meaningful contribution for prediction but the selected features should be aggregated to have a predictive role in the model.In our study, we used logistic regression and random forest classification, which are very common and widely studied machine learning models . For theThis study has several advantages. First, we comprehensively analyzed the imaging feature by respective combination of image features and clinical information. By this way, we were able to find that the best-performing combination of imaging features, preprocessed features by association analysis and PCs from PCA analysis, which showed improved prediction performance combined with clinical information. Second, we performed both the prediction model based on computational analysis and association and correlation study based on statistical analysis. This will help to interpret the complex nature of the liver CT scan image features more intuitively, provide the clinical relevance of it and support an evidence for the results of prediction model. Third, all the patients enrolled in the analyses had at least 5 years of follow-up after primary colectomy. This will make the results of analyses more reliable since the information involves the long-term observations.Despite demonstrating the validity of our proposed approach, there might be a couple of remaining potential limitations. First, as we used the retrospectively collected CT scan, we could not collect the parameters and the product information of the CT scanner, which could influence the imaging features as a batch effect. But since, the CT images were taken on the purpose of preoperative clinical staging in colorectal cancer, the quality of the CT images was well controlled. Second, we simply concatenated the clinical factors and imaging features level for integration. Transformational integration could be applied in a larger set of samples . Third, By analyzing the liver image features by convolutional neural network in stage I-III colorectal cancer patients based on preoperative abdominal CT scan, we were able identify the contribution of imaging features to predict the metachronous liver metastasis. The 1st PC from imaging feature combined with clinical information improved the performance of standardized prediction model using only clinical information such as age and TNM stage. The preoperative liver imaging features generated by CNN might have the potential to predict liver metastasis after 5 years. This result suggests that even though there were no metastasis or liver lesion during the primary colectomy for stage I-III CRC, the features of liver in preoperative CT scan can impose characteristics that could be predictive for liver metastasis during the postoperative follow-up. The findings might conceptualize the importance of the imaging features in liver which could be applied for clinical practice.


To reduce maternal mortality Tanzania introduced Maternal Death Surveillance and Response (MDSR) system in 2015 as recommended by World Health Organization (WHO). All health facilities are to notify and review all maternal deaths inorder to recommend quality improvement actions to reduce deaths in future. The system relies on consistent and correct categorization of causes of maternal deaths and three phases of delays. To assess its adequacy we compared the routine MDSR categorization of causes of death and three phases of delays to those assigned by an independent expert panel with additional information from Verbal Autopsy (VA).Our cross-sectional study included 109 reviewed maternal deaths from two regions in Tanzania for the year 2018. We abstracted the underlying medical causes of death and the three phases of delays from MDSR system records. We interviewed bereaved families using the standard WHO VA questionnaire. The obstetrician expert panel assigned underlying causes of death based on information from medical files and VA according to International Classification of Disease to Death in Pregnancy Childbirth and Puerperium (ICD-MM). They assigned causes to nine ICD-MM groups and identified the three phases of delays. We used Cohen’s K statistic to compare causes of deaths and delays categorization.Comparison of underlying causes was done for 99 deaths. While 109 and 84 deaths for expert panel and MDSR respectively were analyzed for delays because of missing data in MDSR system. Expert panel and MDSR system assigned the same underlying causes in 64(64.6%) deaths (K statistic 0.60). Agreement increased in 80 (80.8%) when causes were assigned by ICD-MM groups (K statistic 0.76). The obstetrician expert panel identified phase one delays in 74 (67.9%), phase two in 24 (22.0%) and phase three delays in all 101 (100%) deaths that were assessed for this delay while MDSR system identified delays in 42 (50.0%), 10 (11.9%) and 78 (92.9%).The expert panel found human errors in management in 94 (93.1%) while MDSR system reported in 53 (67.9%) deaths.MDSR committees performed reasonably well in assigning underlying causes of death. The obstetrician expert panel found more delays than reported in MDSR system