Frailty is a geriatric syndrome that results from multisystem impairment caused by age‐associated accumulation of deficits. The frailty index is used to define the level of frailty. Several studies have searched for molecular biomarkers associated with frailty, to meet the needs for personalized care. Cyclase‐associated protein 2 (CAP2) is a multifunctional actin‐binding protein involved in various physiological and pathological processes, that might reflect frailty's intrinsic complexity. This study aimed to investigate the association between frailty index and circulating CAP2 concentration in 467 community‐dwelling older adults  from Milan, Italy. The selected robust regression model showed that circulating CAP2 concentration was not associated with chronological age, as well as sex and education. However, circulating CAP2 concentration was significantly and inversely associated with the frailty index: a 0.1‐unit increase in frailty index leads to ~0.5‐point mean decrease in CAP2 concentration. Furthermore, mean CAP2 concentration was significantly lower in frail participants  than in non‐frail participants. This study shows the association between serum CAP2 concentration and frailty status for the first time, highlighting the potential of CAP2 as a biomarker for age‐associated accumulation of deficits. Circulating CAP2 concentration is not associated with chronological age, sex and education. CAP2 concentration is significantly and inversely associated with the Frailty Index (a 0.1‐unit increase in Frailty Index leads to ~0.5‐point mean decrease in CAP2 concentration). Mean CAP2 concentration was significantly lower in frail participants than in non‐frail subjects. CAP2 provides a snapshot of the frailty status representing a potential biomarker for age‐related accumulation of deficits. Frailty is a clinically recognized condition characterized by increased vulnerability to stressors due to reduced homeostatic reserve , an actin‐binding protein that controls actin cytoskeleton dynamics and, thereby, regulates cellular processes such as muscle contraction and changes in synaptic morphology that underlie memory and learning . Among participants, 55.7% were women, and 51.8% reported nine or more years of education. Their median FI was 0.22 . Finally, their median CAP2 concentration was 5.21 ng/mL  Table . No signp from t tests >0.05). When the FI was introduced into the model in continuum (middle columns), it was the only variable significantly and inversely affecting CAP2 concentration . Similarly, when the FI was introduced as a dichotomous variable , the β coefficient was still negative and significantly different from 0 . Based on this model, the frail participants showed a lower mean CAP2 concentration  than the non‐frail participants   do not significantly modify CAP2, CAP2 circulating concentration was inversely related to FI. In detail, (i) increases in FI in continuum, indicating greater frailty, were related to decreases in serum CAP2 concentration; (ii) mean circulating CAP2 concentration was significantly lower in frail than in non‐frail participants.Although it is significantly related to FI , serum CAP2 concentration is not significantly related to chronological age alone. This suggests a potential role for CAP2 as a biomarker of age‐related accumulation of deficits that cannot be mirrored by chronological age. Indeed, chronological age can be considered inadequate for capturing individuals' homeostatic weakness, increased vulnerability to diseases, and decreased physiological integrity associated with aging.Our results support the hypothesis that circulating CAP2 concentration provides a snapshot of frailty. Although the FI provides a multidimensional measure of clinical phenotypes related to deficits, biomarkers such as CAP2 may assist in better targeting at‐risk subpopulations, thus (i) ameliorating planning and implementation of personalized preventive and therapeutic interventions and (ii) supporting a more accurate prediction of frailty's trajectory. In addition, several studies have suggested that the age‐associated decline in adaptive homeostasis is a major risk factor for many age‐associated diseases, such as cancer, cardiovascular disease, and AD  validate diagnostic performance of CAP2 in larger samples alone or combined with other biomarkers and (ii) establish the potential prognostic value of CAP2 in frailty and its possible role in tracking the efficacy of clinical interventions in frail older individuals.11.1At the geriatric outpatient clinic, Fondazione IRCCS Ca′ Granda Ospedale Maggiore Policlinico, Milan, Italy, 467 community‐dwelling older adults underwent a comprehensive geriatric assessment and provided biological specimens between 2005 and 2020 Appendix  based on1.2Serum CAP2 concentration was determined using a commercially available ELISA kit .1.3p‐values >0.1 from models including up to the three‐way interaction, the final confounding‐factor‐only model included the main effects for chronological age, sex, and education. Based on likelihood ratio test p‐values >0.1 from models including up to the four‐way interaction, the final selected FI‐based model (FI continuous) included terms for chronological age, sex, education, and FI. The same robust model was also fitted using a dichotomized FI variable  and the EMMs were calculated by averaging predictions from this model over a reference grid  and FI  were considered as independent variables and regressed on the dependent variable CAP2. Based on likelihood ratio test d Lenth, . Detailsd Lenth,  with libs Lenth, .S.P. and C.M. designed, conducted, and analyzed the experiments, revised the manuscript; L.D.A. conducted and analyzed the experiments; P.D.R. revised the manuscript, funding acquisition; M.C.S. analyzed the experiments, revised the manuscript; R.S. analyzed the experiments, revised the manuscript; M.R. designed experiments, revised the manuscript; B.A. designed experiments, revised the manuscript; M.D.L. revised the manuscript; M.C. designed experiments, revised the manuscript; V.E. performed statistical analysis, wrote and revised the manuscript; E.M. conceptualization, funding acquisition, project administration, wrote and revised the manuscript.All authors read and approved the final manuscript.This work was supported by the Fondazione Cariplo (grant number 2018‐0511 to EM and P.D.R), the Italian Ministry of University and Research .The authors declare that they have no competing interests.All participants provided informed consent to use their clinical and biological data for the research .Appendix S1.Click here for additional data file.


APOE) ε4 allele has been associated with cognitive decline in Parkinson's disease (PD), but little is known about its relationship with motor and other nonmotor symptoms and whether APOE ε4 retains an influence on cognition when other factors are considered.The apolipoprotein E  score, Non‐motor Symptom Questionnaire (NMS) score, Movement Disorder Society–Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) Part II score, and physical activity level.We analyzed data from 7616 individuals, comparing motor/nonmotor symptoms in different APOE ε4 heterozygosity was modestly associated with lower cognitive scores , whereas no significant association was found for any other nonmotor and motor symptoms. However, in multivariate analysis, cognitive impairment was associated with higher GDS , NMS , and MDS‐UPDRS Part II  scores, whereas physical activity was negatively associated . APOE ε4 was no longer significant after adjusting for these factors.APOE ε4 in patients with PD; however, when considering multiple factors, APOE ε4 plays a subordinate role. Other factors, such as depression, physical activity, and other nonmotor symptoms, demonstrate a stronger influence on cognitive impairment.There is a link between cognition and  APOE) ε4 allele.APOE ε4 allele is a significant genetic risk factor for sporadic Alzheimer's disease (AD) and dementia with Lewy bodies.APOE ε4 status and cognitive impairment in individuals with PD, indicating its potential role as a genetic risk factor for the development of cognitive decline in these patients.APOE ε4 may also influence motor symptom progression in PD.APOE ε4 on cognition and other nonmotor/motor symptoms in PD, this study aims to investigate the associations between APOE ε4 genotypes and motor/nonmotor symptoms in a large sample of patients. In addition, we aimed to investigate the relationship between APOE ε4 and cognitive impairment while considering the impact of other factors. By exploring these associations, we aim to enhance our understanding of the potential role of genetic variation in PD symptom development and identify targets for personalized interventions to improve patient outcomes.Genome‐wide association studies have identified several genetic susceptibility loci for Parkinson's disease (PD), including the apolipoprotein E (https://foxinsight-info.michaeljfox.org/insight/explore/insight.jsp), is an ongoing, online clinical trial that is building a large cohort of people with and without PD and capturing self‐reported clinical variables. Study methodology for the FI study has been reported elsewhere in detail.APOE genetic data and excluded individuals who had incorrectly filled or missing data  study between 2017 and 2020. The FI study, sponsored by The Michael J. Fox Foundation for Parkinson's Research . Briefly, APOE genotype was genotyped based on 2 single nucleotide polymorphisms (SNPs), rs7412 and rs429358, to distinguish between the APOE ε2, ε3, and ε4 alleles. Illumina Infinium Global Screening  or OmniExpress‐24 Bead Chip array was used to genotype these SNPs .APOE ε2/ε4 alleles (n = 156) because the APOE ε2 allele, which has potential neuroprotective effects, may have attenuated the harm related to the APOE ε4 allele.For To rate cognitive instrumental activities of daily living (IADL), the Penn Parkinson's Daily Activities Questionnaire (PDAQ‐15) was used. The questionnaire includes 15 items that assess the level of difficulty a participant currently experiences as a result of PD on IADLs such as reading comprehension, medication management, navigation, learning to use new gadgets, financial management/understanding, and orientation. Each question is scored 0 to 4, with a total range of 0 to 60 points. Higher scores indicate better performance on IADLs, and the rating scale is as follows: none = 4, a little = 3, somewhat = 2, a lot = 1, or cannot do = 0. To differentiate between participants with normal/mildly impaired cognitive function and those with severe impairment, a previously validated cut‐off score of <43 was applied. This cut‐off score was validated for this particular assessment conducted by knowledgeable informants and was used in this study because of the lack of a currently validated self‐report measure.Physical activity was assessed with the Physical Activity Scale for the Elderly (PASE). The PASE is a self‐reported questionnaire assessing the duration and frequency of physical activities during the previous week. It consists of the following 3 domains: leisure (5 components), household (6 components), and occupation (1 component). The leisure subscore includes questions about light activity (reading), light recreational activity , walking; moderate or vigorous physical activity , and muscle strengthening (weightlifting). Household activities include light  and heavy . The occupational activities include questions about hours spent working or volunteering and the amount of physical activity required for work . However, the occupational data were not published in the FI study because of privacy concerns and therefore have not been included in the PASE calculation. The PASE score was calculated according to the scoring manual and higher scores indicated greater physical activity.Furthermore, for the assessment of depression, we used the Geriatric Depression Scale 15 (GDS‐15), a short form extracted from the 15 questions of the GDS. The total score is 30 points with validated cutoffs