Association between maternal multimorbidity and neurodevelopment of offspring: a prospective birth cohort study from the Japan Environment and Children's Study.

OBJECTIVES: To investigate the association between multimorbidity during pregnancy and neurodevelopmental delay in offspring using data from a Japanese nationwide birth cohort study. DESIGN: This study was a prospective birth cohort study. SETTING: This study population included 104 059 fetal records who participated in The Japan Environment and Children's Study from 2011 to 2014. PARTICIPANTS: Pregnant women whose children had undergone developmental testing were included in this analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: Neurodevelopment of offspring was assessed using the Japanese version of the Ages and Stages Questionnaire, third edition, comprising five developmental domains. The number of comorbidities among the pregnant women was categorised as zero, single disease or multimorbidity (two or more diseases). Maternal chronic conditions included in multimorbidity were defined as conditions with high prevalence among women of reproductive age. A multivariate logistic regression analysis was conducted to examine the association between multimorbidity in pregnant women and offspring development. RESULTS: Pregnant women with multimorbidity, single disease and no disease accounted for 3.6%, 30.6% and 65.8%, respectively. The ORs for neurodevelopmental impairment during the follow-up period were similar for infants of mothers with no disease comorbidity and those with a single disease comorbidity. However, the ORs for neurodevelopmental impairment were significantly higher for children born to mothers with multimorbidity compared with those born to healthy mothers. CONCLUSION: An association was observed between the number of comorbidities in pregnant women and developmental delay in offspring. Multimorbidity in pregnant women may be associated with neurodevelopmental delay in their offspring. Further research is required in this regard in many other regions of the world.

Heavy metals and trace elements in maternal blood and prevalence of congenital limb abnormalities among newborns: the Japan Environment and Children's Study.

BACKGROUND: Heavy metals such as lead (Pb) and cadmium (Cd) have been associated with adverse pregnancy and developmental outcomes, including congenital abnormalities. This study investigated the association between exposure to heavy metals and trace elements during fetal life and congenital limb abnormalities in infants. METHODS: This study is based on a prospective ongoing nationwide birth cohort from the Japan Environment and Children's Study (JECS). The concentrations of Cd, Pb, mercury (Hg), selenium (Se), and manganese (Mn) were measured in maternal blood collected during the mid-late trimesters. Inclusion criteria were available from questionnaires filled in during pregnancy, including information about congenital limb abnormalities at birth or at one month. To examine the associations with limb anomalies and individual chemicals, logistic regression models were applied following log-transformation or division into quartiles of Cd, Pb, Hg, Se, and Mn concentrations. To assess the associations with the heavy metals and trace elements mixture, quantile g-computation was employed. All models were adjusted for age, maternal smoking history, maternal alcohol intake, history of smoking, and infant sex. RESULTS: Data from 90,163 participants were included in the analysis, of whom 369 had congenital limb abnormalities in any of the collected information, and 89,794 had none. Among the 369 cases of congenital limb abnormalities, there were 185 and 142 cases of polydactyly and syndactyly, respectively. The median concentrations of Pb, Cd, Hg, Se, and Mn were 5.85, 0.66, 3.64, 168, and 15.3 ng/g, respectively. There were no associations between maternal blood concentrations of Pb [adjusted odd ratio = 0.83; 95% confidence interval = 0.61, 1.11], Cd [0.87; 0.68, 1.10], Hg [0.88; 0.73, 1.07], Se [1.07; 0.44, 2.59], and Mn [0.91; 0.64, 1.30] with congenital limb abnormalities. No significant association was observed between the mixture of heavy metals and trace elements [0.85; 0.72, 1.02] and any congenital limb abnormalities. Moreover, there was no association with all polydactylies and all syndactylies, or any type of abnormality as a subdivision. CONCLUSION: At the maternal exposure levels of Cd, Pb, Hg, Se, and Mn assessed in the present study, no association was identified with the risk of developing congenital limb abnormalities in children.