indicating difficulties within MDSR teams to critically review deaths. In the past decade, maternal mortality has decreased worldwide during the period that the international community was striving to attain Millennium Development Goal 5 , 2. AcceThe MDSR system, introduced since 2015 in Tanzania , includeMedical files are widely used to determine underlying causes of facility maternal deaths. In view of poor documentation of medical files in health facilities –15 or inAccurate categorization of causes of death facilitates implementation of recommendations that are specific to prevent maternal deaths and reduce possibilities of under- or overestimation of data. While cause of death assignment by health professionals as part of MDSR reviews is commonly preferred, challenges are reported.In view of the importance of correct information of cause of deaths as well as contributing factors to inform strategies, we sought to 1) estimate completeness of reporting of facility maternal deaths and 2) compare categorization of medical causes using ICD MM and 3) three phases of delays to maternal deaths between the MDSR system and an expert panel of independent obstetricians. We aimed to identify existing gaps in categorizing correct underlying medical causes and three phases of delays to derive recommendations to improve the MDSR system.A cross-sectional study was conducted including 132 maternal deaths from two regions in Tanzania. The deaths had occurred between 1st January and 31st December 2018. Routine MDSR categorization of cause of deaths and the three phases of delay was compared with those assigned by an independent expert panel of obstetricians with additional information from VA. To compute the completeness of maternal deaths reported by the MDSR we used the number of infants that received Bacillus Calmette-Guerin (BCG) vaccine,as a proxy for live births as previously recommended,  to calcuThe study was conducted in Lindi and Mtwara regions in Southern Tanzania with a total population of about 2 million . The twoEach health facility that provides delivery services in Tanzania has a standard MDSR committee as stipulated in the guideline . In regiOur main outcome was the underlying medical cause of death defined as disease or condition that started the chain of events that led to death e.g. postpartum haemorrhage (PPH)  . UnderlAs stipulated in Tanzania MDSR guideline, delays in health care seeking or provision of care deemed to have contributed to the maternal deaths were grouped using the three delays model, stipulating delays 1) to decide to seek care; 2) to reach health facilities for care including transport and 3) to receive appropriate care in facilities . SeveralData collection followed three steps: 1) abstracting information from MDSR documents 2) performing VA and 3) independent obstetrician panel review.The first author AS, in close collaboration with regional Reproductive and Child Health Coordinators, abstracted information using a pre-defined checklist from maternal deaths narrative summaries, death review report forms and district monthly death report summaries .The field team (AS and VA interviewers) then traced families using demographic information such as names of the deceased woman, place of death, district and date of death, home address, name of village/street leader, name of husband/partner and other information, for VA interviews.Verbal Autopsy interviews were conducted using the translated standard questionnaire provided by WHO . The queThe field team commenced the process of finding families for VA interviews by visiting and enquiring in the facility where death occurred or where the deceased woman attended antenatal clinic. They were then taken to the family through local government leaders. At the family’s home, after being introduced they explained in detail the purpose of VA. Then one of the interviewers identified person (s) that was (were) present during illness and death and conducted VA with them.Using the coded VA questionnaires as well as copies of available medical files a group of experts, consisting of three experienced obstetricians in MDSR reviewed all maternal deaths. Two of them were from Muhimbili University of Health and Allied Sciences and had never worked in the regions and one was from Mtwara regional hospital. The latter was included to help the panel understand the context better especially information in VA. The author, AS, was among the panel members and had previously been trained on using ICD-MM. All the three panel members neither conducted the VA interviews nor documented any information from the reviews.The three panel members reviewed all the deaths together by reading through the information in VA questionnaire and available medical files. Then they discussed the findings and made their decision by consensus. The cause of death was agreed if at least two of the panel members said the same cause of death. First, the expert panel went through VA questionnaires and determined the underlying cause from the information by consensus. Second, the panel went through the medical files and