for the short assessment, scores below 5 indicate no depression; 5 to 8, mild symptoms; 9 to 11, moderate symptoms; and 12 to 15, severe symptoms.For assessing motor functions, the Movement Disorder Society–Sponsored Revision of the Unified Parkinson's Disease Rating Scale (MDS‐UPDRS) Part II was used (Part II consisting of 13 items). All items were scored on a scale from 0  to 4 (severe), and total scores were obtained from the sum of the corresponding item scores.The Non‐motor Symptom Questionnaire (NMS) was used to assess nonmotor symptoms. This is a 30‐item, self‐completed, yes/no questionnaire to assess patients' nonmotor symptoms in PD. The items contain 9 domains: gastrointestinal, urinary tract, sexual function, cardiovascular, apathy/attention/memory, hallucinations/delusions, depression/anxiety/anhedonia, sleep/fatigue, pain, and miscellaneous. The positive answers were summed for a total score ranging from 0 to 30. Higher scores indicate worse nonmotor symptom conditions.The Rapid Eye Movement Sleep Behavior Disorder Single‐Question Screen (RBD1Q) questionnaire is a screening tool used to identify individuals with probable rapid eye movement sleep behavior disorder (RBD). The RBD1Q consists of a single yes/no question that asks, “Have you ever been told, or suspected yourself, that you seem to ‘act out your dreams’ while asleep ?” This questionnaire has been validated as a useful tool for identifying patients with probable RBD and has been shown to have a high sensitivity and specificity.Using the Comorbidity Questionnaire, patients were asked about 14 health conditions . In addition, all “other” reported health problems were asked: (1) whether they have the condition (yes/no), (2) whether they receive treatment for the condition (yes/no), and (3) whether the condition limits their activities (yes/no).The following demographic variables were used: age, sex, income, employment, and educational level, as well as taking PD medication and disease duration.APOE genotype, ε4 noncarriers , APOE ε4 heterozygotes (ε3/ε4), and APOE ε4 homozygotes (ε4/ε4) and calculated using analysis of variance or Kruskal–Wallis test and the χ2 test where appropriate. To explore the associations between different APOE genotypes and various motor/nonmotor symptoms, we performed binary logistic regression analysis using APOE noncarriers as the reference group. The independent variables included GDS, MDS‐UPDRS Part II, NMS, PDAQ‐15, PASE, and PRBD1Q scores. To adjust for confounding variables, we ran 3 different models: the first adjusted for age and sex; the second additionally adjusted for education, employment, and income; and the final model further adjusted for disease duration, PD medication, and self‐reported comorbidity , and median with minimum–maximum, and frequencies. Participant baseline characteristics were described according to the APOE genotypes, we conducted both univariate and multivariate logistic regression analyses . All calculations were considered exploratory, and we did not adjust for type II error.To assess the associations between cognitive IADL and GDS, NMS, MDS‐UPDRS Part II, PASE, and PRBD1Q scores and APOE genotype data and complete data, yielding a final sample of 7616 individuals with a mean age 65.3 (SD 9.2) years and a slightly lower proportion of female participants (43.9%). With respect to APOE genotype, 78.7% were noncarriers of the ε4 allele, 19.9% were APOE ε4 heterozygotes, and 1.4% were ε4 homozygotes. Detailed information on participants' characteristics can be found in Table APOE ε4 genotypes in all reported variables: age, sex, disease duration, PD medication, education, employment, income, comorbidity score, depression, cognition and daily activities, movement experiences, nonmotor symptoms, physical activities, and PRSBD  of having a higher PDAQ‐15 score (a higher score reflects better cognition) than those without the allele. However, there was no significant association between APOE ε4 homozygosity and PDAQ‐15 across all 3 models. Furthermore, none of the other independent variables, including GDS, NMS, PASE, MDS‐UPDRS Part II, and PRBD1Q scores, were significantly linked to APOE ε4 heterozygosity or homozygosity in any of the models.In Table APOE ε4 homozygotes, as significant. However, multivariate analysis refined these predictors to GDS, NMS, MDS‐UPDRS Part II, and PASE scores. Each 1‐point increment in GDS, NMS, and MDS‐UPDRS Part II score raised the cognitive impairment odds by 28%, 22%, and 7%, respectively. Conversely, higher PASE scores, indicating increased physical activity, lowered cognitive impairment risk. APOE ε4 heterozygotes showed no significant effect in the multivariate analysis.Table APOE ε4 genotype associated with motor and nonmotor symptoms in PD. In the present study, we found a modest but significant association at least in APOE ε4 heterozygotes with disabilities in cognitive IADL status. The fact that the association remained significant even after an adjustment for several cofounders indicates a marginal detrimental impact of APOE ε4 on cognition in IADL of individuals with PD. However, we did not have enough statistical power to detect the effect of the ε4 allele when ε4 homozygote alleles were compared with APOE ε4 noncarriers. Although several studies have demonstrated an overrepresentation of APOE ε4 carriers among individuals with PD cognitive impairment and dementia, others have been equivocal or provided only modest evidence.APOE ε4 allele could be a risk factor for cognitive decline in neurodegenerative diseases. However, the underlying pathophysiological mechanisms remain to be fully elucidated.This is one of the largest data sets of individuals that describes the genetic variability of APOE ε4 allele is widely recognized in scientific literature for its role in accelerating cognitive and motor decline in PD.APOE ε4 homozygotes. In the multivariate analysis, these predictors were refined to the GDS, NMS, MDS‐UPDRS Part II, and PASE scores. APOE ε4 heterozygotes did not show a significant effect in the multivariate analysis. APOE ε4 is associated with the accumulation and deposition of amyloid‐β (Aβ) peptides, a hallmark of neurodegeneration. The interaction of APOE ε4 with the low‐density lipoprotein receptor‐related protein 1 could exacerbate the Aβ pathology, pointing toward a possible mechanism for the APOE ε4–induced cognitive decline.APOE ε4 on cognitive impairment may be more nuanced and possibly modulated by other factors such as depression and physical activity. Although the APOE ε4 allele is linked with an increased risk for depression and cognitive impairments,APOE ε4, depression, physical activity, and cognition in the context of PD is warranted.The APOE ε4 allele and motor or nonmotor symptoms in PD individuals based on the patient‐reported MDS‐UPDRS Part II data. However, the broader literature provides a nuanced perspective. Some studies using the clinician‐rated Part III of the MDS‐UPDRS have suggested an association between the APOE ε4 allele and risk of motor symptoms,APOE ε4 genotype and the progression of motor symptoms in PD. Their findings suggest that the impact of the APOE ε4 allele might be more pronounced in the neocortex, where AD and Lewy body disease pathologies are found, rather than in the subcortical and brainstem structures where Lewy bodies are predominantly found in PD. Specifically, their study indicated that APOE ε4 was associated with the progression of motor symptoms during the middle or late stages of PD, especially in patients who were either Aβ positive or RBD negative. The authors also posited that APOE ε4 might facilitate the propagation of extracellular α‐synuclein in cortical regions during the mid‐to‐late stages of the disease, suggesting a potential direct interaction between APOE ε4 and α‐synuclein. These findings underscore the potential role of APOE ε4 in the propagation of α‐synuclein pathology in PD, highlighting the intricate interplay between genetic and pathological factors in the progression of the disease.Our study did not find an association between the APOE variants in a large cohort of 7616 individuals.Given that longitudinal studies represent a gold standard for tracking disease progression but are traditionally hampered by small sample size, the power of this study is significantly enhanced through its size. In this study, we were able to significantly enhance the power of our analysis by examining The present study has several limitations: First, as a result of the cross‐sectional design of the study, conclusions are limited to a specific point in time. Second, although online surveys have the advantage of collecting data from many individuals in a short period of time, the major limitation is that all results are based on self‐report. In addition, the PD diagnosis was self‐reported without independent review by a movement disorder specialist, which limits the possibility of ruling out that some patients may actually have a form of atypical parkinsonism that may respond differently to cognitive performance in IADL than PD. Therefore, the lack of quantification of the different measures should be considered when interpreting the results. Furthermore, although the PDAQ‐15 measures the potential impact of cognitive IADL and is correlated with cognition, cognition was not directly assessed in this study and was solely reliant on self‐reported data. Studies have revealed that individuals tend to underestimate their cognitive impairments in self‐report questionnaires.It is important to note that individuals with more severe cognitive impairments may have been unable to participate or complete the questionnaire fully, which may have led to an underrepresentation of severe cognitive impairment, potentially limiting the generalizability of the findings. However, compared with academic centers where response rates are traditionally low, the FI study was able to achieve a high response rate and obtain a large sample size, which allows obtaining a comprehensive understanding of people with PD. This online approach can improve accessibility for individuals with mobility or cognitive impairments and could be an effective strategy for future research on PD and other neurological disorders, especially when attempting to recruit underrepresented populations.APOE ε4 genotype (heterozygote) is modestly associated with cognitive impairment in IADL among individuals with PD. However, no significant associations were observed between APOE ε4 and motor or other nonmotor symptoms in PD. Although a link between cognition and APOE ε4 exists in patients with PD, our results suggest that APOE ε4 plays a secondary role when considering multiple factors such as depression, physical activity, and other nonmotor symptoms, which have a stronger impact on cognitive impairment. Further longitudinal studies are necessary to better understand the role of APOE ε4 genotype in cognitive impairment and to develop specific prevention strategies for individuals at high genetic risk.Our study found that (1) Research Project: A. Conception, B. Organization, C. Execution; (2) Statistical Analysis: A. Design, B. Execution, C. Review and Critique; (3) Manuscript Preparation: A. Writing of the First Draft, B. Review and Critique.A.K.: 1A, 1B, 1C, 2A, 2B, 3AS.H.: 1A, 3BM.W.: 2B, 2C, 3BJ.S.: 1A, 1B, 1C, 2C, 3BEthical Compliance Statement: The authors confirm that informed patient consent was not required for this work. We confirm that we have read the journal's position on issues involved in ethical publication and affirm that this work is consistent with those guidelines. The authors confirm that the approval of an institutional review board was not required for this work.Funding Sources and Conflicts of Interest: This research did not receive any specific grant from funding agencies in the public, commercial, or not‐for‐profit sectors. There are no relevant conflicts of interest to disclose.Financial Disclosures for the Previous 12 Months: The authors declare that there are no additional disclosures to report.


