Comprehensive Geriatric Assessment (CGA), the gold standard for detecting frailty in elderly cancer patients, is time-consuming and hard to apply in routine clinical practice. Here we compared the performance of two screening tools for frailty, G8 and SAOP2 for their accuracy in identifying vulnerable patients.We tested G8 and SAOP2 in 282 patients aged 65 or older with a diagnosis of solid cancer and candidate to undergo surgical, medical and/or radiotherapy treatment. CGA, including functional and cognitive status, depression, nutrition, comorbidity, social status and quality of life was used as reference. ROC curves were used to compare two screening tools.Mean patient age was 79 years and 54% were female. Colorectal and breast cancer were the most common types cancer (49% and 24%). Impaired CGA, G8, and SAOP2 were found in 62%, 89%, and 94% of the patients, respectively. SAOP2 had a better sensitivity  than G8 (AUC 0.79), with higher performance in breast cancer patients (AUC 0.93) and in patients aged 70-80 years (AUC 0.87).G8 and SAOP2 both showed good screening capacity for frailty in the cancer patient population we examined with SAOP2 showing a slightly better performance than G8. The risk of cancer increases with age . AlreadyDespite the increasing rate of elderly cancer patients, there is a major gap of knowledge on how to properly stratify older patients according to their biological status, to be able to recommend the most appropriate type of treatment in a personalized fashion.The Comprehensive Geriatric Assessment (CGA) is a multidimensional, interdisciplinary evaluation that leads to the identification and stratification of patients’ clinical vulnerability.According to the International Society of Geriatric Oncology (SIOG), the CGA remains the gold standard for defining the presence and or the degree of frailty in elderly cancer patients; its widespread use is recommended, particularly since the CGA was shown to improve patient overall survival, functional status, and quality of life , 6. In gHowever, the CGA is time and resource-consuming and requires the expertise of geriatricians who are not always available in standard cancer clinics. It remains poorly incorporated in routine clinical practice. Thus, consensus guidelines from the National Comprehensive Cancer Network (NCCN) , the EurSince the 2005 SIOG guidelines, a total of 17 different tools have been studied in 44 different trials to evaluate the best screening test in oncogeriatrics . These iSo far, although the G8, the VES-13 and fTRST tools have shown the best clinometric properties in elderly patients , there rThe purpose of this study was to define the accuracy with which two geriatric screening tools, G8 and SAOP2 identify frail oncogeriatric patients, who are ultimately candidates to receive a CGA.Three hundred three eligible cancer patients were evaluated at the Ospedale Policlinico San Martino in Genoa, Italy, between January 2015 and May 2017. For two patients, clinical data collection was not completed and therefore they were not included in the study. Nineteen patients did not meet the inclusion criteria. Thus, 282 eligible patients were evaluated in the study  showed problems in at least 3 CGA clinical domains, thus resulting as frail. This clinical vulnerability was mainly characterized by multimorbidity, initial functional decline and malnutrition risk   Table .Frail patients (based on CGA assessment) scored positive according to G8 in 89% and to SAOP2 in 94% of the cases, respectively  and G8  with reference to CGA using ROC curves showed a higher accuracy in differentiating patients with abnormal CGA for the SAOP2 screening tool (p<0.032)  was further assessed, separately, in patients with a diagnosis of breast cancer (n=68) versus patients with a diagnosis of colorectal cancer (n=138).The SAOP2 showed higher accuracy in predicting patients’ clinical vulnerability in breast cancer patients  as compared to the G8  (p<0.014). Conversely, a comparison between SAOP2 and G8 in colorectal cancer patients did not show any difference in the ability to detect frail patients (p<0.160)  was higher as compared to G8's accuracy  (p<0.015).Notably, both screening tools failed to accurately detect frailty in patients aged >80, with a high rate of false positive results p<0.40)  in over octogenarian patients. The biological aging involves a loss of homeostasis with enhanced vulnerability to environmental stressors  , 38 thatOn the basis of our results, G8 performance seems to be outweighed by malnutrition risk in cancer patients between 70 and 80 years. Conversely, SAOP2 tool did not show any correlation with GCA domains.Few studies have addressed the association between single geriatric domains, with reference to CGA assessment, and oncogeriatric screening performance . Namely,In our population, SAOP2 was not statistically associated with social domains even an association trend with poorer perceived health status and lack of support was observed. This may be due to the partial diagnostic accuracy of the used Gijon Scale; in turn, the social vulnerability index (SVI) , may be Even preliminary in nature, SAOP2 tool seems to better predict clinical vulnerability, especially at the earlier stages of cancer. This is particularly true in ‘’younger’’ patients with two wide prevalent cancer types.The present study has some limitations. First, non-metastatic colorectal and breast cancer were strongly represented while patients with progression/relapse of the disease were systematically excluded. Thus, stratification of patients with different cancer types and stages is still needed. Moreover, the single centre population may represent a potential bias selection.Notwithstanding that, the strength of the study lies on the real-world assessment of an oncogeriatric population, with the direct comparison of G8 and SAOP2 screening accuracy. Assessing this aspect would warrant more sophisticated study designs, including the feedback of a GA team, the appropriateness of the referral and the time frame of clinical interventions. In line with that, the prospective nature of the present study will help the understating of outcome measures in terms of service utilization, geriatric referrals, complications, functional independence and survival variables.Comparing sensitivity and specificity to CGA has the advantage of feasibility in a study. However, it only indirectly addresses the question of how useful the tool is for selecting patients. Therefore, which screening tool could best suit the older cancer populations is a matter of debate. The inclusion of frailty indicators and biological markers may add knowledge to this intriguing field and is part of the observational study.Eventually, further research is needed to optimize the use of SAOP2 screening tool. On the basis of its multidisciplinary nature and the inclusion of key relevant issues like social vulnerability, health perceived status and quality of life, it has great potential of defining clinical pathways, targeting the quality of life and the quality of care in this outgrowing number of cancer patients .This prospective study was performed at the Ospedale Policlinico San Martino, Genoa, Italy, from January 2015 to May 2017.Inclusion criteria were all patients over 65 years with a first diagnosis of solid tumour, who qualified for surgery and/or chemotherapy, adequate understanding of the Italian language and ability to sign an informed consent.Exclusion criteria were: palliative care patients; severe dementia or pre-existing major neurological and/or psychiatric disorders.The study was approved by the ethical committee of the participating hospital, and written informed consent was obtained by all subjects or their next to kin.Patients were simultaneously tested for G8 and SAOP2 questionnaire and comprehensive geriatric assessment, before oncological treatment , by an expert geriatrician.First visit also included the Short Form 36 , 46 to aDemographic data , tumour characteristics , proposed chemotherapy and/or surgery, geriatric recommended clinical interventions were also collected.The G8 screening tool was developed to identify elderly unfit cancer patients, eligible for geriatric assessment.The G8 test consists of the following eight items: chronological age  and seven clinical items including the Mini Nutritional Assessment, a questionnaire dealing with food intake, weight loss, mobility, neuropsychological comorbidity, body mass index, prescription drug, and self-perception of health status , 22, 40.The total score can range from 0 to 17. A score of ≤14 is considered abnormal, indicating a clinical vulnerability profile. The G8 was compared in terms of clinometric properties with CGA in eight different studies, that cumulatively included 3816 patients , 40, 47.The SAOP2 screening tool was developed by the multidisciplinary clinical team of the SAOP at Moffitt Cancer Centre . In addiIf 1 item is impaired, the respective specialist is called in, with potential secondary referral to other team members. If several items are impaired, the multidisciplinary team is called along with the geriatric referral for CGA assessment. SAOP2 is a sensitive tool, with low internal specificity, addressing the importance of a multidisciplinary team approach , 29, 48.An expert geriatrician administered the CGA assessment in an average time of 50 minutes. It evaluates the following tools to assess several clinical domains  , 64. PolThe primary outcome was to assess the diagnostic accuracy of SAOP2 and G8 screening tool, with reference to CGA, in detecting patient's clinical vulnerability.The descriptive analysis for quantitative variables was expressed as mean and standard deviation (SD) or median and interquartile range (IQR).Sensitivity and specificity of both screening tools were calculated using the pre-specified cut-offs from literature.Further receiver operating characteristic (ROC) curves were used to compare G8 and SAOP2 screening tools.If present, indeterminate results were considered as false-positive or false-negative and incorporated into the final analysis. For example, an indeterminate result in a patient found to be frailty according to CGA was considered to have had a negative test result.Area under the curves (AUC) with 95% CI were reported. AUCs were compared using chi-square test.A non-parametric Mann–Whitney test was used to compare two variable.A p-value <0.05 was considered statistically significant.Stata  was used for the computation.Collection and assembly of data: CR, CG, SS, ER, RM, AB.Data analysis and interpretation: CR, CG, FM.Statistical analysis: CG, AS, FM.Manuscript writing: CR, CG, FM, AN.


