Background. This study evaluates the effect of adjuvant BEMER therapy in patients with knee arthrosis and chronic low back pain in a randomized double blind design. Methods. A total of 50 patients with chronic low back pain and 50 patients with osteoarthritis of knee took part in this study and were randomized into 4 groups. Hospitalized patients received a standardized physiotherapy package for 3 weeks followed by BEMER therapy or placebo. Results. In patients with low back pain, the comparison of the results obtained at the first and second visit showed a significant improvement in resting VAS scores and Fatigue Scale scores. The Oswestry scores and Quality of Life Scale scores showed no change. In patients with knee arthrosis, the comparison of the first and second measurements showed no significant improvement in the abovementioned parameters, while the comparison of the first and third scores revealed a significant improvement in the Fatigue Scale scores and in the vitality test on the Quality of Life Scale. Conclusions. Our study showed that BEMER physical vascular therapy reduced pain and fatigue in the short term in patients with chronic low back pain, while long-term therapy appears to be beneficial in patients with osteoarthritis of knee. During  in vivo . There a in vivo , 4. As r in vivo . PST  for 3-4 weeks depending on the diagnosis. Improvement of microcirculation and reducing fatigue are the clinical applications that have so far been confirmed.Aim of the study was to evaluate the effect of adjuvant BEMER therapy on pain, fatigue, and quality of life in patients with knee arthrosis and chronic low back pain. The primary outcomes were to assess the effect of BEMER therapy on knee and low back pain caused by degenerative changes. The secondary outcomes were to evaluate the adverse effects and to record the changes in fatigue and to investigate the effect on quality of life.This is a single-centre, randomized, placebo-controlled, double blind follow-up study. A total of 50 patients with chronic low back pain and 50 patients with knee arthrosis were enrolled in this study who had been hospitalized for 3 weeks at the Rheumatologic Rehabilitation Department of the Hospitaller Brothers of St. John of God.The patients signed an Informed Consent Form before the study. The study was approved by the Ethics Committee.In addition to complex standard physiotherapy, half of the patients also received additional BEMER therapy, while the other half received additional placebo BEMER therapy; patients could not tell the placebo treatment from the real treatment. Randomisation was conducted by an independent person (by means of drawing lots). During this study, neither the study doctor nor patients or study assistants knew the treatment given. Unblinding took place only after study completion.Demographics: average age was 67.29 years ± 5.44 years  and 66.7 years ± 7.73 years  in patients with chronic low back pain and 67.11 years ± 8.8 years  and 65.3 years ± 7.46 years  in patients with osteoarthritis of knee; with 2 and 3 exceptions, all patients with chronic lower back pain and with osteoarthritis of knee, respectively, were females. There were no differences in gender and age between the treatment and placebo group.Inclusion Criteria for Patients with Low Back Pain. These includepatients with chronic nonspecific low back pain with nonseverely reduced mobility;males and females of 20 to 80 years of age;nonspecific low back pain for at least 12 weeks;palpable tenderness of the paravertebral muscles and/or painful limited mobility of the lumbar spine;low back pain VAS  score of at least 30 mm on a 100 mm visual analogue scale during exercise;the patients have not received systemic or local steroid therapy or physical therapy or balneotherapy, within 2 months prior to the study; physiotherapy was allowed.Exclusion Criteria for Patients with Low Back Pain. These includeacute low back pain;organic neurological deficit associated with lower back pain;the underlying cause is likely to be vertebral compression fracture caused by osteoporosis or other factors;underlying malignancy;pain caused by inflammatory spine conditions;spondylolisthesis (grade 2 or higher);pregnancy.Inclusion Criteria for Patients with Osteoarthritis of Knee. These includemales and females of 30 to 80 years of age with mild or moderate knee arthrosis reporting knee pain characteristic of arthrosis for at least 3 months;diagnosis of knee arthrosis confirmed by imaging meeting ACR (American College of Rheumatology) criteria .Exclusion Criteria for Patients with Osteoarthritis of Knee. These includeinflammatory rheumatic conditions;palpable effusion in the knee;knee injury within 6 months prior to the study;intra-articular steroid within 1 month prior to the study;intra-articular hyaluronic acid within 6 months prior to the study;patients with femoral neuralgia or radiculopathy;NSAID  therapy or chondroprotective therapy modified within 1 month prior to the treatment;knee surgery within 6 months;pregnancy.Intervention. All patients were administered a standardized physiotherapy package during the study : underwater whirlpool massage (10 minutes), TENS therapy on the low back or knee (15 minutes every day), and aquagym (30 minutes every other day)).In addition to the standard complex physical therapy 50% of the patients received BEMER therapy, whereas 50% received placebo BEMER therapy. parameters evaluated: pain intensity on a visual analogue scale (VAS) of 10 cm; General Quality of Life Questionnaire SF 36 , 12; Facit Fatigue Scale (fatigue intensity ranged from 1 to 50) ; Oswestry Index for patients with low back pain , 15; WOMAC Index for patients with knee pain , 17.We used the data from only those patients who received at least 12 sessions of treatment (each patient completed 15 sessions).Each BEMER session lasted 20 minutes; parameters were using mattress applicator (B. Body Pro): 7–35 microTesla, intensive applicator (B. PAD): 60–100 microTesla, or mattress applicator (B. Body Pro) intensity levels 2-3-4–10, intensive applicator (B. PAD) intensity levels 6-7-8-9-10 and using vascular motion signal configuration . The device was a BEMER International AG (Liechtenstein) product. Accessories were mattress with therapy unit (B. Body), flexible, intensive, small surface unit (B. PAD) and B. SPOT (intensive point-like unit), and B. LIGHT unit as needed (may be connected to light therapy unit). The B. BOX Professional control units have 10 different levels of intensity and 3 predefined programmes. The intensity levels are applied during the general full body surface treatment according to the basic programme, while programmes P1–P3 are used gradually to achieve the “deep effect” during targeted treatments. Patients in both groups were treated in supine position while receiving B. BODY mattress applicator treatment. Low back pain patients received B. PAD therapy placed in the low back region at the same time as the full body treatment. Knee pain patients had the B. PAD applicator placed on their knees at the same time as the full body treatment. Therapy sessions lasted 20 minutes each with the B. BODY and B. PAD applicator, respectively:Study Procedure. It is as follows.Before starting the therapy, the physician takes a detailed history, performs a physical examination, checks whether the patient meets the inclusion and exclusion criteria, informs the patient about the study, obtains a signed Informed Consent Form, and administers the questionnaires .At the end of the therapy sessions, the physician examines the patient, administers the abovementioned questionnaires, and asks about the adverse effects.Follow-up period after 15 weeks .The analyses focused on pairwise comparisons among the two selected study arms based on the intention-to-treat (ITT) principle. The One-Sample Kolmogorov-Smirnov Test was applied for testing normality. Analysis of covariance (ANCOVA) was used with the analgesic as a covariate to measure effectiveness by comparing study arms. The significance level was set at alpha = 0.05 (two-tailed). All analyses were carried out using the R-software version 2.9.1 .Of the 25 patients with low back pain, 4 patients (2 cases with acute fever and 2 older patients have misunderstood how to fill out the questionnaires) and 6 patients (2 cases with acute fever and 4 patients had poor compliance) were excluded from the placebo group and the treatment group, respectively, after the second measurement. Of the patients with knee arthrosis, 2 patients (because of gastroenteritis) and 6 patients  were excluded from the placebo group and the treatment group, respectively. No adverse effects were seen. In the group with low back pain, the comparison of the results from the first and second visit showed a significant improvement in resting VAS scores and Fatigue Scale scores and the exercise VAS scores were close to the level of significance, while no changes were found in the Oswestry scores and Quality of Life see . Based oAs regards knee complaints, the comparison of the results of the first and second measurements showed no significant improvement in either parameter  see . Based oPreliminary data suggest that BEMER therapy may have a pain-relieving and fatigue-reducing effect in the treatment of chronic low back pain, even in the short term (studies conducted on large patient populations are required to confirm this). However, for long-term improvement, the therapy should be applied for a long period of time . There was no short-term beneficial effect during knee therapy, probably due to the fact that Program P2 should be used instead of Program P3 , although it was effective in the long term. In this study, we used BEMER therapy not as monotherapy but as adjuvant physiotherapy for inpatients. Many studies with pulse electromagnetic field (PEMF) in patients with locomotor diseases have been published. In patients with osteoarthritis of knee, PEMF was administered as adjuvant therapy in a total of 483 patients in 9 studies, which showed an improvement in the total clinical score . Based oThis is a pilot study. Unfortunately, many of the patients failed to return or misunderstood how to fill out the questionnaires during the three-month period for returning them, which led to a decrease in the number of cases. Furthermore, the Fatigue Scale includes many questions that are uncharacteristic of inpatients and can be evaluated only in outpatients: if, for example, a patient fails to answer Questions 2, 3, and 4, the entire questionnaire will be unevaluable (the software will ignore it). Some patients failed to completely fill out the SF 36 and the Fatigue Scale because they did not perform a specific physical activity during hospitalization. In the case of SF 36, patients failed to answer 1 or 2 questions in a number of item groups. They either did not understand the questions or were not allowed to perform a specific activity. Unfortunately, these cases also led to worse results because more significant improvements would have been possible in many cases in a larger study population.Our study suggests the possibility that BEMER therapy administered in combination with traditional physiotherapy procedures reduces chronic lower back pain in the short term and may be effective in the long-term treatment of patients with osteoarthritis of knee. However, well-performed studies with a larger sample size are required for a more exact evaluation of the abovementioned effects.