Solar‐driven water purification is considered as an effective and sustainable technology for water treatment using green solar energy. One major goal for practical applications is to improve the solar evaporation performance by the design of novel photothermal materials, with optimized heat localization and water transport pathways to achieve reduced energy consumption for water vaporization. Recently, some emerging materials like polymers, metal‐organic frameworks (MOFs), covalent organic frameworks (COFs) and also single molecules were employed to construct novel solar evaporation systems. In this minireview, we present an overview of the recent efforts on materials development for water purification systems. The state‐of‐the‐art applications of these emerging materials for solar‐driven water treatment, including desalination, wastewater purification, sterilization and energy production, are also summarized. Research on solar‐driven water purification has grown rapidly over the past years and emerging materials such as polymers, MOFs and COFs have been employed in this field recently. This minireview gives an overview of the basic concepts and principles for solar evaporation systems and on the development of novel photothermal materials. Photothermal materials have thus been utilized to enhance the evaporation rates. In general, water is soaked into the photothermal material and then evaporated by the heat produced from the material under sunlight irradiation. Systems for solar evaporation were however first developed in form of volumetric heating, using e.g. homogeneously dispersed metal nanoparticles (NPs) as solar absorbers, which convert the incident photons to thermal energy to heat the aqueous dispersion medium. These systems however require a long‐term and intense light source to heat the bulk water, resulting in a low light‐utilizing efficiency.Drinkwater shortage has become a global concern due to the inherent freshwater scarcity on earth and water pollution caused by industrial/agricultural production and domestic sewage.Comprehensive reviews have elaborated on the topics of photothermal materials,2In an interfacial SVG system, the following four key principles should be optimized.2.12O3),As essential component of SVGs, diverse solar absorbers have been applied, such as plasmonic metallic NPs ,2.2ηs‐t is a standard metric to evaluate the performance of solar‐radiation‐to‐thermal‐energy conversion. It can be estimated by the equation ηs‐t=m hLV/Copt 0P at the steady state, where m denotes the net evaporation rate, hLV is the equivalent evaporation enthalpy of water through the solar evaporators, P0 represents the intensity of the incident radiation and Copt is the optical concentration .The mechanisms for light‐to‐heat conversion are related to the nature of the materials. The photothermal conversion ability can be controlled via varying band gaps, introducing defects sites, changing morphologies or creating composites.7b, 142.3−1 K−1, 20 °C), the downward conduction dissipation is a main loss factor and related to the thermal conductivity of the materials.Ideally, the converted thermal energy should be utilized entirely for heating the water. However, in a typical system, heat loss is inevitable, which takes place through three routes: downward conduction loss to bulk water, upward convection to the environment and radiation loss. Minimizing the thermal loss is crucial to ensure heat localization at the evaporation interface and promoting evaporation efficiency. Due to the high thermal conductivity of the water itself  (PVA) showing high affinity to water molecules, has become the most popular polymer to fabricate solar evaporators, usually in the form of hydrogels. As PVA does not absorb light in the visible, various absorbers, for instance, PPy,12b actis Figure  to reguls Figure  and f, yn Figure  have been Figure . Such str Figure  and k.−2 h−1 is achieved under 0.5 sun, which even surpasses the most reported SVG yields under one sun. Extracting the energy from environments would be a rational and effective guidance in the construction of solar evaporators.Very recently, tuning of channel size to control water transport rates was emphasized to influence the SVG performance in PVA hydrogels.−2 h−1 under one sun ,n Figure .3.26O4(OH)4(fumarate)6] to capture water from the atmosphere.Owing to their high surface area and porosity and their tunable structure and functionality, MOFs and COFs have been broadly investigated for water treatments such as organic contaminants adsorption/degradation, ion capture or seawater desalination.2+ as photothermal material.−2 h−1 SVG rate (70.3 % efficiency) under 2 sun, much higher than when just the porphyrin monomer was applied. Still, the sometimes‐observed low water stability impedes the applications of many MOFs to construct solar evaporators.[2(OH)(BTC)(H2O)]n (Cu‐BTC) nanorods.−2 h−1 and 90.8 % efficiency under one sun.Most MOFs have pore sizes in the micropore range (pore diameter less than 2 nm), which is suitable for the adsorption and separation of small guest molecules.−2 h−1 under one sun and could even treat a high‐concentration VOCs up to 400 mg L−1 ‐linked COF as solar absorber, which also showed broad absorption from 400 to 2400 nm  into a COF backbone, which again yielded broad light absorption  as solar evaporators by a one‐step hydrothermal process.s Figure –f. By chs Figure  and 5h. s Figure . Consequn Figure .3.3−2 h−1. Li et al. doped aggregation induced emission (AIE) molecules into 3D fiber aerogels to assemble solar evaporators . Very recently, phosphomolybdic acid as electron acceptor was combined with porphyrins as electron donors, to assemble 2D dendritic nanosheets (DNS) with enhanced photothermal ability  for effective photothermal conversion.s Figure .[45] Ty Figure  and d. T4.32 in both air and water generated O2.− and H2O2, acting as disinfecting agents (Figure Bacillus subtilis, Escherichia coli, and Pseudomonas aeruginosa, in the collected water was reduced to a level below the drinking water standards of WHO (Figure Disinfection of drinking water from microorganism pollutants is an important topic. The anti‐bacterial/viral ability of SVG systems was therefore exploited to produce fully purified water.2b, 14s Figure . When apO Figure . When ABO Figure .4.4−2 and a stable SVG rate of 1.3 kg m−2 h−1 under one sun (Figure When thermal energy is localized on the surface of SVG systems for steam generation, energy loss into surroundings and salinity/temperature gradients can be converted to electricity through several energy conversion technologies.n Figure –c.[12b]n Figure . Combini5The use of solar energy has considerable potential for water purification applications. In this minireview, we describe the recent progress of different materials, such as polymers, MOFs, COFs, and some novel molecules applied as solar water evaporators. Figure The authors declare no conflict of interest.Sijia Cao received her master degree in 2021 from Humboldt Universität zu Berlin. In Master's period, she studied COF/graphene hybrid materials for environmental applications at the Technische Universität Berlin with Prof. Arne Thomas. Afterwards, she started her Ph.D. research in the group of Prof. Yan Lu in Helmholtz‐Zentrum Berlin für Materialien und Energie, Germany. Her research focuses on COF‐based functional materials for energy applications.Changxia Li received her Ph.D. in inorganic chemistry from Beijing Institute of Technology, China under the supervision of Prof. Liangti Qu in 2018. From 2019 to 2020, she carried out postdoctoral research with Prof. Arne Thomas at the Technische Universität Berlin, Germany. Then, she joined the group of Prof. Freddy Kleitz at the University of Vienna, Austria as a postdoctoral researcher. Her research interests focus on the design and synthesis of COF/graphene‐based functional materials for energy and environment applications.Arne Thomas received his Ph.D. from the Max Planck Institute of Colloids and Interfaces in Potsdam, Germany. After a postdoctoral stay at the University of California, Santa Barbara, as an AvH fellow, he rejoined the MPIKGF as a group leader. In 2009, he became a professor at the Technische Universität Berlin, where he is leading the Department of Functional Materials. His research focuses on porous materials, from mesoporous inorganic to microporous organic materials.


