The chemical composition of human milk has long-lasting effects on brain development. We examined the prognostic value of the human milk metabolome and exposome in children with the risk of neurodevelopmental delay (NDD).This retrospective cohort study included 82 mother–infant pairs . A total of 59 milk samples were from mothers with typically developing children and 23 samples were from mothers of children at risk. Milk samples were collected before 9 months of age . Neurocognitive development was assessed by maternal report at 14.2 ± 3.1 months using the Ages and Stages Questionnaires-2.Metabolome and exposome profiling identified 453 metabolites and 61 environmental chemicals in milk. Machine learning tools identified changes in deoxysphingolipids, phospholipids, glycosphingolipids, plasmalogens, and acylcarnitines in the milk of mothers with children at risk for future delay. A predictive classifier had a diagnostic accuracy of 0.81 (95% CI: 0.66–0.96) for females and 0.79 (95% CI: 0.62–0.94) for males.Once validated in larger studies, the chemical analysis of human milk might be added as an option in well-baby checks to help identify children at risk of NDD before the first symptoms appear.Maternal milk for infants sampled before 9 months of age contained sex-specific differences in deoxysphingolipids, sphingomyelins, plasmalogens, phospholipids, and acylcarnitines that predicted the risk of neurodevelopmental delay at 14.2 months of age.Once validated, this early biosignature in human milk might be incorporated into well-baby checks and help to identify infants at risk so early interventions might be instituted before the first symptoms appear. Some symptoms, once developed, may continue throughout life, causing enormous social and economic burdens. Children diagnosed with neurodevelopmental disorders have better treatment outcomes if detected and treated at younger ages.3 Therefore, new methods for early prediction of an infant’s later neurodevelopmental risk are urgently needed.Neurodevelopmental delay (NDD) refers to the delays in skill development of infants and young children, including physical, linguistic, behavior, or learning areas.6 However, limited information is available about the impact of milk composition on breastfed infants’ neurocognitive outcomes.Early infancy is a critical period for neurodevelopment. As the ideal nutrition recognized by the World Health Organization for infants, human milk (HM) can have lasting effects on the structure and function of the developing brain. Compelling evidence supports the overall beneficial effects of HM intake on neurodevelopment.8 A recent study reported that protein content in HM is not correlated with preterm infant neurodevelopment at 2 years of age.9 Metabolites in HM are the nutritional building blocks that support physical growth, immune system development, and brain maturation for infants.11 The associations between metabolites in HM and the neurodevelopment and cognitive function in young offspring have only been sparsely examined and primarily focused on the total fat content and long-chain polyunsaturated fatty acid in HM.13 For example, a recent systematic review has shown the association between fat content in HM for premature infants and neurodevelopmental outcomes.14 In addition, higher linolenic acid and docosahexaenoic acid was found in milk collected at the third month of life were reported to be correlated with better psychomotor development of exclusively breastfed infants at the sixth month of life.15 However, total fat concentration does not reflect different lipid classes and other endogenous metabolites in HM, which also appears to be important for infant growth.8 Milk composition varies somewhat over the course of a day.16 For example, the fat content varies from about 4.1–5.2% over a day,17 and the time since the last feeding can influence the composition of milk available in the next feeding.18 No studies have yet been published on the comprehensive analysis of the metabolomic differences in human milk between typically developing (TD) children and children with NDD.Compositionally, human milk contains various amounts of protein, fat, carbohydrates, amino acids, and other metabolites, prebiotic factors like HM oligosaccharides, hormones, peptide growth factors, cytokines, microRNAs, minerals, and microbiota.19 Infant early life exposure to environmental chemicals through milk is uniquely influential to their long-term outcomes due to heightened neuroplasticity during early childhood sensitive periods.20 Experimentally, several common classes of environmental chemicals in HM have been assessed for their associations with neurodevelopmental outcomes of the offspring.21 For example, in a mother–infant cohort from Vietnam, the neurodevelopmental scores for children at 4-month-old and 2-year-old were decreased with the increase in dioxin  in HM collected at 1 month.23 The level of diethylhexyl phthalate in HM collected at 30 days after delivery was associated with impaired mental development in children aged 1–2 years.24 Scores for cognitive function were also lower in children who were exposed to higher amounts of the flame retardant brominated diphenyl ether-209 (BDE209) through HM.25 However, most of the existing epidemiological studies for neurodevelopmental effects of HM have only focused on a small set of environmental chemicals and hence may exclude important chemicals from consideration. Since the general population can be simultaneously exposed to a wide range of environmental chemicals, a comprehensive characterization of the human milk exposome will allow an in-depth evaluation of the potential long-term neurodevelopmental effects of the HM exposome.A mother is exposed to various environmental chemicals through food, personal care products, household products, medical and recreational drugs, pollutants, or through her occupational environment. Some of this maternal body burden can be transferred to HM during lactation.In this study, we conducted integrated metabolomic and exposomic profiling of HM. This broad-based approach combined with multiple machine learning (ML) algorithms aimed to (1) determine the potential metabolomic and exposomic differences in HM for infants with and without NDD risk, and (2) develop and evaluate multi-analyte models using HM metabolome and exposome data to predict infants with the future risk of NDD.26 Inclusion criteria for this study were milk samples collected from mothers of infants less than 9 months of age and the valid completion of an Ages and Stages Questionnaire version 2 (ASQ2) during follow-up of the infant between 12 and 25 months of age. Exclusion criteria included two or more conflicting ASQ2 communication scores above and below the age-specific developmental threshold for a given child. HM samples were either collected at the participant’s home and shipped on ice within 24 h of sample collection or collected on-site at UC San Diego and processed in the laboratory within 3 h of sample expression. Selected HM samples were aliquoted and stored at −80 °C before transfer to the Naviaux lab for analysis. All the study protocols were approved by the Institutional Review Board (IRB) at UC San Diego, and written consent was obtained upon the collection of the HM samples under Dr Chamber’s UC San Diego IRB-approved protocol #130658. Metabolomics and exposomics were conducted on deidentified samples under Dr Naviaux’s UC San Diego IRB-approved protocol #140072. Metabolomic and exposomic analysis of the samples was conducted from July 21, 2020, to September 22, 2020. Detailed participant and sample characteristics are summarized in Supplementary Table The study population is based on the subjects from the University of California, San Diego (UC San Diego) Human Milk Biorepository, which contains over 5900 HM samples from over 2200 women who reside in the U.S. and Canada along with subsequent neurodevelopmental screening assessments for their infants/children. We first excluded maternal participants who reported diabetes, depression, other medical conditions, or pregnancy complications. HM samples were collected using a standardized protocol previously described.28 The overall sensitivity of the screening instrument for correctly identifying children with delays and the specificity of the instrument for identifying TD children are 72 and 88%, respectively.27 The questionnaire encompasses the domains of communication, gross motor, fine motor, problem solving, and personal social. Scores by domain are computed and compared to established cut-offs by child age and sex to identify areas of concern. For purposes of this study, children were classified as “screen positive for developmental delay” if they had an ASQ2 score that was critical or borderline in any single domain of the assessment. No genetic or biochemical data were available from the infants who tested at risk by ASQ2 score. Children were classified as “typically developing” (TD) if their scores in all domains were above the respective cutoff scores.Screening for neurocognitive abilities of the breastfed children was assessed by maternal reports using the ASQ2. ASQ2 is a parent/caregiver screening instrument that pinpoints developmental progress in children between the ages of one month and 5.5 years. The ASQ screening questionnaire has been found to have 84% overall agreement with validated tools such as the Bayley Scales of Infant Development.30 with minor modifications to expand the list of targeted chemicals. To acquire comprehensive metabolomic profiles, we performed LC-MS/MS analysis in both hydrophilic interaction liquid chromatography (HILIC-MS/MS) mode and reverse phase (RP-MS/MS) mode. A total of 659 endogenous metabolites covering all major human metabolic pathways and diverse chemical classes were targeted. We used a Shodex polymer-based aminopropyl column (HILIC mode) for most endogenous metabolites and a Raptor biphenyl column (RP mode) for some of the lipophilic metabolites that cannot be measured well by the HILIC method, such as gangliosides, deoxyceramides, phosphatidylcholines, some sphingomyelins, and steroid hormones.HM samples were thawed on ice and mixed with a set of internal standards. The endogenous metabolites and xenobiotic compounds were extracted with four volumes of 100% ethanol prechilled at −20 °C. Targeted, broad-spectrum, metabolomic analysis of endogenous metabolites was performed using a UFLC XR HPLC system  coupled with a Qtrap 5500 triple quadrupole mass spectrometer (SCIEX) (LC-MS/MS) as described earlierExposome profiling of xenobiotic chemicals in HM was conducted using both RP-MS/MS and gas chromatography (GC)-MS/MS methods based on the chemical properties of the targets. A total of 937 anthropogenic compounds were targeted, including 145 analytes on the RP-MS/MS platform and 792 chemicals on the GC-MS/MS platform. These xenobiotics were selected to broadly monitor human exposure to commonly used pesticides, food additives, personal care products, pharmaceuticals, persistent organic pollutants, phthalates, and flame retardants in the US. MRM transitions and the retention time (RT) were optimized using the purified standards, and two MRMs transitions were used for each analyte. The full targeted list and the associated MS/MS parameters and RTs are listed in Supplementary Tables 31 All compounds were mapped to standard