We first show that Calcium Orange indicator is compatible in vitro with a commonly used Channelrhodopsine-2 (ChR2) variant, as standard calcium imaging conditions did not alter significantly the activity of transduced cultures of rodent primary neurons. A fast, robust and scalable process for micro-chip fabrication was developed in parallel to build micro-compartmented cultures. Coupling optical fibers to each micro-compartment allowed for the independent control of ChR2 activation in the different populations without crosstalk. By analyzing the post-stimuli activity across the different populations, we finally show how this platform can be used to evaluate quantitatively the effective connectivity between connected neuronal populations.In this paper we report the combination of microfluidics, optogenetics and calcium imaging as a cheap and convenient platform to study synaptic communication between neuronal populations  The MEAs have excellent temporal resolution, extended coverage, and their non invasiveness makes them particularly suited for long-term experiments. While originally low, their spatial resolution has significantly improved through recent advances . On the On the one hand, the field of optogenetics has developed alternative methods to control neuronal activity by transducing genes coding for light-gated ion channels into neurons –22. Depein vivo the possibility to use a standard ChR2 variant in combination with a red-shifted calcium indicator [On the other hand, since action potentials in neurons go along with a calcium influx, it is possible to indirectly measure the activity of neurons by calcium imaging using standard fluorescence microscopy –30. Combndicator , 30.in vitro [Besides the stimulation and recording aspects, the experimental toolbox for neuronal cultures has been recently enriched by new nano- and micro-technologies allowing the reconstruction of neuronal pathways under controlled geometry –37 and win vitro , 45, 46.In this paper, we demonstrate the possibility of combining optogenetics, calcium imaging and microfluidics to study neuronal connectivity. First we report that the Calcium Orange indicator, which spectrum is red-shifted compared to Fluo-4, is a suitable tool to record the activity of neuronal cultures transduced with ChR2 for extended periods of time. Secondly we evaluate the possibility to combine optical fibers and micro-compartmented cultures to increase the spatial resolution and the specificity of stimulation. Finally, we demonstrate the ability of our platform to measure functional connectivity between small neuronal populations connected through different types of micro-channels.We used PDMS-on-glass chips consisting in micro-wells (2 mm in diameter) in which neurons were seeded, with bundles of micro-channels  connectiMaster molds were created by spin coating a silicon wafer with a first 3 μm thick layer of SU-8 2005 (Microchem) and were then exposed with a first photomask so as to create the axon-selective micro-channels. A second layer of SU-8 3035 was spun at low speed on top of the axon channels and exposed with a second photomask in order to create pillars of 2 mm in diameter and 1 mm tall. Although we don’t present them here, we successfully created with this method smaller pillars of 1 and 0.5 mm in diameter. These pillars constitute the templates for the micro-wells. Master molds were replicated in PDMS  using two cycles of silanization, casting and curing. The parts to be silanized were first treated with plasma to activate their surfaces , placed in a petri dish with a drop of silane  deposited on the cover, and incubated 15 min at ambient pressure and temperature. PDMS molds are more resistant to casting-peeling cycles and can be produced easily from the intermediate PDMS counter molds, reducing clean room time and costs tremendously.The PDMS molds were silanized before the first use and spin coated with fresh PDMS so that the top of pillars from the mold emerge. This step is critical as overflowing the pillars will yield closed micro-wells in the final product. In the event that the micro-wells are closed, they might be opened by simply tearing the PDMS membrane off the wells with clean tweezers. The PDMS parts featuring the wells and micro-channels were assembled on clean glass coverslips (18 mm diameter No.0) using plasma bounding . After plasma activation of both coverslip and PDMS, a thin film of Poly-L-Lysine  was created on the coverslip by adding 10 μL of 0.01% PLL, and the PDMS was deposited on the coverslip immediately. Mild pressure insured good contact between PDMS and glass while rejecting the excess of PLL in the wells and micro-channels. Curing 15 min in an oven at 70°C allows the PDMS to attach covalently to the coverslip and the PLL to dry. The microchips were rinsed thoroughly 3 times with double distilled water (DDW) to remove excess PLL, detrimental to neurons. During the rinsing step, it is important to prevent resuspended PLL from adsorbing on the top surface of the PDMS, since this would create unwanted routes for axonal growth. The rinsed chips were finally incubated with plating medium in 12 well plates (1 mL per well) for at least one hour before seeding.Animal care and experiments were conducted in accordance with standard ethical European Committee Guidelines on the Care and Use of Laboratory Animals and the guidelines published by the Institutional Animal Care and Use Committee of the Weizmann Institute of Science (IACUC of the Weizmann Institute of Science). Experiments were approved by the IACUC of the Weizmann Institute of Science (WIS application 00420113-2) and were performed in an authorized establishment under the supervision of authorized investigators. Pregnant rats were ordered from Harlan . Animals were sacrificed the day of arrival using an intraperitoneal injection of pentobarbital followed by cervical dislocation.2 100 mM 10 μL, dissection solution 10 mL) at 37°C for 20 minutes. After digestion, the supernatant was carefully removed and replaced with 10 mL of plating medium  supplemented with 25 mg of trypsin inhibitor and 25 mg of Bovine Serum Albumine for 5 min to inactivate the papain. Supernatant was removed, replaced with plating medium and tissues were triturated with fired polished pasteur pipettes. The medium covering the microchips was aspirated and the dissociated neurons were seeded in compartment at a density around 5000 neurons/mm2. The seeded chips were incubated half an hour in a humidified, 37°C and 5% CO2 incubator to allow the attachment of neurons to the PLL substrate. Each chip was finally covered by 1 ml of serum-free medium  and the cultures were placed back into the incubator.Primary neurons were obtained from rat embryos at stages E19. Dissection and papain dissociation of hippocampi was done according to established protocols. BrieflyGlial proliferation was stopped 4 days after plating by adding in the medium 20 μg/mL 5-fluoro-2’-deoxyuridine and 50 μg/mL uridin . All media contained 50 μg/mL gentamicin so as to minimise contaminations.Neurons were transduced with an Adeno-Associated Virus (mixed serotype AAV1-2) containing a pAAV-CaMKIIa-ChR2(E123T/T159C)-P2AE-YFP construct kindly given by the group of Pr. Ofer Yizhar. Due to the low cytotoxicity of AAV, we used a saturating amount of viral particles (extracted from culture supernatant) in accordance to the amounts used in Pr. O. Yizhar lab, yielding a fraction of ChR2-YFP positive neurons of around 70%. Cultures were infected by adding the virus directly to the wells a few hours after seeding. Experiments were performed at least 5 days after infection.2 1 mM, CaCl2 2 mM, Glucose 10 mM, HEPES 10 mM) to obtain a loading solution with a final concentration in calcium indicator of 5–10 μM. The culture medium was replaced with loading solution and the cultures were incubated at room temperature for 40 minutes in the dark. The loading solution was then removed and replaced by fresh recording buffer, and the cultures were allowed to recover at 37°C for 30 min in the dark before imaging. This step also facilitates hydrolysis of the internalized AM ester precursor into its active form. Images were acquired with a scanning confocal microscope (Leica SP5) and a white-light laser set to 549 nm was used to excite the Calcium Orange. The scanning line was 1 by 256 pixels, with a pixel size of 4 μm by 4 μm. The line was positioned parallel to the axon micro-channels, astride the two populations  were resuspended into 10 μL of anhydrous DMSO to yield ∼ 4 mM stock solutions. Calcium Orange stock solutions were diluted into filtered Recording Buffer  Rebel LEDs with 10 mm Square CoolBases . The LEDs were mounted on an aluminum heat-sink with thermal tape. MOSFETs (IRF840) controlled with an Arduino Leonardo board were used to achieve fast modulation of the LED forward currents (from 0 to 700 mA). The LEDs were butt coupled to 0.5 mm diameter PMMA optical fibers with a homemade adaptor keeping the fibers aligned with the LEDs. Home-made spacer and adapter were used to position the other end of each fiber on top of the different neuronal populations, at a distance of approximately 1 mm from the neurons. Since the acceptance half-angle of the PMMA fibers we used is about 26°, the spot receiving the vast majority of the light was about 1.5 mm in diameter. Each 0.5 mm fiber delivered about 3 mW of light in this configuration, as measured with a laser power meter .To be able to combine optogenetic stimulations of neurons with calcium imaging, we needed to select a red-shifted calcium dye and check if the excitation of this dye did not trigger neuronal activity in population of neurons expressing ChR2. The calcium dye we have selected, Calcium Orange indicator from Molecular Probe, presents a maximum of excitation at 549 nm . The minThe effect of increasing intensities of 549 nm light on ChR2 was then investigated on cultures expressing ChR2-E123T/T159C. Recorded bursting rates were normalized by the bursting rate observed under minimal light on the same culture, so as to exclude inter culture variability in term of basal activity. The results showed that activity was largely unaffected by light at 549 nm. Increasing the intensity of excitation light by 400% (compared to the reference level) yielded no significant increase  in the frequency of bursts . This shBased on the theoretical figures of merit, other ChR2 variants (such as H134R presented in 2 (We characterized the response of transduced neurons to brief pulses of light (from 1 ms to 1024 ms), at 470 nm and for a dose of about 3 mW/mm2 . StimulaIt is important to notice that two mechanisms are involved in the activation of neurons in these responses. The first one is the direct activation by light (via ChR2) which thus applies only to transduced neurons; the second one is an indirect activation by synaptic transmission and it applies to all neurons. To better characterize the contribution of each of these two mechanisms, we did experiments in the presence and absence of CNQX.CNQX is an antagonist of AMPAR, a ionotropic transmembrane receptor for glutamate that mediates most excitatory transmission at synapses. In consequence, under saturating concentration of CNQX, excitatory synaptic transmission is blocked and the observed response only represents the fraction of neurons that is directly activated by light.In the absence of CNQX we observed that each stimulation elicited a bursting event, the amplitude of which, as recorded using calcium imaging, was largely independent from the stimulus duration . Notice After the addition of 10 μM CNQX , the response to the shortest stimulus was significantly reduced compared to normal condition, and increased gradually with the stimulus duration  in contrSurprisingly, responses to longer stimulations were consistently higher in the presence of CNQX. This paradoxal finding could be explained by the simple hypothesis that inhibitory neurons do not express ChR2. This hypothesis is supported by the observed mutually exclusive marking of GAD2-positive (inhibitory) and ChR2 positive neurons , in lineThe possibility that the first neurons to respond might be only excitatory would change the modality of recruitment of neurons in the burst. It therefore raises the more general question of whether individual neurons experience evoked bursts differently from endogenous ones based on the timing of activation of their pre and post-synaptic partners. Although our data does not allow us to conclude, other groups have developed complementary methods to monitor fast propagation of neuronal activity in small neuronal populations, 46. SucIn order to study neuronal functional connectivity, we used a system in which two populations (contained in 2 mm diameter chambers) were connected by symmetrical micro channels, but only one population was expressing ChR2 . To indeStimulating the