Hypertension (HTN) patients have higher risk of all‐cause and cardiovascular disease (CVD)‐specific mortality. Dietary patterns have been reported related to the risk of mortality, but their roles in HTN patients is unclear.To explore the relationships between different dietary patterns and all‐cause/CVD‐specific mortality and provide dietary guidance for HTN patients' prognosis improvement.Data of 27 618 HTN patients were extracted from the National Health and Nutrition Examination Survey (NHANES) database in this retrospective cohort study. The associations between Healthy Eating Index (HEI)‐2015, Alternate Healthy Eating Index (AHEI)‐2010, Dietary Approaches to Stop Hypertension (DASH), and Mediterranean (MED) diet and all‐cause and CVD‐specific mortality were explored using univariate and multivariate Cox regression analyses with hazard ratios (HRs) and 95% confidence intervals (CIs). Subgroup analyses of age, gender, body mass index, and comorbidity were also performed.The median follow‐up time was 83 months. A total of 3462 patients died for all‐cause and 1064 died due to CVD. After adjusting for covariates, we found that high adherence to AHEI‐2010 , and MED  diet were associated with decreased risks of both all‐cause and CVD‐specific mortality. In patients who aged ≥65 years old, were normal/overweight, without complications, the relationships between different dietary patterns and risk of mortality were different.High scores of AHEI‐2010 and MED may be associated with decreased risks of all‐cause and CVD‐specific mortality in patients with HTN. The role of dietary patterns in the risk of mortality in patients with HTN has been unclear. This study explored the relationship between different dietary patterns and all‐cause/CVD‐specific mortality in HTN patients. The study data were extracted from the NHANES database. After average 83 months' follow‐up, we found that high adherence to AHEI‐2010, and MED diet were associated with decreased risks of both all‐cause and CVD‐specific mortality. AHA/ACCthe American Heart Association/American College of CardiologyAHEI‐2010the Alternate Healthy Eating Index‐2010BMIbody mass indexCacalciumCDCthe Centers for Disease Control and PreventionCKD‐EPIthe Chronic Kidney Disease Epidemiology CollaborationCNPPthe USDA's Center for Nutrition Policy and PromotionCOPDchronic obstructive pulmonary diseaseCrcreatinineCRPC‐reactive proteinCVDcardiovascular disease mortalityDASHthe Dietary Approaches to Stop HypertensionDGAthe Dietary Guidelines of AmericansDHAdocosahexaenoic acidDMdiabetes mellituseGFRestimated glomerular filtration rateEPAtimnodonic acidFFQsfood frequency questionnairesHBhemoglobinHDL‐Chigh‐density lipoprotein cholesterolHEI‐2015the Healthy Eating Index‐2015HIVhuman immunodeficiency virusHTNhypertensionICDthe International Statistical Classification of Disease.KpotassiumLDL‐Clow‐density lipoprotein cholesterolMCQmultiple‐choice questionMECmobile examination centerMEDthe Mediterranean scoreMETmetabolic equivalentMgmagnesiumNasodiumNCHSthe National Center for Health StatisticsNHANESthe National Health and Nutrition Examination SurveyPIRpoverty‐to‐income ratioPUFAspolyunsaturated fatty acidsTGtriglyceride1Hypertension (HTN) is a clinical syndrome characterized by elevated systolic blood pressure (SBP)/diastolic blood pressure (DBP) ≥ 140/90 mmHg, affecting 1.13 billion adults worldwide.Diet adjustment, a mean of nondrug treatment, is an important part of HTN management.Herein, this study aims to explore the effects of different diet patterns on the all‐cause and CVD‐specific mortality in HTN patients, in order to provide some dietary guidance for the management and prognosis improvement of these patients.22.1https://wwwn.cdc.gov/nchs/nhanes/Default.aspx.Data of participants in this retrospective cohort study were extracted from the National Health and Nutrition Examination Survey (NHANES) database in 2005–2018. NHANES includes publicly available 2‐year cross‐sectional surveys that focus on the health and nutrition of noninstitutionalized US residents, which is administered by the Centers for Disease Control and Prevention (CDC) and the National Center for Health Statistics (NCHS).A total of 27 618 adults who diagnosed with HTN were absorbed in this study. The exclusion criteria were (1) aged <20 years old, (2) having extremely low or high total energy intake , and (3) missing the information of dietary. After excluding those who lost to follow‐up, finally, 21 757 of the adults were eligible. Since the NHANES protocol was approved by the Institutional Review Board (IRB) of the NCHS of CDC, and the data were publicly available, no ethical approval of our IRB was required.2.2The NHANES database uses two 24‐hour recalls as the principal methodology to provide detailed quantitative food and nutrient intake assessment, and uses the food frequency questionnaires (FFQs) to provide typical or qualitative data.The DASH dietary pattern aims at lowering BP which focuses on high amounts of fruits, vegetables, and low‐fat dairy products. DASH score was calculated based on protein, fiber, magnesium (Mg), calcium (Ca), potassium (K), total fat, saturated fat, cholesterol, and sodium (Na) to examine the degree of adherence to the DASH. Provide one point when meeting the goal for each component, provide 0.5 points when meeting an intermediate goal of DASH and give zero points when the nutrient content of the DASH control diet and meeting neither goal. The control diet targets are from the previous DASH study.The AHEI‐2010 includes information of 11 components of food groups and nutrients that are associated consistently with lower risk of chronic diseases in clinical and epidemiologic investigations, which is based on a comprehensive review of the relevant literature and discussions with other nutrition researchers.The HEI‐2015 is a diet quality index to assess the adherence to the Dietary Guidelines in Americans, which were developed by the USDA's Center for Nutrition Policy and Promotion.https://www.ars.usda.gov/northeast-areaeltsvillee-md-bhnrceltsvillee-human-nutrition-research-center/food-surveys-research-group/docs/fped-databases/. In summary, the calculation was based on assessed intakes of total fruits, vegetables (except potatoes), whole grains, legumes, nuts, fish, red and processed meat, ratio of monounsaturated to saturated fat, and alcohol. A single point was assigned to participants whose intakes were greater than the median of the study cohort, except for red and processed meat and alcohol. For alcohol and red/processed meat, one point was assigned to those who had moderate alcohol consumption (10–25 g/day for men and 5–15 g/day for women) or a meat intake that was less than the median for the cohort. If these criteria were not met, participants received point of zero. Hence, higher MED  score represents greater adherence to the MED diet.The MED score was calculated according to the information in the Food Patterns Equivalents database (developed by the US Department of Agriculture): In the current study, the four dietary patterns were divided into three levels, respectively, according to their tertiles, including HEI‐2015: ≤46.64, , and >58.28, AHEI‐2010: ≤30.5, , and >39, DASH: ≤1.5, , and >2.5, and MED score: ≤3, , and >5.2.3We collected the variables including age, gender, race, education level, marital status, poverty‐to‐income ratio (PIR), smoking status, drinking status, height, weight, body mass index (BMI), waistline, physical activity, total energy intake, estimated glomerular filtration rate (eGFR), platelet, hemoglobin (HB), creatinine (Cr), C‐reactive protein (CRP), CVD, diabetes mellitus (DM), dyslipidemia, cancer, asthma, anemia treatment, gout, chronic obstructive pulmonary disease (COPD), dialysis, hypothyroidism, human immunodeficiency virus (HIV) infection, and drug abuse from NHANES.Physical activity was calculated by the physical activity questionnaire in NHANES, using the following formula: energy expenditure  = recommended MET × exercise time of corresponding activity (min). Participants who had laboratory diagnosis of fasting plasma glucose ≥7 mmol/L, HbA1c ≥ 6.5% (48 mmol/L), self‐reported previous diagnosis of DM by medical professionals, or taking glucose‐lowering agents or insulin (survey or prescription medication check) were considered to have DM. Dyslipidemia was defined as having total cholesterol ≥ 200 mg/dL (5.2 mmol/L), or triglyceride (TG) ≥ 150 mg/dL (1.7 mmol/L), or low‐density lipoprotein cholesterol ≥ 130 mg/dL (3.4 mmol/L), or high‐density lipoprotein cholesterol ≤ 40 mg/dL (1.0 mmol/L), or self‐reported hypercholesterolemia, or use of a cholesterol medication.2.4https://www.cdc.gov/nchs/data-linkage/mortality.htm. CVD‐specific mortality was defined and classified by the International Statistical Classification of Disease (ICD) codes, 10th revision (coded I00‐I78).The study outcomes were all‐cause mortality and CVD‐specific mortality. The mortality information of patients was queried using the NHANES‐linked social security account through the website: 2.5t test was used for comparation. Data with skewed distribution were described by median and quartiles [M ], and Mann–Whitney U rank sum test was used for comparation. The composition ratio (N [%]) was used to describe the distribution of measurement data, and we used chi‐square test for comparation.Normal distributed data were described by mean ± standard deviation (mean ± SD), and p < .05 was considered as significant.The MEC exam weight (wtmec2yr) was used for weighting since data sets in the current study incorporated seven cycles of NHANES 2‐year data collection. Weighted univariate Cox regression analysis was used to screen covariates related to all‐cause mortality and CVD‐specific mortality. Univariate and multivariate Cox regression analyses were used to explore