reviewed all available information. Based on these two sources, the panel determined the 1) underlying cause of death including the ICD coding, 2) contributing medical causes and 3) three phases of delays, all by consensus . The thrData were processed using MS Excel and then transferred to SPSS computer program version 25. Proportions of each underlying medical cause categorized by MDSR system and the expert panel of obstetricians were computed. Underlying medical causes and differences between the routine MDSR system and obstetricians panel were tabulated. As the routine MDSR system used a shortlist of ICD codes while the expert panel used the full number of ICD-MM codes and groups, comparison had to use a pragmatic approach. For example, when the obstetricians panel categorized a death to be caused by PPH due to atony, coagulopathy or retained placenta, this was considered to be in agreement if MDSR system categorized the same death as PPH (non traumatic). Also PPH (traumatic) for MDSR system was decided to be in agreement if obstetricians’ panel categorized the same case as PPH .Cohen’s K statistics were used to determine the level of agreement in categorizing the underlying causes and the three phases of delays. We defined < 0 as no agreement, 0–0.2 as slight agreement, 0.21–0.4 as fair, 0.41–0.6 as moderate, 0.61–0.8 as substantial and 0.81–1 as almost perfect agreement .In the year 2018, a total of 132 maternal deaths were reported in the study regions. According to District Health Information System, the total number of children that received BCG vaccine (as a proxy for live-births) for that year in the two regions was 96,265. Thus according to the MDSR system, MMR was 137 per 100,000 live births with 95% Confidence Interval of 115 to 163 deaths per 100,000 live births. Our final analysis included 109 deaths  of women who died were ≥ 30 years (median 31 years and Inter-quartile range of 25–36), 64(58.7%) had primary education, 76(69.7%) were married/living with partner and 69(63.3%) were peasants. Most 52(47.7%) women were sick for less than a day and 56 (51.4%) died within 24 h of delivery. More than three quarters died in the postpartum period, more than half 65 (70.6%) had a live-born baby before dying and 56 (60.2%) gave birth by caesarean section, 49 (45%) of deaths were reported in district hospitals and 20 (18.3%) in regional hospitals (Table Traumatic and non-traumatic PPH was the most common cause of death categorized by both groups. Obstetricians panel and MDSR system categorized the same underlying causes in 64/99 (64.6%) maternal deaths  Table .Table 2The obstetricians’ panel categorized 21 deaths as caused by hypertensive disorders in pregnancy, childbirth and puerperium and 15 agreements occurred with the MDSR system. The MDSR system categorized two of these deaths in group 3 (obstetric Haemorrhage), two in group 4 (pregnancy-related infection), one in group 5 (other obstetric complications) and one in group 7 (non-obstetric complications). Overall, out of 99 deaths both obstetricians` panel and MDSR system categorised 80(80.8%) in the same ICD-MM group   while there was slight agreement in specifying phase-two and phase one delays (K statistic 0.2) Table .Table 4“Delays in decision-making” was the predominant phase-one delay according to the obstetricians’ panel 57 (77.0%) and MDSR system 23 (54.8%). In phase two delays, MDSR system identified more 6 (60.0%) “Delayed arrival to health facility” than obstetricians’ panel 10 (41.7%). The obstetricians’ panel indicated that human errors and mismanagement was assessed to occur in 94 (93.1%) of maternal deaths as compared to 53 (67.9%) by the MDSR system. Agreement in identifying delays ranged from no agreement to slight in most of the categories. There was moderate agreement (K statistic 0.41) in identifying delayed referrals  was found when ICD-MM groups were compared.nddelay) and 93% (3rd delay) identified by the MDSR system. The obstetrician panel found that human errors or mismanagement had occurred in 93.1% of deaths while the MDSR system reported this in 67.9% of deaths with moderate agreement in identifying delays in referral system. The MMR in the two regions was estimated at 137 per 100,000 live births.Phase-one delays were identified in 68%, phase-two in 22% and phase-three in 100% by the obstetrician panel as compared to delays in 50% (1st delay), 12%  and phase three (100%) compared to the MDSR system. It is of note that the MDSR system indicated no phase-three delays (provider factors) in six maternal deaths while the obstetricians’ panel identified phase-three delays in all deaths. It was not clear whether the MDSR system could not identify delays or efforts tried to shift blame to the deceased women. A culture of blame is one of the major obstacles in maternal death reviews , 29. TheThe two regions reported a total of 122 facility- and 10 community-related maternal deaths for the year 2018. This corresponds to a MMR of 137 per 100,000 live births, which falls short of most recent national and international estimates of 556/100,000 and 524/100,000 respectively [Underreporting of deaths was also revealed during our field activities as we identified three facility deaths that were not reported to higher levels of the health care system. Furthermore, four deaths had no records in facilities where they were reported to have taken place and had not been reviewed. This shows existing inconsistencies in identification, notification and reporting of maternal deaths in facilities. The problem of underreporting of maternal deaths is universal all over the world even in countries with well-developed vital registration systems. Studies in the United States, France, Taiwan and Netherlands have all revealed underreporting of maternal death in the health system –37. The The main strength of this study is the use of an expert panel consisting of experienced obstetricians who have knowledge of both ICD-MM and MDSR. All three obstetricians received explicit training and have taken part in MDSR in their home institutions. The study used data previously documented, thus the committees did not have prior knowledge of the study which removed social desirability bias if data collection would have been done prospectively.The expert panel was supported with additional information from the VA. Using both VA and medical files ensured that the expert panel had enough information to identify underlying causes and delays. The fact the VA was used, and thus community factors were explicitly investigated, could possibly explain more delays in first phase identified by the expert panel. However, it is also difficult to know how much information was sought from the family by the MDSR committees. The committees are required to seek information from family members even though there is no formal tool such as VA questionnaire to guide that process.This study adds to the body of evidence of the reliability of cause of deaths assignment by MDSR teams. Most other studies have assessed how well the providers categorize the underlying medical causes of death in comparison to an external expert panel or computer programs using ICD 10, thus two other systems with major limitations. Our strategy to enhance the work of the expert committee with VA is likely to have led to a more robust assignment of the cause of deaths, thus comparing with a likely gold standard.Main limitation is the fact that there were multiple MDSR committees that reviewed the deaths. This could pose a problem for Cohen’s K statistic which is recommended for comparing two raters. But each death was reviewed by one MDSR committee and one panel, indicating a two-way comparison for each case. Also, the nature of the study led to missing of some data due to lost documentation, but VA helped in filling information gap in some of these cases.Another limitation is that only facility deaths were included. While community deaths ought to be reported, the MDSR system is not clear on how these deaths are to be reviewed. In addition, there are no available summaries, review information or any other information about circumstances that would have been needed to include these deaths in our study. It is important to note that the distribution of causes of deaths is not representative for the total population in the two regions.The MDSR system performed reasonably well in assigning the underlying cause of deaths according to ICD-MM in contrast to what other studies have indicated. The obstetricians’ panel found more delays of care provision than reported in the MDSR system, indicating difficulties within MDSR committees to critically review maternal deaths. The committee members should have training and support in identifying lessons to be learned in facilities and avoid shifting of blame to the family and deceased.


A 45-year-old male patient went to our observation for a white lesion in the left lower gum. At the anamnesis, he reported previous diagnosis of oral erosive and genital lichen planus (discovered 2 and 12 years before respectively) and the excision of 2 small oral squamous papillomas 2 year ago. A PCR-real time test using a saliva sample was carried out a year ago, but it did not confirm the presence of the HPV DNA. During the clinical examination, we observed a white not scrapable lesion of 2.2cm in the vestibular gum around the teeth 3.6-3.7. The lesion is not linked with a traumatic event and we decided to make an incisional biopsy to verify if it was another injury linked to the spread of oral erosive lichen planus or a new entity. The histological examination confirmed a diagnosis of oral HPV lesion, precisely squamous papilloma, but in-situ hybridation did not find HPV DNA. The presence of a mild dysplasia with unaffected margins was also confirmed. At our first knowledge, this is the first documented case in literature of an oral squamous papilloma with a keratin-like appearance, rather than exophytic and/or cauliflower form. The differential diagnosis could be with oral lichen planus, leukoplakia or verruca vulgaris. The patient was advised of the risk of malignancy connected with the simultaneous presence of oral erosive lichen planus and HPV lesions.