transduced side with 5 ms pulses reproducibly evoked bursts in the transduced population, as well as in the non transduced population . As non In contrast, neither side produced a bursting event when the non-transduced population was stimulated. In some cases like presented on When both populations are transduced, the absence of crosstalk previously demonstrated guarantees that post-stimulus activity evoked reproducibly in the non stimulated population is due to synaptic transmission only. We thus applied our system to a pair of transduced populations in order to evaluate transmission independently in each direction, by initiating a burst on either side and measuring the response in the opposite side over 30 repetitions, so as to average out any non specific response (like spontaneous activity occurring by chance in the peri-stimulus time window). Thanks to the short data collection window allowed by line-scanning see , we coulIn a second approach we used narrowing micro-channels called ”axon-diodes”, already known to orient axonal growth and synaptic transmission  see met. In the Interestingly, reducing the overall excitatory neurotransmission by adding onto these cultures an under-saturating concentration of 5 μM CNQX restored an absolute unidirectional transmission . Our intThese experiments show that the functional connectivity yielded by axon diodes is highly polarized and confirm the previous results of Peyrin et al. . The resIn this paper, we have described an experimental setup, combining optogenetics, calcium imaging and microfluidics, as a tool to measure functional connectivity between different populations of neurons in reconstituted networks.The optical recording and stimulation method used in this setup has by itself significant advantages over micro-electrode arrays. Besides alleviating the need for micro-electrodes fabrication technologies and tedious alignement of electrodes with micro-patterned substrates, the method allows for a fully non-contact system where all the sophistication of the measurement is retained in the non-disposable optical components of the instrument, and all the elements in contact with neurons are disposable, reducing the costs and facilitating sample preparation. In systems like the presented micro-chips, where a high spatial resolution is not necessary, a temporal resolution comparable to that of MEAs could be achieved at a far lesser cost, using photodiodes instead of a scanner or an ultra fast camera.The prospect of measuring effective synaptic strength without heavy and expensive electrophysiology techniques is very promising for large scale studies on synaptic plasticity, memory and their importance in neurodegenerative diseases.With ongoing developments aiming at increasing the throughput of the experiments, by recording and stimulating multiple chips at once, a large number of parameters such as neuronal type, culture conditions, network topology, or stimulation patterns could be systematically examined to get deeper insights into fundamental aspects of neuronal networks behavior.S1 FigA. The scanning line of the confocal microscope was positioned following the dashed line so that it covered both populations in roughly equal proportions. B. Resulting imaging data are x-t pictures, from which we extracted the fluorescence traces presented in (PNG)Click here for additional data file.S2 FigThe image shows a primary culture of neurons extracted from GAD2-Cre × ROSA:loxp-stop-loxp:tdTomato embryos (JAX 010802 and 007909 respectively). Only the GAD2-positive inhibitory neurons expressing the Cre recombinase can excise the floxed stop codon and express tdTomato (in red). It appears clearly that ChR2-YFP (green), controlled by the CamKIIa promoter, is not expressed in GAD2-positive inhibitory neurons.(PNG)Click here for additional data file.


The purpose of this study was to determine whether the development of a standardized approach to the collection of intestinal tissue from healthy volunteers, isolation of gut associated lymphoid tissue mucosal mononuclear cells (MMC), and characterization of mucosal T cell phenotypes by flow cytometry was sufficient to minimize differences in the normative ranges of flow parameters generated at two trial sites. Forty healthy male study participants were enrolled in Pittsburgh and Los Angeles. MMC were isolated from rectal biopsies using the same biopsy acquisition and enzymatic digestion protocols. As an additional comparator, peripheral blood mononuclear cells (PBMC) were collected from the study participants. For quality control, cryopreserved PBMC from a single donor were supplied to both sites from a central repository (qPBMC). Using a jointly optimized standard operating procedure, cells were isolated from tissue and blood and stained with monoclonal antibodies targeted to T cell phenotypic markers. Site-specific flow data were analyzed by an independent center which analyzed all data from both sites. Ranges for frequencies for overall CD4+ and CD8+ T cells, derived from the qPBMC samples, were equivalent at both UCLA and MWRI. However, there were significant differences across sites for the majority of T cell activation and memory subsets in qPBMC as well as PBMC and MMC. Standardized protocols to collect, stain, and analyze MMC and PBMC, including centralized analysis, can reduce but not exclude variability in reporting flow data within multi-site studies. Based on these data, centralized processing, flow cytometry, and analysis of samples may provide more robust data across multi-site studies. Centralized processing requires either shipping of fresh samples or cryopreservation and the decision to perform centralized versus site processing needs to take into account the drawbacks and restrictions associated with each method. There is increasing interest in characterizing and quantifying T cell populations in lymphoid tissue as a component of translational studies focused on HIV pathogenesis and/or the evaluation of novel strategies to treat or prevent HIV infection –4. The cIn multi-site studies involving flow cytometric evaluation of MMCs, investigators can ship mucosal samples to a central processing and analysis facility or process and evaluate samples locally with subsequent compilation of site-acquired/analyzed data. However, it is unclear whether MMC flow data generated at one site can subsequently be compared with data generated at a second or third site. Differences in study populations, tissue acquisition, and T cell isolation may prevent direct comparison between data sets. The problem is further exacerbated by use of different flow cytometer platforms and/or gating strategies.In contrast, there is a well-established process to monitor inter- and intra-laboratory performance of peripheral blood mononuclear cell (PBMC) flow cytometry in multi-site trials through the use of standardized flow cytometry staining panels and use of cryopreserved aliquots of the same PBMC sample for quality control . RepeateThe purpose of this pilot study was to evaluate a standardized approach to the collection of intestinal tissue, isolation of gut associated lymphoid tissue (GALT) MMCs, and characterization of T cell phenotypes (activation and memory) at two participating sites: the McGowan laboratory at the Magee-Womens Research Institute (MWRI) at the University of Pittsburgh School of Medicine and the Anton laboratory at the David Geffen School of Medicine at UCLA.The protocol for this study is available as supporting information .This study was approved by the University of Pittsburgh School of Medicine Institutional Review Board (IRB# PRO10090390) and UCLA Office of Human Research Protection Institutional Review Board (IRB# 11–000666) with all participants providing written informed consent.The primary objective of this study was to determine whether development of a standardized approach to the collection of intestinal tissue from healthy volunteers, isolation of GALT MMC, and flow cytometric characterization of T cell populations was sufficient to minimize differences in the normative ranges generated by multiple sites. A second goal was to assess the need for fluorescence-minus-one (FMO) controls to be performed on all tissue types on all samples .Chlamydia trachomatis or Neisseria gonorrhea as well as any other gastrointestinal disorders or chronic systemic conditions (details in Protocol). At the start of the study we excluded participants with positive serology for either herpes simplex virus type 1 (HSV-1) or HSV-2. UCLA found it difficult to enroll HSV-1/2 negative participants and so in order to expedite study recruitment the HSV serology exclusion criteria was dropped. Seven of the UCLA participants were HSV-1 seropositive and none of the participants were HSV-2 seropositive.To minimize study population heterogeneity only male participants were enrolled into the study. Exclusion criteria included positive HIV-1 serology or evidence of rectal infection with 6 PBMC obtained from a single donor. These were obtained from a single leukapheresis. PBMC were isolated by density centrifugation with Histopaque  and were cryopreserved in aliquots. These cells are referred to as qPBMC to differentiate them from the study participants’ PBMC and were included as a quality control to be used with each participant’s MMC and PBMC sample. As required, each vial of frozen qPBMCs was placed in a 37°C water bath after removal from the -80°C freezer. The vial of cells was then transferred to 50 ml conical tubes containing 8 ml of cRPMI medium. The cells were centrifuged at 1600 rpm (515 x g) for 10 minutes. After decanting the supernatant fluid, the volume of remaining medium and cells was adjusted to 1 ml. Cell counts were obtained using a hemocytometer.The HIV Vaccine Trials Network (HVTN) laboratory  provided each study site with 45 frozen PBMC aliquots each containing 25 x 10Ten ml of heparinized whole blood was collected from each participant at the time of endoscopy. Whole blood was diluted with an equal volume of Dulbecco's Phosphate-Buffered Saline  and placed in a 50 ml conical tube. Lymphocytes were isolated using differential centrifugation with Histopaque 1077 . The mononuclear cell layer (interface) was removed carefully with a Pasteur pipette and placed in a clean, sterile 50 ml conical tube and washed with 50 ml of D-PBS. The cells were pelleted by centrifugation for 5 min at 2000 rpm (800 x g) and washed twice with 50 ml D-PBS. The cell pellet was resuspended in approximately 5 ml D-PBS. Cell counts were obtained using a hemocytometer.Flexible sigmoidoscopy was performed with collection of 15 rectal biopsies acquired at approximately 15 cm from the anal verge. Biopsies  were collected and immediately placed into 15 ml of tissue culture medium . Mucosal mononuclear cells were isolated from rectal biopsies using a combination of mechanical and enzyme digestion as previously described . GeneralImmunophenotyping by flow cytometry was performed by pre-staining with a viability dye followed by staining with one of two different eight-color study panels . Panel 1Compensation was set individually for each of the fluorochromes in the panels. One set of singly-stained compensation samples was prepared for each flow cytometry run, using BD positive and BD negative compensation beads  Compensation Particles Set, #552843, BD Biosciences, San Jose, CA). Compensation staining for the Aqua fluorescent dye was performed separately using the ArC Amine Reactive Compensation Bead Kit .Flow cytometric analysis was performed independently at both sites. The BD LSRFortessa cytometer  was used at the MWRI site and the BD LSRII cytometer  was used at the UCLA site. Each site prepared a baseline report using the same lot number of BD Cytometer Set Up & Tracking beads (CS&T) . The baseline report provided information of the cytometer’s performance by measuring key factors, such as determining a target median fluorescent intensity (MFI) and a linear range for each parameter. Application settings were calculated using both instruments to provide consistent photomultiplier tube (PMT) target MFI values for reproducible cytometer settings across the two flow cytometers at the two sites over the course of the study. This was achieved using unstained qPBMCs supplied for this study and adjusting the voltages for each detector to set the robust standard deviation of the electronic noise (rSDEN) of the instrument 2.5–3.0 above the value provided on the baseline report. Once the voltages were determined, a stained sample labeled with all the reagents used in the study was run to ensure the brightest populations for each parameter were within the linear max as defined on the cytometer baseline report. These settings were saved as application settings for the entire study. Using the newly created application settings, that lot of CS&T beads was collected on the cytometer