the relationship of different dietary patterns and all‐cause mortality and CVD‐specific mortality, respectively. Model 1 was the crude model. Model 2 adjusted for demographic data including age, gender, education level, and marital status. Model 3 of all‐cause mortality adjusted for age, race, education level, marital status, PIR, physical activity, smoking, total energy intake, eGFR, drinking, BMI, DM, CVD, dyslipidemia, cancer, anemia treatment, gout, COPD, HB, dialysis, CRP, and hypothyroidism. Model 3 of CVD‐specific mortality adjusted for age, race, education level, marital status, PIR, physical activity, smoking, eGFR, drinking, DM, dyslipidemia, cancer, anemia treatment, gout, COPD, HB, dialysis, CRP, and hypothyroidism. We further drew the Kaplan–Meier (KM) curves to reflect the influences of different dietary patterns on all‐cause mortality and CVD‐specific mortality, respectively. Subgroup analyses of age, BMI, CVD, DM, and dyslipidemia were also performed. The evaluation indexes were hazard ratios (HRs) and 95% confidence intervals (CIs). Two‐sided Statistical analyses were completed by SAS 9.4 (SAS Institute.), and R version 4.2.0 (Institute for Statistics and Mathematics). Missing data were interpolated. We further performed the sensitivity analysis on characteristics of participants before and after the interpolation of missing data (Supporting Information: Table 33.1n = 662), missing dietary data (n = 2255), and having extreme energy intakes  (n = 147). Then, individuals who lost to the follow‐up were also excluded (n = 2795). Finally, 21 757 of the patients were eligible.A total of 27 618 patients with HTN were initially included. We excluded those who aged less than 20 years old (p < .05).Table 3.2p < .05).Covariates respectively related to all‐cause mortality and CVD‐specific mortality were showed in Supporting Information: Table Table 3.3p < .05). Additionally, high DASH score was only linked to a decreased risk of all‐cause mortality .Table Higher adherence to AHEI‐2010 was related to lower risks of both all‐cause mortality  and CVD‐specific mortality (HR = 0.73 95% CI: [0.54–0.99]) in patients who were normal or overweight. Besides, higher MED score was only linked to a decreased risk of all‐cause mortality .In HTN patients with CVD, high adherence to AHEI‐2010 was associated with a decreased risk of CVD‐specific mortality . Differently, patients without CVD who had high HEI‐2015  and AHEI‐2010  were seemed to have a decreased risk of all‐cause mortality.p < .05). Moreover, higher MED score was only related to lower risk of all‐cause mortality in patients with dyslipidemia .AHEI‐2010 was also found to be linked to decreased risks of all‐cause mortality and CVD‐specific mortality in patients without DM and with dyslipidemia (all 4The current study explored the association between different dietary patterns and all‐cause mortality and CVD‐specific mortality in patients with HTN. Our results showed that high AHEI‐2010 and MED scores were linked to decreased risks of both all‐cause mortality and CVD‐specific mortality. Also, these relationships in patients with different age, BMI, CVD, DM, and dyslipidemia were different.Although there are few reports on the relationship between different diet quality scores and the prognosis in patients with HTN, previous research can still be instructive. Zhong et al.Greater adherence to HEI‐2015 has been reported to be inversely correlated with the risk of incident HTN, and among its components, a higher score of total fruits, fatty acids, and Na was associated with a reduced risk of incident HTN.Due to HTN may be a result of and can be influenced by multiple factors, we further performed subgroup analyses. Age, low educational attainment, overweight, obesity, abdominal obesity, and high cholesterol and blood glucose were strongly associated with HTN.4.1The present study used samples from the NHANES database which is representative. In this cohort study, the follow‐up time was long, and the mortality information was obtained from the death certificate so that the outcome assessment was accurate. Besides, we assessed different dietary scores which related to HTN may provide dietary guidance for prognostic management of patients with HTN. However, we only dietary intake data were collected at baseline, which may not reflect long‐term dietary patterns and changes during the follow‐up, so we chose the 2‐day average of dietary survey data. Although confounders such as socioeconomic factors and chronic diseases were adjusted, other potential confounders such as disease status and treatment during follow‐up were difficult to adjust.4.2Further prospective large‐sample cohort studies are needed to explore the association between different dietary patterns and prognoses in patients with HTN and the potential mechanisms, which may provide some guidelines on dietary patterns for the management and treatment of high‐risk populations.5High AHEI‐2010 and MED scores were related to decreased risks of both all‐cause mortality and CVD‐specific mortality in patients with HTN.The authors declare no conflict of interest.Supporting information.Click here for additional data file.Supporting information.Click here for additional data file.


In Alzheimer's disease (AD), the presence of circadian dysfunction is well‐known and may occur early in the disease course. The melanopsin retinal ganglion cell (mRGC) system may play a relevant role in contributing to circadian dysfunction. In this study, we aimed at evaluating, through a multimodal approach, the mRGC system in AD at an early stage of disease.We included 29 mild–moderate AD (70.9 ± 11 years) and 26 (70.5 ± 8 years) control subjects. We performed an extensive neurophtalmological evaluation including optical coherence tomography with ganglion cell layer segmentation, actigraphic evaluation of the rest‐activity rhythm, chromatic pupillometry analyzed with a new data‐fitting approach, and brain functional MRI combined with light stimuli assessing the mRGC system.We demonstrated a significant thinning of the infero‐temporal sector of the ganglion cell layer in AD compared to controls. Moreover, we documented by actigraphy the presence of a circadian‐impaired AD subgroup. Overall, circadian measurements worsened by age. Chromatic pupillometry evaluation highlighted the presence of a pupil‐light response reduction in the rod condition pointing to mRGC dendropathy. Finally, brain fMRI showed a reduced occipital cortex activation with blue light particularly for the sustained responses.Overall, the results of this multimodal innovative approach clearly document a dysfunctional mRGC system at early stages of disease as a relevant contributing factor for circadian impairment in AD providing also support to the use of light therapy in AD. Circadian rhythms are characterized by a period of about 24 h, synchronized to the light–dark cycle by the projections of melanopsin retinal ganglion cells (mRGCs) to the suprachiasmatic nucleus (SCN) through the retinohypothalamic tract.post mortem studies on retina and optic nerves demonstrated an age‐related loss of RGCs including also mRGCs.post mortem retinas.In vivo optical coherence tomography (OCT) and Chromatic pupillometry, allowing to assess the mRGC contribution to PLR, has been applied to evaluate this system in several neurodegenerative disorders including hereditary optic neuropathies, glaucoma, Parkinson's disease, isolated Rapid Eye Movement sleep Behaviour Disorder,Blue light is particularly effective in stimulating mRGCs and previous brain functional magnetic resonance (fMRI) studies conducted in controls, people suffering seasonal affective disorder (SAD) and blind subjects, demonstrated the presence of activation/deactivation of specific cerebral areas crucial for cognitive functions using light stimuli tasks designed to stimulate mRGCs.In this study, we aimed at expanding on our previous investigation by evaluating in vivo the mRGC system in relation to circadian rhythms through a novel multimodal approach including cognitive testing, neurophthalmological assessment and OCT measures, chromatic pupillometry, actigraphy, and brain fMRI studies in a cohort of 29 mild–moderate AD patients compared to 26 controls. We also evaluated the mRGC system in 3 centenarians.In this study, we prospectively screened at the IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy, 33 AD patients and 42 controls. All participants signed institutional review board–approved consent forms. The local Ethical Committee approved the study . We also included 3 centenarians , who underwent the neuropthalmological evaluation, pupillometry, and rest‐activity assessment by actigraphy.For AD, the inclusion criterion was AD diagnosis at mild–moderate stage of diseaseExclusion criteria for patients and controls were as follows: (1) spherical or cylindrical refractive errors more than 3 or 2 diopters, (2) presence of posterior pole pathology including age‐related macular degeneration and known optic neuropathies including open‐angle glaucoma, (3) ocular pressure more than 20 mmHg, (4) severe lens opacity, (5) retinal detachment, (6) vascular retinal pathology including diabetic retinopathy, (7) shift‐workers in the last year, (8) travels through more than one time zone during the last 3 months, and (9) contraindications for MRI.Exclusion criteria for controls included (1) excessive daytime sleepiness as assessed by the Epworth Sleepiness Scale (ESS score > 10), (2) presence of poor sleep quality as determined by the Pittsburgh Sleep Quality Index Questionnaire (PSQI index ≥7),Neurophthalmological evaluations included best corrected visual acuity (VA) by Snellen's chart, color vision tests (Ishihara test), slit‐lamp biomicroscopy, Goldman applanation tonometry, color fundus photography, and OCT . OCT protocols