Psychiatric readmissions negatively impact patients and their families while increasing healthcare costs. This study aimed at investigating factors associated with psychiatric readmissions within 30 days and 1 year of the index admissions and exploring the possibilities of monitoring and improving psychiatric care quality in China.Data on index admission, subsequent admission(s), clinical and hospital-related factors were extracted in the inpatient medical record database covering 10 secondary and tertiary psychiatric hospitals in Beijing, China. Logistic regressions were used to examine the associations between 30-day and 1-year readmissions plus frequent readmissions (≥3 times/year), and clinical variables as well as hospital characteristics.p < 0.001). Male patients were more likely to have frequent readmissions . Receiving electroconvulsive therapy (ECT) was significantly associated with a lower risk of 30-day readmission  and frequent readmissions .The 30-day and 1-year psychiatric readmission rates were 16.69% (1289/7724) and 33.79% (2492/7374) respectively. 746/2492 patients (29.34%) were readmitted 3 times or more within a year (frequent readmissions). Factors significantly associated with the risk of both 30-day and 1-year readmission were residing in an urban area, having medical comorbidities, previous psychiatric admission(s), length of stay > 60 days in the index admission and being treated in tertiary hospitals  or a longer length of stay were more likely to be readmitted, and men are more likely to be frequently readmitted. ECT treatment may reduce the likelihood of 30-day readmission and frequent admissions. Targeted interventions should be designed and piloted to effectively monitor and reduce psychiatric readmissions. China Mental Health Survey, the lifetime prevalence of mental disorders was estimated to be 16.6% in China [According to the in China : almost in China , and aroin China . Understin China .Although repeated hospitalizations for patients with psychiatric disorders may reflect the nature and course of the illnesses, socio-demographic or environmental factors, underlying inefficiencies during the hospitalization and in the pre- and post-discharge settings may also play a role . ReadmisPrevious studies have identified several factors associated with psychiatric readmissions, although some of these factors were found to be specific to settings and timeframes and the findings were somewhat mixed , 7. LiteMost of the existing studies were conducted in developed countries, where the psychiatric care had been drastically reshaped after the de-institutionalization movement  and the China is currently facing a shortage in psychiatric beds and mental health professionals. For example, a recent study pointed out that there were 3.15 psychiatric beds per 10,000 population in China, nearly half of the rate in high-income countries  . At the Furthermore, China is a geographically vast country, and regional differences in service volume and clinical practice may exist. Take ECT treatment as an example, a survey of 45 psychiatric hospitals from 10 provinces in China, showed that the rates of ECT utilization among schizophrenia patients varied from 0.5% in Jiangsu province (eastern China) to 12.2% in Jilin province (northeast China) .A few previous studies have focused on psychiatric readmission in patients in China. Zhou et al. 2014) identified previous hospitalizations to be significantly associated with 1-year readmission 014 ident. HoweverTo address this research gap, we designed this study, which aimed to investigate factors associated with 30-day and 1-year psychiatric readmissions as well as frequent readmissions (≥3 times/year). We utilized a regional, multi-center database to ensure a larger sample size and examine hospital-level variables. This study also utilized an all-payer database that included inpatient records regardless of their insurance types or payment methods, thus allowing us to include uninsured patients or patients covered by insurances other than the 3 main social health insurance schemes in China  .The initial dataset was obtained from the Beijing Municipal Health Commission Information Centre (referred as the Information Centre). It contained data extracted from the face sheets of inpatient medical records  uploadedThe eligibility criteria of the index admissions and the data cleaning process were illustrated in Fig. This project was approved by the Ethics Committee of Chinese Clinical Trial Registry . The informed consent of patients was waived.We constructed 3 multi-variate logistic models primarily: 1) the 30-day readmission model. The dependent variable was a binary variable indicating 30-day readmissions status (yes or no). All the eligible index admissions were included in this model; 2) the 365-day (1-year) readmission model. The dependent variable of this model was a binary variable indicating 365-day readmissions status (yes or no). The eligible index admissions with a discharge date after Dec 31, 2016 were excluded in this model. We applied this exclusion because we only had inpatient records up to Dec 31, 2017; 3) the frequent readmissions model. The dependent variable of this model was a binary variable with patients who had three or more psychiatric readmissions within 365 days of the index admissions versus patients with at least one and less than 3 readmissions within 365 days. The eligible index admissions with a discharge date after Dec 31, 2016 as well as eligible index admissions that were not followed by any readmission within 365 days after the index admissions were excluded. This model was designed to explore factors associated with repeated readmissions.We used the same set of independent variables