and recorded. This procedure was performed on the cytometer at MWRI. These values were exported to a flash drive and were provided to the UCLA site where they were imported onto the LSRII. UCLA, using the same lot of the CS&T beads, adjusted the voltages so that the MFI in each channel matched the established targets MFI’s established at MWRI. These were, recorded and saved as application settings on that instrument. Each time the samples were run, application settings at both sites were used to yield consistent results.Approximately 5,000 compensation beads per tube were acquired. Approximately 20,000 CD8+ positive events from the cell samples were recorded per immunophenotyping tube.Flow cytometry standard (FCS) files generated by both sites were analyzed at each site and also transferred to the HVTN laboratory where they were analyzed by a single investigator using FlowJo software version 9 . The hierarchical gating strategy used to identify cell populations of interest is illustrated in All flow cytometric data are percentages. CD4+ and CD8+ refer to the percent of CD3+ T cells expressing CD4 or CD8. The memory and activation markers refer to the percent of CD4+ or CD8+ T cells expressing (or not expressing) the indicated markers. All statistical testing for the manuscript was performed by a statistician at the MWRI site (AA). For each of the cell populations of interest (6 from the activation panel and 12 from the memory panel), we present the mean and standard deviation (SD) for the entire study population and for each of the laboratories (MWRI and UCLA) as well as boxplots showing the median, quartiles, and range for each cell population. To test for between-laboratory differences in measurement, we compared MWRI measurements to UCLA measurements using both the two-sample t-test and the non-parametric Wilcoxon rank-sum test. The t-test is the most common test used to compare the distribution of a continuous variable in two independent samples, but given the presence of outliers and the relatively small sample size, we also present Wilcoxon rank-sum tests, which are more robust and less influenced by outliers.A significant result for one marker on both the t-test and the Wilcoxon rank-sum test is strong evidence of a difference between labs in that particular marker. The spread of the respective distributions was also compared using a Folded F-test to compare the variances for the MWRI lab versus the UCLA lab; a significant result indicates that one distribution had significantly greater variability than the other (regardless of the center of the respective distributions). The data are displayed graphically using box-and-whisker plots; the p-values reported on these figures correspond to the Wilcoxon rank-sum test for each marker.The primary between-laboratory comparisons were conducted using the qPBMC data. All of the same methods described above were also used to analyze the PBMC data and MMC data but these should be interpreted with caution as they are taken on different participant samples at the respective sites and thus, significant differences may not be indicative of between-laboratory differences in measurement, but differences in the participant populations recruited at the two sites. In addition, Levey-Jennings plots were prepared separately for qPBMC data from each site and visually show the mean and +/- 3 SD from the mean. Values beyond 3 SD were considered outliers and reasons for potential assay irregularities were investigated for those flow data sets with outliers. All statistical analyses were performed using SAS version 9.4 . P-values less than 0.05 were considered statistically significant; no adjustments were made for multiple comparisons.A total of 77 participants were screened (34 at MWRI and 43 at UCLA) for the study to enable enrollment of 20 participants at both sites . The meaBecause of the assay standardization and the standardization of the instrument settings, it was possible to apply a standard gating template to nearly all the data and this template was almost identical for the data from the two sites  and seven cell populations from the memory panel . There were also significant between-laboratory differences in variance for five populations from the activation panel and seven populations from the memory panel, all indicating stable but greater variance in the MWRI measurements than the UCLA measurements. Using data from subjects not excluded by the above-mentioned criteria, 17 paired PBMC-MMC data sets from MWRI and 18 data sets from UCLA were compared in the centralized analysis. In the participant PBMC data , there wThe MMC data  had signGraphical representations of 6 selected populations are shown in As mentioned above, 6 of the 18 participants at UCLA were HSV-1 positive. A number of significant differences were noted in the activation and memory phenotype of PBMC and MMC in the HSV-1 positive group (N = 6) compared to the HSV-1 negative group (N = 12), These differences included the percentage of CD4+ or CD8+ PBMC expressing the CD27-/CD45RA+ phenotype. Differential expression of CD4+ or CD8+ MMC expressing the CD27+/CD45RA+, CD27+/CD45RA-, CD27-/CD45RA-, CD27-/CD45RA+, and CD38+/DR+ (CD4+ MMC only) was also seen. These differences are summarized in In this study we developed standardized protocols for cell isolation, cell staining, and flow cytometry acquisition using instruments with similar configurations. This approach resulted in data that were consistent over time within each of two laboratories allowing for template gating to be used. Flow cytometry data across the laboratories were very similar with only minor modifications needed in the gating template that was shared between the sites. Centralized analysis was used to avoid potential variability introduced by site-specific gating of the data, a structure that enabled within study identification of differences between the laboratories. Control qPBMC from a single subject were included in each experiment and provided a method for trend analysis and allowed for comparisons between sites.Results showed that although overall CD3+, CD4+ and CD8+ T cell frequencies were comparable between sites, many of the memory and activation subsets were not. This suggests that even with careful standardization, there is limited ability to attain comparable data when samples are processed, stained and acquired at different sites. This places emphasis on the need to either arrange for centralized processing/flow cytometric acquisition or to perform concordance testing with iterative remediation to ensure data from multiple sites are comparable.Our study clearly demonstrated that FMO controls are not necessary in each experiment or for each specimen type as long as standardized protocols are used. These standardized protocols allowed for template gates to be used and FMOs performed at the beginning of the study can be used to establish the gates that will be used throughout the study. This finding is especially important in mucosal studies since it avoids the loss of precious tissue-derived cells in FMO-tubes.A major aim of this study was to determine whether standardized procedures performed at individual sites with centralized analysis of flow data would allow generation of PBMC and MMC data from healthy controls that were similar across two trial sites. For this pilot study, our goal was to recruit similar participants at both sites; however, differences in the study participants at each site could be the reason for statistical differences in some cell populations for PBMC and MMC. The Los Angeles participants were slightly older than the Pittsburgh participants (35.4 ± 12.5 versus 28.9 ± 9.3 years) although this difference was not significant. Additionally, the original goal of the study was to enroll a population that was seronegative for both HIV-1 and HSV-1/2. Unfortunately, this lead to a high screen/enrollment ratio at UCLA and a decision was made to drop the HSV-1/2 exclusion criterion. As a consequence, the UCLA participants included seven individuals who were HSV-1 seropositive. It is uncertain as to whether asymptomatic HSV-1 seropositivity might account for some of the observed flow cytometric differences between the two groups of participants as none of the MWRI participants were HSV-1/2 seropositive. HSV-2 activation can be associated with accumulation of CD4+/CCR5+ T cells in affected genital tissue . As HSV-In future studies it would be appropriate to obtain PBMC and MMC samples, divide the samples and distribute these to sites for staining and flow cytometric analysis. With this approach the inherent variability associated with study participants would be removed and the study could focus on differences that might be associated with technical aspects of the procedures. Overnight shipping of fresh mucosal specimens can be confounded by weather-associated delays, and there is also concern about the changes in cell phenotype that might be introduced by cryopreservation . FurtherThere were no significant differences in the mean/median qPBMC CD4+ and CD8+ T cell percentage data between the two sites although there was significant (but stable within-laboratory) variance between the two sites . It is rWe observed significant differences in qPBMC subsets of CD4+ and CD8+T cells, subsets such as CD38+, HLA-DR+, CCR5+, CD27+, and CD45RA+. These markers do not have clear separation between positive and negative cells, and thus percentage of positive cells is highly dependent on the position of the gate. These markers have not routinely been evaluated in the IQA Program, perhaps because of the more challenging gating.Although the qPBMC inherently serve as the best standard control sample across the sites, there may be site-based variability in maintaining cryopreservation during/post shipping, storage, or thawing that may lead to qPBMC sample heterogeneity. Additionally, there may be site-specific technical variables that are not entirely accounted for in the standardized protocols such as performance of flow cytometers, isolation of cell populations, and flow cytometry staining technique. These latter variables could influence the qPBMC, PBMC, and MMC data. Thus, perhaps with additional optimization across sites and more extensive concordance testing, it may be possible to achieve improved performance across other T cell subsets. Small comparative studies with iterative protocol modifications, perhaps using only qPBMC, may be needed to achieve concordance between sites. This likely requires a major investment in time and resources. Our study only included two sites already expert in isolating, staining, gating and analyzing colorectal mucosal biopsy samples. Harmonizing sites with less experience of mucosal flow cytometry would be even more complex.The use of qPBMC within any one site effectively tracks performance over time and highlights samples producing outlier results with likely invalid data. In this study, further investigation into excluded data exposed irregularities in some aspect of the cell staining or collection on the cytometer demonstrating that the use of standard cells included in each experiment and trended over time is a useful quality-control measure to identify flow data that may be compromised. This type of control should be considered in any type of longitudinal study, and pre-established acceptability criteria can use used to exclude potentially unreliable data. Standardized procedures and instrument set-up allow for template gating and remove the need to perform FMO staining controls within each experiment. If the expectation is to perform processing/data acquisition at multiple sites, then it is important to plan for sufficient time and effort needed to achieve concordance between sites. Alternatively, cryopreservation techniques may need to be developed, distinct from those for PBMC, and especially designed for mucosal samples (MMC) to allow for shipment to a centralized testing facility without loss or shift in cellular phenotype. As the number of multi-center trials collecting cellular samples is likely to increase there is an urgent need to optimize the techniques used to analyze these samples.S1 FigShown is an example from one experiment at one site. The upper graphs show the FMO for PE CCR5, the middle for APC CD27, and the lower for FITC CD45RA. The three specimen types are shown with MMC on the left, PBMC in the middle and qPBMC on the right. For each, graphs are paired with the full stain on the left and the FMO on the right. Note that the FMO defines the lower limit of the gate; often the gate is placed higher.(PDF)Click here for additional data file.S2 FigThe bold black line shows the mean, and the dotted red line shows +/- 3 SD from the mean. Green and yellow lines are for 1 and 2 SD.(PDF)Click here for additional data file.S1 File(PDF)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.