included the evaluation of peripapillary retinal nerve fiber (RNFL) thickness and ganglion cell layer (GCL) segmentation analysis of the macula (GCL is defined as the thickness from the inner boundary of the GCL to the outer boundary of the Inner Plexiform Layer).All AD patients and control subjects underwent an extensive neuropsychological assessment. In particular, global cognition was evaluated by Mini Mental State Examination (MMSE)MotionWatch8, CamNtech Ltd, Fenstanton, UK) worn on the non‐dominant wrist for 7 consecutive days. Subjects were instructed to wear the device continuously over the 24 h and to complete a daily sleep‐log to track: bed and raise times, daytime naps, and periods of device removal. Subjects with <5 consecutive days of recording were excluded from analysis.Rest‐activity rhythm was evaluated using a wrist actigraph  to assess sleep and NPCRA  measures.https://www.camntech.com/motionware‐software/). Circadian and sleep parameters were used for comparisons between AD patients and controls, and for correlations with clinical data.All weekly participants' n = 19 basis permutations. Groups' differences in motor activity were assessed through non‐parametric permutation F‐test.Moreover, to assess group differences in circadian motor activity profile, we processed the time‐series of raw motor activity data through functional linear modeling (FLM).2, 472 nm) under dark‐adaption;rod‐condition: low luminance 1 s blue flash  under dark‐adaption;mRGC‐condition: high luminance 1 s blue flash  presented against 6 cd/m2 rod‐suppressing blue‐adapting field.cone‐condition: 1 s red flash  as reported in details in Romagnoli et al.exp1 function, MathWorks, Inc., Natick, MA, USA) and deriving the exponential coefficient  and AUC 5–7 s   Fig. .Brain fMRI methods can be found detailed in Evangelisti et al.Before the MR acquisitions, participants underwent a dark adaption period of 1 h and a training session for the cognitive task.3).MR acquisitions were performed with a 1.5T system (GE Signa HDx 15), equipped with an 8‐channel phased array coil. The protocol included fMRI  and high‐resolution T1‐weighted images .visual cognitive paradigm, duration ~20 min) consisting of an alternation of blue and red illumination periods of 50 s and darkness (duration from 20 to 30 s). This paradigm allowed the evaluation of the effects on brain responses of the interactions between light stimulation and cognitive task. Finally, the pure visual paradigm was meant to investigate the possible role of mRGCs in a pure visual setting. Participants were exposed to blue or red lights for periods of 10 s separated with 5 s of darkness, with a random color alternation, for a total duration of ~10 min.As for the fMRI paradigms Fig. , for theMonochromatic light stimulation was achieved with a metal‐free purpose‐built optic fiber combined with narrow (FWHM 10 nm) interference band‐pass filters .https://fsl.fmrib.ox.ac.uk/fsl).The image analyses were carried out using the FSL software , Bonferroni's correction method was used, and two‐sided p‐values are presented. Statistical analyses were carried out using R software (version 4.0.0) and IBM SPSS Statistics for Windows, version 20.0  software.Between group analyses were performed to compare demographics, OCT, actigraphy, and pupillometry outcomes. Individual patient's data were compared by using chi‐square, independent sample p‐value for Group×Age decade interaction term was calculated with log‐likelihood ratio test (LR test) comparing nested LMMs with and without the interaction term, and stratified β coefficients  for the variables turning out to be effect modifiers .Between groups (AD versus controls) analyses to compare OCT outcomes were performed by following the “all‐eyes approach”.t‐ and Mann–Whitney U‐ tests. Moreover, p‐value for Group × Age decade interaction term was calculated with LR test comparing nested linear models with and without the interaction term, and stratified β coefficients (95% CI) for the variables turning out to be effect modifiers .Comparison of actigraphy measures between AD and control groups were performed through independent For all conditions (rod/mRGC/cone), pupillometry metrics were obtained by their individual average values. Statistical analyses included Wilcoxon signed‐rank or independent t‐tests and aimed to compare variables among groups (AD/controls).p < 0.05).Group comparisons were performed by non‐parametric permutation statistics. For the visual paradigm, comparisons were made within the primary visual cortex  and AD (n = 29) subjects are reported in Table p = 0.034). All other GCL measurements were not significantly different between groups.According to exclusion criteria, OCT scans of 4 controls were excluded from analysis. Therefore, OCT data of control n = 2 and AD β coefficients stratified by group (AD/control), we found for controls a significant decrease for the average, superior and supero‐nasal GCL thickness for the 60–69, 70–79, and over 80 decades if compared to the decade 50–59, while in AD we observed a significant decrease only for the average GCL thickness in the comparison between the decade over 80 and 50–59 , TIB (p = 0.001), AWT (p = 0.04), SL (p = 0.04) and TAS (p = 0.04)  from the mean of controls, as previously proposedConsidering the Group × Age decade interaction term analysis, we found a significant difference between groups for RA, L5, and TAS Fig. , for L5 Moreover, AD patients presented significantly lower motor activity from 24:00 to 2:00 , from 11:00 to 14:00 (point‐wise test of significance), and from 18:00 to 21:00 (point‐wise test of significance) compared to controls , as well as AWS (p = 0.02) and TAS (p = 0.005) were significantly reduced in centenarians compared to AD in the group over 80s.We also evaluated the actigraphic recordings of the 3 centenarians which did not significantly differ from the controls in the decade 80–90 (data not shown). Furthermore, SE was significantly higher in the centenarians compared to AD in the group over 80s (p = 0.001] as well as a significantly lower average slope , and also a significantly reduced |B|  indicative of a lower exponential decay amplitude. Under mRGC‐condition, no significant differences were observed among AD and control groups. Finally, we evaluated the PLR in the 3 centenarians demonstrating a significant reduction only for the PIPR parameter (p = 0.008) in the comparison with controls in the 80–90 decade (data not shown).Pupillometry recordings included in the final analysis, according to inclusion/exclusion criteria, were available for 22 controls and 26 AD. Under rod‐condition, AD patients showed a significantly delayed onset of transient PLR response [contraction onset timing (ms): AD = 362 (50); Control = 323 (24); For each group (control/AD), a Spearman correlation coefficient was computed to assess the relationship between actigraphy/pupillometry/clinical variables (disease duration and MMSEc), the latter only for AD group.r = −0.84 , and a strong, positive correlation between M10 and the slope (rod‐condition) of PLR with r = 0.83 . The same bivariate relationships were lost in AD group (data not shown). In AD, there was only a moderate, negative correlation between TIB and |B| with r = −0.65   with = 0.048) .Among the 55 subjects enrolled in the study, 7 participants were not available to perform the fMRI. From the remaining 48, 14 subjects were not included because of claustrofobia or not‐compatible MRI implanted devices. For 34 participants (14 AD and 20 controls), the fMRI acquisitions were completed but 6 were excluded for suboptimal image quality.p‐value 0.001), while there was no statistically significant difference for age nor gender .The final dataset for fMRI studies consists of 12 AD  and 16 controls . The two groups differed significantly, as expected, for the cognitive level assessed with MMSEc  and negative correlations with the inferior RNFL thickness at OCT . Instead, in the control group, the sustained response to blue light positively correlated with some actigraphic parameters .Control subjects showed primary occipital cortex activation in response to both transient and sustained (10 s and 50 s) stimuli for both red and blue light without significant differences between the two light conditions. Instead, AD patients showed reduced cortical activations to blue light compared to red light in response to transient and sustained (10 s) stimuli. Moreover, when considering only activations under blue light, they did not show significant responses to sustained stimuli (10 s and 50 s) Fig. , panel ACerebral responses to PVT, in both groups, involved brain regions associated with sustained attention; however, these responses were significantly reduced in AD patients compared to controls within occipital, temporal, insular, parietal, and cerebellar cortices Fig. , panel Bp < 0.001).Reaction time in AD was overall longer compared to controls even though this difference was not significant due to large variability of responses, especially in the AD group. Moreover, the lower brain responses for AD compared to controls correlate with performance obtained in different cognitive tasks, such as MMSEc, those evaluating executive functions, memory and language abilities   and in brain regions crucial for vigilance and sustained attention  Fig. , panel Cblue > PVTred in the comparison HC > AD the cingulate gyrus correlate with the SE  in AD patients.Brain activations for the contrast PVTIn this study, we evaluated in vivo the mRGC‐system through a multimodal approach including OCT, actigraphy, chromatic pupillometry, and fMRI in a cohort of mild–moderate AD patients demonstrating that mRGCs are affected in AD, as highlighted by actigraphic, pupillometry, and fMRI findings. Each approach provides a specific layer of information that deserves