chemical identifiers such as PubChem, CAS, and HMDB using the metabolite ID conversion tool in MetaboAnalyst 5.0, and the data harmonization results were manually checked. A web application DeepVenn was then used for the comparison and visualization of two lists.To compare the HM endogenous metabolites detected in our study with known HM metabolome data, we downloaded the curated HM metabolites from the milk composition database and human metabolome database (HMDB). We also conducted an extensive literature review of known HM metabolites using the text-mining tool PolySearch.t-test (parametric), Mann–Whitney U test (non-parametric) for continuous variables, and Fisher’s exact test for categorical variables. A two-tailed P value < 0.05 was considered statistically significant. Sample size calculations for future validation studies were performed using Pearson’s correlation-based effect size of 0.25, two-sided α of 0.05, and β value of 0.8 (https://sample-size.net/correlation-sample-size/).All general analyses were performed using GraphPad Prism 9.3.1 unless specified. Data were presented as the number n and percent (%) for categorical variables and median (IQR) or mean (±standard deviation) for continuous variables. Differences between TD and risk groups were assessed by Student’s z-scores. Spearman correlation r values, false discovery rate–adjusted P values, and q values were generated with all chemical pairs. Only correlations between each pair variable with absolute r > 0.5, and q < 0.05 were used to construct the final correlation networks by MetScape 3.0, a plug-in for Cytoscape 3.8.2.32Spearman’s rank correlation was used to build the correlations between HM metabolome and exposome data using the processed 33 See details in the The predictive performance was evaluated using the area under the receiver operator characteristic (AUROC) curve and nomograms were also created using R 4.0.5 with corresponding R packages. Confidence intervals (CI) for the ROC curves were calculated by bootstrap resampling. The models were validated by permutation analysis (1000 times). Sensitivity, specificity, and accuracy were calculated by 2 × 2 contingency table analyses. Matthews correlation coefficient (MCC) was also calculated, which provided complementary information to common binary classification metrics, especially for class-imbalanced datasets.Figure P < 0.0001   Fig. . Interesr > 0.5 or <–0.5 and q value < 0.05). Besides the positive correlations with phospholipid and deoxysphingolipid metabolism as observed in the female group, the HM exposome for male infants was positively correlated with ceramides and negatively correlated with sphingomyelins  analysis is a common ML method for omics-based predictive models due to its resilience to high dimensionality, insensitivity to noise, and robustness to overfitting.35 We used the RF algorithm to identify which variables have a more determinant impact on the prediction outcomes. The importance of the variables in HM metabolome and exposome contributed to the classification between the risk and TD group was ranked by mean decrease accuracy scores in Supplementary Table U test was further applied to verify the results of ML. Finally, pathway enrichment analysis was conducted, and the optimal features were refined to cover different biochemical pathways  to select the optimal features from HM metabolome and exposome for predicting the future risk of NDD in the offspring   of −1 (Z-score) will be assigned to 30 element points  Fig. . In addi03) Fig. . A nomog03) Fig. . The nomP = 0.012)  Fig. . Other pr of 0.25. In the current discovery phase study, 22 metabolites in the female group, and 34 metabolites in the male group had absolute r values > 0.25, which represents a medium to large effect size. Based on these calculations, a total of 124 females and 124 males, with 62 children at risk and 62 TD controls from each sex, will be required for a future validation study.We estimated the sample size required for a future large validation study by Pearson’s correlation-based effect size (variance explained) 3 In this study, by the combination of ML methods and knowledge-based approaches, we successfully developed sex-specific, multi-analyte predictive models from the HM metabolome and exposome to predict future risk of NDD in the toddler age with over 75% accuracy. Our models include novel metabolic and exposomic features in HM that are likely to be informative of disease etiology. If replicated, this finding could have a significant impact on clinical practice by permitting infants to be stratified according to their future risk and new preventive treatments to be tested in clinical trials.To our knowledge, this is the first metabolomics and exposomics study to demonstrate an early signature in human milk for infants at risk of future NDD. The primary patterns of metabolic alterations we found include critical metabolites involved in myelination and brain development, such as deoxysphingolipids, acylcarnitines, sphingomyelins, phospholipids, and glycosphingolipids, including several globosides and cerebrosides. Predicting cognitive outcomes based on infant assessments is known to be challenging because problems such as language impairment, learning disabilities, and social-emotional disorders emerge over time and cannot be evaluated accurately at an early age.36 total protein,37 fat content,38 and microbiota diversity.39 Beyond the existing literature, in this study, we show dramatic differences in HM metabolome between male and female offspring. HM for male offspring had higher phospholipids, sphingomyelins, cardiolipins, acylcarnitines, and plasmalogens than those in the HM produced for female infants. Moreover, we found that the HM metabolome responds to the infant’s stress differentially between the sexes. Compared to the female TD children, participants with female infants at risk produced more phospholipids in their HM to meet their children’s needs. In contrast, phospholipids in HM for male risk infants were lower than those in the male TD group. Even though the HM exposome did not differ substantially according to the infant sex, the inter-omics analysis revealed significant positive correlations between the xenobiotics and ceramides in HM only for male children.Offspring sex-specific differences in human milk composition have been observed for energy content,40 Our metabolomic analysis confirms the presence of plasmalogens from the phosphatidylethanolamine family in HM, which have been implicated in infant brain development.41 Increased HM levels of plasmalogens were reported to be associated with a remarkable increase in brain size in preterm infants.43 NAD+ is a central redox cofactor of virtually all metabolic processes. Postpartum supplementation of nicotinamide riboside to rodent mothers increased NAD+ content in the rodent milk, leading to improved neurobehavioral development of the offspring, whose advantages persist into adulthood.44Low levels of certain critical metabolites in human milk may logically have important effects on infant brain development. In this study, plasmalogens, and NAD+ were observed to be lower in the HM for male infants in the risk group compared to the TD group. Plasmalogens are the main ethanolamine phospholipids in brain myelin and neural membranes, and because of their unique molecular structure, plasmalogens facilitate rapid membrane fusion, which is critical for synaptic neurotransmission.45 During the lactation period, the mother and her breastfed offspring maintain a reciprocal regulation of responsiveness to stress in both rodents and human beings.46 Activation of CDR represents the mother’s metabolic responses to the offspring’s stress, resulting in the tailoring of breast milk composition to maximize health protection and development for infants at risk.Compared to the TD group, we also observed higher levels of some essential metabolites for brain development in mother’s milk for the infants with future NDD risk in a sex-specific manner, including higher phospholipids and sphingomyelins for female risk infants, and higher glycosphingolipids and acylcarnitines for infants in the risk group of both sexes. Interestingly, increased glycosphingolipids, phospholipids, and acylcarnitines are typical metabolic features of cell danger response (CDR).47 The potential effects of early caffeine exposure on the long-term neurodevelopment of breastfed infants require further investigation.Caffeine is a methylxanthine that acts as a central nervous system stimulant and naturally occurs in several foods. Neurostimulation occurs because caffeine is an inhibitor of the widely expressed purinergic adenosine receptors 1 and 3 . Here, we show that HM for the infants in the risk group had higher caffeine than that for the TD infants. The effects of maternal caffeine consumption on breastfed children are inconclusive, and both negative and positive outcomes have been reported in the literature.17 The samples in this study were well matched with a mean sampling time around 1 pm. Another limitation was that the outcome was measured by maternal report using the ASQ at about one year of age, and thus, the assessment of risk for NDD was not diagnostic. However, the ASQ screening instrument is widely used in pediatric practice and can be an important early indicator of future risk, leading to potential further evaluation, ongoing monitoring, or specialized intervention.A limitation of the current study was the relatively small size of the cohort of children at risk. Sample size calculations estimated that at least 124 males and 124 females, with 62 TD and 62 AR of each sex, will be needed for a larger validation phase study. It should also be noted that the nutrient content of HM varies in samples collected at different times of the day.In summary, multi-omics profiling of the maternal milk metabolome and exposome revealed significant differences between children at risk for neurodevelopment delay (NDD) and typically developing (TD) infants. Other well-known risk factors such as prematurity, low birth weight, perinatal difficulties, and socioeconomic variables were well controlled and were unable to account for the differences between groups in this study. The early biosignature in human milk for infants who later screened positive for NDD was dominated by the sex-specific abnormalities in deoxysphingolipids, sphingomyelins, plasmalogens, phospholipids, and acylcarnitines. Multi-analyte models of the biosignature of milk samples collected before 9 months of age showed over 75% accuracy in predicting NDD documented nearly one year later, raising the possibility that milk metabolomics might provide an early warning of later developmental risk. Caution is warranted in interpreting these results. This was a small pilot study. Larger validation studies will be needed to determine the sensitivity, specificity, cost/benefit, and clinical practicality of milk metabolomics as a tool in pediatrics. However, if validated in larger studies, milk metabolomics might one day be used as another early screening tool to help identify infants at risk of NDD with the aim of instituting treatment before the first symptoms appear.Supplementary materialsSupplementary Table S1