Candida infections is an urgent need. Regarding this, the present study was performed to evaluate the antifungal activities of crocin (Cro) alone and in combination with fluconazole.The incidence of invasive fungal infections has been increased in recent years. The growing use of azole drugs for prophylactic and therapeutic purposes has resulted in the gradual emergence of azole-resistant species. Accordingly, the introduction of a new strategy to improve the management of This study was conducted on 50 clinical isolates of four different Candida species. The identity of the isolates was confirmed using the internal transcribed spacer identification system. The interactions of Cro with fluconazole were investigated using a microdilution checkerboard method based on the Clinical and Laboratory Standards Institute reference technique with 96-well microtiter plates. Furthermore, the assessment of the interaction of drug combinations was accomplished using the fractional inhibitory concentration index (FICI) based on the Loewe additivity theory. Candida species. Our results demonstrated indifferent interactions between Cro and fluconazole with a FICI range of 0.5-4 against Candida strains.According to the results, Cro alone showed a relatively high MIC50 value (1 g/ml) against Candida species indicated its failure to show appropriate antifungal activity against this species. The MIC of this agent was not significantly reduced even by the addition of fluconazole. Therefore, other mechanisms which are not related to the mechanism of azole drugs are involved at high concentration of Cro.The high MIC value for Cro against  Candida species, mostly azole- and echinocandin-resistant species, have recently become a serious clinical challenge [Candidiasis is a serious life-threatening infection and will undoubtedly continue to grow in parallel with the significantly increasing number of patients receiving medical care, especially those with immunodeficiency -3. Therehallenge -12. Candida infections, antifungal prophylaxis, and empirical or pre-emptive treatment given their good safety profile and high therapeutic index. Among the azole drugs, fluconazole is the most widely used agent for systemic candidiasis due to its high solubility, low toxicity, and wide tissue distribution [Azoles have been used for many years as the first-line therapy for the treatment of ribution . Candida infections [However, it seems that the increasing clinical use of fluconazole for prophylactic and therapeutic purposes has resulted in the gradual emergence of azole-resistant species . Accordifections . Synergistic properties of combination therapy contribute to the enhancement of antifungal activities. Combination of natural antimicrobials with synthetic chemical therapeutic agents  may help develop new antifungal drugs. Currently, it is assumed that natural products have potent synergistic activities against fungi when combined with an antifungal agent. Crocus sativus) is currently used with a wide range of applications, such as a source of food additive, colorant, and component of traditional medicines. Studies have shown that Cro isolated from C. sativus has numerous beneficial properties, including remarkable antibacterial, antioxidant, and antitumoural effects; moreover, it may have cardioprotective effects [Candida infections.Saffron flower ( effects . HoweverIsolates Candida isolates from four different types of fluconazole-susceptible (n=40) and fluconazole-resistant (n=10) species. The isolates were obtained from the Reference Culture Collection of Invasive Fungi Research Center (IFRC) in Sari, Iran  was obtained from the respective manufacturers in form of powder and used for the preparation of the Clinical and Laboratory Standards Institute (CLSI) microdilution trays. The Cro (98% purity) was obtained from Mashhad University of Medical Sciences, Mashhad, Iran. It was stored at a concentration of 2 mg/ml in dimethyl sulfoxide (DMSO). Furthermore, fluconazole was prepared at a final concentration of 128-0.125 µg/ml. C. krusei ATCC 6258 and C. parapsilosis ATCC 22019, were used in each susceptibility test to ensure quality control.Antifungal susceptibility testing of the yeast strains to fluconazole and Cro was performed according to the CLSI guidelines documents M27-A3 and M27-S4 , 19. TheCheckerboard microdilution assayInteractions of Cro with fluconazole were investigated using a microdilution checkerboard technique in a 96-well microtitre plate. Drug dilutions were prepared to obtain four times the final concentration. Concentrations of fluconazole and Cro had the ranges of 8-0.016 µg/ml and 1-0.063 mg/ml, respectively. For the two-dimensional microplate preparation , 50 µl of each concentration of Cro was added to columns 1-11. Subsequently, 50 µl of fluconazole was added to rows A to H, respectively. The wells of column 11 and row H entailed 50 µl of RPMI medium containing 1% of the solvent. Row H and column 11 contained the fluconazole and Cro alone, respectively. Column 12 was the drug-free well that served as the growth control. The model-fractional inhibitory concentration index (FICI) method was applied to interpret the results. The FICI was calculated by the following equation: FICI=FIC A+FIC Bwhere, FIC A is the ratio of the MIC of the combination to the MIC of drug A alone, and FIC B is the ratio of MIC of the combination to the MIC of drug B alone. The interaction was defined as synergistic, indifferent, and antagonistic if the FICIs were ≤ 0.5, > 0.5 to ≤ 4.0, and > 4, respectively .Time-killing assay Candida albicans isolates were prepared with an initial inoculum of 105 cells/ml. The Cro concentrations were 2 and 1 mg/ml, and fluconazole concentrations were 4, 8, and 16 µg/ml. The DMSO comprised more than 1% of the total test volume. At predetermined time points  after incubation with agitation at 35ºC, 100 ml of aliquot was removed from every solution and serially diluted by 10-fold in sterile water. In the next stage, 100 ml aliquot from each dilution was spread on the sabouraud dextrose agar plate. Colony counts were determined after incubation at 35ºC for 48 h. Fungicidal activity was defined as ≥ 3 log 10 reduction from the initial inoculum. Synergism and antagonism were defined as a respective decrease or increase of ≥ 2 log10 CFU/ml in antifungal activity produced by the combined preparation, compared with that of the more active agent alone .In vitro susceptibility testing Candida isolates obtained from four different species are shown in The MIC values of Cro tested alone or in combination with fluconazole against 50 clinical Investigation of fungicidal effect by time-killing testCells were inoculated onto a potato dextrose agar plate to count the colony forming unit for determining synergistic fungicidal effect. The interaction of Cro with fluconazole at a concentration of 1 mg/ml was confirmed by the time-killing test . HoweverCandida isolates. Accordingly, the combination of two or more antifungal agents may be a good alternative and feasible policy to solve this problem. Considering the large population of high-risk individuals, the incidence of severe candidiasis has undergone a dramatic increase , 3. ProlA large number of natural products originated from plants are reported to possess potent antifungal properties in recent years, such as terpene derivatives, flavans, nucleosides, peptides, alkaloids, saponins, and sterols . Many reCandida effects of two bioactive compounds obtained from Crocus sativusstigmas, namely Cro 1 and safranal. In the mentioned study, some semisynthetic derivatives of safranal led to promising biological results in terms of MIC/minimum fungicidal concentration values, synergism, and reduction in the germ tube formation [A study involved the exploration of the anti-ormation . Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, Yersinia enterocolitica, and C. albicans [Glycyrrhiza glabra root. They reported that glabridin had a weak antifungal activity against different fungi, such as Candida species and Cryptococcusneoformans [In another study, the ethyl acetate extract of Cro was found to inhibit the growth of such microorganisms as albicans , 26. Liuoformans -29. To tCandida species when using Cro alone. To reduce this concentration, fluconazole was applied in combination with Cro. Although the MIC values for Cro was reduced, the interaction of the two agents was obtained in the “indifferent” category of FICI (FICI: 0.5-4). Therefore, no additive effect was observed when using fluconazole. Moreover, the MIC values were similar for both fluconazole -susceptible and -resistant isolates. In contrast to other publications, a high MIC value was obtained against Candida by checkerboard microdilution assay. Unlike the current study, the previous studies that used the petroleum ether and methanolic extracts of saffron flower demonstrated that these compounds showed a strong activity against bacteria and fungi. Consequently, the mechanism of action of Cro is not related to 14-a-demethylase enzyme, which is the target for azole drugs. According to the literature, synergistic effects mostly depend on the concentration of the compounds . In ordeCandida species indicated that this agent failed to show any appropriate antifungal activities. The MIC of this agent was not significantly reduced even by the addition of fluconazole. Therefore, other mechanisms, which are not related to the mechanism of azole drugs, are involved at high concentration of Cro.The high MIC value for Cro against