Adipsic hypernatremia with marked hyperprolactinemia and GH deficiency in a 9-year-old boy.

Adipsic hypernatremia is typically caused by congenital dysplasia of the hypothalamus and pituitary or brain tumors. However, cases of adipsic hypernatremia without underlying organic abnormalities are rare, and some cases have been reported to be complicated by hypothalamic-pituitary dysfunction. The patient in this case was a 9-yr-old boy who was referred to our hospital because of hypernatremia. His growth chart revealed that he had rapidly become obese since infancy, with growth retardation since the age of seven. His hands and feet were very cold, and he had erythema on his abdomen, indicating possible autonomic dysregulation due to hypothalamic dysfunction. Several hormone load tests showed severe GH deficiency (GHD) and marked hyperprolactinemia (peak: 302.8 ng/mL). Magnetic resonance imaging revealed no organic abnormalities in the hypothalamus and pituitary gland. GH replacement therapy was initiated. Although his growth rate improved, obesity persisted. To the best of our knowledge, this is the first report of adipsic hypernatremia without organic intracranial abnormalities that was treated with GH. Moreover, the patient's prolactin levels were higher than those reported in previous studies. In conclusion, adipsic hypernatremia requires the evaluation of pituitary function and appropriate therapeutic interventions.

The association between flooring materials and childhood asthma: A prospective birth cohort in the Japan Environment and Children's Study.

BACKGROUND: Childhood asthma is known to be affected by a range of factors, including conditions in the indoor environment. While flooring material influences indoor air conditions, the potential association between flooring materials and childhood asthma remains poorly understood in Japan. OBJECTIVE: The present study aims to assess the association between childhood asthma incidence and the primary flooring material with the ongoing prospective nationwide birth cohort data of the Japan Environment and Children's Study (JECS). METHODS: The JECS gathered data on mothers and children through 15 Regional Centres across Japan. The present study assessed flooring materials used in the home and asthma incidence at age four among children born between 2011 and 2014. We implemented logistic regressions, setting asthma incidence among the children as the outcome and home floor type as the exposure. Additional analyses were conducted, stratifying the home's age as a proxy for tatami age, to assess whether the potential effect of tatami flooring on asthma risk is influenced by its age. RESULTS: The present study included total of 75,629 infants. For tatami flooring, the main multivariable regression and additional sub-group regression for homes over ten years old produced odds ratios of 1.09; 95% Confidence Interval (CI) [1.01-1.17] and 1.10; 95% CI [1.00-1.21] compared with flooring, respectively. CONCLUSION: These results imply that exposure to tatami flooring, particularly in older homes, may be associated with childhood asthma incidence. Moreover, our study highlights the importance of evaluating the relationship between regional and cultural differences between asthma and flooring materials.

Maternal pre-pregnancy body mass index and related factors: A cross-sectional analysis from the Japan Environment and Children's Study.

Socioeconomic status and smoking are reportedly associated with underweight and obesity; however, their associations among pregnant women are unknown. This study aimed to investigate whether socioeconomic factors, namely educational attainment, household income, marital status, and employment status, were associated with pre-pregnancy body mass index (BMI) categories, including severe-moderate underweight (BMI ≤ 16.9 kg/m2), mild underweight (BMI, 17.0-18.4 kg/m2), overweight (BMI, 25.0-29.9 kg/m2), and obese (BMI ≥ 30.0 kg/m2) among Japanese pregnant women using data from the Japan Environment and Children's Study (JECS). In total, pregnant women were included 96,751. Age- and parity-adjusted multivariable multinomial logistic regression analyses assessed socioeconomic factors and smoking associations with falling within abnormal BMI categories (normal BMI as the reference group). Lower education and lower household were associated with overweight and obesity, and, especially, lowest education and household income had relatively higher point estimate relative ratios (RRs) of 3.97 and 2.84, respectively. Regarding the risks for underweight, however, only junior high school education had a significantly higher RR for severely to moderately underweight. Regarding occupational status, homemakers or the unemployed had a higher RR for severe-moderate underweight, overweight, and obesity. Unmarried, divorced, or bereaved women had significantly higher RRs for mildly underweight status. Quitting smoking early in pregnancy/still smoking had higher RRs for all four not having normal BMI outcomes; however, quitting smoking before pregnancy had a higher RR only for obese individuals. Lower educational attainment and smoking are essential intervention targets for obesity and severe-moderate underweight prevention in younger women. Lower household income is also a necessary target for obesity.