Label-freeoptical sensors are attractive candidates, for example,for detecting toxic substances and monitoring biomolecular interactions.Their performance can be pushed by the design of the sensor throughclever material choices and integration of components. In this work,two porous materials, namely, porous silicon and plasmonic nanoholearrays, are combined in order to obtain increased sensitivity anddual-mode sensing capabilities. For this purpose, porous silicon monolayersare prepared by electrochemical etching and plasmonic nanohole arraysare obtained using a bottom-up strategy. Hybrid sensors of these twomaterials are realized by transferring the plasmonic nanohole arrayon top of the porous silicon. Reflectance spectra of the hybrid sensorsare characterized by a fringe pattern resulting from the Fabry–Pérotinterference at the porous silicon borders, which is overlaid witha broad dip based on surface plasmon resonance in the plasmonic nanoholearray. In addition, the hybrid sensor shows a significant higher reflectancein comparison to the porous silicon monolayer. The sensitivities ofthe hybrid sensor to refractive index changes are separately determinedfor both components. A significant increase in sensitivity from 213± 12 to 386 ± 5 nm/RIU is determined for the transfer ofthe plasmonic nanohole array sensors from solid glass substrates toporous silicon monolayers. In contrast, the spectral position of theinterference pattern of porous silicon monolayers in different mediais not affected by the presence of the plasmonic nanohole array. However,the changes in fringe pattern reflectance of the hybrid sensor areincreased 3.7-fold after being covered with plasmonic nanohole arraysand could be used for high-sensitivity sensing. Finally, the capabilityof the hybrid sensor for simultaneous and independent dual-mode sensingis demonstrated. Fordecades, optical sensors have been studied to detect toxicand dangerous substances such as volatile organic compounds (VOC),explosives, pesticides, or antibiotics in food and also to monitorbiomolecular interactions in order to develop safe and effective drugs.1 Surface plasmonsare collective oscillations of conduction band electrons and are highlysensitive to refractive index changes in the vicinity of the goldfilm. They can be excited in thin gold films by matching the momentaof the incoming light and the surface plasmon polaritons using prismcoupling.2 Alternatively, a grating ora scattering structure can be integrated into the gold film for thispurpose.3Most prominent for the latter application are optical sensors basedon surface plasmon resonance (SPR) in thin gold films, which werecommercialized in the 1990s.4 The sensitivity of these plasmonic nanohole arrayscan be significantly improved, for example, with the geometrical layoutof the plasmonic structure5  or usingthe supporting material.6 In the lattercase, not only the refractive index of the supporting material dictatesthe optical signature of the sensor but the supporting material alsodefines the accessible surface area for capturing and detecting targetanalytes. Consequently, plasmonic nanohole arrays were not only fabricatedon top of solid substrates but also combined with porous inorganicmaterials7 and polymers9 oreven investigated as free-standing sensors.10 Using another optical sensor platform as a support for plasmonicnanohole arrays in order to provide a second sensing channel has notbeen published until now, to the best of our knowledge. However, firstattempts to harness the advantages of combining plasmonic and interferometricsensors have recently been reported. For example, gold nanostructuresshowing localized SPR were combined with porous silicon (PSi) sensors.12Several advantages havebeen found in using gold films with integratedgratings such as periodic plasmonic nanohole arrays for sensor applications,ranging from lower production costs to easier optical readout in thetransmission or reflection mode and enabling multiplexing.13 PSi has successfully been used as optical sensors for decades enabling,for example, the detection of DNA, antigen/antibody interactions,14 explosives, or VOCs.15 Different optical properties of PSi have been exploited for thispurpose including specular reflectance and fluorescence. For improvingthe optical response of the PSi sensor, other materials were oftendeposited on top of the porous layer or filled into the pores.16 For example, gold nanoparticles were integratedinto PSi sensors for interferometric sensing and for providing materialshighly suitable for surface-enhanced spectroscopy.18The appeal in using PSi for optical sensing is based on itshighsurface area, simple functionalization chemistry, and efficient fabricationby electrochemical etching, which give easy access to sophisticatedoptical structures such as photonic crystals.11 Reflectance spectraof the resulting hybrid sensors were characterized by a fringe patternresulting from the Fabry–Pérot interference at the bordersof the PSi monolayer, in which a broad dip due to localized SPR (LSPR)in the nanostructured gold film could be noticed. Both the interferencepattern and LSPR responded sensitively to refractive index changesin the surrounding medium. Similar results were presented for PSirugate filters that were decorated on the surface and approximatelyin the upper third of the pores with wet-chemically synthesized goldnanoparticles.12 Nevertheless, all thesehybrid sensors utilize gold nanoparticles showing LSPR, which limitsthe detection of analytes to a few nanometers (∼20 nm) abovethe gold surface. Plasmonic nanohole arrays probe the refractive indexat 100–200 nm above the gold surface, facilitating highly sensitivedetection of target analytes in a larger volume. Furthermore, almostall reported hybrid sensors were composed of PSi layers whose poreswere at least partially filled with gold nanoparticles, preventingsimultaneous dual-mode sensing of two target analytes. In addition,the pore size of the PSi was reduced by incorporation of gold nanoparticles,making the admission of biomolecules into the porous layer difficult.In a nutshell, these hybrid sensors leave a lot of room for improvementconcerning the readout and analysis of their reflectance spectra,the design of the plasmonic structure, and the accessibility of thepores in the PSi layer—to name a few.For realizing a hybrid sensor with two sensing channels, goldnanostructureswere deposited onto the surface of PSi monolayers using a galvanicdisplacement reaction.In this work, opticalsensors based on plasmonic nanohole arrays,PSi monolayers, and the combination of both were fabricated and investigatedin order to demonstrate the benefits of these hybrid sensors, whichpossess two transduction channels, provided by, namely, SPR and interferometry.19 which is based on a combinationof colloidal lithography and chemical gold deposition. In this work,polystyrene@poly-N-isopropylacrylamide (PS@poly-NIPAM)core–shell particles were utilized instead of pure poly-N-isopropylacrylamide microgels for preparing the colloidalmask. The PS@polyNIPAM core–shell particles were synthesizedaccording to Kim and Ballauff.20 Detailsare given in the Supporting Information.Plasmonicnanohole arrays were fabricated using a modified method from Quintand Pacholski,21 After complete assemblyof the Teflon etching cell, 4 mL of the electrolytic solution waspoured into the cell, and a Pt ring electrode was immersed in thesolution. Then, a current density of 442 mA cm–2 was applied for 30 s using a Kepco power supply in order to obtainthe desired PSi monolayer. Freshly etched PSi samples were thoroughlywashed with ethanol and immersed in pentane for 10 min. Then, thePSi samples were dried in a stream of N2.PSi monolayers were preparedby wet electrochemical etching using a solution of ethanol and hydrofluoricacid (48%) in a volumetric ratio of 1:3. Silicon wafer pieces of ∼1.6× 1.6 cm  were placedon top of an aluminum foil, which was part of a custom-made Teflonetching cell.To increasethe stability of the fabricated PSi monolayers, they were thermallyoxidized at 600 °C for 1 h using a muffle oven (Thermo FisherScientific). Furthermore, the surface of the oxidized PSi monolayerswas functionalized with 3-aminopropyltriethoxysilane (APTES) by vapordeposition. For this purpose, PSi samples were placed in a desiccatortogether with a small container filled with 30 μL of APTES.A vacuum of 1 mbar was applied for 45 min. Finally, the APTES-functionalizedPSi samples were thermally annealed at 110 °C for 1 h.Figure S1a). Thisprocedure lifted the plasmonic nanohole array layer from the glasssubstrate, and it floated on the surface of the water .22 APTES-functionalizedPSi samples were immersed in water  and used to lift the plasmonic nanohole array layer from the air/waterinterface . Finally, the drysensors, consisting of the PSi structure and plasmonic nanohole array,were tempered for 24 h at 45° C in order to support the adhesionbetween the gold and the APTES-functionalized silicon surface.In order to detach the plasmonic nanoholearray from the glass substrates, the samples were immersed in 0.1M aqueous NaOH solution overnight. Then, the edges of the gold layerwere scratched with a diamond tip of a glass cutter. The samples wereremoved from the basic solution and slowly immersed vertically ina MilliQ water bath (Scanning electron microscopy(SEM) was exploited for characterizing all samples regarding the sizeand the arrangement of nanoholes/pores and the thickness of the layers(gold/PSi). For this purpose, samples were equipped with a thin layerof carbon (∼5 nm) deposited using a combined system for carbonand sputter coating from Quorum Technologies (model Q150R ES). A scanningelectron microscope from Zeiss (model Gemini ultra plus) was utilizedfor taking micrographs at an acceleration voltage of 3 keV with anInLens detector.2 was illuminated with the aid of the optical fiber. Reflectivityspectra were recorded every 15 s in a wavelength range of 400–1000nm with a spectral acquisition time of 0.5 s and a total integrationtime of 20 ms, resulting from averaging 25 spectral scans. Reflectivityspectra were collected at normal incidence. In order to obtain reflectancespectra, the reflectivity spectrum of the respective sample was dividedby a reflectivity reference spectrum, which was recorded previouslyfrom a clean silicon wafer. Transmittance spectra were collected byutilizing an appropriate optical setup obtained from Ocean Optics,Inc. (USA). The illuminated area on the samples was ∼5 mm indiameter. Transmittance spectra were recorded every 15 s in a wavelengthrange of 400–1000 nm with an integration time of 1 ms and 25scans to average, resulting in an acquisition time of 25 ms.A charged-coupleddevice (CCD)spectrometer (model Flame) from Ocean Optics, Inc. (USA) was utilizedfor collecting reflectance and transmittance spectra. For recordingreflectance spectra, a bifurcated optical fiber was equipped witha microscope objective lens and connected to both the spectrometerand a tungsten light source. A spot of ∼1–2 mm23 Briefly, the liquid was pumped through the flow cell via an inletand outlet using a Perimax 12 SPETEC peristaltic pump and a flow rateof 0.57 mL/min. Reflectance/transmittance spectra were collected byguiding the light through the transparent flow cell using the alreadydescribed optical setups.A custom-made flow cell madeof plexiglass was used for biomolecule adsorption/penetration experiments,whose layout has been reported previously.www.wavemetrics.com)equipped with the freely