Standard analysis methods, i.e., voxel-based morphometry, tract-based spatial statistics and resting state functional network analyses, were used in a within-subject design to compare 8 and 32 channel head coil data. Signal-to-noise ratios (SNR) for both head coils showed similar ranges, although the 32 channel SNR profile was more homogeneous. Our data demonstrates specific patterns of gray and white matter volume differences between head coils (relative volume change of 6 to 9%), related to altered image contrast and therefore, altered tissue segmentation. White matter connectivity  showed hemispherical dependent differences between head coils (relative connectivity change of 4 to 6%), and functional connectivity in resting state networks was higher using the 32 channel head coil in posterior cortical areas (relative change up to 27.5%). This study shows that, even when acquisition protocols are harmonized, the results of standardized analysis models can be severely affected by the use of different head coils. Researchers should be aware of this when combining multiple neuroimaging MRI datasets, to prevent coil-related bias and avoid misinterpretation of their findings.Neuroimaging MRI data in scientific research is increasingly pooled, but the reliability of such studies may be hampered by the use of different hardware elements. This might introduce bias, for example when cross-sectional studies pool data acquired with different head coils, or when longitudinal clinical studies change head coils halfway. In the present study, we aimed to estimate this possible bias introduced by using different head coils to create awareness and to avoid misinterpretation of results. We acquired, with both an 8 channel and 32 channel head coil, T1-weighted, diffusion tensor imaging and resting state fMRI images at 3T MRI (Philips Achieva) with stable acquisition parameters in a large group of cognitively healthy participants ( Large multicenter data samples are increasingly used to establish and reproduce MRI neuroimaging findings. Although pooling MR imaging data contributes to increased study power, the reliability and results from such studies may be compromised by the use of different hardware elements . For exaFor the present study, we included 77 cognitively healthy participants who underwent MRI of the brain on a 3Tesla Philips Achieva scanner  at the Leiden University Medical Center, Leiden, Netherlands. The MRI protocol contained T1 weighted, DTI and resting state functional MR images, acquired with both an 8 channel SENSE head coil (8CH) and an 32 channel SENSE head coil (32CH) within one MRI session  in which families with autosomal dominant inherited FTD gene mutations are followed using standardized assessment protocols including an MRI of the brain every year, as described previously . To confFor signal to noise ratio’s (SNR) assessment, we acquired proton density weighted single-slice images with one noise-only image, i.e., without radiofrequency pulses, in one healthy volunteer, following the procedures described by For the 3DT1 weighted acquisition, scanning parameters were as follows: MPRAGE, TR = 9.7 ms, TE = 4.6 ms, FOV = 224 × 177 × 168 mm, flip angle = 8°, slices = 140, voxel size = 0.88 × 0.88 × 1.2 mm, SENSE = none, acquisition time = 4 min and 56 s. Identical parameters were used for both the 8CH and the 32CH coils.b = 1000 s/mm2, TR = 8250 ms, TE = 80 ms, FOV = 256 × 208 × 140 mm, flip angle = 90°, slices = 70, voxel size = 2 × 2 × 2 mm, SENSE = 2.0, one b = 0 s/mm2 acquisition, scan time = 8 min and 48 s, 2 signal averages. For the 32CH coil, we increased the number of slices to 80, to have sufficient coverage to include the cerebellum in the imaging volume for our longitudinal FTD-RisC study  version 5.0.8. .For both the 8CH coil and 32CH coil, we isolated an average signal image and one noise-only image for each orientation plane. We subdivided the signal and noise images into non-overlapping regions of interest (ROI) of 16 by 16 voxels. Next, we calculated the mean signal of the ROI using the averaged signal image, and the standard deviation of the noise of the ROI using the noise-only image. Since the noise images were amplitude-reconstructed, the measured standard deviation was corrected for the Rician noise distribution . UltimatThird, we translated the SNR ROI matrices into color-coded maps, in order to visualize the SNR distribution throughout the brain for both coils.To assess the influence of the head coil on gray and white matter volume measurements, we applied the standard voxel-based morphometry (VBM) pipeline as implemented in FSL. Preprocessing of the T1 weighted images included brain extraction followed by radiofrequency (RF) inhomogeneity correction, tissue segmentation and realignment to Montreal Neurological Institute (MNI) standard space using non-linear registration. We performed quality control to ensure good brain extraction, that was not different between both head coils. Next, FMRIB’s Automated Segmentation Tool (FAST) was used for correction for spatial intensity variations, also known as bias field or RF inhomogeneity, and segmentation of the T1 weighted images . The corb = 0 image using the FMRBIB Diffusion Toolbox. The tensor was fitted each voxel to create fractional anisotropy (FA) and mean diffusivity (MD) images. Subsequently, we applied standard tract-based spatial statistics (TBSS) as implemented in FSL  as implemented in FSL, consisting of motion correction with MCFLIRT and spatial smoothing with a kernel of 6 mm FWHM. The data-driven independent component analysis (ICA) based Automatic Removal of Motion Artifacts (ICA-AROMA) approach was used to identify and remove noise components from the resting state fMRI data . After dt-tests with each subject’s mean effect to analyze head coil differences in gray and white matter volume, FA, MD, and resting state functional connectivity. We performed voxel-based non-parametric permutation testing  technique and family-wise error (FWE) correction to correct for multiple comparisons across voxels. We quantified the severity of head coil differences by calculating effect sizes and percentage of change.For all analyses, we designed within-subject paired sample  testing  with 500n = 39) and validation dataset (n = 38), matched for age and sex. For the template dataset, we merged and averaged the images into a mean 8CH coil image and 32CH coil image. The averaged 32CH coil image was divided by the averaged 8CH coil image, resulting in a voxel-based scaling factor. We reduced noise by applying a median filter with a kernel of 5 mm. Next, the 8CH coil images from the validation set were multiplied with the voxel-based scaling factor, equalizing the 8CH coil images to the signal intensity of the 32CH coil images. We repeated previous described statistical analysis on the validation set to complete verification of the scaling factor.For our own longitudinal clinical study , we aimeIn total 77 participants were included in this study . CognitiVisualization of the SNR ROI matrices revealed a quite homogeneous distribution of SNR throughout the brain using the 32CH coil, with the highest SNR in posterior cortical areas of the brain. SNR of the 8CH coil was highest in the frontal lobe, but dropped in central and medial areas .pFWE < 0.05, effect size = 2.096, increase = 6.2%), particularly in the middle and inferior frontal lobe, the superior and middle temporal lobe, the anterior insular cortex, the temporo-parietal junction, the paracingulate, and the cuneus  in frontal and deeper cerebral areas, such as the medial temporal lobe, medial frontal lobe, basal ganglia, posterior insular cortex, anterior cingulate, superior frontal cortex, occipital lobe, and the cerebellum  with the 32CH coil compared with the 8CH coil in all tracts of the right hemisphere and frontal tracts of the left hemisphere, such as the forceps minor, anterior parts of the uncinate fasciculus, anterior thalamic radiation, inferior fronto-occipital fasciculus, and superior longitudinal fasciculus  in part of the left-sided posterior tracts, such as the forceps major, the corticospinal tract, the inferior longitudinal fasciculus, and the central and posterior parts of the anterior thalamic radiation  in the entire right hemisphere, and some tracts of the left hemisphere located in the prefrontal and the occipital lobe  in all tracts of the left hemisphere, except for the prefrontal and occipital lobe  is larger than previous studies (n = 26), we assume that the increased power in our study allowed for detection of coil differences in resting state functional connectivity. Concordant with the results from our study, past research demonstrated that the 32CH coil has particularly increased SNR in posterior cortical areas compared to coils with less channels, caused by a difference in coil geometry  with stable acquisition parameters allowed for the use of voxel-specific information for VBM, DTI, and resting state fMRI scaling. We validated the use of the scaling factors on independent data and demonstrated that coil differences can be substantially reduced when using voxel-based scaling in all modalities . Voxel-specific scaling factors from this study are available upon reasonable request to AH (a.hafkemeijer@lumc.nl).The datasets for this manuscript are not publicly available yet as they are part of the ongoing longitudinal FTD-RisC study and the international GENFI cohort. Requests to access the raw MRI datasets should be directed to JS (The study has been carried out in accordance with the Declaration of Helsinki and has been approved by the Medical and Ethical Review Committees of the Erasmus MC University Medical Center, Rotterdam, Netherlands and the Leiden University Medical Center, Leiden, Netherlands. Written informed consent has been obtained from all participants.JLP designed the study, collected and analyzed the data, and was involved in data interpretation and writing of the manuscript. YT, MO, JG, and JMPa contributed to data analyses, interpretation and writing of the manuscript. EE, JMPo, LJ, LM, ED, and JS were involved in data collection and writing of the manuscript. MB and SR contributed to data interpretation and writing of the manuscript. AH was involved in study design, analyses and interpretation of the data, and writing of the manuscript.MO received funding from Philips, Best, the Netherlands. The funder was not involved in the study design, collection, analysis, interpretation of data, writing of the article or the decision to submit it for publication. The funder did permit publication of a figure depicting their hardware. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