Bridging the Diagnostic Gap between Histopathologic and Hysteroscopic Chronic Endometritis with Deep Learning Models.

Chronic endometritis (CE) is an inflammatory pathologic condition of the uterine mucosa characterized by unusual infiltration of CD138(+) endometrial stromal plasmacytes (ESPCs). CE is often identified in infertile women with unexplained etiology, tubal factors, endometriosis, repeated implantation failure, and recurrent pregnancy loss. Diagnosis of CE has traditionally relied on endometrial biopsy and histopathologic/immunohistochemistrical detection of ESPCs. Endometrial biopsy, however, is a somewhat painful procedure for the subjects and does not allow us to grasp the whole picture of this mucosal tissue. Meanwhile, fluid hysteroscopy has been recently adopted as a less-invasive diagnostic modality for CE. We launched the ARCHIPELAGO (ARChival Hysteroscopic Image-based Prediction for histopathologic chronic Endometritis in infertile women using deep LeArninG mOdel) study to construct the hysteroscopic CE finding-based prediction tools for histopathologic CE. The development of these deep learning-based novel models and computer-aided detection/diagnosis systems potentially benefits infertile women suffering from this elusive disease.

Visualization of strain distribution in rubber bulk during friction.

This study employed a digital image correlation method (DICM) to experimentally quantify horizontal strain distribution in silicone rubber bulk during horizontal displacement against a stainless-steel sphere with/without glycerol. The strain distribution at different depth levels was measured by capturing the position of white powders in transparent rubber bulk. The experimental results indicated that each point in the rubber bulk moved while describing a horizontal loop during horizontal displacement depending on the position and lubrication conditions. This caused changes in the horizontal strain during horizontal displacement. These results suggest that the hysteresis term could be caused by changes in the vertical and horizontal strains.

Construction of deep learning-based convolutional neural network model for automatic detection of fluid hysteroscopic endometrial micropolyps in infertile women with chronic endometritis.

OBJECTIVE(S): Chronic endometritis (CE) is a localized mucosal inflammatory disorder associated with female infertility of unknown etiology, endometriosis, tubal factors, repeated implantation failure, and recurrent pregnancy loss, along with atypical uterine bleeding and iron deficiency anemia. Diagnosis of CE has traditionally relied on endometrial biopsy and detection of CD138(+) endometrial stromal plasmacytes. To develop a less invasive diagnostic system for CE, we aimed to construct a deep learning-based convolutional neural network (CNN) model for the automatic detection of endometrial micropolyps (EMiP), a fluid hysteroscopy (F-HSC) finding recognized as tiny protrusive lesions that are closely related to this disease. STUDY DESIGN: This is an in silico study using archival images of F-HSC performed at an infertility center in a private clinic. A total of 244 infertile women undergoing F-HSC on the days 6-12 of the menstrual cycle between April 2019 and December 2021 with histopathologically-confirmed CE with the aid of immunohistochemistry for CD138 were utilized. RESULTS: The archival F-HSC images of 208 women (78 with EMiP and 130 without EMiP) who met the inclusion criteria were finally subjected to analysis. Following preprocessing of the images, half a set was input into a CNN architecture for training, whereas the remaining images were utilized as the test set to evaluate the performance of the model, which was compared with that of the experienced gynecologists. The sensitivity, specificity, accuracy, precision, and F1-score of the CNN model-aided diagnosis were 93.6 %, 92.3 %, 92.8 %, 88.0 %, and 0.907, respectively. The area under the receiver operating characteristic curves of the CNN model-aided diagnosis (0.930) was at a similar level (p > .05) to the value of conventional diagnosis by three experienced gynecologists (0.927, 0.948, and 0.906). CONCLUSION: These findings indicate that our deep learning-based CNN is capable of recognizing EMiP in F-HSC images and holds promise for further development of the computer-aided diagnostic system for CE.