available online plugin Fringe_24_1 fromM.J. Sailor (http://sailorgroup.ucsd.edu/software/index.html). A total of20 optical spectra at each water/sucrose concentration were averagedto obtain the magnitude of EOT, IntEOT, and/or SPR correspondingto that concentration. The resulting values were determined for atleast three individual sensors of each type .Real-time monitoringof changes in theoptical spectra of the sensors was achieved using IGOR Pro software. The holes had a diameter of 173 nm ± 30 nm, and the latticeconstant of the array was 399 ± 19 nm . The thickness of the goldfilm was determined to be 80 ± 7 nm by atomic force microscopy. In eq L is the lattice constantof the array, i and j are integers,and εd and εm(λPSP) represent the permittivity of the surrounding dielectric mediumand the metal, respectively. The most pronounced transmittance maximumlocated at ∼680 nm can be assigned to the  gold/glassmode.27Plasmonic nanohole arrays 1d were frographs S2. The h22 After slow immersion of the sample in water,the plasmonic nanohole array floated on the water surface, from whereit was picked up with a PSi sensor functionalized with 3-aminopropyltrimethoxysilane(APTES). The resulting hybrid sensor composed of a plasmonic nanoholearray on top of a PSi monolayer is presented in S4). In d).Similar reflection spectra have already been reported for PSi structureswith gold nanoparticles, in which the nanoparticles are either depositedon top of the PSi or partially infiltrated into the porous layer.28Plasmonic nanohole arrayswere lifted off the glass substrate byimmersion in alkaline solution overnight and scratching the gold film.n2OH = 1.3330 and n50wt% sucrose = 1.4201) of the surrounding medium with a shift in the opticalspectrum to higher wavelengths—as expected.The significant differences between the reflectance spectraareimmediately apparent. In addition to the presence of a dip in theinterference pattern of the hybrid sensor, its reflectance spectrumis characterized by an enormous increase in reflectivity, which mightbe explained by the differences in the reflectivity of the top interface.Furthermore, the presented reflectance spectra were taken in MilliQwater (solid line) and 50 weight % sucrose in MilliQ water (dashedline). Both sensors, namely, PSi and hybrid sensors, respond to anincrease in the refractive index , whichrepresents the refractive index of the porous layer multiplied withits thickness, can be directly obtained from the position of the FFTpeak. The amplitude of the FFT peak gives information on changes inthe refractive index contrast of the layer.29 In 2O (n = 1.3330) to 50 weight % sucrose inMilliQ water (n = 1.4201) with a significant increasein EOT. This observation is in accordance with published results onPSi sensors decorated with gold nanoparticles.17One of themost broadly applied methods for monitoring changesin the interference pattern of, for example, PSi sensors is basedon applying a fast Fourier transform (FFT) to their spectra.n = 1.3478, 30% w/w ofsucrose—n = 1.3812, and 50% w/w of sucrose—n = 1.4201) as analyte solutions. The optical response ofboth sensors to refractive index changes in the course of the experimentwas monitored by determining the EOT from the FFT plots of the correspondingreflectance spectra (n≠1 – EOTn=1) versus the refractive index of the surroundingmedium  was calculated. A plot of the FFT peak amplitude vs the refractiveindex of the medium is displayed in n =3). The standard deviation of these three measurements was calculatedand is given as error bars in the graph. The raw data can be foundin the Supporting Information . In addition, the reproducibility of the optical responseof one sensor (PSi and hybrid sensors) for different sucrose solutionsis shown in Figure S8. Here, the errorbars represent the standard deviation for changes in the optical signalcorresponding to one sucrose solution . However, these values depend on the wavelengthrange of the reflectance spectrum, which is chosen for calculatingthe FFT (here: 600–950 nm).2O (solid lines)and 50 wt % aqueous sucrose solution (dashed lines) are shown. Inboth cases, the spectral position of the plasmonic peak increaseswith a rise in the refractive index of the surrounding medium. In In n = 3). The standard deviation of these three measurementswas calculated and is given as error bars in the graph. The raw datacan be found in the Supporting Information . Plasmonic nanohole arrays on top of PSi monolayershad a sensitivity of 386 ± 5 nm/RIU, whereas sensors preparedon glass substrates had sensitivities of 213 ± 12 nm/RIU. Thehigher sensitivity of the hybrid sensors might be based on the largeraccessible surface area for sensing (PSi vs solid glass).31Determination of the sensitivities of the two sensorswas achievedby calculating the slopes of the linear relationships between thespectral positions of the SPR peaks in the reflectance and transmittancespectra 3c.Three33 In contrast,PSi functionalized with APTES has a positive charge in acidic media,and its electrostatic repulsive interaction with protein A, whichis also positively charged , facilitates the removalof protein A from the PSi layer. Similar observations have alreadybeen described for BSA.34 Afterward, PBSbuffer was flowed over the sensor in order to establish a new baseline(from ∼81 to ∼88 min). Subsequent to immersion of thehybrid sensor in PBS buffer, a solution of 1 mg/mL BSA in PBS inducedonly an optical response of the PSi layer (∼88 to ∼116min). The plasmonic nanohole array already covered with protein Aresponded to the presence of BSA with a spectral shift in the SPRto shorter wavelengths, indicating a decrease in the refractive index.Thus, as expected, no binding between BSA and protein A was detected.The shift is most likely related to the inaccuracy of the fit of therelatively broad SPR signal used to determine the position of theSPR signal on the wavelength scale. In other experiments, this opticalresponse could not be detected . After the removal of BSA from the PSi layer by flowing PBS (from∼116 to ∼126 and from ∼136 to ∼144 min)and 0.1 M acetic acid (from ∼126 to ∼136 min) over thesenor surface, the sensor was exposed to a solution of NHS–PEG4–biotin in PBS (∼144 to ∼175 min). Thismolecule can bind to primary amine groups via its NHS group (NHS: N-hydroxysuccinimide) and reacts with amine groups presentin the PSi layer due to its functionalization with APTES and withthe amine groups of protein A, which is adsorbed to the plasmonicnanohole array.35 In this reaction, NHSis released, and PEG4–biotin is covalently boundto the amine groups of protein A and APTES.36 Consequently, changes in the SPR spectral position and EOT wereexpected. Indeed, a small shift in the spectral position of the SPRsignal is evident. However, due to the coverage of the plasmonic nanoholearray with protein A, only a small change in the refractive indexin this protein layer is achieved by the binding of the small-moleculePEG4–biotin to some of the accessible amine groupsin protein A. In the PSi layer, the free amine groups of the self-assembledmonolayer of APTES are all readily accessible to the binding of PEG4–biotin, resulting in a larger change in the refractiveindex and consequently a significant change in the EOT. Afterward,PBS buffer (from ∼175 to ∼184 and from ∼196 to∼207 min) and 0.1 M acetic acid (from ∼184 to ∼196min) were flowed over the sensor in order to establish a new baseline.Due to the PEG4–biotin covalently bound to the APTS-functionalizedPSi surface, the EOT signal did not return to the baseline obtainedbefore the sensor was exposed to NHS–PEG4–biotin.Finally, the hybrid sensor was immersed in a solution of 0.025 mg/mLrabbit IgG, which selectively binds to protein A (from ∼207to ∼236 min). Indeed, protein A is often used for functionalizingbiosensor surfaces because it allows for directed immobilization ofimmunoglobulins (IgGs).38 Only the plasmonicnanohole array covered with protein A showed an optical response,indicating that rabbit IgG did not infiltrate into the PSi layer andshowed a typical association and dissociation to protein A adsorbedto the plasmonic nanohole array. The covalent binding of a small amountof PEG4–biotin obviously did not prevent this specificbiomolecular interaction. Degradation of the PSi sensor or removalof APTES from its surface was not detected throughout the biosensingexperiments. The interactions between biomolecules, buffer, APTES,and the PSi surface are rather complex, and the stability of surfacefunctionalization using APTES has been studied in detail previously.39Finally, thecapability of the hybrid sensor composed of a PSi monolayer coveredwith a plasmonic nanohole array for dual-mode sensing was investigatedby performing flow cell experiments. For this purpose, the sensorsurface was subsequently exposed to different solutions of biomolecules,namely, protein A, bovine serum albumin (BSA), functionalized biotin,and rabbit IgG. In summary, three different optical sensors–plasmonicnanoholearrays on glass substrates, PSi monolayers, and their combination(plasmonic nanohole arrays on top of PSi monolayers)—were fabricatedand characterized, and their sensing performance was investigated.Plasmonic nanohole arrays were obtained using a bottom-up strategyand showed extraordinary transmission of light provided by SPR inthe gold layer. In their optical transmittance spectra, a strong maximumcan be noticed, which is related to the  gold/glass mode andresponded to refractive index changes with a sensitivity of 212.6± 11.6 nm/RIU. By transferring plasmonic nanohole arrays on topof PSi layers, the accessible surface area for sensing was enlarged,and the sensitivity of the plasmonic structure significantly increasedto 386.3 ± 5.4 nm/RIU. The same trend was observed for detectingthe adsorption of proteins to the sensor surface. Furthermore, theunderlying PSi monolayer can be exploited for interferometric sensingby monitoring changes in its EOT, and its sensitivity was determinedto be 11,958.6 ± 149.6 nm/RIU. In this case, the presence ofthe plasmonic nanohole array on top did not have an influence on thesensitivity of the interferometric sensor, which had a sensitivityof 11,377.7 nm/RIU ± 23.6 without the plasmonic structure ontop. However, if no changes in the EOT were considered except variationsin the amplitude of the EOT peak, a drastic gain was found in sensitivityfrom 8.6 ± 0.1 to 32.7 ± 0.5 per RIU for PSi sensors withoutand with plasmonic nanohole arrays on top, respectively. Hence, theincrease in the sensitivity of PSi sensors covered with gold nanostructuresmight be traced back solely to an enhanced reflectivity of the toplayer. Furthermore, dual-mode sensing of the hybrid sensor was successfullydemonstrated by simultaneously monitoring the optical response ofboth channels, namely, SPR (plasmonic nanohole array) and EOT changes(PSi monolayer), upon exposure to different biomolecule solutions.The benefits of combining plasmonic and interferometric sensorplatforms can be manifold, ranging from increased sensitivities andthe independent utilization of different optical transduction methodsfor monitoring refractive index changes to the vast opportunitiesfor functionalizing their surfaces with different capture probes.These kinds of sensors pave the road to multiparametric sensors andcan be easily miniaturized due to their bottom-up fabrication strategy.