a comment.First, the OCT analysis failed to show significant differences between AD and controls except for the infero‐temporal GCL thickness. This sector corresponds to the supero‐nasal sector of the optic nerve, in line with previous results pointing to a preferential loss of the magnocellular component of the optic nerve in AD.Second, the actigraphic recordings showed a significant difference between controls and AD for the TST, TIB, SL, and TAS. The increased TST found in the AD group is in line with previous findings.post mortem AD retinas.Third, in relation to pupillometry studies, we recently published a preliminary study demonstrating a significant reduction of the peak amplitude for the rod‐condition in AD compared to controls as well as a significant correlation with age only in the AD group and a significant increase of the PIPR variance in the melanopsin protocol in the same AD patients here investigated.The significant correlation emerging between pupillometric and actigraphic measurements in controls argues in favor of a cross‐validity of these two approaches in capturing the mRGC‐mediated non‐visual functions, further validating the sensitivity of these tools to explore the mRGC system in vivo.post mortem studies,As final approach, brain fMRI results revealed reduced occipital cortex responses to blue light compared to red light stimulation in AD compared to controls, in particular for the sustained responses to light. This result further complements in vivo the mRGC loss previously reported in Overall, the findings of our multimodal approach point to a dysfunctional mRGC system since the early stages of AD. Considering also the results of previous studies, we envisage a specific pathology affecting the mRGC cell body, reflected at initial stages by the dendropathy involving the connections with rods, detected by pupillometry, as well as by the reduction of the GCL thickness. Only at later stages, this mRGC pathology evolves into axonal damage, included in the general RNFL thinning. The dysfunction of the mRGC system ultimately contributes to the well‐known circadian and sleep disturbances reported in AD, documented by actigraphic studies. The timing of this process may vary in subgroups of AD patients, as reflected in the great variability observed for the multiple readouts that we used in our multimodal approach. Our findings may also support the use of light therapy as a counteracting measure for circadian dysfunction in AD aimed at stimulating the still functional mRGCs.This study has some limitations which include the limited number of patients enrolled, the absence of complete data for all of the patients examined and the inclusion of mild cases, which was necessary for compliance in running the tests.In conclusion, the use of such a multimodal approach for investigating the mRGC system, particularly in neurodegenerative disorders characterized by circadian and sleep dysfunction, is here proposed as an effective tool for evaluating in vivo the mRGC‐dependent occurrence of circadian dysfunction, and biomarking patients for risk stratification.CL, MM, RL, and VC contributed to the conception and design of the study; CL, MM, MR, MSM, SE, MD, SC, CB, CT, GV, MC, PD, MF, PA, CZ, and FB contributed to the acquisition and analysis of data; CL, MM, MR, SE, SC, CT, GV, AS, MF, PA, PB, RL, CT, RL, and VC contributed to drafting the text or preparing the figures.The authors declare no conflicts of interest related to this manuscript.Table S1. Spearman correlations between actigraphic and pupillometric variables in controls.Table S2. Spearman correlations between actigraphic and pupillometric variables in AD.Click here for additional data file.


Single‐step genomic predictions using SNP (H‐BLUP) or haplotypes (HAP‐BLUP) from blocks with different linkage disequilibrium (LD) thresholds  were evaluated. We also considered different blending parameters when constructing the genomic relationship matrix used to predict the genomic‐enhanced estimated breeding values (GEBV), with alpha equal to 0.95 or 0.50. The GEBV were compared to the estimated breeding values (EBV) obtained from traditional pedigree‐based evaluations (A‐BLUP). The mean theoretical accuracy ranged from 0.499 (A‐BLUP for PWT) to 0.795 . The prediction accuracies ranged from 0.143 (A‐BLUP for PWT) to 0.330 (A‐BLUP for YGFW) while the prediction bias ranged from −0.104 (H‐BLUP for PWT) to 0.087 . The GEBV dispersion ranged from 0.428 (A‐BLUP for PWT) to 1.035 (A‐BLUP for YGFW). Similar results were observed for H‐BLUP or HAP‐BLUP, independently of the LD threshold to create the haplotypes, alpha value, or trait analysed. Using genomic information  provided similar or higher prediction and theoretical accuracies and reduced the dispersion of the GEBV for body weight, wool, and reproductive traits in Rambouillet sheep. However, there were no clear improvements in the prediction bias when compared to pedigree‐based predictions. The next step will be to enlarge the training populations for this breed to increase the benefits of genomic predictions.Rambouillet sheep are commonly raised in extensive grazing systems in the US, mainly for wool and meat production. Genomic evaluations in US sheep breeds, including Rambouillet, are still incipient. Therefore, we aimed to evaluate the feasibility of performing genomic prediction of breeding values for various traits in Rambouillet sheep based on single nucleotide polymorphisms (SNP) or haplotypes (fitted as pseudo‐SNP) under a single‐step GBLUP approach. A total of 28,834 records for birth weight (BWT), 23,306 for postweaning weight (PWT), 5,832 for yearling weight (YWT), 9,880 for yearling fibre diameter (YFD), 11,872 for yearling greasy fleece weight (YGFW), and 15,984 for number of lambs born (NLB) were used in this study. Seven hundred forty‐one individuals were genotyped using a moderate (50 K;  Further scientific innovation, with its greater application through increased education and training, is needed to meet this demand , originally used to obtain EBV from Best Linear Unbiased Predictions (BLUP), can be replaced or combined with the genomic relationship matrix (G) to predict genomic‐enhanced breeding values , are commonly used to include a proportion of A in the G that is used in the genomic evaluation  has been the standard method used in genomic analyses. However, haplotypes can also be used for both genomic prediction . Furthermore, in these studies, the factors that might contribute to better haplotype‐based predictions  were not fully explored. Second, the method used to create the haplotypes can influence the results , postweaning weight (PWT), and yearling weight (YWT)], two wool traits [yearling fibre diameter (YFD) and yearling greasy fleece weight (YGFW)] and one reproductive trait [number of lambs born (NLB)] as described in Table http://nsip.org/wp‐content/uploads/2015/04/Lambplan‐TC‐Report‐Notter.pdf). Briefly, the preadjustment considered birth and rearing type (fixed levels as a multiplicative adjustment), and age of dam (measured in days) at recording (quadratic and linear regressions). No preadjustments were done for NLB. The data underwent quality control (QC) with observations deviating more than three standard deviations from the mean removed from further analyses.The phenotypic datasets used for the genetic and genomic prediction analyses for the body weight and wool traits were processed previously by the NSIP, which provided preadjusted phenotypes (Contemporary groups (CG) were created by concatenating flock, year, season, management group, sex, recording date, and 70‐day age groups to split lambing (birth) dates into 70‐day consecutive periods for PWT, YWT, YFD, and YGFW. For BWT, the CG included all the effects previously mentioned for body weight and wool traits, excluding recording date; 35‐ rather than 70‐day age group were also used. The CG for the NLB were created considering ewe's flock, birth year, season, management group, and parturition number . The preadjusted phenotypes for body weight and wool traits and the NLB phenotypes were then adjusted for the CG effect, so that the phenotypes analysed—henceforth referred to as corrected phenotypes—accounted for all systematic environmental effects considered in the NSIP genetic evaluations. The CG for all analysed traits were treated as fixed effects and, as a final QC step, CG with less than three animals and with no phenotypic variability within CG were removed.2.2Neogen . These individuals were chosen to be genotyped based on pedigree‐based relatedness (genetic connectedness) aiming to capture as much genetic diversity as possible in animals with DNA samples, coming from nine representative NSIP Rambouillet flocks. Connectedness values were derived from prediction error co‐variances among EBV   and BovineHD BeadChips (Illumina Inc.)  