Acute Myeloid Leukemia (AML) is a fatal hematological cancer. The genetic abnormalities underlying AML are extremely heterogeneous among patients, making prognosis and treatment selection very difficult. While clinical proteomics data has the potential to improve prognosis accuracy, thus far, the quantitative means to do so have yet to be developed. Here we report the results and insights gained from the DREAM 9 Acute Myeloid Prediction Outcome Prediction Challenge (AML-OPC), a crowdsourcing effort designed to promote the development of quantitative methods for AML prognosis prediction. We identify the most accurate and robust models in predicting patient response to therapy, remission duration, and overall survival. We further investigate patient response to therapy, a clinically actionable prediction, and find that patients that are classified as resistant to therapy are harder to predict than responsive patients across the 31 models submitted to the challenge. The top two performing models, which held a high sensitivity to these patients, substantially utilized the proteomics data to make predictions. Using these models, we also identify which signaling proteins were useful in predicting patient therapeutic response. Acute Myeloid Leukemia (AML) is a hematological cancer with a very low 5-year survival rate. It is a very heterogeneous disease, meaning that the molecular underpinnings that cause AML vary greatly among patients, necessitating the use of precision medicine for treatment. While this personalized approach could be greatly improved by the incorporation of high-throughput proteomics data into AML patient prognosis, the quantitative methods to do so are lacking. We held the DREAM 9 AML Outcome Prediction Challenge to foster support, collaboration, and participation from multiple scientific communities in order to solve this problem. The outcome of the challenge yielded several accurate methods  capable of predicting whether a patient would respond to therapy. Moreover, this study also determined aspects of the methods which enabled accurate predictions, as well as key signaling proteins that were informative to the most accurate models. AML is a potent malignancy of the bone marrow. It is characterized by the production of dysfunctional myeloid cells, incapable of carrying out their normal differentiation into mature blood cells, ultimately leading to hematopoietic insufficiency, infection, hemorrhage, and anemia , 2. The High-throughput proteomics studies, such as Reverse Phase Proteomic Arrays (RPPA), have the potential to bridge the gap between the underlying genetic alterations and functional cellular changes. Thus far, proteomics has been used successfully to profile AML patients based on alterations in several key signaling pathways, including highly implicated proteins like FLi1  and FOXOThe Dialogue for Reverse Engineering Assessment and Methods (DREAM) is a crowdsourcing platform which has accelerated the development of computational tools in the most pertinent areas of biology and medicine, unraveling gene networks , 9, predThe DREAM9 AML-OPC was designed to facilitate both the improvement and comprehensive assessment of quantitative AML prognosis methodologies. Challenge participants were provided access to data from 191 AML patients (the training set) seen at the MD Anderson Cancer Center , while data from an additional 100 AML (the test set) patients was withheld for model evaluation. We chose Response to Therapy (RT) as the primary clinical endpoint because it is a potentially actionable prognosis criterion. However, since a patient’s Remission Duration (RD) and Overall Survival Time (OS) can be informative in planning patient care, these were also included in the challenge objectives.The DREAM9 AML-OPC included over 270 registered participants and 79 contributing teams, many of which contributed to multiple sub-challenges. Over 60 algorithms were contributed, many of which were refined during the challenge, yielding several innovative and accurate top performing models. We identify these models, test them for robustness, and determine which scoring metrics differentiate the top performers. We also evaluate whether prediction accuracy can be improved by aggregating predictions from the many diverse models we tested. In addition, we evaluate RT predictions over the population of models to determine which outcomes are more difficult to predict accurately. Finally, we investigate the top two performing models to determine the extent their RT predictions were improved by the RPPA data.The challenge data consisted of 40 clinical indicators see  and 231 th week following the challenge opening. In SC1 .A unique facet of community based model development is the ability to examine whether the diverse population of submitted models can be combined to either assure or improve predictive power. Previous DREAM challenges have shown that this approach, often referred to as the “wisdom of crowds”, generates ensemble prediction scores that are comparable in performance, and often times better, than the top performing models . This isSimilarly, aggregating RD predictions from the top 5 models in SC2 , re-scoring each model, and then repeating 1000 times for each sub-challenge . For SC1We next investigated prediction errors in more detail, focusing on SC1, since RT is a potentially actionable part of prognosis. Specifically, we asked whether either outcome, Complete Remission (CR) or Resistant, was more difficult to predict. Patients in the test set were grouped based on outcome and the predictions from each model were re-scored. The resulting accuracy, taken as the positive prediction value, was distributed distinctly for each outcome . The medOne of the goals of the DREAM9 AML-OPC was to promote the development of a quantitative method which could utilize the high-throughput RPPA proteomics data to make more accurate prognosis predictions. We examined RPPA data usage for the two highest ranked models from SC1. To do so, we tested each model on scrambled RPPA data, meaning the original trends and RPPA data patterns that were present during model training were removed. Note, scrambled protein data was generated by randomly shuffling patient protein values for each individual protein, meaning the distribution and associated statistics were maintained for each protein. Both models were first tested on data with protein values simultaneously scrambled for all 231 proteins a total of 100 times and scored using the AUROC and BAC metrics. Neither model completely lost predictive power, having median scores of 0.69 and 0.65 for the first and second ranked model, respectively, as evaluated using the average of the AUROC and BAC. However, the resulting scores were much lower when the models made predictions using scrambled data compared to the original scores using the actual RPPA data . For botWe next wanted to determine which specific signaling proteins were most pertinent to the performance of the two top models from SC1. To test this, we scrambled the data for each of the 231 proteins separately over 100 iterations, running each model on a total of 23,100 scrambled data sets. We then evaluated these predictions using a combined metric based on the average of the AUROC and BAC. The percent difference between the original score (unscrambled data) and the score achieved using data with individually scrambled proteins was used to describe the models dependence on each protein . If a prThe absence of new and informative prognostic information has stunted the improvement of AML prognosis accuracy and the advancement of treatment for the last two decades. The DREAM 9 AML-OPC gathered researchers from all around the world to address this problem, successfully providing a competitive incentive for progress while maintaining a collaborative environment. This was evident from both the improvement seen in the challenge leaderboards and the wide use of the challenge forums during the competition to convey ideas and voice questions and concerns. In addition, the DREAM9 AML-OPC carried out a webcast “hackathon”, a collaborative tool new to DREAM challenges, where several teams shared insights and local experts presented ideas.By evaluating the predictions from both good and poor performing models, we were able to use the DREAM9 AML-OPC as a crowdsourcing platform to gain general insight into making more accurate RT predictions. Although many of the models in SC1 were robust, we determined that higher ranked models were distinct in having an elevated and stable median BAC score. In this case, it is likely that the AUROC metric was less sensitive to the class imbalance inherent in the AML data (as discussed in the methods). As this implies the top performers held greater capacity to predict the minority class, i.e., the Resistant patients, we investigated performance on each class in more detail. Indeed, the overall accuracy observed across all the contributed models was lower in predicting the Resistant cases. The top performing models, however, held accuracies well above the median accuracy for the Resistant class, indicating their ability to predict these patients allowed them to obtain higher BAC scores and higher ranks. Accordingly, future efforts in developing RT prognostic models would benefit from improving predictive ability for Resistant patients.Each sub-challenge resulted in the development of a refined and robust quantitative method to predict a different aspect of patient prognosis. The top model in SC1 used a random forest learning algorithm coupled with a novel form of feature selection called “evolutionary weighting”. Since no general class of learning algorithms could be identified as more accurate in predicting RT, the success of this algorithm likely stems from its implementation and effective feature selection. While the DREAM 9 AML-OPC focused on clinically actionable RT predictions, the challenge also resulted in the development of a refined Cox regression model capable of predicting RD and OS. In addition, some participants were also inspired to pursue interesting lines of research beyond the specific aims evaluated by the DREAM9-AML-OPC, for example, exploring characteristics specific to subpopulations of patients .It is