FMR1 premutation carriers who present without clinical symptoms of the fragile X-associated tremor/ataxia syndrome (FXTAS), but who on MRI demonstrate white matter hyperintensities in the middle cerebellar peduncles (MCP sign) and other brain regions, a rare finding. MCP sign is the major radiological feature of FXTAS; it is therefore remarkable to identify five cases in which this MRI finding is present in the absence of tremor and ataxia, the major clinical features of FXTAS. Subjects underwent a detailed neurological evaluation, neuropsychological testing, molecular testing, and MRI evaluation utilizing T2 imaging described here. Additional white matter disease was present in the corpus callosum in four of the five cases. However, all cases were asymptomatic for motor signs of FXTAS.Here we report five cases of male  FMR1 gene located on the X chromosome  is a neurodegenerative condition resulting from a premutation (55-200 CGG repeats) in the r age 80 , 4. FXTAanifests , 7. The anifests , 10.FMR1 mRNA that occurs in the premutation, leading to RNA toxicity  repeats, who denied tremor and ataxia. On examination his blood pressure was 177/87 mmHg and his heart rate was 62 bpm. This was consistent with reported history of and treated with metoprolol and candesartan. His body mass index (BMI) was 29.3. On neurological examination, finger-to-nose touching was without tremor and his arm movements were normal. His deep tendon reflexes were 1 to 2+ in the upper extremities, 3+ at the knees, and 2+ at the ankles. His temperature sensation was normal and his vibration sensation was absent in both great toes. Tandem walking was performed normally. No cognitive abnormalities were present on neuropsychological examination and no psychiatric symptoms were reported.His MRI demonstrated the emergence of a faint MCP sign Figure . His CC Case 2 is a 50-year-old, right-handed male carrier with 102 CGG repeats who denied tremor and ataxia. He had a history of multiple concussions from sports injuries in high school and college.On examination his blood pressure was 147/82 mmHg and his heart rate was 48 bpm. His BMI was 25.8. Finger-to-nose touching was without tremor, and deep tendon reflexes were symmetrical and 1+ at the upper extremities, 2+ at the knees, and 2+ at the ankles. His vibration sense was mildly decreased in the lower extremities. His tandem gait was normal. No cognitive abnormalities were present on neuropsychological examination and no psychiatric symptoms were reported.He presented with the MCP sign on MRI Figure . He alsoCase 3 is a 62-year-old, right-handed male carrier with 86 CGG repeats who denied tremor and ataxia. On examination his blood pressure was 125/88 mmHg and his heart rate was 88 bpm. His blood pressure was being controlled with irbesartan and hydrochlorothiazide. His BMI was 26.9. Finger-to-nose touching showed no tremor and his tandem walk was without difficulty. He had slight increased tone on the right and left extremities with symmetrical movement. Deep tendon reflexes were normal, and he scored a 2+ in all four extremities. His vibration sense, tactile sensation, and cold sensation were also normal. No cognitive abnormalities were present on neuropsychological examination and no psychiatric symptoms were reported.The MRI revealed the MCP sign Figure . AdditioCase 4 is a 60-year-old right-handed, male carrier with 74 CGG repeats who denied tremor and ataxia. On examination he had a blood pressure of 152/86 mmHG and a heart rate of 89 bpm. He had a BMI of 27.9. His neurological examination showed decreased vibration sense in the lower extremities. Deep tendon reflexes were 1–2+ in the upper and 2+ in the lower extremities. He had no rest or action tremor, gait ataxia, or dystonia. No cognitive abnormalities were present on neuropsychological testing and no psychiatric symptoms were reported.The MRI showed mild cerebellar volume loss, with MCP sign bilaterally Figure  and subtCase 4 visited for follow up examination at age 64, at which time he had developed a head tremor and mild intention tremor. He also demonstrated subtle gait ataxia, taking only three steps on tandem walking. His MRI at age 64 confirmed the previous findings Figure .Case 5 is a 61-year-old, right-handed male carrier with 89 CGG repeats, who denied any history of tremor. He noted that he had no history of falling but he experienced some instances of unsteadiness when turning around, possibly attributable to a 4 cm difference in leg lengths secondary to a congenital vascular malformation in one leg that impacted growth. On examination, his blood pressure was 107/68 mmHg and his heart rate was 55 bpm. He had a BMI of 26.5. There was no sign of tremor during finger-to-nose touching. Along with having mild balance problems while turning, he had instability on tandem walking during the first few steps. With practice, he was able to perform at least 6 steps without missteps. He had normal reflexes in his upper extremities and knees, all +2. His right ankle reflex was 1+, and there was no reflex in his left ankle. He had decreased vibration sense bilaterally in both big toes but normal vibration sense at the ankles bilaterally. Additionally, pinprick sensation was slightly decreased in the great toe. No cognitive abnormalities were present on neuropsychological examination and no psychiatric symptoms were reported.On MRI, the T2 images showed the MCP sign Figure  and whitFMR1, and MRI. All participants were English-speaking men between the ages of 50 and 70 years, and each was known to be a carrier of the FMR1 premutation prior to study enrollment.This study was carried out in accordance with the recommendations of Institutional Review Board at the University of California, Davis with written informed consent from all subjects. All subjects gave written informed consent in accordance with the Declaration of Helsinki. All subjects gave written informed consent for publication of their cases. The protocol was approved by the Institutional Review Board at the University of California, Davis. The study protocols involved a detailed medical history and neurological examination, along with neuropsychological testing, molecular studies of The neurological examinations were performed by a medical doctor and included cranial nerve function, tone, rigidity, reflexes, sensation, extrapyramidal function, and gait. In all five cases, signs of tremor and ataxia were judged to be absent.Neuropsychological testing included the Wechsler Adult Intelligence Scale, Wechsler Memory Scale, and Behavioral Dyscontrol Scale. Cases 1, 2, 4, and 5 completed the Wechsler Adult Intelligence Scale, Third Edition (WAIS-III). Case 3 was tested with the Wechsler Adult Intelligence Scale, Fourth Edition (WAIS-IV); however the entire test protocol was not completed. Scores for each case are reported in Table Psychiatric symptoms were evaluated using the Symptom Checklist-90-Revised (SCL-90-R) and the Structured Clinical Interview for DSM-IV (SCID-4).FMR1 specific primers lying outside the CGG repeat  following standard procedure. CGG trinucleotide repeat size was determined using a combination of PCR and Southern blot analysis. PCR was performed using G repeat . SoutherG repeat .2, TR of 4,000 ms, TE of 84 ms, and matrix size of 384 × 384. The second pulse sequence was a T2-weighted fluid-attenuated inversion recovery (FLAIR) in which images were acquired in 104 sagittal slices of 1.9 mm thickness (no gap) with FOV 243 mm, 512 Å~ 512 matrix, TR of 5,000 ms, TE of 455 ms, and inversion time 1,700 ms. In case 4, the MRI included 1.5 mm corona spoiled gradient echo (SPGR) and 3 mm axial proton density and T2-weighted scans.Presence of the MCP sign was confirmed by neuroradiological examination of MRI images collected on a Siemens 1.5T or 3T scanner using pulse sequences. In cases 1, 2, 3, and 5, two different pulse sequences were used. The first was a turbo spin echo (TSE) acquired in 48 axial slices of 3 mm thickness (no gap) with FOV of 240 mmHere we present five cases of male premutation carriers who have white matter disease in the MCP, a classic sign of FXTAS, but do not demonstrate clinically significant tremor or ataxia. Case 5 has noted mild balance problems, but he also had a leg length discrepancy, which may have accounted for this subtle sign. Although some cases have medical problems such as high blood pressure, or decreased vibration sense in the lower extremities typical of mild neuropathy, the major clinical criteria for FXTAS are not present in these cases. This causes a concern regarding their prognosis for FXTAS since the radiological findings show evidence of neurodegeneration but there are no signs of tremor and ataxia; symptoms that would form the basis of the diagnosis of FXTAS , 23. TheIt is not clear what exactly is the cause of the white matter disease, but the RNA toxicity leading to sequestration of critical proteins for neuronal function, chronic DNA damage repair, mitochondrial dysfunction, dysregulation of iron transport, and the production of the neurotoxic FMRpolyG protein from RAN translation, may all contribute to cell death and white matter disease , 27.Martinez-Cerdeno et al. suggested that the white matter disease in the MCP may be related to microhemorrhages . SpecifiOnce there is significant white matter disease, the progression of further neurological dysfunction is thought to escalate, although the progression of FXTAS may be quite variable . A recenA preliminary study demonstrated that the onset of FXTAS correlates with increased CGG repeats, such that the higher the repeat number, the earlier the onset of FXTAS symptoms . In the In the cases reported here, the neurological symptoms of tremor and ataxia are nonexistent so we do not have the clinical findings for a diagnosis of FXTAS. We consider these patients to be at high risk for FXTAS, or pre-FXTAS, because they already have the MRI findings and therefore evidence of neurodegeneration. We have seen a progression of symptoms in Case 4, who had developed FXTAS at his 4-year follow up, and we will continue to follow the remaining four subjects.The presence of the MCP sign may be more common than originally thought in premutation carriers without neurological symptoms. Their follow-up will guide our recommendations for these carriers in the future. We suspect that avoidance of toxins  and other insults , in addition to exercise and treatment of medical problems including hypertension or obesity may help to stabilize or slow the progression of FXTAS even in those with white matter disease as reported here , 37. PerThe raw data supporting the conclusions of this manuscript will be made available by the authors, without undue reserve, to any qualified researcher.JF, FM, YM, JG, FT, DH, SR, VM-C, and RH drafting/revising the manuscript for content. FM formatting the manuscript. FT providing the molecular data. DH, RH, and SR funding study. RH analysis and interpretation of the data.FT one of the topic editors; has received funding from Asuragen, Inc. DH has received consulting fees from Ovid for FXS clinical trial development. RH one of the topic editors; has received funding from Novartis, Marinus, Alcobra and Neuren to carry out trials in Fragile X Syndrome (FXS) and consulted with Zynerba, Fulcrum and Ovid regarding treatment trials in FXS. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer FN and handling Editor declared their shared affiliation at the time of the review.