Detection of biomagnetic signals from induced pluripotent stem cell-derived cardiomyocytes using deep learning with simulation data.

The detection of spontaneous magnetic signals can be used for the non-invasive electrophysiological evaluation of induced pluripotent stem cell-derived cardiomyocytes (iPS-CMs). We report that deep learning with a dataset that combines magnetic signals estimated using numerical simulation and actual noise data is effective in the detection of weak biomagnetic signals. To verify the feasibility of this method, we measured artificially generated magnetic signals that mimic cellular magnetic fields using a superconducting quantum interference device and attempted peak detection using a long short-term memory network. We correctly detected 80.0% of the peaks and the method achieved superior detection performance compared with conventional methods. Next, we attempted peak detection for magnetic signals measured from mouse iPS-CMs. The number of detected peaks was consistent with the spontaneous beats counted using microscopic observation and the average peak waveform achieved good similarity with the prediction. We also observed the synchronization of peak positions between simultaneously measured field potentials and magnetic signals. Furthermore, the magnetic measurements of cell samples treated with isoproterenol showed potential for the detection of chronotropic effects. These results suggest that the proposed method is effective and has potential application in the safety assessment of regenerative medicine and drug screening.

Parenchymal renal rupture due to an obstructive ureteric calculus in an incompletely duplicated renal pelvis and ureter.

Introduction: Parenchymal renal rupture due to a ureteric calculus is extremely rare and an emergency. Case presentation: A 54-year-old man was brought to the emergency room with left back pain without trauma. Computed tomography showed left parenchymal renal rupture with an incompletely duplicated renal pelvis, ureter, and an 11-mm ureteric calculus in the ureterovesical junction. A ureteral stent was placed, and the patient was treated conservatively as his vital signs were stable. We performed transurethral lithotripsy after resolution of the perirenal hematoma. Conclusion: To best of our knowledge, this report is the first to present a case of parenchymal renal rupture due to a ureteric calculus in an incompletely duplicated renal pelvis and ureter. Ureteric calculus within an incompletely duplicated renal pelvis and ureter is at risk of parenchymal renal rupture. Therefore, the aggressive treatment of ureteric calculus could be important.

In Vivo Intelligent Fluorescence Endo-Microscopy by Varifocal Meta-Device and Deep Learning.

Endo-microscopy is crucial for real-time 3D visualization of internal tissues and subcellular structures. Conventional methods rely on axial movement of optical components for precise focus adjustment, limiting miniaturization and complicating procedures. Meta-device, composed of artificial nanostructures, is an emerging optical flat device that can freely manipulate the phase and amplitude of light. Here, an intelligent fluorescence endo-microscope is developed based on varifocal meta-lens and deep learning (DL). The breakthrough enables in vivo 3D imaging of mouse brains, where varifocal meta-lens focal length adjusts through relative rotation angle. The system offers key advantages such as invariant magnification, a large field-of-view, and optical sectioning at a maximum focal length tuning range of ≈2 mm with 3 µm lateral resolution. Using a DL network, image acquisition time and system complexity are significantly reduced, and in vivo high-resolution brain images of detailed vessels and surrounding perivascular space are clearly observed within 0.1 s (≈50 times faster). The approach will benefit various surgical procedures, such as gastrointestinal biopsies, neural imaging, brain surgery, etc.