Although uncommon in dentistry, the concept of the life cycle holds great importance for dental professionals in identifying crucial intervention opportunities and determining the optimal timing for treatments and procedures. To carry out a review of the literature on life cycle classifications and their distinct phases, evaluating their applicability in healthcare. A literature review was performed, searching for articles in PubMed, SciELO, National Health Library (BvB), and Google Scholar databases, as well as relevant books. The keywords “life cycle,” “life stages,” “human development,” “age groups,” and “biological age” were used. Relevant articles were selected by analyzing their titles and abstracts, and read in full to confirm their inclusion in the research. Nine distinct life cycle classifications were found, each with unique criteria.  Based on the comprehensive literature review, a novel classification was proposed (The 10-phase Life Cycle), which encompasses dental, growth, physiological aging, sociocultural, and behavioral characteristics, aiming to enhance communication among healthcare professionals, particularly those engaged in the growth, development, and aging processes of human beings. The increase in life expectancy has led to increased longitudinal studies and studies related to the life course, including the changes that occur during the life cycle.A person’s life cycle is commonly divided into sequential phases, which follow their age, from birth to death. These phases, which have a predictable duration, can also be demarcated from a perspective of biological events  and from a social events perspective .The use of well-defined phases of the life cycle in the health area is essential for professionals to recognize the windows of opportunity for intervention and the most appropriate time to conduct treatments. Identifying the characteristics of each phase of the life cycle becomes essential for health professionals to act appropriately in growth, development, and aging. This knowledge makes it possible to understand the normal patterns, conflicts, and expected disorders,The present study comprehensively reviewed the existing literature on life cycle classifications, considering their distinct phases. Based on the findings, a novel life cycle classification was proposed, based on dental, growth, physiological aging, sociocultural, and behavioral characteristics that facilitate communication between health professionals, particularly those who act in human growth, development, and aging.ciclo vital”, “estágios do ciclo de vida”, “desenvolvimento humano”, “faixas etárias”, “grupos etários”, “idade biológica”, and their equivalents in English  were used. The terms were associated with each other by the Portuguese operators “E” and “OU,” and their English counterparts, “AND” and “OR.” From the search results, the pertinent publications were selected through the analysis of their titles and abstracts, both in Portuguese and in English. The chosen articles were thoroughly read to confirm whether they should be included or excluded from the research. The criteria for inclusion encompassed articles and book chapters that were published with full text, in Portuguese or English, presenting characterizations of the life cycle phases, with no limitation on publication date . In order to identify other relevant publications, the reference list of the included studies was manually checked for relevant or missing studies using the keywords previously mentioned. Studies that met all inclusion criteria were selected. When there was no mutual agreement between the two authors responsible for selecting the articles, a third author was involved to make a final decision. The literature review was divided according to the different classifications found, and the results were presented and discussed.A review of the literature was carried out, searching for articles in the PubMed, SciELO, National Health Library (BvB), and Google Scholar databases, from 1972 to 2020. The Portuguese keywords “Based on the bibliographic material consulted, 9 classifications were registered for the life cycle, which will be described according to the number of phases, presenting their criteria and nomenclatures. From these nine classifications found, graphs with colors were created, representing the life cycle phases, as described by each author.According to Camarano,According to Bogin and Smith,» Infancy: starts with birth, or, more accurately, when the person first receives nutrition via lactation, be it exclusive or non-exclusive breastfeeding, and ends with weaning. In pre-industrialized societies, this occurs from 0 to 36 months.» Childhood: the period after weaning, in which the person still depends on someone for food that must be prepared considering the dentition and digestive tract immaturity, for protecting the person against diseases. The important milestones for the completion of the stage are the eruption of the first permanent molar and complete growth of the brain, which correspond to the possibility of nutrition like that of an adult, because of the masticatory capacity and increased cognitive capacity. The events that determine the end of childhood occur around 6.5 to 7 years of age.10 and have greater physical and cognitive capacity.,» Juvenile: phase preceding puberty, in which persons are no longer so dependent on their parents for food» Adolescence: starts with puberty, when signs of sexual maturation are manifested, such as the appearance of pubic hair. In this phase, growth is accelerated, characterizing the “growth spurt”. The end of this phase is related to the achievement of stature and complete sexual maturity. For women, it occurs, on average, at 19 years of age, and in men, around 21 to 25 years.» Adulthood: bone apposition ends, and resorption begins around the fifth decade of life.This classification of the life cycle into six phases  is based» Infancy and first childhood phase: from birth to 5 years of age; period in which learning to walk, speak, eat solid foods, and readiness to read occur.» Second childhood and preadolescence: from 6 to 12 years of age, when personal independence and appropriate sex role learning are achieved.» Adolescence: from 13 to 18 years of age; phase in which mature relationships are maintained, and physical acceptance and career preparation occur.» Young adult: from 19 to 29 years of age; period of choosing a partner, family formation, and civic responsibility.» Medium adult: the phase between 30 and 60 years of age, when the career achieves more satisfactory results, adapting to the aging of the parents.» Third age: 60 years or more, corresponds to the retirement phase, understanding of the proximity to the end-of-life, reduction of income, strength, and health.The United Nations» Infancy (under 1-year old).» Youth (1 to 14 years).» Young adulthood (15 to 24 years).» Middle adulthood (25 to 44 years).» Older adulthood (45 to 64 years).» Retirement age (65 years and beyond).The United Nations addresses different definitions, with a greater or lesser range of ages, for use according to the required purpose, subdividing them into the following phases: preschool children (from 1 to 4 years); school-age children (from 5 to 14 years); adolescence ; third age (persons aged 64 years or older).The classification of the life cycle into seven phases is based on chronological criteria with subdivisions proposed by Gallahue et al.Period of the zygote - conception to 1 week;Embryonic period - 2 to 8 weeks;Fetal period - 8 weeks to birth.» Prenatal life (conception to birth): Neonatal periodEarly infancyLater infancy» Infancy (from birth to 24 months): Toddler period - 2 and 3 years;Early childhood - 3 to 5 years;Middle/late childhood - 6 to 10 years.» Childhood (2 to 10 years): Prepubescence - 10 to 12 years for girls/11 to 13 years for boys;Postpubescence - 12 to 18 years for girls/14 to 20 years for boys.» Adolescence (10 to 20 years): Novice period - 20 to 30 years;Settling period - 30 to 40 years.» Young adulthood (20 to 40 years): Midlife transition - 40 to 45 years;Middle age - 45 to 60 years.» Middle adulthood (40 to 60 years): Young old - 60 to 70 years;Middle old - 70 to 80 years;Oldest-old - 80 years and beyond.» Older adulthood (over 60 years): Papalia et al.» Prenatal period: from conception to birth, when structure and basic body organs are formed.» First childhood: from birth to 3 years. Corresponding to the phase in which all the senses work to different degrees, and great dependence on parents occurs.» Second childhood: 3 to 6 years, when many cognitive skills are developed, the body has proportions more equivalent to that of an adult.» Third childhood: 6 to 11 years, a period in which logical thinking is developed, and language and memory improve.» Adolescence: 11 to approximately 20 years old, when a search for identity and maturity of the reproductive system occurs.» Young adult: 20 to 40 years, being a period of the relative stability of personality traits, with an increased number of marital relationships and parenting.» Middle age: 40 to 65 years, corresponding to the onset of some degree of loss of sensory capacity, health, and dexterity and, for women, hormonal changes because of menopause.» Third age: 65 years and older, the last phase, being described as when retirement and a decrease in physical capacity and health commonly occur.For this classification , Erik Er» Baby (0 to 18 months): oral-sensory phase, in which the person’s relationship is centered on the mother.» First childhood (18 months to 3 years): phase characterized by the contradiction between impulses and social norms.» Intermediate childhood (3 to 5 years): stage in which motor, language, and thinking skills fully develop.» School-age (6 to 11 years): latency phase, in which school integration and formation of new interpersonal relationships occur.» Adolescence (12 to 18 years): phase of acquisition of psychosocial identity.» Young adult (19 to 40 years): a period in which the main point is the establishment of mature and lasting relationships.» Adult (41 to 65 years): phase of personal affirmation at work and in the family.» Older adults (65 years and beyond): last phase, in which the lived experiences are reassessed.Barry Bogin» Neonatal stage (birth to 28 days): characterized by postnatal adaptation, ability to distinguish breast milk odor and reflexes guided by sound and light.Early nursling (2 to 12 months): is characterized by the introduction of other foods from 6 months onwards, the eruption of some deciduous teeth, and completion of the phase marked by bipedal walking.- Late nursling (12 to 30-36 months): development of verbal skills and finalized with the complete eruption of deciduous teeth, besides weaning.- » Breastfed infant : with food coming totally or partially from milk, whether maternal or industrialized, associated with rapid cognitive, behavioral, and motor development. » Childhood (3 to 6.9 years): period with moderate growth rate, well-developed bipedal walking, improvement in communication, independence, and self-care. This phase ends with the eruption of the first permanent molars and incisors and with adrenarche.» Youth or prepuberty (7 to 9 years): characterized by a low growth rate, legal school age in most societies, and ability to feed independently.» Puberty (9 to 10 years): neuroendocrine changes in the reproductive system, appearance of secondary sexual characteristics , intensification of friendships and social activities.Prefertility (11 to 13 years old): subcategory ending with menarche for girls and spermarche for boys.- Fertility (13 to 22 years): This phase is characterized by changes in lipid and muscle tissues related to sex, and increased physical and cognitive levels for work. This phase ends with the eruption of the third molars, the end of the fusion of epiphyses of long bones, and the maximum height reached.- » Adolescence (11 to 22 years): There is a growth spurt and intensification of social, economic, and sexual activities. The duration of this phase is different for girls (11 to 18 years) compared to boys (11 to 22 years). Prime: a phase that corresponds to the maximum performance of adult life, ending at the age of 35.- Gradual decline (35 to 50 years): It is observed the clinical detection of the first signs of physical degeneration, ending with menopause for women and, for men, reduced sperm quality.- Transition or degenerative age (50 years to senility): decreased cognitive and functional abilities.