for these three models. We only included admissions with primary diagnoses of mental and behavioral disorders (ICD-10 code: F00-F99). We did not include admissions for non-psychiatric conditions within the timeframe.We included a set of variables based on literature review and availability in our database. The variables included in this study were socio-demographic factors  for the models were also calculated to flag multi-collinearity. For model discrimination and calibration, the C-statistic was calculated from the receiver-operator characteristic curve to assess model discrimination, where a C-statistic of 0.50 or more indicates acceptable predictive power . Hosmer-P-values were two-sided and were considered significant at p < 0.05.To check the robustness of the results, we conducted sensitivity analyses to examine the same set of factors for index admissions for patients with schizophrenia and related disorders (F20-F29 as their primary diagnosis) and affective disorders (with F30-F39 as their primary diagnosis) respectively. All analyses were conducted using the Stata, version 15 . All Among the 7724 patients (index admissions) included in the analyses, 1289 patients  were readmitted within 30 days, and 2492/7374 (33.79%) were readmitted within 1 year after the index admissions in which 746/2492 patients (29.94%) were readmitted 3 or more times , nearly half of the patients were married (48.90%), and 87.52% had health insurance coverage. Clinically, more than one fourth (26.39%) had previous admission(s) within a year prior to the index admissions. The largest diagnosis group was schizophrenia and related disorders (44.64%), followed by bipolar disorders (31.17%). More than half of the sample (57.69%) had at least 1 medical comorbidity listed in the Elixhauser comorbidity index. The mean inpatient length of stay (LOS) was 66.90 days, with a great range of variation . Details about the basic features of the sample can be found in the Additional File p < 0.001) , as well as being treated in tertiary hospitals 1) Table . The use Table . p < 0.05). Compared to patients with depressive disorders, patients with bipolar disorders and schizophrenia and related disorders were significantly associated with risk of 1-year readmission (p < 0.001). Full results are displayed in Table 2 of this model was 0.23 and C-statistics was 0.78.We found being married was significantly associated with a low risk of 1-year readmission risk  were readmitted for 3 or more times (frequent readmission). Compared with those with 1 or 2 readmissions, being male, residing in an urban area, having medical comorbidities, previous hospitalizations and being treated in a tertiary hospital were significantly associated with risk of frequent readmissions  were re-admitted three times or more within a year. Several factors were significantly associated with a high risk of 30-day and 1-year readmission, including an urban residency, having more medical comorbidities and previous psychiatric admission(s), a length of stay of > 60 days in the index admission as well as being treated in tertiary hospitals. The use of ECT was significantly associated with a lower risk of 30-day readmissions and frequent readmissions in the whole sample and the schizophrenia subgroup.The 30-day readmission rate 16.69%) in this study was consistent with some studies conducted in other countries and settings , 9; neve% in thisWe found that being male, with a younger age and being unmarried were significantly associated with a higher risk of readmission. Such findings were consistent with the results of several other studies according to a systematic review published in 2016 . This maWith respect to the LOS, we found the LOS in China (mean: 66.90 days) was considerably longer than that in many developed countries (mostly less than 40 days) , 4, 42. Our findings provide an opportunity to further examine the effects of different LOS on risk of readmission. Compared with patients with LOS less than 20 days, patients with LOS between 20 to 60 days had a lower readmission risk while the ones with LOS > 60 days had a higher risk. This may suggest that the optimal LOS is between 20 and 60 days in the study population although further studies are needed in this area. It is reasonable to assume that for the patients with LOS longer than 60 days, they were more likely to have a more severe disorder, and they were more likely to be treatment-resistant and to have limited family or social support.The study found that the use of ECT was significantly associated with a low risk of the 30-day and frequent readmissions in the entire sample and among the patients with schizophrenia and related disorders. Among the patients with affective disorders (depressive and bipolar disorders), a trend of association was found between ECT treatment and a low risk of readmission but the association was not statistically significant.ECT is widely used in China and unlike in United States, where ECT is predominantly used for patients with major depressive disorder, it is used in patients with numerous other psychiatric disorders, with schizophrenia and mood disorders being the two most common indications , 46.Our findings about the association between ECT and readmission risk are important and intriguing. It is important because we found that ECT was associated with a lower risk of readmission in the whole sample including patients from 10 hospitals, and in the sample with schizophrenia and related disorders. It is intriguing because the association was not significant among patients with affective disorders, which is inconsistent with the findings by Slade et al. (2017) , who fouThese discrepancies may due to different inclusion and diagnostic