This approach not only admits random input and output environment, but also allows the evaluation unit to exceed the front edge under the given probability constraint. Moreover, observing the change in outcome variables when a group of explanatory variables are deleted provides an additional strategic technique to measure the effect of the remaining explanatory variables. It is found that: (1) For 2013–2016, the influencing factors of PM2.5 pollution days included wind speed, no precipitation day, relative humidity, population density, construction area, transportation, coal consumption and green coverage rate. In 2016, the number of cities whose PM2.5 pollution days was affected by construction was decreased by three from 2015 but increased according to transportation and energy utilization. (2) The PM2.5 pollution days in southern and central Jiangsu Province were primarily affected by the combined effect of the meteorological factors and social progress, while the northern Jiangsu Province was largely impacted by the social progress. In 2013–2016, at different risk levels, 60% inland cities were of valid stochastic efficiency, while 33% coastal cities were of valid stochastic efficiency. (3) The chance constrained stochastic DEA, which incorporates the data distribution characteristics of meteorological factors and human activities, is valuable for exploring the essential features of data in investigating the influencing factors of PM2.5.A chance constrained stochastic Data Envelopment Analysis (DEA) was developed for investigating the relations between PM Every autumn and winter, the Ministry of Ecology and Environment (MEEC) of China releases regularly information regarding heavy air pollution conditions, especially fine particulate matter  is the risk level (or significance level), that is, the risks faced in decision-making. A correct decision leads to a lower risk, and a wrong decision results in a higher risk. In our study, the higher the risk level, the higher the probability of PM2.5 pollution days after the management decision is made.According to Lan , in Equa−1 (α) is the value of the inverse distribution function of the standard normal distribution function at α; Iijσ and 0ijσ are the standard deviations of: Φjλ is the parameter of jDMU;oθ at different α levels:oθ at different α levels. The reciprocal of 0θ is the stochastic efficiency of DMU0.2.5 pollutions in these cities was relatively effective in recent years.Referring to Before the result analysis, it should be noted that the analysis process is like the evaluation results obtained from 2013 to 2016. In view of space constraint, this study took 2013 as an example to give detailed analysis process and conclusions on stochastic DEA results of 13 cities. For the assessment results from 2014 to 2016, this study only provided a comprehensive conclusion. In addition, the random efficiency values obtained from 2014 to 2016 were shown in At the 95% risk level, the efficiency values of Wuxi, Xuzhou, Changzhou, Lianyungang, Huai’an, Yancheng, Zhenjiang and Suqian were 1, and the remaining cities were ranked by efficiency values (from high to low) as follows Taizhou, Nanjing, Yangzhou, Nantong and Suzhou. But at 50% risk level or less, the efficiency values of all 13 cities were 1.2.5 pollution days in these cities were greatly affected by meteorological factors and human activities.Referring to In order to investigate the relationship between input variables and the output variable, firstly this study obtained the relevant efficiency values by deleting grouping variables. As the risk levels are between 0.05 and 0.5, after deleting the grouping variables, the efficiency values of DMUs had not changed; When the risk level is 0.8, 0.9 and 0.95, the DMUs whose stochastic efficiency values changed by deleting grouping variables are shown and analyzed in To investigate the relationship between different input variables and the output variable, this study further derives the efficiency values of DMUs by deleting single input variable. The number of cities which stochastic efficiency values changed by deleting single input variable in diffident risk levels in 2013 is shown in 2.5 pollution days in most cities were dominated by meteorological factors and social progress, and few cities were affected by transportation, energy utilization, ecological protection. The main factors affecting PM2.5 pollution days were wind speed, relative humidity, urbanization rate, population density, building construction area, number of public transportation vehicles under operation, energy consumption of per 10,000 yuan industrial cross output value, total coal consumption and green coverage rate of built-up areas.In general, as the risk level increased, cities with efficiency values of 1 decreased in 2013. At the 95% risk level, only eight cities had efficiency values of 1. The PMReferring to 2.5 pollution days were a little different compared with 2013, which also included no precipitation day and negative pressure change but did not included urbanization rate.In general, the results in 2014 were similar to in 2013. But the specific influencing factors on PMRefer to 2.5 pollution days in most cities were dominated by meteorological factors and social progress, but unlike 2013–2014, the PM2.5 pollution days in Nanjing were also affected by industrial development. The specific influencing factors in 2015 were wind speed, no precipitation day, relative humidity, population density, building construction area, number of public transportation vehicles under operation, total coal consumption and green coverage rate of built-up areas.Compared to 2013-2014, cities with efficiency values of 1 in 2015 have decreased. At the 95% risk level, only 7 cities have stochastic efficiency values of 1. The PMReferring to 2.5 pollution days were a little different compared with 2015, which also included positive temperature change, and the cities affected by various factors had also increased.In 2016, the results were similar to in 2015. But the specific influencing factors on PMAccording to the economic development, geographical location and other factors, the selected 13 cities in Jiangsu Province are divided into three regions: Southern Jiangsu Province, Central Jiangsu Province and Northern Jiangsu Province. The geographical areas can also be divided into coastal and inland area. Among them, Southern Jiangsu Province includes Nanjing, Zhenjiang, Suzhou, Wuxiand Changzhou. Central Jiangsu Province includes Yangzhou, Taizhou and Nantong. Northern Jiangsu Province includes Xuzhou, Lianyungang, Huai’an, Yancheng and Suqian. Coastal area includes Nantong, Lianyungang and Yancheng. Inland area includes ten cities, Nanjing, Xuzhou, Changzhou, Suzhou, Yangzhou, Taizhou, Wuxi, Zhenjiang, Huai’an and Suqian.2.5 pollution for different regions. In view of the space limitation, the detailed analysis process is no longer listed, but the comprehensive conclusions of regional analysis are provided.To examine the relationship between input variables and output variable among cities of different regions, the stochastic efficiency values obtained by deleting grouping variables and deleting single input variables were sorted by regions see  to furthFrom 2013 to 2016, referring to The results obtained by deleting grouping input variables and deleting single input variables are summarized as follows:2.5 pollution days in Southern Jiangsu Province were dominated by meteorological factors and social progress, less affected by industrial development, transportation, and energy utilization. Due to the high level of urbanization, dense population and advanced industrial pollution control, energy utilization and traffic management in Southern Jiangsu Province, besides meteorological factors, social progress is an important factor affecting PM2.5 pollution in recent years. The specific influencing factors were wind speed, relative humidity, population density, building construction area, total coal consumption and green coverage rate of built-up areas.From 2013 to 2016, the efficiency values of Wuxi and Zhenjiang were 1 at different risk levels, which indicates that the two cities have relatively high levels of particulate pollution control. The efficiency values of Nanjing, Changzhou and Suzhou showed a monotonous non-increasing trend as the risk level increases. The PMReferring to 2.5 pollution days in Central Jiangsu Province were affected by many factors, such as meteorological factors, social progress, transportation, energy utilization, but have little to do with the industrial development. Since Central Jiangsu Province is inferior to Southern Jiangsu Province in energy utilization and traffic management, the two factors become important factors of PM2.5 pollution in Central Jiangsu Province. The specific influencing factors were wind speed, no precipitation day, positive temperature change, negative pressure change, relative humidity, population density, civil car ownership, number of public transportation vehicles under operation, energy consumption of per 10,000 yuan industrial cross output value, total coal consumption and green coverage rate of built-up areas.From 2013 to 2016, PM2.5 pollution days in Northern Jiangsu Province were more affected by social progress, followed by meteorological factors and transportation, and the impact of industrial development, energy utilization and ecological protection was minimal.Referring to 2.5 pollution in Northern Jiangsu Province. The specific influencing factors were relative humidity, population density and civil car ownership.This indicates that Northern Jiangsu Province is in a period of rapid urbanization and population growth, and the number of motor vehicles increases sharply. Therefore, in addition to meteorological factors, social progress and motor vehicles become important factors affecting PMReferring to 2.5 pollution days in Nantong was largely affected by the meteorological factors and social progress at different risk levels, Lianyungang and Yancheng were mainly affected by social progress. The main factors affecting the PM2.5 pollution days in Nantong were wind speed, no precipitation day, positive temperature change, population density, number of public transportation vehicles under operation, and energy consumption of per 10,000 yuan industrial cross output value. The main factors affecting the PM2.5 pollution days in Lianyungang and Yancheng were population density and building construction area.From 2013 to 2016, the PMReferring to 2.5 pollution days in inland area was affected by meteorological factors and social progress at different risk levels, followed by transportation, energy utilization, and ecological protection, less affected by industrial development. The PM2.5 pollution days in inland area were related to most of input variables, in addition to the two input variables of “gross output value of industrial enterprises above designated size” and “urbanization rate”.From 2013 to 2016, the PMWe summarized the analysis results of the fourth part and drew conclusions of generality and personality, with the results shown in 2.5 is mainly produced by human activities, but its migration, as well as the formation in some cases, is largely driven by meteorological factors. This study aimed at the influencing factors of PM2.5 in different regions. We adopted the chance constrained stochastic DEA model, took meteorological factors and human activities as input variables, and PM2.5 pollution days as output variables. By deleting grouping input variables and single input variable, we study the stochastic efficiency values of 13 cities in Jiangsu Province under different risk levels. If one or a grouping input variables was deleted and the stochastic efficiency value of the DMUs changed, it is considered that the deleteing input variable or the grouping input variables had an impact on PM2.5 pollution day, and the influencing factors in different regions were sorted out from 2013 to 2016.PM2.5 pollution for the selected 13 cities in Jiangsu Province. From the perspective of time series, cities affected by NPD, PTC, GOVIE, PD and CCO variables in 13 cities in Jiangsu Province increased, while cities affected by WS, NPC and UR variables decreased from 2013 to 2016. From the perspective of the subregion, the number of PM2.5 pollution days in southern Jiangsu Province was greatly affected by meteorological factors and social progress, but less affected by industrial development, transportation and energy utilization. The number of PM2.5 pollution days in central Jiangsu Province was affected by meteorological, social progress, transportation, energy utilization, but it had little relationship with industrial development. In the northern Jiangsu Province, the number of PM2.5 pollution days was greatly affected by social progress, followed by meteorological factors and transportation, and the least affected by industrial development, energy utilization and ecological protection. In coastal area, the number of PM2.5 pollution days in Nantong city was greatly affected by meteorological factors and social progress, while Lianyungang and Yancheng were only greatly affected by social progress. In inland area, the number of PM2.5 pollution days was largely affected by meteorological factors and social progress, followed by transportation, energy utilization and ecological protection, and less affected by industrial development.It is concluded that there were generality and personality factors of PM(1)Consider the distribution characteristics of data.