- Senility: variable duration phase, according to somatic and functional reserve levels.- Death: reduction of tissue and organ performance below the minimum support necessary for the maintenance of life.- » Adult life (from 18-22 years to immediately before death) Gonçalves,» Conception: begins with fertilization of the ovum.» Prenatal (between 0 and 14 days up to 9 months): zygote (cell multiplication), embryo (beginning of organ formation) and fetus (body growth and improvement) stages.» First childhood (0 to 3 years): in this phase the child develops two important skills: speech and locomotion.» Second childhood (3 to 6 years): discovery of sexuality and construction of gender identity and frequent use of imagination.» Third childhood (6 to 11 years old): slower physical development occurs, compared to the two previous stages, besides the beginning of school learning and friendships building.» Preadolescence (11 to 14 years): intense body changes occur abruptly.» Adolescence (14 to 17 years): is marked by the relationships building, in the form of friendship “groups”.» Youth (17 to 20 years): transition to adult life, with increased social pressures and, therefore, with increased depression rates.» Young adult (20 to 40 years): the physical and cognitive peak occurs, and is characterized by the themes of maternity/paternity, profession, and marriage.» Middle age (40 to 60 years): beginning of decline in physical capacity, procreation, children leaving home, preparation for retirement.» Older adults or old age (60 years and older): increased physical and cognitive frailty, retirement, and free time.» Death: represents the end of the life cycle.The literature presents different classifications of the life cycle phases, which focus on chronology and social or biological issues. However, these classifications are not standardized, making communication between researchers and scholars difficult.It was observed in the researched literature that among the classifications that were found, five described the life cycle in at least seven phases, concentrating more than half of the cycles before the adult phase. The phases related to old age were scarcely described. To classify the phases of the vital human cycle, besides biological age, morphological (measurements of height and weight compared to normative indices), skeletal , dental (sequence of eruption and tooth formation), and sexual aspects  should be considered. One could also include emotional  and mental aspects , self-perception (the person’s assessment of their ability), or perceptive age .The classifications by GonçalvesBased on chronological age, the United Nations,,,,,,,-,,,,A complete life cycle classification should contain comprehensive dental, growth, and aging elements and socio-cultural and behavioral characteristics. No classification was found that also included skeletal and dental development. The pubertal growth spurt can be assessed using carpal radiographs or vertebral maturation analysis (CS3 and CS4).Thus, in the present article, the authors suggest a classification for the life cycle, divided into 10 phases , based o» Breastfeeding.» Predental period, known as the period of gingival rollers, is characterized by the absence of teeth.» Physiological distocclusion: The mandible is retruded in relation to the maxilla, with a discrepancy of 3 to 5 mm.» Normally, from birth to 29 days.» Breastfeeding and, beginning at six months, solid food introduction.» Period of the deciduous dentition, which begins with the eruption of the first deciduous tooth.» Physiological growth of the mandible, enhanced by breastfeeding, promotes necessary muscular stimulation for development.» Great dependence on parents.» Normally, from 30 days to 23 months.» The complete deciduous dentition marks the beginning of this life cycle’s phase.» Development of cognitive skills.» The body assumes proportions like that of an adult.,» Intersphenoidal synchondrosis fusion.» Normally, from 2 to 5 years old.» Phase characterized by mixed dentition; starts with the eruption of the first permanent molars.» Ugly duckling phase.» Logical thinking development, language, and memory improvement.,» Fusion of spheno-ethmoidal synchondrosis.21,22 , or in the beginning of the pubertal growth spurt (G1) or in the ascending phase of the pubertal growth spurt (Psi), or at the peak of the pubertal growth spurt (between FPcap and FMcap), determined by analyzing the middle finger of the left hand through carpal radiography21,22 .» Normally, from 6 to 12 years old.» Transition from the late phase of the mixed dentition to permanent dentition: the eruption of permanent second molars and/or permanent canines.» Post-menarche phase for girls (estimated growth of approximately five years more).» Vertebral maturation analysis: stages CS3 and CS4.,» Through the analysis of the carpal radiograph, it can be determined whether the patient is at the ascending phase of the pubertal growth peak curve (between R= and G2), at the peak of the pubertal growth spurt (between FPcap and FMcap), at the beginning of the descending curve (Rcap and M-Fdui); at the descending curve of pubertal growth (between Fpui and Riu); or at the end of growth (Rut)» Search for identity and maturity of the reproductive system.» Normally, from 13 to 18 years.» Characterized by permanent dentures and the presence of third molars.» Stability of personality traits.,» Physical and cognitive peak.» Maximum performance of adult life.,» Maturation of the cervical vertebrae shows that the patient is no longer growing;» Beginning of synostosis of the coronal and sagittal sutures,» Normally, from 19 to 24 years.» Complete permanent denture.-» Maturation of the cervical vertebrae and carpal radiography: the patient no longer shows growth (Rut stage).» Increase in the number of marital relationships and paternity.» Synostosis of the lambdoid suture.» Normally, from 25 to 44 years.» Characterized by mature permanent dentition, dental wear, arch length, and perimeter reduction, and decrease in the overbite, besides an increase in lower anterior crowding.» First signs of physical degeneration, characterized by menopause for women,» Affected reproductive capacity.» Normally, from 45 to 64 years old.» Greater susceptibility to tooth fractures and loss; use of dental prostheses and/or implants.» Teeth in more yellowish, brown, or gray tones; teeth with greater susceptibility to erosion, abrasion, plaque accumulation, and periodontal disease.» Adaptation of the stomatognathic system in the face of morpho-functional conditions, which become slower, uncoordinated, and adapted to the structural damage that occurs over the years.» Process of bone resorption in the maxilla and mandible, increasing its porosity; jawbone thickness reduction and mandibular angle decrease.» Decreased proximal femur mineral density, increasing the risk of hip fractures.» Increased physical and cognitive frailty.» In general, from 65 to 79 years old.» Partial or total absence of teeth associated with the use of prostheses.» Partial or complete obliteration of the volume of the canals and pulp chamber.,» Reduced dry mouth perception and difficulty for eating; the higher need for prosthesis.» Higher frequency of chronic pain.The distinct phases of the life cycle proposed in this study can be seen in This article was written in response to the need for a life cycle classification that provides clear and well-defined boundaries for its phases, specifically for use in scientific research within the healthcare field. Moreover, due to the growing life expectancy, some existing classifications grouped individuals into vastly different stages of maturation, cognition, and requirements. As a result, the proposed life cycle classification seeks to expand the viewpoint and promote contemplation on this matter.In this study, we conducted a comprehensive review of existing studies on life cycle classifications, focusing on their distinct phases. Based on the present analysis, the authors propose a classification that considers dental, growth, physiological aging, sociocultural, and behavioral characteristics. The primary aim of this classification is to enhance communication among healthcare professionals, particularly those involved in human growth, development, and aging. By providing a framework that encompasses these various aspects, the authors believe it will facilitate a more comprehensive understanding and effective collaboration in this field.


Gas explosions, particularly those involving methane–air mixtures, present considerable hazards in confined spaces, such as coal mines. Comprehending the explosion characteristics and their correlations with initial gas concentrations is vital for devising effective safety measures. This study examines the influence of initial gas concentration on explosion temperature, overpressure, and flame evolution in methane–air premixed gas explosions, utilizing a custom-built 20-L spherical explosion experimental apparatus. The explosion temperatures display an oscillatory pattern, reaching maximum values at 6.5%, 9.5%, and 12% initial gas concentrations, with corresponding temperatures of 995 K, 932 K, and 1153 K. The maximum overpressure exhibits an initial rise and fall trend, modeled by an exponential function. Notably, in proximity to the 9.5% concentration, the pressure wave fosters the reverse propagation of the flame wave, leading to a secondary temperature increase. Flame sensors were employed to investigate the presence, absence, and duration of flames, demonstrating that elevated initial gas concentrations resulted in more prolonged flame durations and increased harm. At an initial gas concentration of 9.5%, a persistent flame is generated instantaneously during the explosion. Furthermore, the study analyzes the interplay between temperature and overpressure, underscoring the significance of mitigating high-temperature burns near tunnel walls and enclosed spaces. These findings advance the understanding of gas explosion dynamics and hold substantial implications for safety measures in coal mines. Gas explosions release an immense amount of energy instantaneously, resulting in a high-temperature environment. In underground coal mines, explosions typically occur in excavation tunnels and mining faces, where environmental factors prevent the easy dissipation of explosion energy, causing sustained high temperatures in the tunnels and posing considerable risks to personnel and equipment7. The initial methane concentration influences the peak temperature and duration of the explosion. Investigating the effects of varying initial methane concentrations on temperature characteristics during the explosion process can help provide a crucial theoretical basis for understanding temperature characteristics in coal mine methane explosions, thus preventing the risks posed by gas explosions11.Methane gas explosions are a major hazard in coal mines, leading to significant consequences, including economic losses for coal enterprises, human casualties, environmental damage, and severely hindering the coal industry's production12 first derived an expression for explosion temperature through theoretical research. Scholars worldwide now use numerical simulation software such as FLACS14, FLUENT16, AutoReaGas18, and CHEMKIN19 to study methane explosion temperatures or establish specific mathematical physics equations to explore temperature variation rules under fixed volume or pipeline propagation conditions20. Some researchers have also simulated temperature variation rules for methane explosions in confined spaces, laying the groundwork for methane explosion temperature studies21. However, most simulations are conducted under isothermal or adiabatic conditions, leading to discrepancies with actual experimental data and preventing accurate simulations of temperature changes during real methane explosions.In 1967, Olsen22 used voltage signals to represent temperature, unveiling the temperature change trend of methane explosions as they propagate through pipelines. Subsequent researchers have studied temperature variations at different positions during pipeline propagation, finding that the flame temperature in the upper part of the pipeline is higher than in the lower part24. Cui et al.25 used R-type micro-thermocouples to examine the temperature variations of small-scale pipeline methane explosions. Li et al.26 employed C2-7-K and C2-1-K thermocouples to investigate temperature