important to note, however, potential limitations in our challenge design. The scarcity of AML patient proteomics data available required us to use data from the test set to provide participants with feedback on the weekly leaderboard. This represents an indirect form of information leakage which could potentially lead to the development of over-optimistic models. However, we limited feedback to 12 scorings per participant and used random test set subsamples to minimize potential model overfitting. Moreover, the top model from sub-challenge 1 only submitted to the leaderboard 1 time prior to final judging. Another potential source of information leakage was the availability of data describing clinical variables and outcomes for a limited number of patients that were used in this study . This daBeyond developing accurate prognostic models, participants were provided novel clinical RPPA proteomics data and tasked with developing a means to use this information in conjunction with clinical data to improve prognosis accuracy. To our knowledge, the DREAM9 AML-OPC represents the first attempt at both developing a quantitative means to utilize this information and providing a rigorous way to assess the resulting models. Accordingly, we tested the two top performing models for SC1 to see the extent to which their RT predictions depended on the RPPA data. Our findings indicate that the performance of these models was enhanced by using RPPA data, suggesting that clinical proteomics has the potential to become a valuable component to AML prognosis. Moreover, the performance of both models, though derived from very different approaches, was heavily dependent on PI3KCA, suggesting PI3KCA could be a highly informative protein biomarker for predicting AML patient response to therapy. This is congruent with recent studies suggesting PI3KCA mutation is a prognostic factor for AML , 20 and Synapse online repository. Note, the clinical outcomes in the overall dataset were imbalanced, with the percent of CR and Resistant patients being approximately 71% and 29% respectively. This ratio was believed to be generally congruent with the overall low survival rate for AML patients and was preserved in both the training and test datasets.The dataset used for the DREAM 9 AML-OPC consisted of 291 patients seen at the MD Anderson Cancer Center , for which clinical attributes and RPPA data from bone marrow biopsies was obtained, processed, and normalized as described previously –28. A geth, 2014. Participants were allowed to submit test set predictions for feedback once a week for 13 weeks, from June 23rd to September 8th, 2014  using the final test set and the models ranked 2nd, 3rd, etc  are shown in 1k the difference in performance metrics of the Rank #1 model (M1) and model Rank k (Mk), then ΔM1k = M1—Mk, under the same sub-sample. 1k as a function of k. For SC1, the Rank #2 model scores better than Rank #1 in the averaged AUROC and BAC score  in 13.7% of the sub-samples tested. Therefore, while the Rank #1 model does not perform better than the Rank #2 model in all sub-samples, it scores higher with a frequency of 86.3%. If we call Prob(M1 > Mk | D) the probability that Model Rank#1 scores higher than model Rank k, and Prob(Mk > M1 | D) the probability that model Rank k scores better than model Rank #1 given the data, then the posterior odd ratio is defined as:To check if the ranking resulting from the final model predictions is robust to perturbations of the test set , we re-evaluated each model’s predictions on 1000 sub-samples of the final (week13) test patients. The results of the performance comparison between the model ranked 1This ratio measures the fold change of the frequency of model Rank #1 performing higher than model Rank k to the frequency of model Rank k performing better than model Rank #1 given the data at hand. This unprejudiced prior was that mode Rank #1 and model Rank k have equal odds of winning. Therefore the prior odds ratio is given by:The Bayes Factor K is defined as the ratio between posterior odds and prior odds ratios:1 > Mk.For hypothesis testing, where the conventional statistical significance is given by p-values < 0.05, well established guidelines for the interpretation of Bayes Factors  suggest For sub-challenges 1, 2 and 3 we have that BF is equal to 6.3, 332 and >999, indicating a robustness of the relative ranking between Rank #1 and Rank #2 models in the Challenge. This robustness holds for all metrics and all sub-challenges, except for metric AUROC in SC1, for which model Rank #1 cannot be considered to be better than model Rank #2, #3 or #4.Use of the RPPA data was determined for the two top scoring models in SC1 by scrambling the protein data, making predictions with the previously trained models, and comparing the scores to those from the original predictions that were made with the unscrambled RPPA data. The data was scrambled by randomly shuffling the values for each individual protein across the 100 patients in the test dataset. In this way, the statistical properties, e.g., the mean, variance, range, etc, were preserved for every protein. All proteins in the dataset were scrambled in this manner for each assessment and a total of 100 assessments were conducted. Note, each model was scored using the final (week 13) test dataset (74 patients). Reduction in model performance was measured by the percentage of scores that were lower than the original predictions, i.e, dividing the number of scores that were less than the original (unscrambled) by the total number of scores from scrambled RPPA assessments.The procedure to determine which specific proteins were informative to the two top performing models was the same as described above, with the exception that only 1 protein was scrambled for each of the assessments. Again, this was repeated 100 times, making a total of 23,100 scrambled assessments for the 231 proteins. To more accurately determine the percentage of perturbations that decreased model performance, an additional 10,000 assessments were performed for proteins that altered model performance under the initial 100 assessments.Challenge results were analyzed using the statistical computing language R . Figure S1 Fig(A) The number of teams participating in each sub-challenge each week. (B-D) Box plots comparing the distribution of scores each week with scores generated from random predictions for each sub-challenge. The red line indicates the maximum score seen for each week.(TIF)Click here for additional data file.S2 FigModel performance was evaluated on the week 13 test data (red) and training data (blue). (A) Performance for SC1 was determined using the AUROC (top) and BAC (bottom) scores (B) Performance for SC2 was determined using the CI (top) and PC (bottom) scores. (C) Performance for SC3 was determined using the CI (top) and PC (bottom) scores.(TIF)Click here for additional data file.S3 FigModel stability was evaluated by scoring final predictions on 1000 different random subsets of the week 13 test set patients (81%). For each specific subset, the difference between the Rank #1 model score and each lower ranking model was determined. Positive differences are indicated by blue points while negative differences are shown in red. The text above each set of points indicates the fraction of scores in which a lower ranking model outperformed the rank #1 model.(TIF)Click here for additional data file.S4 FigEach model was classified by its base machine learning method as documented in the write-ups submitted by each participant. The accuracy in predicting both CR and Resistant patients, taken as the positive predictive value, was then determined for each model. Note, “Meta” refers to models that used a combination of multiple different machine learning approaches, while other refers to approaches that did not use machine learning methods. These methods included various implementations of descriptive statistics, probability analysis, and sparse matrix analysis.(TIF)Click here for additional data file.S5 Figth and 75th percentile respectively. (B) Table showing the percentage of randomizations that yielded reduced scores with respect to the original (unscrambled) RPPA data for the Rank #1 model. (C) Box plots, as described in A, showing the distribution of scores obtained by scrambling individual protein data over 100 assessments for the Rank #2 model. (D) Table showing the percentage of randomizations that yielded reduced scores with respect to the original (unscrambled) RPPA data for the Rank #2 model.(A) Box plots comparing the distribution of scores obtained by scrambling individual protein data over 100 assessments see  for the (TIF)Click here for additional data file.S6 FigA histogram showing the number of patients for different levels of PIK3CA. The vertical centerline at 0 denotes the boundary between low and high PIK3CA levels.(TIF)Click here for additional data file.S7 Figth and 75th percentile respectively. (C) Table showing the percentage of perturbations that resulted in reduced scores after scrambling each individual clinical variable for the Rank #1 model. (D) Box plots, as described in B, showing the distribution of scores obtained by scrambling data pertaining to individual clinical variables over 100 assessments for the Rank #2 model. (E) Table showing the percentage of perturbations that resulted in reduced scores after scrambling each individual clinical variable for the Rank #2 model.(A) Heat map showing the percent difference in score (average of BAC and AUROC) between predictions obtained using the original clinical variables (not scrambled) and predictions made using data where each clinical variable was scrambled separately over 100 assessments. The y-axis indicates the result for each scrambled assessment, 1–100, while the x-axis indicates each clinical variable. (B) Box plots comparing the distribution of scores obtained by scrambling data from individual clinical variables over 100 assessments see  for the (TIF)Click here for additional data file.S1 Table(TIF)Click here for additional data file.S1 TextThe description of the best performing methods, as provided by participants, for SC1, SC2, and SC3.(PDF)Click here for additional data file.S2 Text(PDF)Click here for additional data file.