(2)Comprehensively investigate the meteorological factors and human activities.(3)Differentiate multiple effective decision units.In addition, the evaluation model adopted in this study has the following characteristics:The chance constrained stochastic DEA focuses on processing large sample data, especially panel data with incomplete data, incomplete information and only general distribution. This method pays attention to the input-output relationship between variables, that is, efficiency, so the stochastic DEA and other similar techniques can also be used for environmental performance, environmental efficiency, energy efficiency and other evaluation studies in terms of environmental sciences.2.5 pollution and intensify haze forecasting and early warning. At the same time, each city should comprehensively consider the diffusion or agglomeration effects of pollutants under different meteorological conditions. When formulating management policies, timely selects measures to prevent and reduce haze pollution caused by adverse meteorological conditions. Second, cities should reach consensus and strengthen regional joint defense and control. Haze pollution often has regional and compound characteristics. The neighboring cities should strengthen the regional joint prevention and control, jointly formulate and implement the joint control measures for air pollution and co-improve the regional air quality.First, the cities in Jiangsu Province should pay attention to the impacts of meteorological conditions on local PM2.5. The prevention and control of PM2.5 pollution need to maintain a continuous long-term effort. Future research can further explore the reason of invalid stochastic efficiency and investigate deeper relationship between PM2.5 pollutions, meteorological factors and human activities. For example, study the impact of cooperation and competition in different regions on PM2.5 pollution, in order for providing useful reference and support for local environmental protection measures.The limitation is that this is a qualitative study on the factors affecting PM


Intensive research on photoacoustics (PA) for imaging of the living human body, including the skin, vessels, and tumors, has recently been conducted. We propose a PA measurement system based on a capacitive micromachined ultrasonic transducer (CMUT) with waterless coupling, short measurement time (<1 s), backward light irradiation, and a low-profile ultrasonic receiver unit (<1 cm). We fabricate a 64-element CMUT ring array with 6.2 mm diameter and 10.4 MHz center frequency in air, and 100% yield and uniform element response. To validate the PA tissue characterization, we employ pencil lead and red ink as solid and liquid models, respectively, and a living body to target moles and vessels. The system implements a near-field imaging system consisting of a 6 mm polydimethylsiloxane (PDMS) matching layer between the object and CMUT, which has a 3.7 MHz center frequency in PDMS. Experiments were performed in a waterless contact on the PDMS and the laser was irradiated with a 1 cm diameter. The experimental results show the feasibility of this near-field PA imaging system for position and depth detection of skin, mole, vessel cells, etc. Therefore, a system applicable to a low-profile compact biomedical device is presented. Recently, photoacoustic imaging (PAI) in soft tissue has been studied extensively in the context of biomedical imaging. Photoacoustics (PA) combines the physics of optical and ultrasound imaging and exploits the PA effect, in which a photon incident on tissue generates scattered ultrasonic waves through optical absorption and thermal expansion. Compared with optical imaging techniques, PAI provides deeper penetration and superior spatial resolution and contrast, as determined by the ultrasonic transducer (UT) specifications such as the center frequency, cell size and pitch, element size and pitch, and receiving sensitivity ,2. Thus,Most previous PAI studies have targeted spatial resolution and contrast improvement. PA microscopy (PAM) is an excellent method for obtaining high resolution and high contrast. Optical resolution PA microscopy (ORPAM) uses the optical focus to emit ultrasonic waves at the focal point of light sources. Further, as acoustic signals are generated at a focal spot, high resolution and high contrast can be achieved ,7. Thus,In this article, motivated by ultrasound medical imaging, we demonstrate PA volumetric imaging using a capacitive micromachined UT (CMUT), which can be applied in compact devices such as smart watches and bands. A 2D transducer array is highly desirable for realization of 3D volumetric PAI. Previous studies on PA volumetric imaging have been performed, but the system application is limited by far field imaging, the immersion method, and bulkiness ,15,16. HIn the PAI method, lights are used as acoustic signal generators and UTs as receivers. The acoustic signal generation characteristics, such as the radiated light direction, intensity, and uniformity, and the acoustic signal receiving characteristics, such as the receiving sensitivity, spatial resolution, and grating lobe effects, are all important. Calibration of the light path to determine the illumination uniformity and intensity of the generated acoustic signal is the key to constructing an excellent PA system. Fabrication of transparent UTs  and the The UT used as the receiver is as important as the light irradiation. A CMUT has advantages such as wide bandwidth, ease of array fabrication, potential for integration with electronics, and high receiving sensitivity . In addi2, lower than the safe use limit of 20 mJ/cm2 (ANSI Z136.1). Thus, this measurement technique can be applied to the living human body. Note also that a 2D array-based volumetric imaging system has the advantage that no scanning was required.In this work, as shown in We conducted an experiment to verify the feasibility of a single-shot PAI method. Ultrasonic signals detected by the CMUT were measured simultaneously on several channels by an oscilloscope  with 5000 points, 500 MHz sampling frequency, and 8 averaging acquisition. The data of all elements were automatically obtained by using eight 8-channel multiplexers. The signal data were acquired in a measurement time interval of less than 1 s and processed using a digital band pass filter from 1.5 to 8 MHz, with reference to the detected main frequency of 3.7 MHz. Image reconstruction was performed using the delay and sum method based on the PA effect.First, we performed PA experiments featuring point targets of pencil lead and red ink with 0.3 and 2 mm diameter, respectively, to estimate spatial resolution and validate the imaging feasibility for both solid and liquid materials. In each case, the target contacted the top of the PDMS. As a result of the geometric nature of the ring array, the target image qualities differed on-axis and off-axis. Thus, the objects were targeted on-axis and off-axis to confirm the reliability of the obtained images . The c-mTo demonstrate the PA volumetric imaging capability, line-depth and near-field curved imaging experiments were performed. As shown in 2 was employed, being lower than the safe use limit of 20 mJ/cm2. All experiments on a living human body were conducted on the first author (W.Y.C.) with his informed consent. In To determine our system’s applicability in biomedicine and biometrics, the living human body was investigated. A 532 nm wavelength with an estimated intensity of less than 5 mJ/cmIn the photoacoustic imaging, CMUT is operated in the only reception mode. In receiving mode, CMUT is supplied in very small AC voltage. Since it makes micro current, CMUT has low electrical stress. We measured various phantoms using the system for one month with a 40 V bias voltage and a 10 Hz pulsed laser, and on average, the experiment was conducted for 15 h per week. Any sign of degrading the performance of this system has not been found.Pulsed photoacoustic signal characterization for near and far-field diffraction effects from 100 kHz to 12.5 MHz. This work presents the measurement result at 8.6 mm distance .Photoacoustic imaging system applied to various wavelengths in biological tissues from 0.5 to 3 MHz. This work presents the concept of a near-field imaging system, and an imaging result at 17.5 mm depth .We implemented the single shot photoacoustic imaging system in near field with waterless contact. The PA system is separated into a laser for the transmitting part and an acoustical transducer for the receiving part. This is suitable for near-field imaging because the transmit signal generated by the ultrasonic pulse-echo system does not cause an interference effect on the received transducer. Near field photoacoustic imaging using conventional mechanical scanning has been studied as follows.Compared to the previous work, our system is competitive with a system that is imaging at 6 mm depth using a receiver with a center frequency of 3.7 MHz.This study examined the feasibility of a PA volumetric imaging system for biomedical and biometrics applications. We implemented a single-shot PA system based on a CMUT integrated with electronics, which had wide bandwidth and high receiving sensitivity. The ring array CMUT, which has center frequency of 10.4 MHz in air and 3.7 MHz in PDMS with 6.2 mm array diameter is used. This system is constructed to a 6 mm PDMS matching layer to validate near-field imaging application with a dry contact method. The PA experiment is performed with point targets of solid (pencil lead) and liquid (red ink) phantoms, artificial line-depth vessel phantoms, an artificial curved vessel phantom, and a mole and vein in the living body. We successfully obtained volumetric image results. In addition, a depth-dependent intensity analysis for vessels in the living body was performed.The proposed system has the following advantages: waterless coupling for dry subject contact, backward-mode laser irradiation for uniform light illumination, a low-profile ultrasonic receiving unit (<1 cm) for the compact device application required for near-field imaging, and short measurement intervals (<1 s) for feasible commercial applications. We expect this technique can be applied to further applications such as vein recognition, under skin diagnosis, and skin disease detection as functional imaging using various wavelengths.