variations during explosion propagation, with the highest recorded temperature reaching 1292.27 K. Liu et al.27 analyzed the relationship between flame propagation and temperature during pipeline explosions, discovering that increased temperatures promote flame propagation. Nie et al.28 employed a two-dimensional temperature field radiation method to study temperature variations surrounding the explosion flame, finding that the temperature at the flame front initially rises sharply, then slows, and finally decreases after reaching its peak.Under experimental conditions, Wang and He29 found that increasing ambient temperature reduces the maximum explosion pressure of CH4, while the maximum pressure rise rate does not change significantly. Shi et al.30 used a 20 L spherical explosion vessel to study the explosion characteristics and influencing factors of CH4-coal dust mixed explosions, finding that under equivalent concentration conditions with an initial pressure equal to atmospheric pressure for coal dust and CH4, the greater the initial pressure of the combustible gas, the higher the maximum explosion pressure and pressure rise rate. Shirvill et al.31 found that adding less than 25% H2 by volume to the gas network does not significantly increase the intensity of methane explosions. Jiao et al.32 established a relationship between overpressure and temperature during tunnel explosions, obtaining a relatively reliable distribution of air temperature in the tunnel as a function of propagation distance shortly after the explosion. This provided a direction for studying the relationship between temperature and pressure during the explosion process. Tran et al.33 used ANSYS Fluent to create a three-dimensional cylindrical geometric model, simulating the characteristics of CH4 explosions and concluding that the maximum explosion pressure occurs when the equivalence ratio of the CH4-air mixture is 1.2, and adding H2 to the fuel mixture will increase the maximum explosion pressure.Explosion pressure under different initial methane concentrations is the primary parameter for studying methane explosions. Currently, due to their explosive nature, methane explosion experiments are mainly conducted in sealed containers. Pekalski et al.In summary, current research on temperature characteristics during methane explosions primarily focuses on numerical simulations, with relatively fewer studies on explosion temperature characteristics and the interactions between temperature and pressure under different initial methane concentrations. To address this, this paper experimentally investigates the temperature variations during the explosion process and the interactions between temperature and pressure under different initial methane concentrations. The findings contribute to the theoretical understanding of the mechanisms behind temperature variations during methane explosions, thereby enhancing strategies for gas explosion disaster prevention and control. Furthermore, a deeper comprehension of the interaction mechanisms between temperature, pressure, and flame during a methane explosion can provide a theoretical foundation for planning escape and rescue operations during gas explosion incidents.The explosion experimental system comprises three primary modules: a 20 L spherical explosion module, a gas supply and distribution module, and a data acquisition module, as illustrated in Fig. The 20 L spherical explosion module is constructed with a stainless-steel material, and the vessel is designed to withstand a pressure of 4 MPa with a thickness of 20 mm. This design ensures the necessary strength and integrity of the vessel during the experiment.4 concentration sensors. The CH4 concentration is initially estimated using the pressure proportioning method and is then fine-tuned with the aid of laser CH4 concentration sensors. These sensors can accurately calibrate the CH4 concentration within the vessel with a precision of 0.1%.The gas supply and distribution module primarily consist of solenoid valves, gas distribution pressure sensors, and laser CHThe data acquisition module comprises testing sensors and a transient signal test system. The testing sensors include temperature, pressure, and flame sensors, as shown in Fig. 4 gas was introduced into the vessel using the partial pressure proportioning method. A compressor was employed to inject air, ensuring the CH4/air mixture pressure reached 0.15 MPa. Following a 15-min resting period, the exhaust hole of the vessel was connected to a gas flow meter and a laser CH4 concentration sensor. The gas flow rate was adjusted until it stabilized at 200–300 mL/min, and the CH4 concentration was measured after 2 min. By adding air, the CH4 concentration in the vessel was fine-tuned until it reached the desired experimental value. The excess gas mixture was discharged to maintain the pressure inside the vessel at 0.12 MPa. It is important to note that the laser CH4 concentration sensor was calibrated using standard gas to ensure measurement accuracy of 0.1%.Initially, the spherical vessel was evacuated to 0.05 MPa. Subsequently, the required amount of CHUpon preparing the experiment, the trigger switch was activated, sequentially triggering the solenoid valve, high-pressure pulse generator, and data acquisition instrument to collect temperature, pressure, and flame data from the explosion vessel. The ignition electrode was then employed to ignite the gas combustion, initiating the explosion process.Following each experimental set, combustion products were discharged, and the data was recorded and saved. Additionally, the recorded data underwent automatic filtering and noise reduction processing to enhance its accuracy and reliability. The gas in the vessel was replaced 2 to 3 times, and subsequent experiments were conducted after the temperature in the vessel returned to room temperature. The initial conditions for the experiments were room temperature, 0.12 MPa, and an experimental step of 0.5%. It is well-known that the lower explosion limit (LEL) and upper explosion limit (UEL) of a methane–air mixture are 5.0% and 16.0% respectively. In this study, we specifically focused on testing concentrations within the range of 5.5% to 14.0%.34. In this study, the relationship between explosion temperature and initial gas concentration was experimentally investigated, as illustrated in Fig. Gas explosions generate high temperatures that can lead to devastating consequencesFollowing ignition, there was a 41 ms delay before the temperature increased sharply. The first peak temperature of 909.22 K was reached at 331 ms, then decreased to 894.35 K at 450 ms, followed by a second rise to a peak of 937.66 K at 570 ms, and subsequently declined gradually. This phenomenon can be attributed to the explosion temperature test sensor being positioned 103 mm from the center of the vessel. The confining effect of the explosion vessel and pressure, along with other factors, cause the flame in the 20 L vessel to oscillate and superimpose, resulting in the secondary temperature rise. The enclosed space, such as the vessel wall, exerts a restraining effect on the explosion, emphasizing the importance of preventing high-temperature burns near tunnel walls and closed ends in actual gas explosion scenarios.The maximum explosion temperature refers to the highest temperature reached during an explosion at a specific gas concentration. Figure 35. In the current experiment, the energy predominantly exists in high-pressure expansion gas, high-temperature environment, reaction products, and heat transfer. The energy of the reaction before the explosion varies with the initial gas concentration, and the high-temperature environment generated during the explosion represents one of the ways energy is released from the explosion process.The observed variation in the maximum explosion temperature with changes in the initial gas concentration can be attributed to the nature of the energy released during the gas explosion. The energy release during the gas explosion primarily occurs in the form of gas expansion and heatFigure 2 > 0.9.The evolution of overpressure during gas explosions was investigated under varying initial gas concentration conditions, and the results are presented in Fig. Flame signals can be collected through a flame sensor, which detects voltage signals. The presence of a voltage signal indicates the existence of a flame. The voltage signal is utilized to study the presence, absence, and duration of the flame. As shown in Fig. , the flaWhen the initial gas concentration is near 9.5%, the explosion occurs at the same time as the flame signal is picked up, near the UEL and LEL, the flame sensor picks up the flame signal after a certain period of time before a stable explosion flame exists. The signal image of a typical flame is taken to study the change in flame during the explosion of low concentration gas (5.5%), equivalent concentration gas (9.5%) and high concentration gas (14%), as shown in Fig. When the initial gas concentration is near 9.5%, the explosion occurs simultaneously with the flame signal detection. Near the UEL and LEL, the flame sensor detects the flame signal after a certain period of time before a stable explosion flame exists. Figure Analysis of the flame signal images revealed that at an initial gas concentration of 5.5%, intermittent flames first appear in the vessel after ignition, indicating that the low gas concentration causes a weak initial explosion with an unstable flame. However, after a period, the explosion temperature and pressure rise, resulting in a stable explosion with a flame stability duration of 1432 ms. At an initial gas concentration of 9.5%, the explosion is more intense and the flame appears immediately after ignition, with a stable flame duration of 1232 ms. Conversely, at an initial gas concentration of 14%, the high gas concentration makes ignition difficult, causing the gas around the ignition source to ignite first and, after a period, produce a stable flame with a flame stability duration of 2189 ms.Figure The analysis suggests that high initial gas concentrations result in longer flame durations during explosions, leading to greater harm to individuals. In addition to the toxic effects of post-explosion gases and the possibility of secondary explosions, the prolonged burning effect on the human body can cause severe damage to personnel. Therefore, in coal mines, preventing high concentrations of gas explosions is crucial for mitigating the hazards posed by high temperatures. Implementing measures to monitor and regulate gas concentrations in mines can help reduce the likelihood of gas explosions and ensure the safety of miners.1 = 216 ms. Additionally, the temperature peaked for the second time at 936.34 K for 570 ms, with a pressure peak time interval of t2 = 455 ms. Notably, the analysis demonstrates that the pressure wave propagated faster than the flame during the explosion process, causing the temperature to reach its first peak later than the pressure wave peak time.Understanding the relationship between temperature and overpressure during gas explosions is crucial for comprehending the dynamics of the explosion process and developing effective safety measures. In this context, the temperature and overpressure of a 9.5% concentration gas explosion were analyzed, as illustrated in Fig. Furthermore, as the explosion pressure wave reaches the vessel wall, it encounters a reverse force, resulting in its reverse propagation. Simultaneously, the slower burning wave converges with the reverse-propagating wave, causing some of the flame burning waves to also propagate in the opposite direction. The superposition of the forward and reverse burning waves leads to a further increase in the flame temperature. It is worth noting that this phenomenon can be hazardous, especially when the explosion strength is high, as it can result in a secondary increase in the gas explosion temperature.3 and t4, are significantly shorter than t2, thereby diminishing the reverse propagation of the flame wave. As a result, the phenomenon of secondary heating during the temperature rise process is weakened, and in some cases, non-existent.However, when the initial gas concentration is near the UEL and LEL, for example at 6.5% and 12%, as shown in Fig. 2 > 0.9).The explosion characteristics of methane–air premixed gas are strongly influenced by the initial gas concentration. The explosion temperature exhibits an oscillatory