criteria, varied ECT utilization rates  and even different treatment parameters in different studies. Other factors, such as other treatments (including medications) during the index admissions, and the number of ECT treatments, may also play a role. Although the current finding of this study may suggest that ECT is an effective treatment in reducing the risk of readmissions in the study population, it is also possible that patients or their families are avoidant of future admissions, due to actual or perceived side effects or other reasons (such as stigma or fear) related to ECT treatments. Unfortunately, we do not have the relevant data to further understand the mechanism behind the findings.Clearly, the association between ECT and readmission rate warrants further research. Since the association between ECT use and the risk of readmission can be disorder and study setting specific (rate of utilization varies), future studies may need to include more details, such as data on the patients’ symptoms, medication use, treatment parameters, adverse events during the procedure, etc. .We found that index admission in tertiary hospitals was significantly associated with 30-day and frequent readmissions in this study. This could be explained by the fact that patients treated in the tertiary hospitals tended to be more severe and treatment-resistant, and such severity was not fully adjusted for using the existing variables in the multi-variate model. Previous studies using data from the Centre of Medicare and Medicaid Services (CMS) in United States had identified that larger, urban, academic facilities tended to have slightly higher risk-standardized readmission rates , 49. In One of the strengths of this study is that we utilized the regional all-payer medical record database from 2015 to 2017, providing evidences on the 30-day, 1-year and frequent readmissions of psychiatric patients in Beijing. The sample is very large (> 7000) and multiple hospitals in different levels (tertiary versus secondary) and different setting  were included. The regional multi-center database in this study is better than data from a single hospital, as it may detect readmissions across hospitals and institutional factors can be examined as well.Another strength is the rigorousness of inclusion/exclusion criteria and the inclusion of ECT treatment in the analysis. For example, we excluded patients with unusual LOS (< 24 h) and patients who were non-Beijing residents to reduce the bias introduced by patients admitted for rehabilitation purposes or patients travelled to Beijing for treatments.A few limitations also need to be acknowledged. Firstly, since we only used data from the face sheets of discharge medical records, we were unable to include several potentially important factors. For example, we were unable to include demographic factors such as employment, income, living situation and education levels; clinical factors such as the severity of the psychiatric symptoms, the status of voluntarily versus involuntary admission, patients’ response to medications; as well as post-discharge factors like medication adherence, support system etc. While this study proved that face sheets of medical records can be used as a cost-effective way to examine factors associated with readmission risk, further prospective studies are needed to replicate our findings and explore the mechanism behind the associations between the identified factors and the readmission risk. Due to the limitation of our dataset, some of our findings, especially the findings regarding ECT treatment, should be interpreted with caution, since we only used ECT treatment as a categorical variable (yes or no). Future studies need to include treatment-related factors, including number of treatments, legal status, treatment parameters  and adverse events.Secondly, we did not have access to the outpatient records of those patients thus we were unable to examine the outpatient service utilization after the index admissions. We also did not have information on the community care availability and intensity  so we weThirdly, since this study used data from Beijing, the capital city of China and it is considered as a regional and even national medical center with better resources, the results of this study may not be generalizable to other parts of the country, especially less developed regions. A national database or a national represented sample of psychiatric patients would be more ideal data sources for future studies.This study estimated that the rate of 30-day and 1-year psychiatric readmission was 16.69 and 33.79% respectively; and 10.12% were considered as frequent readmissions (≥3 admissions within 12 months after discharge). Those who resided in an urban area, with medical comorbidities, previous psychiatric admissions, and length of stay of > 60 days in the index admissions were more likely to have psychiatric readmissions. Receiving ECT treatment may be associated with a lower risk of readmission.Further studies might consider exploring the clinical and epidemiological mechanisms behind the risk and protective factors for readmission or designing interventions at hospital and community levels to reduce psychiatric readmission in China.Additional File 1. Results of supplementary analyses and the list of comorbidities included in the Elixhauser Comorbidity Index (ECI). Supplementary Table 1 listed the comorbidities of ECI. Supplementary Table 2–4 showed the results of the supplementary descriptive and univariate analyses. Supplementary Table 5–7 presented the results of the sensitivity analyses. Supplementary Table 8–9 showed the results for post-regression diagnostics and calibration plots of the primary and sensitivity analyses models.