Acrylamide, a probable carcinogen to humans, forms during high temperature cooking. Dietary exposure to acrylamide among the Japanese population is unknown. We aimed to establish and validate a method to assess acrylamide exposure among the Japanese population using a food frequency questionnaire (FFQ) from the Japan Public Health Center-based prospective study.n = 215) and 1996 . The 28-day dietary records (DRs) were collected over 1 year. The FFQ was distributed before and after DR collection. Data for acrylamide exposure were based on reported measurements in Japan, and calculations considered the cooking process for specific vegetables in a home setting. Spearman’s rank correlation and weighted kappa coefficients were calculated from energy-adjusted data.Validation studies for the FFQ were conducted in 1994  µg/day and 7.25  µg/day, and corresponding levels estimated from the FFQ were 7.03  µg/day and 7.14  µg/day, respectively. Deattenuated correlation coefficients for men and women were 0.54 and 0.48 in Cohort I and 0.40 and 0.37 in Cohort II, respectively. Weighted kappa coefficients were over 0.80 in all cases. The main contributing food groups from DRs were beverages, confectioneries, vegetables, potatoes and starches, and cereals.High kappa values validate the use of FFQ in epidemiological studies. The marked contribution of cooked vegetables indicates the importance of considering household cooking methods in assessing acrylamide intake levels in the Japanese population. Since FFQs can be used to rank participants according to levels of intake, associations, such as those between food intake and risk of disease, can be examined. However, there has been no study establishing an acrylamide database and validating acrylamide intake estimates from FFQs in the Japanese population.4–6 However, in a report of acrylamide content in foods commonly consumed by Japanese people, foods associated with household cooking, such as stir-fried vegetables, also contained acrylamide.7 Therefore, the cooking method of foods prepared at home is an important consideration in the estimation of dietary acrylamide intake levels.Foods contributing to acrylamide intake are expected to differ between Asian and western populations. The main foods contributing to acrylamide intake among western people are potato-based foods, wheat-based products, and coffee.We oversee the Japan Public Health Center-based (JPHC) Study, a prospective study of a large Japanese cohort that was launched in the 1990s and continues to be followed today. The present study aimed to establish a database of acrylamide-containing foods commonly consumed by Japanese people and to validate acrylamide intake from a self-administered dietary questionnaire compared to a 28-day weighted dietary record (DR) in the JPHC Study.8–10 In brief, validation studies for FFQ were conducted among subsamples from two cohorts: Cohort I from February 1994 and Cohort II from May 1996. In the Cohort I area, 215 participants completed 28-day (14-day for participants in Okinawa) DRs and the FFQ, and 209 participants completed DRs and two FFQs. In the Cohort II area, 350 participants completed DRs and the FFQ, and 289 participants completed DRs and two FFQs. Cohort I and Cohort II were independently conducted studies that collected dietary records among different populations. This study did not undergo ethics approval, since it was conducted before the administration of ethics guidelines for epidemiological research in Japan, which mandate such approval. However, oral or written informed consent was obtained from the participants before the start of the study. Further, for statistical analysis, our study protocol was approved by the ethics committee of Osaka University (approved number 15131).Details of the study design and participants’ characteristics were described elsewhere.7,11–19 were available through July 2016, and we selected the measurements in these reports according to the following criteria: 1) sample size of measurements was two or more; 2) mean or median value of measurements was reported or able to be calculated from reported data; 3) if there were several reports for the same food, values measured in a Japanese sample and from larger sample sizes were preferentially used; 4) if both mean and median were reported, median was preferentially used; and 5) if the measurements were below quantitative or qualitative limits, values equating to half that of the quantitative or qualitative concentrations were used.A total of 10 reportsTo calculate acrylamide intake from DRs, we used the Standard Tables of Food Composition in Japan, Fifth Revised and Enlarged Edition (5th FCT), which contains 1,878 food items. Since acrylamide measurement values in existing literature are limited, we attempted to organize relevant acrylamide-containing items of the 5th FCT into more discrete categories for which acrylamide measurements are available. We categorized the 1,878 food items from the 5th FCT using the following criteria: a) same or similar species, same or similar parts, and same or similar cooking methods as an acrylamide-containing food with available data; b) when a food consisted of several foods containing acrylamide , the acrylamide values were calculated using measurements from the database for each food item; further, c) oils and fats, fresh food, non-processed food, and non-heated food were assigned a value of zero; and d) other foods that did not meet the above criteria were treated as missing values. After applying these acrylamide criteria to the 5th FCT, 282 (15%) out of 1,878 food items from the 5th FCT were designated as acrylamide-containing foods. The number of foods treated as non-acrylamide-containing foods (criteria c) was 1,276, and the number of foods treated as missing (criteria d) was 320.7 Therefore, in addition to the food items from the 5th FCT, the following foods were also used to calculate acrylamide intake from cooked foods: potatoes , onions , bean sprouts , asparagus , sweet peppers , squash , cabbage , string beans , eggplant , broccoli , podded peas , sweet potato (deep-fried), toasted bread, deep-fried batter, and stir-fried rice.7 Ultimately, we expanded the food items listed in the 5th FCT from 1,878 to 1,917, and 321 food items (17%) were designated as acrylamide-containing foods.We did not apply available acrylamide concentration data for mixed dishes, which were not listed in the 5th FCT , because cooked foods listed in the 5th FCT were limited and both DR and FFQ were conducted using food-based methods. However, acrylamide concentration varies depending on the cooking method, even for the same food.Participants recorded menus, food and beverage names, and the amount consumed  in the specific food diary. Dieticians checked the records and coded each food using the food item code of the 5th FCT. To calculate acrylamide intake from specific cooked foods not listed in the 5th FCT, dieticians used the menu to determine the cooking methods of these foods. Nine cooking methods  were coded. Dietary acrylamide was calculated by multiplying the concentration and amount consumed for each food and summing these values by day. Energy intake was also calculated using the 5th FCT.9,10 Out of 147 food items, the following 28 (19%) were designated as acrylamide-containing foods: rice, miso, beer, baked fish paste, bread, rice cake, Japanese-style confectioneries, cakes, biscuits and cookies, chocolates, peanuts, fried tofu, sencha (a type of green tea), bancha (a type of green tea), oolong tea, black tea, coffee, canned coffee, soup, potatoes, sweet potato, onions, bean sprouts, sweet peppers, squash, cabbage, snap beans, and broccoli. For rice, bread, potato, sweet potato, and vegetables , we calculated acrylamide intake by considering the cooking methods used because our original FFQ only estimated the amount of raw food intake. Weighted average values of acrylamide content for these foods were calculated using the proportion of each cooking method, obtained from the DRs of Cohort I and Cohort II separately. We inserted the proportions of cooking method calculated from Cohort I in the calculation of acrylamide intake in Cohort II and vice versa. Acrylamide intake from each food was calculated by multiplying the concentration of acrylamide for each food by the eating frequency and portion size.Our FFQ was designed to assess habitual dietary intake for the previous 1 year.We also estimated acrylamide intake from fried batter. Our original FFQ contained a question inquiring about eating frequency for fried foods with batter: “How often do you consume deep fried foods with batter?” Respondents chose their response from six frequency categories: almost never, 1–3 times/month, 1–2 times/week, 3–4 times/week, 5–6 times/week, and daily. Acrylamide intake from fried batter was estimated by multiplying the eating frequency for fried foods with batter with the amount of acrylamide intake from fried batter per day calculated from the DRs. Total daily acrylamide intake from the FFQ was calculated by summing the acrylamide intake for each food item and fried batter.λ is the ratio of intra- to inter-individual variation of DRs, and n is the number of DRs for each participant (28 days). For cross-classification analysis, energy-adjusted acrylamide intake from DRs and the FFQ were divided into five categories by quintile, and the percentage of participants among the same, adjacent, and opposite categories were calculated using both quintile numbers. Moreover, weighted kappa coefficients were calculated. Contribution of each food to total acrylamide intake were calculated as the percentage of acrylamide intake from each food to the total amount of acrylamide intake from DR data. For sensitivity analysis, we conducted the same analysis but excluded acrylamide intake from fried batter. All analyses were performed using SAS .Mean dietary acrylamide intake was calculated by sex and cohort. Percentage differences of acrylamide intake between DR and FFQ were calculated using the following formula: (acrylamide intake from FFQ − acrylamide intake from DR)/acrylamide intake from DR × 100. Spearman’s rank correlation coefficients (CC) between DRs and the FFQ were calculated for crude and energy-adjusted values. Energy adjustment was conducted using the residual method. In addition, de-attenuated correlation coefficients were also calculated because the CC between DRs and the FFQ are attenuated by individual variations in daily intake. De-attenuation was calculated using the following formula: de-attenuated CC = observed energy-adjusted CC × Mean acrylamide intake from DRs was about 7 µg/day, and the 95th percentile value was 0.23 µg/kg body weight/day Table . About 4Ranges of energy-adjusted CCs for Cohorts I and II were 0.34 to 0.48 and deattenuated CCs were 0.37 to 0.54 Table . From crr = 0.37 and r = 0.48 in men and r = 0.31 and r = 0.43 in women; Cohort II, r = 0.29 and r = 0.37 in men and r = 0.23 and r = 0.33 in women, respectively.In the sensitivity analysis, where acrylamide intake from fried batter was excluded, the Spearman’s CCs for crude and energy-adjusted intake were unchanged: Cohort I, Beverages, confectioneries, vegetables, potatoes and starches, and cereals contributed to the total acrylamide intake Table . The topWe established an acrylamide database to calculate acrylamide intake for Japanese people and found that 17% of food items in the DRs and 19% of food items in the FFQ were designated as acrylamide-containing foods in the FCT. The deattenuated CC between 28-day DRs and the FFQ for validation was 0.37 to 0.54, weighted kappa coefficients were over 0.80, and energy-adjusted CC between FFQs for reproducibility was 0.56 to 0.62 in both sexes and both cohorts. The main contributing food group based on DR data was beverages, followed by confectioneries, vegetables, potatoes and starches, and cereals.20 Our results are consistent with this previous report.Our study showed a low-to-moderate correlation between DRs and the FFQ, and kappa coefficients indicated a high degree of coincidence. Moreover, correlations between the two FFQs were moderate. Therefore, acrylamide intake estimated from the FFQ is suitable for analysis as categorical variables in epidemiological studies, particularly for the JPHC prospective cohort study. The European Prospective Investigation into Cancer and Nutrition (EPIC) study reported a crude correlation of 0.35 between DRs and their FFQ.7 The percentage difference between DRs and the FFQ in our study was about ±10%, and the distribution was also similar. Hence, we concluded that estimation of dietary acrylamide intake from DRs and the FFQ was relatively accurate. On the other hand, the estimated dietary acrylamide intake from Nagata et al’s FFQ is almost three times higher than our estimate.21 This difference may be partly due to differences in constructing the acrylamide database and calculating acrylamide intake. For example, meat products accounted for 12.4% of the total acrylamide intake in Nagata et al’s report,21 while no meat-based food items were designated as acrylamide-containing foods and the meat products group accounted for 0% of acrylamide intake in our study. In another example, in contrast to WHO’s report of a measured value for crumbed or battered poultry,19 we preferentially used a value for fried batter measured in Japan7 and did not use the value for crumbed or battered poultry to avoid double assessment of acrylamide intake from fried batter. Moreover, we categorized acrylamide intake from fried batter as cereals. These methodological differences may account for the differences in the contribution of the meat group and cereal food group to acrylamide intake. Therefore, differences in mean acrylamide intake levels among studies may be attributed to differences in study criteria and calculation methods used.The mean intake and distribution in our study were similar to the results from the previous Monte Carlo simulation in Japan .22 Moreover, the acrylamide intake of Japanese people is about a quarter of that of the Dutch population (0.45 µg/kg body weight/day),5 which corresponds to the amount of acrylamide derived only from potato crisps among the Dutch. These results suggest that acrylamide intake by Japanese people is much lower than that of western populations. However, a previous study reported that the margin of exposure is less than 10,000 when the benchmark dose lower confidence limit is 0.31 mg/kg body weight/day for mammary tumors in rats.23 Therefore, the Food Safety Commission of Japan calls for vigilance regarding the possibility of a carcinogenic effect of dietary acrylamide.23 Further, the results of epidemiological studies from western countries are inconsistent.24 Hence, epidemiological studies should be conducted to clarify the risk of dietary acrylamide intake on carcinogenicity in the Japanese population.Our results indicate that acrylamide intake by Japanese people is about one tenth of that estimated by JECFA .7,21 Contributing foods in western countries are primarily potato-based foods , wheat-based products , and coffee, but not vegetables.25–27 Therefore, cooked vegetables are an important source of acrylamide intake, at least in the Japanese population.We found that acrylamide intake from vegetables accounted for a proportion of the total acrylamide intake, a finding which is consistent with previous reports in Japan.28–33 Hence, an association analysis between acrylamide intake and cancers in the Japanese population is warranted and is likely to differ from the results from western countries.In our study, coffee, green tea, and vegetables accounted for about half of the total acrylamide intake. These foods are often reported to have preventive effects on cancers.The main strength of this study was the use of 28-day weighted DRs to calculate acrylamide intake. The use of 28-day records allowed us to better estimate the association between acrylamide intake and contributing foods from DRs than previous studies.3 we used median or mean values of measurements to reduce the effect of such variations. Therefore, a validation study using duplicate methods is needed to clarify the accuracy of calculating acrylamide intake from DRs. Third, we could not compare dietary acrylamide intake and biomarkers, such as hemoglobin adduct concentrations of acrylamide and glycidamide. The Nurses’ Health Study II reported a moderate correlation between dietary acrylamide intake and hemoglobin adduct concentrations.34 However, dietary acrylamide levels are affected by individuals’ metabolism35 and lifestyle,36 causing sensitivity to dietary acrylamide to vary. This was reflected in the EPIC study, where, although acrylamide intake from the FFQ was correlated with that from DRs, the correlation between acrylamide intake from the FFQ and hemoglobin adduct concentrations was low.20 Therefore, analysis using biomarkers is needed to understand dietary acrylamide exposure in Japanese people.There are several limitations of this study. First, acrylamide intake from DRs and the FFQ may have been underestimated because the list of acrylamide-containing foods covered only about 17–19% of food items from each FCT. However, we considered home cooking methods for specific foods (mostly vegetables) and found that the contribution of vegetables was relatively high. This result indicates that cooking methods are important for estimating acrylamide intake. Second, we could not validate the calculated acrylamide intake from DRs. As acrylamide concentration varies even within the same food product depending on the cooking temperature and time,The validity of estimating acrylamide intake using a FFQ was low to moderate in this study. However, the high validity of categorization suggests that the FFQ is suitable for use in epidemiological studies, particularly for the JPHC prospective cohort study. Further, the contributing foods to total acrylamide intake in the Japanese population were different from those in western countries, possibly due to differences in home food preparation methods. Therefore, considering household cooking methods, especially for vegetables, is important for assessing the acrylamide intake level in Japanese people.