Rape has been found to be the trauma most commonly associated with Posttraumatic Stress Disorder (PTSD) among women. It is therefore important to be able to identify those women at greatest risk of developing PTSD. The aims of the present study were to analyze the PTSD prevalence six months after sexual assaults and identify the major risk factors for developing PTSD.Participants were 317 female victims of rape who sought help at the Emergency Clinic for Raped Women at Stockholm South Hospital, Sweden. Baseline assessments of mental health were carried out and followed up after six months.Thirty-nine percent of the women had developed PTSD at the six month assessment, and 47% suffered from moderate or severe depression. The major risk factors for PTSD were having been sexually assaulted by more than one person, suffering from acute stress disorder (ASD) shortly after the assault, having been exposed to several acts during the assault, having been injured, having co-morbid depression, and having a history of more than two earlier traumas. Further, ASD on its own was found to be a poor predictor of PTSD because of the substantial ceiling effect after sexual assaults.Development of PTSD is common in the aftermath of sexual assaults. Increased risk of developing PTSD is caused by a combination of victim vulnerability and the extent of the dramatic nature of the current assault. By identifying those women at greatest risk of developing PTSD appropriate therapeutic resources can be directed. Rape has been found to be the trauma most commonly associated with Posttraumatic Stress Disorder (PTSD) among women Empirical research on risk factors for developing PTSD and the impact of sexual assault has used a broad variety of ways approaching the subject. Many studies tend to divide the numerous potential risk factors into three categories: pre-assault variables, assault variables, and post-assault variables Identifying risk factors for the development of PTSD following sexual assault and subsequently permit early interventions with those victims at greatest risk may reduce the incidence A majority of the conducted studies have a cross-sectional design, often in a large sample of college students, based on the participant’s ability to recall circumstances from past traumatic events. These designs have a risk of causing a non-representative sample with a high proportion of recall bias. Only a few longitudinal studies with acute trauma samples have been conducted and they often suffer from selection bias and problems with too small sample sizes, making generalizations hazardous The aims of the present study were to: (a) estimate the incidence of PTSD 6 months following a sexual assault using a structured clinical interview, and (b) by using self-report questionnaires at baseline and after 6 months further explore psychiatric morbidity in this population, and (c) prospectively identify factors associated with the development of PTSD at 6 months following a sexual assault. We hypothesized that about one third of the victims would develop PTSD, and that those not diagnosed as having a full PTSD would have major disabling symptoms. We also hypothesized that current PTSD status  would be higher than in the general population and that victims of sexual assault represent a group with more psychiatric morbidity and history of earlier traumatization. Furthermore, we hypothesized that earlier victimization, psychiatric morbidity, perceived life threat during the assault, and more severe assaults would be predictive of PTSD at 6 months post-rape.Out of 1,047 eligible women, 317 female victims of rape or attempted rape who had been in contact with the Emergency Clinic for Raped Women at Stockholm South Hospital, Sweden, between February 2009 and December 2011 agreed to participate in the study . All womThe PTSD Module of the Structured Clinical Interview for DSM-IV (SCID I) was used to establish current PTSD 6 months post-rape. The SCID-I is a widely used structured clinical interview The level of physical violence used during the assault was defined according to the NorVald Abuse Questionnaire The Beck Depression Inventory, BDI The Stanford Acute Stress Reaction Questionnaire, SASRQ The Posttraumatic Stress Diagnostic Scale, PDS At a medical check-up appointment approximately 10 days after the acute visit, eligible women were asked to participate in the study. Consenting women completed the 3 self-rating questionnaires: BDI, PDS, and SASRQ. Information about history of earlier sexual assaults, sexual assault in childhood, and number of other earlier traumas were taken from the PDS questionnaire. Information on demographics, employment status, psychiatric treatment history, assault characteristics, and possible injuries were gathered from the clinic's structured data files. Six months after the rape, study participants were diagnosed regarding PTSD using the Structured Clinical Interview for DSM-IV (SCID I) and they filled in 2 of the self-rating questionnaires .a/(a+b), where a stands for the women with ASD that also developed PTSD and b stands for the women with ASD that did not develop PTSD Group comparisons between completers and non completers, PTSD versus non-PTSD participants, were performed using independent t-tests for continuous variables and chi-square tests for categorical variables. The main outcome of the study, the occurrence of PTSD 6 months post-rape, was analyzed using logistic regression. Our model strategy was as follows: First, correlates of PTSD were examined including psychometric variables, victim characteristic variables, and assault characteristic variables in 3 separate regression models. Variables were entered in these models if they were found significant in the crude analyses. Second, a simultaneous multivariate regression model was tested with significant factors from the three domains analyzed to predict the occurrence of PTSD. Variables were considered significant if the Wald test resulted in a p<0.05. The associations are presented as odds ratios (OR) and 95% confidence interval (CI). Hosmer-Lemeshow goodness-of-fit test was used to examine if the model adequately fitted the data  The study was approved by the local medical ethics committee in Stockholm (2008/759-31)..The age range was 18 to 59 years . The average time between the assault and the initial acute visit was 3.06 days (SD = 5.56 days) and the time from the assault to the initial psychiatric assessment was 19.9 days (SD = 6.7 days). A majority (70.6%) had experienced a completed rape . Twenty percent had experienced other sexual assaults (including attempts), and 10.1% could not remember what type of assault they had been subjected to p>.05), apart from current alcohol abuse being more common among non-completers . Non-completers were more depressed  and had more avoidance symptoms at baseline  than completers, but there was no difference in pre-existing PTSD at baseline .Of the 317 women entering the study, 201 women completed the six month assessment. The socio-demographics  of non completers were not different from completers  of the women met all 6 criteria for PTSD. When women who had not experienced a Criterion A trauma (22/201) were excluded, the PTSD prevalence was 41.3% (74/179). After excluding the 39 women who, according to the PDS questionnaire, had signs of pre-existing PTSD at baseline, 38.6% (54/140) of the women developed PTSD six months post-rape.n = 134) met the duration criteria (E). Only 51% (n = 101) reported significant impairment in their daily lives (F).Fifty-four percent of the women (97/179) had a high symptom load of B- (re-experience), C- (avoidance), and D-  symptoms at 6 months and 74.9.% (According to Beck Depression Inventory (BDI), 47.5% suffered from moderate or severe depression 6 months after the assault regardless of PTSD status.p<.001). They also reported higher PTSD symptom scores from all symptom clusters at baseline according to the SASRQ. The correlation coefficient between SASRQ and the SCID at 6 months was high, . A summary of the psychometrics in the two groups is shown in Women who fulfilled criteria for a full PTSD status (n = 74) using the SCID-I at 6 months (regardless of pre-existing PTSD status at baseline) were compared with those not having PTSD (n = 105) on various psychosocial measures. The women in the PTSD group reported being more depressed both at the baseline assessment and at 6 months post-rape .Differences between the two groups concerning characteristics of the victim and assault characteristics are shown in AOR = 2.75, 95% CI , p = .002) and acute stress disorder (ASD)  at baseline were associated with the development of PTSD.Three regression analyses were conducted separately that analyzed psychometric variables, victim characteristics, and assault characteristics. Variables in these regressions were all found significant in crude analysis see , and 3. AOR = 2.02, 95% CI , p = .040) and psychiatric treatment history  were associated with the occurrence of PTSD.Of the victim characteristics , a history of ≥2 traumatic events , having been sexually assaulted by a group , having been subjected to several sexual acts , and having been injured  were found to be significant risk factors. Suffering from amnesia was found to be a protective factor for PTSD  in the crude analysis; however, it was not so in the adjusted analysis.Of the assault variables , perceived life threat  and in the final model. However, even though a vast majority of the women developed ASD, only 43% of these women continued to develop PTSD. Unlike what has been found after other kind of traumas Suffering from ASD shortly after the sexual assault was as mentioned associated with the development of PTSD, both in crude measures (The major findings of the present study were as follows: (a) with considerations taken to pre-existing PTSD status at baseline 39% of the women developed PTSD 6 months following rape. Furthermore (b) we found that 19% of the study population already suffered from PTSD at the time of the current sexual assault due to earlier victimization. The major risk factors for developing PTSD found (c) were having been sexually assaulted by a group, suffering from ASD shortly after the assault, having been subjected to several acts during the assault, having been injured, and having a history of ≥2 traumatic events.To our knowledge the present study is one of the largest longitudinal studies performed in a clinical setting. The use of parallel psychometrics  to assess PTSD symptoms give strength to the study. Unlike many other studies we used six months as the time for diagnosis of PTSD. This was based on evidence from earlier studies that the number of people with PTSD decreases substantially within the first three months and that symptoms persisting beyond three to six months have a high probability of becoming chronic Our finding of a PTSD prevalence of 39% is in line with two earlier longitudinal studies from clinical settings The second finding in our study that one out of five women had a pre-existing PTSD at the time of the current sexual assault is interesting, yet not surprising. We know that earlier trauma substantially increase the risk of re-victimization The main focus of the present study was to identify the factors associated with the development of PTSD at 6 months following a sexual assault. There was an alarmingly high occurrence of psychopathology found shortly after the sexual assault with almost 80% of the women suffering from ASD at the 2 weeks assessment. The use of ASD to predict development of PTSD after various traumatic events have been supported in earlier studies In the present study dissociation was found a significant risk factor for PTSD in univariate analysis, however, the association did not remain significant in the multivariable model. This finding was consistent with another study Out of the victim characteristic variables entered in the regression model earlier victimization remained significant. In this study we saw an almost linear effect between the number of earlier traumas and the risk of developing PTSD. As two recent studies have suggested When exploring the assault variables, the only factors that remained associated with PTSD in adjusted analyses were having been subjected to several acts, having been sexually assaulted by a group and having been injured during the assault. The association between having been subjected to several acts is concordant with another study There are some clinical implications of our findings. By focusing on potential risk factors for PTSD, easily assessed in a clinical setting  they can be used even in small clinics where psychological treatment is not a matter of course for everyone. With increased knowledge about the largest risk factors for PTSD these clinics more easily can identify those women at greatest risk and resources could be directed to these women. “High risk” women could be referred to specialized crisis centers more quickly and maybe some cases of development of PTSD can be avoided.The present study has some limitations that need to be mentioned. The fact that almost 37% of the consenting patients were lost to follow-up is of course a potential bias. However, the proportion of women by victim-assailant relationship were the same for the completers in this study as the consecutive patients that sought help during the 13 months evaluated in another study from the same clinic The overall finding confirms that sexual assault represents one of the most traumatic experiences a person can be exposed to regarding the high incidence of PTSD in its aftermath compared to other traumas