Machine learning prediction of footwear slip resistance on glycerol-contaminated surfaces: A pilot study.

Slippery surfaces due to oil spills pose a significant risk in various environments, including industrial workplaces, kitchens, garages, and outdoor areas. These situations can lead to accidents and falls, resulting in injuries that range from minor bruises to severe fractures or head trauma. To mitigate such risks, the use of slip resistant footwear plays a crucial role. In this study, we aimed to develop an Artificial Intelligence model capable of classifying footwear as having either high or low slip resistance based on the geometric characteristics and material parameters of their outsoles. Our model was trained on a unique dataset comprising images of 37 indoor work footwear outsoles made of rubber. To evaluate the slip resistant property of the footwear, all samples were tested using a cart-type friction measurement device, and the static and dynamic Coefficient of Frictions (COFs) of each outsole was determined on a glycerol-contaminated surface. Machine learning techniques were implemented, and a classification model was developed to determine high and low slip resistant footwear. Among the various models evaluated, the Support Vector Classifier (SVC) obtained the best results. This model achieved an accuracy of 0.68 ± 0.15 and an F1-score of 0.68 ± 0.20. Our results indicate that the proposed model effectively yet modestly identified outsoles with high and low slip resistance. This model is the first step in developing a model that footwear manufacturers can utilize to enhance product quality and reduce slip and fall incidents.

Comparison of sensitivity among dynamic balance measures during walking with different tasks.

Although various measures have been proposed to evaluate dynamic balance during walking, it is currently unclear which measures are most sensitive to dynamic balance. We aimed to investigate which dynamic balance measure is most sensitive to detecting differences in dynamic balance during walking across various gait parameters, including short- and long-term Lyapunov exponents (λs and λl), margin of stability (MOS), distance between the desired and measured centre of pressure (dCOP-mCOP) and whole-body angular momentum (WBAM). A total of 10 healthy young adults were asked to walk on a treadmill under three different conditions (normal walking, dual-task walking with a Stroop task as an unstable walking condition, and arm-restricted walking with arms restricted in front of the chest as another unstable walking condition) that were expected to have different dynamic balance properties. Overall, we found that λs of the centre of mass velocity, λs of the trunk velocity, λs of the hip joint angle, and the magnitude of the mediolateral dCOP-mCOP at heel contact can identify differences between tasks with a high sensitivity. Our findings provide new insights into the selection of sensitive dynamic balance measures during human walking.

Hokkaido birth cohort study in Japan on the growth trajectory of children born with low birth weight until 7 years of age.

BACKGROUND: Low birth weight (LBW) is a significant global health concern with potential health risks and developmental implications for infants. Catch-up growth, an accelerated growth following an inhibition period, may partially compensate for growth deficits in LBW children. AIMS: This study investigated the prevalence of LBW and catch-up growth in height, weight, and body mass index (BMI) among LBW children in Japan, identified factors associated with LBW, and explored the potential for catch-up growth at different ages up to seven years. STUDY DESIGN AND SUBJECTS: The Hokkaido birth cohort study included 20,926 pregnant Japanese women recruited during their first trimester from 37 hospitals and clinics. Follow-up assessments were conducted in children up to seven years of age, tracking LBW children's growth and development using the Maternal and Child Health Handbook, and providing valuable insights into catch-up growth patterns. OUTCOME MEASURES: LBW was defined as a neonatal birth weight of <2500 g. The primary outcomes were catch-up growth in height, weight, and BMI at different ages. Z-scores were calculated to assess growth parameters with catch-up growth, defined as a change in z-score (> 0.67) between two time points. RESULTS AND CONCLUSIONS: A LBW was prevalent in 7.6 % of the cohort, which was lower than that reported in other Japanese studies. Among LBW children, 19.3 % achieved catch-up growth in height by age seven, and 10.6 % in weight. Catch-up growth in LBW children could partially offset these deficits. Further research will help understand the long-term outcomes and inform interventions for healthy development.