pattern with increasing gas concentration, with maximum values occurring at 12% and 6.5% concentrations. The maximum overpressure follows an initial rise and fall trend and can be modeled with a fitting equation The relationship between temperature and overpressure during gas explosions reveals the causes of temperature changes. Pressure wave propagation is faster than flame propagation, resulting in a secondary increase in temperature during strong explosions. Near the 9.5% gas concentration, the explosion temperature exhibits two peaks over time. However, when the initial gas concentration is near the UEL and LEL, the phenomenon of secondary heating is weakened or non-existent. This highlights the importance of preventing high-temperature burns near tunnel walls and closed spaces in actual gas explosion scenarios.Flame sensors were employed to study the presence, absence, and duration of flames, revealing that high initial gas concentrations led to longer flame durations and greater harm. Therefore, monitoring and regulating gas concentrations in potentially explosive environments, such as coal mines, are essential for mitigating hazards posed by gas explosions and ensuring personnel safety.The explosion intensity near the explosion limits is reduced due to the influence of the initial gas concentration, leading to a slower rate of pressure rise during the explosion process, a longer time to reach the pressure peak, and a weakened ability of pressure waves to reverse propagate after reaching the explosion tank. The time intervals between pressure and temperature peaks during the explosion process are smaller, which diminishes the reverse propagation of the flame wave and weakens secondary heating during the temperature rise process, or even renders it non-existent. Methane–air premixed gas with a 9.5% methane concentration exhibits the most intense explosion effect, generating stronger pressure waves and a higher flame propagation speed.This study has investigated the explosion characteristics of methane–air premixed gas and the influence of initial gas concentration on explosion temperature, overpressure, and flame evolution. The findings provide valuable insights into the dynamics of gas explosions and have significant implications for the development of effective safety measures. The main conclusions of this study are as follows:


Obstructive sleep apnea (OSA) is common in obese patients with type 2 diabetes mellitus (DM) and may contribute to diabetic microvascular complications.2) with DM undergoing retinal screening and respiratory monitoring during sleep. OSA was defined as apnea-hypopnea index (AHI) of ≥15 events/hour, resulting in two groups (OSA+ vs. OSA−).To investigate the association between OSA, hypoxemia during sleep, and diabetic retinal complications in severe obesity. This was a prospective observational study of 93 obese patients mean (SD) age: 52(10) years; mean (SD) body mass index (BMI): 47.3(8.3) kg/m1c, and insulin treatment (p>0.05). The OSA+ group was significantly more hypoxemic. There was no significant difference between OSA+ and OSA− groups for the presence of retinopathy (39% vs. 38%). More OSA+ subjects had maculopathy (22% vs. 13%), but this did not reach statistical significance. Logistic regression analyses showed that AHI was not significantly associated with the presence of retinopathy or maculopathy (p>0.05). Whilst minimum oxygen saturation was not significantly associated with retinopathy, it was an independent predictor for the presence of maculopathy OR = 0.79 , after adjustment.Forty-six patients were OSA+: median (95% CI) AHI = 37(23–74)/hour and 47 were OSA–ve (AHI = 7(4–11)/hour). Both groups were similar for ethnicity, BMI, cardiovascular co-morbidities, diabetes duration, HbAThe presence of OSA, as determined by AHI, was not associated with diabetic retinal complications. In contrast, severity of hypoxemia during sleep (minimum oxygen saturations) may be an important factor. The importance of hypoxia in the development of retinal complications in patients with OSA remains unclear and further studies assessing the pathogenesis of hypoxemia in patients with OSA and diabetic retinal disease are warranted. Routine clinical data collected included age, gender, self-reported ethnicity, weight, BMI, cardiovascular co-morbidities (hypertension and coronary artery disease), duration of DM, DM medications, HbAPatients underwent home portable respiratory monitoring during sleep using the Embletta™ (Embla systems). The protocol for this system is used as part of standard clinical care in the current service setting. All patients were shown how to utilize the equipment by a trained physiologist in sleep medicine on the afternoon of the test night. Measurement channels included airflow using a nasal pressure device, chest and abdominal movements by inductance plethysmography, oxygen saturation through pulse oximetry, heart rate, and body position. The staff observed the patient undergoing a trial of self-application before leaving the sleep physiology department. Devices were returned the next day and studies were downloaded and reviewed for data quality. Patient filled in a sleep diary for the night of time to bed and time to estimated sleep and time wake up to assess the matching with the objective sleep respiratory signals. Studies showing at least four hours of good quality respiratory signals were regarded as acceptable. If any data were deemed inadequate, the patient was offered a retest. All data were manually scored by blinded sleep trained physiologists and then rescored by author DB for confirmation. Author DB was blinded to any diabetic eye screening data. All parameters were scored based on respiratory event during sleep scoring guidelines published by the American Academy of Sleep Medicine DR data were obtained from the routine local screening program based on the English National Screening Program for Diabetic retinopathy (ENSPDR). Two 45° fields per eye  are obtained on an annual basis. ENSPDR has developed guidance on the classification of the severity of diabetic retinopathy in England and Wales 2); and 4) % time spent under 90% oxygen saturation; and DR as well as DMac. Model 1 was unadjusted, Model 2 adjusted for age, gender and ethnicity, and Model 3 was further adjusted for DM duration, insulin treatment and HbA1c, hypertension, and coronary artery disease. Results were reported as odds ratio (OR) with 95% confidence interval (95% CI). A p value of <0.05 was considered statistically significant.All statistical analyses were carried out using the Statistical Package for Social Sciences, version 19 . Data analysis was performed on anonymized patients as part of service evaluation, therefore no formal ethical approval was required as per recommendation from the UK National Research Ethics Service There were a total of 121 DM patients eligible, but three patients’ sleep respiratory data were not analyzable, and 26 patients did not have the complete available retinopathy screening data; therefore, a total of 93 eligible patients were included in the study. No patient had a diagnosis of central sleep apnea.2, respectively. Sixty-three per cent of patients suffered from hypertension. Median (IQR) duration for DM was 6(2–10) years with a median (IQR) HbA1c of 62(51–77) mmol/mol or 7.8(6.8–9.2)%. The majority of patients were on diabetes medications (81.7%) and 33% of the patients were on insulin treatment. Median (IQR) AHI was 14(7–36) events/hour with a median (IQR) minimum oxygen saturation 82(74–85)% and median (IQR) % time spent under 90% oxygen saturation of 3(1–12)% for the total sample.The mean (SD) age of the patients was 52(10) years and 59% of the patients were females. The majority of patients were of white European origin (69%) and 23% were of South Asian origin. Mean (SD) weight and BMI were 130.4(25.3) Kg and 47.3(8.3) Kg/m1c , DM duration , insulin treatment  and hypertension . The results of our multivariate logistic regression analyses are shown in Univariate logistic regression identified factors significantly associated with diabetic retinal disease. Diabetic retinal disease was significantly associated with diabetes duration  and insulin treatment . DMac was associated with HbARetinal disease is a well-recognized micro-vascular complication of DM Our study did not show any significant difference in the prevalence of diabetic retinal disease between those who had moderate to severe OSA compared to those who did not. We examined the relationship between diabetic retinal disease and respiratory parameters using logistic regression analysis. After correction for age, gender, ethnicity, diabetes duration, usage of insulin and the presence of hypertension, there was no association found between mean oxygen saturations, and % time spent under 90% oxygen saturations and retinal disease. Our results were consistent with the Japanese studies, which did not find any correlation with the above respiratory parameters 1c with DR There has been much interest in the role of OSA in the development of micro-vascular complications in patients with DM; this study showed that OSA, when measured by using the apnea – hypopnea index, has no association with retinal complications. Only one study investigated the effect of AHI and DR and it was performed in 98 veterans who are predominantly male. Although this study found a significant positive correlation between DR and AHI, the result was not adjusted for potential confounders 2 = 0.3) Logistic regression analyses showed that the degree of hypoxemia, determined by minimum oxygen saturation during sleep, was associated with the development of DMac, but not DR. As the OSA group was more hypoxemic than the group without moderate to severe OSA, this may explain why the OSA+ had a higher prevalence of DMac. The relationship between OSA and diabetic retinal disease is important given the risk of blindness The findings of our study show that minimum oxygen saturation may be associated with DMac supports the findings by Shiba et al 1c level was also 0.4% higher in the OSA+ group and this is also associated with severe DR Apart from intermittent hypoxia, the other potential mechanisms for greater DMac in OSA patients may be due to elevated BP and severity of DM. Hypertension is strongly associated with OSA Our study also examined creatinine and eGFR levels. There was a significant higher creatinine level and a non-significant lower eGFR in OSA+ group. These findings may act as a reassurance in the probable greater early development of diabetic retinal disease as part of DM micro-vascular complications in our OSA+ group. The impact of correcting OSA on micro-vascular complications remains unknown but a study recently by Mason et al looking at the impact of CPAP on patients with OSA (defined as ODI>10 events/hr or AHI>15 per hour) on macular edema showed no improvement in the disease, but interestingly, some improvement in visual acuity Our study benefits from well-matched groups that are representative of the severely obese population and the ethnic diversity of our local population. The OSA+ and OSA− groups were well matched for BMI, co-morbidities, diabetes duration, and diabetes medications. The OSA+ group had more men and was older than the OSA− but these differences were accounted for when undergoing statistical analyses. Also, in our analyses, we observed the expected and previously reported factors associated with diabetic retinal disease. The limitations of our study include its cross-sectional design and lack of information regarding other potentially important factors such as smoking. Visual acuity data were also not available.This study of severely obese patients with DM found no association between OSA and DR but there was a higher prevalence of DMac in those with moderate to severe OSA. Further analysis suggests that the severity of hypoxemia, particularly minimum oxygen saturation has an independent association with the development of maculopathy but the exact mechanism for this remains unclear. As this OSA group was more hypoxemic, it is hypothesized that hypoxemia and not apnea/hypopnea frequency per se is the key factor in developing diabetic micro-vascular complications. The other likely potential mechanisms were greater systemic hypertension and severity of DM in OSA. The impact of CPAP on micro-vascular retinal complications in OSA patients remains undetermined, but further RCTs may be warranted to specifically look at the hypoxemic OSA group.