Self-management plays an important role in maintaining good control of diabetes mellitus, and mobile phone interventions have been shown to improve such self-management. The Health Promotion Board of Singapore has created a caloric-monitoring mobile health app, the “interactive Diet and Activity Tracker” (iDAT).The objective was to identify and describe short-term (8-week) trajectories of use of the iDAT app among patients with type 2 diabetes mellitus in a primary care setting in Singapore, and identify patient characteristics associated with each trajectory.A total of 84 patients with type 2 diabetes mellitus from a public primary care clinic in Singapore who had not previously used the iDAT app were enrolled. The app was demonstrated and patients’ weekly use of the app was monitored over 8 weeks. Weekly use was defined as any record in terms of food entry or exercise workout entry in that week. Information on demographics, diet and exercise motivation, diabetes self-efficacy , and clinical variables  were collected at baseline. iDAT app use trajectories were delineated using latent-class growth modeling (LCGM). Association of patient characteristics with the trajectories was ascertained using logistic regression analysis.Three iDAT app use trajectories were observed: Minimal Users , Intermittent-Waning Users , and Consistent Users . The adjusted odds ratio of being a Consistent User, relative to a Minimal User, was significantly higher for females  and for those with higher exercise motivation scores at baseline . The adjusted odds ratio of being an Intermittent-Waning User relative to a Minimal User was also significantly higher for those with higher exercise motivation scores at baseline .This study provides insight into the nature and extent of usage of a caloric-monitoring app among patients with type 2 diabetes and managed in primary care. The application of LCGM provides a useful framework for evaluating future app use in other patient populations. The prevalence of type 2 diabetes mellitus is expected to rise globally with an increasingly urbanized and aging population . In SingA1c) levels  through the self-management of diabetes using mobile phone interaction [thin the world at 75% [A meta-analysis of 22 trials attested to the possibility of significant reductions in glycosylated hemoglobin  is a statistical technique that exploits the existence of latent groups of individuals who share similar time trajectories of a particular trait, the characterization of which allows better understanding of the pattern of change in that variable ,13. LCGMThe primary aim of our study was to assess iDAT app usage in patients with type 2 diabetes. More specifically, our goal was to identify and characterize short-term (8-week) trajectories of use of the iDAT app among patients with type 2 diabetes mellitus in a primary care setting in Singapore and to identify patient characteristics associated with different trajectories.The study was conducted at one of the 18 public primary care clinics (polyclinics) located in the northeastern part of Singapore. It is a typical polyclinic, which managed almost 5000 patients with type 2 diabetes in 2013. Patients enrolled for the study had to meet all of the following inclusion criteria: (1) above 21 years of age, (2) type 2 diabetes mellitus diagnosed based on World Health Organization criteria , (3) ownA1cwere also collected. The questionnaire was primarily self-administered, with assistance from the researcher as needed. Initially, the plan was to recruit only newly diagnosed patients. But due to slow recruitment and to reach our preliminary target of 80 patients, recruitment was expanded, 3 months into the study, to all patients who otherwise satisfied the inclusion criteria.Participants were enrolled over a 5-month period from November 2013 to March 2014. Patients attending the diabetes counselling and screening services for eye and foot complications at the polyclinic were approached. Patients who declined participation, did not feel comfortable using apps, or could not understand English were not recruited . RecruitPatient use of the iDAT app was monitored weekly over a period of 2 months post-enrollment. There were no financial reimbursements to the patients for study participation. This study was approved by the SingHealth Centralized Institutional Review Board E (CIRB) (Ref: 2013/743/E), in accordance with all applicable regulations, and informed consent was obtained after the nature and possible consequences of the study were explained. Participants were informed when consent was taken and in the Participant Information Sheet that the email addresses used for iDAT registration would be collected and used to track app usage. This personal information, together with the other data collected, were to be kept confidential and only used on a need-to-know basis as approved by the CIRB.The iDAT app  was deveThe app is free to download through Apple’s iTunes/App Store and Android’s Google Play. It functions as a calorie counter, helping users to balance calories consumed with calories burned on a daily basis. The “Meal” section allows the user to input food consumed via a food database with their estimated calories, including local ethnic foods. The “Workout” section enables the user to tap on their smartphones’ Global Positioning System (GPS) to monitor fitness workouts and calculate estimated calories burned. Workouts can be added manually or by using the app’s “Step Counter”. Other functions include social features such as Facebook-sharing and a “Weight and Goal” feature that allows users to set a weight loss goal and track weight loss over time.Demographic variables and clinical characteristics at baseline were summarized as mean with standard deviation for continuous variables and counts and percentages for categorical variables. HPB provided the iDAT app backend information in the form of weekly use. To summarize this data, any record in terms of food entry or exercise workout entry in a week was considered as usage for that week.A statistical analysis software (SAS) macro, PROC TRAJ, was used to apply LCGM to analyze weekly iDAT app usage data and to identify the latent groups characterizing the iDAT app use trajectories for the cohort. LCGM uses maximum likelihood to estimate model parameters ,19. To d2, mean systolic blood pressure was 130.5 (SD 18.5) mmHg, mean diastolic blood pressure was 77.6 (SD 10.9) mmHg, and mean HbA1clevel was 8.7 (SD 2.5) %. A minority smoked  while 31% (26/84) were regular or social drinkers.Of 153 patients approached, 84 who consented and satisfied the inclusion/exclusion criteria were enrolled . DemograAs we prioritized the enrollment of newly diagnosed patients, only 21% (18/84) of the enrolled participants had been diagnosed with diabetes more than a year prior to enrollment. Therefore, most of the participants had “diet only” treatment without medications  or were using one diabetes medication but not insulin .When asked to rate how healthy their diet was on a scale of 0-9 , the participants reported a mean score of 4.8 (SD 1.9). Their reported exercise frequency ranged from 25% (21/84) who stated they have “not exercised for the past year” to 7% (6/84) who indicated that they exercise “between 1 to 3 times per month”. They were generally quite motivated to improve their diet and exercise, giving similar mean scores of 7.3 (SD 1.5) and 6.7 (SD 1.5) respectively when asked to rate their motivation on a 0-9 scale.Most owned Android-based smartphones . Most used their smartphones and apps frequently, with 87% (73/84) indicating that they used their smartphones more than 5 times a day and 76% (64/84) used apps more than 5 times a day. After being shown how to use iDAT, they gave a positive baseline rating for its usefulness with mean score of 6.7 (SD 1.5) on a 0-9 scale.Using the LCGM approach and applying goodness-of-fit criteria, weekly iDAT app use was best characterized as three latent trajectory groups . The modA total of 78.6% (66/84) of study participants were Minimal Users with a typical usage pattern of no iDAT input or iDAT input only during the first 2 weeks post-recruitment; 11.9% (10/84) were Intermittent-Waning Users with a typical input pattern of an occasional weekly input, mainly in the first 4 weeks post-recruitment. The remaining 9.5% (8/84) were Consistent Users with a typical input pattern of weekly input throughout all or most of the 8-week post-recruitment period.Univariate  and multTo our knowledge, this is the first study to apply LCGM to delineate trajectories of app usage. We were able to distinguish usage patterns of a caloric-monitoring mobile health app into three latent trajectory groups: Minimal (76.8%), Intermittent-Waning (11.9%), and Consistent Users (9.5%). While a majority of patients did not use or rarely used the app, about 20% used the app, with close to 10% using the app on a regular basis during the 8-week post-enrollment period. The adjusted odds of being a Consistent User, as opposed to a Minimal User, were significantly higher for females and for subjects with higher exercise motivation scores at baseline. The adjusted odds of being an Intermittent-Waning User were also significantly higher for those with higher exercise motivation scores at baseline. The application of LCGM allowed us to delineate distinct trajectories of iDAT app usage and then identify predictors of specific patterns of app use.There is strong evidence that good self-management in the chronic care of diabetes leads to better outcomes of the condition ,21,22. TOur study protocol initially aimed at enrolling newly diagnosed diabetes patients, defined as patients in their first year following a diagnosis of diabetes. We felt these patients would benefit most from using the iDAT app, since they would likely be learning new diets and making lifestyle changes. In addition, there have been few studies focused on patient self-motivation in newly diagnosed diabetes . As the The younger relative age of our study cohort could also be attributed in part to the larger proportion of the younger generation owning mobile phones or being familiar with app usage. The larger representation of Indians and Malays among our study participants was consistent with the demographic profile of patients with diabetes in Singapore’s multi-ethnic population .A1clevel was relatively high at 8.7% (SD 2.5), indicating that some of the patients had not been meeting their HbA1ctargets or due to late diagnosis of diabetes.In appraising patient clinical characteristics, it was not surprising that average BMI in our study was in the “high risk category”. Obesity is a well-known risk factor for diabetes and urbanized Singapore has a rising obesity trend . AverageWhile the medical literature has not been clear in reflecting the differences in app usage between genders, this topic has been thoroughly analyzed in marketing research studies so that app development could be directed toward a targeted audience. Their results have shown that, while well-known and popular apps like Facebook and Twitter have equal gender usage, there are differences in the type of apps that males and females download or use -29. For Our findings also showed that patients with higher exercise motivation scores had greater app usage. There are many barriers to initiating or increasing an exercise routine, so patients who indicate higher exercise motivation may be more determined to take active steps toward improving their diabetes, including more diligent use of the app.Implementation of this study in a primary care environment void of external pressure or add-on facilitation such as regular reminders, reimbursements, or financial incentives for participants to use the iDAT app, underpins its strength. The study provides insight into the potential of a typical mobile phone app to reach out to a target group of users in a patient population. We believe our results provide a good indication of the extent and pattern of use of this caloric-measuring app based largely on self-motivation, in a naturalistic “real-world” setting.The analysis was limited by the fact that the app database could only provide information on app usage on a weekly basis. This limitation was considered during the study design process and was accepted on the basis of what we felt were realistic expectations for participant compliance and diligence in entering data. A database with daily app input would likely have enabled a more detailed picture of usage patterns, assuming adequate participant compliance for daily data entry.The study has practical implications and applications. Health care providers who recommend health-related apps alongside diet and exercise instructions should be aware that only 2 in 10 are likely to use the apps and only 1 in 10 is likely to be a consistent user. Males and those with lower motivation for exercise are less likely to be frequent users of such apps. Further research is needed to understand the user’s psychological construct in the three trajectory groups, which will influence their app adoption. The design, features, and functionalities of the respective app are other potential factors that can facilitate or hinder the user’s engagement with the app and this requires further investigation.This inaugural study using LCGM as the modality of analysis is limited by the relatively small study sample and short length of observation. However, the information gathered, especially the variations in uptake of the app across the three trajectory groups will inform the design and sample size estimation of a future study to determine the effectiveness of a caloric-measuring mobile phone app on clinical outcomes among users with diabetes.Our successful, novel application of the statistical method, LCGM, provides insightful analysis of longitudinal data to determine app utility among a target population. For selected patients with diabetes, the iDAT app can serve as an adjunct tool to facilitate lifestyle changes in conjunction with the usual modality of counselling.