We aimed to find out the prevalence and associated risk factors of GDM among females who attended antenatal clinic at King Abdulaziz University Hospital (KAUH), Jeddah, Saudi Arabia (SA).th September 2016 till 20th December 2016, at the Endocrine Clinic, Faculty of Medicine, KAUH, Jeddah, SA. A total of 5000 women attended antenatal clinic and 637 women were referred to the endocrine clinic for GDM. The data of only 103 GDM was included in the study because only these patients’ complete data was available. The electronic record of 93 pregnant age and BMI matched females, not having GDM were selected as a control group.This retrospective study was carried out from 252=16.82, P=.001) and GDM was significantly higher in multigravida while no association of GDM was found with working status, place of living, hypertension, family history of DM and BMI. Logistic regression analysis revealed that grand multigravida female had the lower risk of GDM as compared to multi, primi and nulligravida while age, working status, place of living, hypertension, family history of DM and BMI were not found significant risk factors for GDM. In GDM group, according to nationality, 68(66%) women were Saudi while 35(34%) were expatriates’ .The prevalence of GDM was 12.75% (637/5000). Parity was associated with GDM (XThe prevalence of GDM was 12.75% and it was not associated with working status, place of living, hypertension, family history of diabetes and BMI. Gestational Diabetes Mellitus (GDM) is defined as glucose intolerance that can be identified during pregnancy, usually, happening following the 24th week of gestation.It is important to recognize and treat the problem in early stages because GDM associated complications in mother and fetus are mostly preventable.4The pattern of prevalence of GDM has notable regional and ethnic differences. Asian inhabitants are considered at higher risk than white populations.7The prevalence of GDM ranges from 8 to 19% in Saudi Arabia.th September 2016 till 20th December 2016, at the Endocrine Clinic, Faculty of Medicine, KAUH, Jeddah, SA. One-year data of the GDM subjects were collected from their electronic records from 1st January 2015 to 31st December 2015. A total of 5000 women attended the antenatal clinic, and 637 women were referred to the Endocrine Clinic because of GDM. So the prevalence of GDM was 12.75% (637/5000). We collected GDM subjects’ data from the electronic record; however, the data of only 103 GDM was taken because these patients’ complete data according to our questionnaire was available. The electronic record of 93 pregnant age and BMI matched females, not having GDM were selected as a control group.This retrospective study was carried out from 25With their record, a questionnaire was filled regarding their age, BMI, blood pressure, living place, working status, family history of diabetes, and parity. We included only those subjects who were diagnosed with GDM and did not have DM simultaneously.The American Diabetes Association (ADA) cut-off values using the one-step approach were used to classify the study subjects as GDMFasting plasma glucose> or equal 5.1 mmol (92mg/ dl).1-Hour plasma glucose ≥ 10.0 mmol/l (180mg/ dl) following a 75 g oral glucose load.2-Hour plasma glucose 8.5 mmol/l (153 mg/dl) following a 75 g oral glucose load.”2, prior history of gestational diabetes, earlier large baby weighing 4.5 Kg or more, and family history of diabetes. The present study was approved by the ethical committee of the KAUH, Jeddah, SA. We kept the confidentiality of the subject and did not disclose any data relating to their identification.Two or more criteria must be met or exceeded for a positive diagnosis. We excluded the diagnosed DM patients and having any other endocrinology problems such as thyroid disorders, Polycystic ovary syndrome, Cushing syndrome that affect blood sugar level. All the participants were screened during 24–28 weeks of gestation or before if they were at high risk for developing GDM. The patients were considered high risk if they had BMI greater than 30 Kg/mThe data were analyzed by SPSS  version 23. Mean ± SD was given for quantitative variables. Frequency and percentages were given for qualitative variables. Student t-test was used to compare the mean of age, HBA1C and fasting blood sugar between cases and controls. Pearson Chi-square test was used to determine the relationship of age, parity, working status, place of living, history of hypertension and BMI. Logistic regression was employed to compute the unadjusted and adjusted odds ratio by using age, parity, working status, place of living, history of hypertension, diabetes, and BMI as independent variables. The p-value < 0.05 was taken as significant.In our study, the prevalence of GDM was 12.75% (637/5000). We compared the data of 103 GDM, and 93 age and BMI matched control subjects. Our results show that HBA1C and fasting plasma glucose (FPG) were significantly higher in cases as compared to control (P<0.001) while there was no significant difference of age (P=0.14) and BMI (P=0.13) between both groups. In GDM group, 60 (58.3%) women were obese (BMI>30) while in the control group there were 45 (48.4%) had BMI greater than 30 . The nat2=16.82, P=0.001) and GDM was significantly higher in multigravida while no association of GDM was found with working status, place of living, hypertension, family history of DM and BMI ] while increased age (≥ 30 years), working status, place of living, hypertension, family history of DM and BMI were not found significant risk factors for GDM .The prevalence of GDM in our study is 12.75%, in comparison to several other studies published from SA, our results are similar to Serehi AA et al., (2015)Logistic regression analysis revealed that age ≥ 30 years increases the risk of GDM, but it was not significant. Our results are similar to a study.The increased age (≥30 years), working status, place of living, hypertension, family history of DM and BMI were not found significant risk factors for GDM. Similar to our results a study reported no significant association between GDM and working status.We found that grand multigravida have a lower risk for GDM after adjusting age, working status, place of living, hypertension, family history of DM and BMI. This observation is also in accordance with our result that increasing age is not a risk factor for GDM. Recently, Nhidza G et al., (2018) demonstrated that gravida is not a predictor of the GDM.A recent study reported that the women suffering from GDM had increased chances of having a macrosomic baby without having the increased risk for other maternal or neonatal complications.24In GDM group, according to nationality, 68(66%) women were Saudi while 35(34%) were expatriates’ . The Jeddah city is the second largest and thickly populated cosmopolitan city, and thousands of expatriates’ families reside in the city. Therefore, our data comprised of women of several nationalities, so our results do not reflect the frequency of GDM among Saudi women.It is suggested that changes in dietary pattern and sedentary lifestyle causing obesity and the more prolonged period of education and better access to birth control techniques causing increasing age at first pregnancy.There is a need to educate the women regarding the drastic effects of the GDM for them, and for their fetus. Such education should be started at school and college level. Research indicates that prevalence of GDM is directly related to the prevalence of T2DM in the society28First, it has been conducted only in one hospital; consequently the sample size was small. Secondly, the data was collected from the electronic record of the patients, so, little important information could not be collected such as the history of their dietary, physical activities, socioeconomic status, educational level and others.Our results indicate that the prevalence of GDM was 12.75% in our setup and no association was found with working status, place of living, hypertension, family history of diabetes and BMI.SA & HM: Contributed to conception and design of the work, acquisition of data and final approval of the version to be published.MB: Contributed to interpretation of data, manuscript writing and revised the manuscript for intellectual content.AMN & FMA: Contributed to data collection, analysis and write up.