SNP panels by plink 1.9 software , located on non‐autosomal chromosomes, or a duplicate on the array removed. Samples with call rate < 0.90 were also removed. A total of 32,584 SNP and 722 samples remained for further analyses. The average distance between adjacent markers was 75.433 kb, with standard deviation, minimum, median, and maximum equal to 67.275 kb, 0.002 kb, 59.492 kb, and 2307.471 kb, respectively, in the 32 K panel used for the analyses.Approximately 35 K  autosomal SNP were in common between the two panels. The QC for the genotypic data was done using the 2.3fimpute v.3.0 software  is a repeatability model, in which p is the random permanent environment effect, y2 contains the repeated corrected phenotypic records, and the other vectors are the same as in model (1). The model (3) also includes the random maternal additive genetic and maternal permanent environment effects, m and q, respectively. The 1′ is a vector of ones used to calculate the overall mean and Z, W, Z2, and S are the incidence matrices that relates the corrected phenotypic records to the random direct additive, permanent environment, maternal additive, and maternal permanent environment effects, respectively. The random effects for the above models were assumed to be normally distributed with (co)variance structures as follows:σu2, σp2, σm2, σq2, andσe2 are the additive genetic, permanent environment, maternal genetic, maternal permanent environment, and residual variances, respectively, and model (1) was used to make the EBV prediction for the YFD, model (2) for NLB, and model (3) for BWT, PWT, YWT, and YGFW.Three linear mixed models for the pedigree‐based BLUP (A‐BLUP) were used in this study, which are defined as follow:blupf90 software  and is centred based on twice of the allelic frequencies . The pregsf90 software . Different values of α and β were used to evaluate the impact of increasing the proportion of A22 on G, with α = 0.95 and 0.50, and thereby β = 0.05 and 0.50, respectively, in which the former is the default value in the pregsf90 software and the later was suggested as the choice in Terminal Sire sheep populations  approach were similar to the ones used in A‐BLUP, except for the inclusion of genomic relationships from the genotyped individuals. In the assumptions of the H‐BLUP, the Vanraden :(8)G=M2.4.3G used in the αG+βA22−1 component was constructed using non‐LD‐clustered SNP (NCSNP) and ps‐SNP. A ps‐SNP corresponds to one of the unique haplotype alleles present within a haploblock, coded as 0, 1, or 2 to account for the number of copies of the reference haplotype allele, similar to Teissier et al.  approach were similar to those described for H‐BLUP. However, the r et al.  and Araur et al. . As a har et al. . All the2.5The whole (complete) and partial datasets used to compare the genetic and genomic predictions  to evaluate the connectedness between the genotyped samples considered as training and validation  in the whole and partial datasets. The VED is based on the variance of unit effects, which approximates the mean prediction error variance (PEV) over unities  and partial (GEBVP) datasets, F¯ is the average inbreeding, ave represent the arithmetic average function, uGEBVW^ and uGEBVP^ are the predicted GEBV or EBV in the whole and partial datasets, respectively, and varGEBVP is the variance of the GEBV or EBV. The other components were previously described.The performance of genetic and genomic predictions was evaluated using the linear regression (LR) method as described by Legarra and Reverter . The LR sei2 is the square of the GEBV or EBV standard error for the individual i, fi is the inbreeding coefficient for the individual i, and the other variables were previously described. The TA was calculated for both whole and partial datasets but presented only for the whole dataset to highlight the overall increase in the TA comparing GEBV and EBV considering all phenotypes available.In addition to the LR statistics, the individual theoretical accuracies (TA) were calculated for the focal individuals according to Van Vleck :(12)TA2.6α = 0.95 and 0.50 to construct G, and (3) HAP‐BLUP using ps‐SNP from different LD thresholds  also with α = 0.95 and 0.50 to construct G. In total, 13 scenarios were evaluated for each of the six traits, resulting in 78 analyses.The scenarios consisted of combinations of (1) A‐BLUP, (2) H‐BLUP with 33.1α value and trait evaluated. We, therefore, only presented the results for the HAP‐BLUP considering the LD threshold of 0.50 (HAP‐BLUP‐LD_0.50) to compare the predictions between pedigree, SNP, and haplotype‐based methods for all traits , ~62% (~0.12), ~8% (~0.02), and ~37% (~0.05) in the GEBV prediction accuracies was observed for the PWT, YWT, YFD, and NLB, respectively, when using 3.2α of 0.95 for YGFW; Appendix The prediction bias ranged between −0.104 .Incorporating genomic information in the analyses resulted in similar or more bias when compared to the pedigree‐based prediction. Alpha equal to 0.95 tended to reduce the bias for BWT and YWT, while the opposite was observed for the other traits , showing reduced dispersion for genomic‐based models. Pedigree‐based models presented similar or lower dispersion for BWT and wool traits. The dispersion with H‐BLUP and HAP‐BLUP‐LD_0.50 showed similar results regardless of the α values. Alpha equal to 0.95 tended to present better dispersion for PWT, YWT, and NLB compared to 0.50, while the opposite was observed for the other traits.A dispersion of −0.29, −0.16, and − 0.35 was observed for PWT, YWT, and NLB, respectively, using H‐BLUP and HAP‐BLUP‐LD_0.50 with 3.4The mean TA ranged from 0.499  was observed in the increase of the mean TA between H‐BLUP and HAP‐BLUP‐LD_0.50 with α of 0.95. Using αequal to 0.50 resulted in the smallest increase in the mean TA (less than 2%) with both SNP‐ and haplotype‐based methods for all traits. At the individual level, the TA using H‐BLUP with α of 0.95 were higher compared to A‐BLUP for the younger individuals and those with no phenotypic information (sires and dams with genotyped progeny) in the partial datasets for all traits , ~9% ~0.05), ~4% (~0.03), ~3% (~0.02), ~5% (~0.03), and ~6% (~0.04) for BWT, PWT, YWT, YFD, YGFW, and NLB, respectively, using H‐BLUP and HAP‐BLUP‐LD_0.50 with , ~4% (~0Increase in the TA for ungenotyped close relatives (progeny or parents) of the genotyped individuals when using genomic information was also observed Appendix , althoug4Genomic selection is the state‐of‐the‐art in modern sheep breeding programs. Here we present the first results of genomic predictions for body weight, wool, and reproductive traits in North American Rambouillet sheep. We performed single‐step genomic predictions fitting SNP or haplotypes to create the genomic relationship matrices used to compute GEBV. Despite the small dataset, promising results were obtained, which can be used as a starting point for the implementation of genomic selection in Rambouillet sheep as well as in other sheep populations.4.1The accuracy of genomic predictions relies mainly on the trait heritability, LD between SNP and QTL  phenotypes or EBV on the GEBV is usually used to measure the “bias” of GEBV  when increasing α, accuracies increased until reaching an asymptote at around 0.50, which was not the case in the current study; (2) the GEBV using α between 0.50 and 0.95 were highly correlated; and (3) less variation was observed in the GEBV of genotyped individuals without phenotypes with α equal to 0.50; and (4) with higher α values, GEBV bias (over‐prediction) increased.In general, appropriate α of 0.50 showed only half of the increase in the accuracies compared to 0.95. No clear advantage in GEBV bias, dispersion, or TA was observed with one α value compared to the other. Therefore, we recommend α of 0.95 for single‐step genomic evaluations in U.S. Rambouillet sheep. However, it is important to highlight that our study included a smaller number of genotyped individuals compared to McMillan and Swan . Haplotype‐based genomic predictions could outperform SNP‐based models in sheep datasets because the former can capture epistasis and these populations could have more complex interactions within haplotype blocks due to higher effective population size. Liang et al. (n = 722) as well as the density of the SNP panel used (~32 K SNP) could have affected both SNP and haplotype genomic predictions.The HAP‐BLUP‐LD_0.50 scenario was chosen to represent the haplotype‐based methods because the LD of 0.50 was the level most likely to estimate the recombination hotspots properly, which are the specific points in the genome with higher probability of recombination . In general, GEBV prediction results for haplotype‐based methods are scarce in small ruminants and additional studies are needed.The haplotypes can be multiallelic markers  relationship matrix for both genomic prediction and variance component estimation using haplotypes or SNP  than the one used in this research (35 K), genotype imputation prior to haplotype blocking and prediction can also be hampered by the number of reference individuals , generally predictions using the imputed 50 K were more biased and the GEBV were more dispersed for all traits. Legarra and Reverter  emphasiz5Using genomic information (SNP or haplotypes as pseudo‐SNP) provided similar or higher GEBV prediction and theoretical accuracies, and reduced the dispersion of the GEBV, for body weight, wool, and reproductive traits in Rambouillet sheep. However, there were no clear improvements in prediction bias compared to pedigree‐based predictions. Alpha value equal to 0.95 is recommended to weight the genomic relationships when modelling the covariances between individuals. The use of haplotypes showed no advantage compared to SNP at the current reference population size and SNP panel density used, regardless of the LD threshold used to create the haploblocks. SNP‐based genomic predictions are therefore recommended since they are easier to implement than those based on haplotypes. Efforts to increase the number of genotyped individuals are paramount to take full advantage of genomic information to accelerate genetic progress in the U.S. Rambouillet sheep breeding program.ACA, PLSC, RML, and LFB: conception of the work; RML and LFB: data acquisition; ACA: SNP and haplotype‐based single‐step genomic prediction analyses; ACA, RML, and LFB: results interpretation; ACA: drafting the manuscript; ACA, PLSC, HRO, RML, and LFB: critical revision of the manuscript; and, ACA, PLSC, HRO, RML, and LFB: final approval of the version to be published. All authors contributed to the article and approved the submitted version. All authors have read and agreed to the published version of the manuscript.This research was funded by the National Sheep Industry Improvement Center (NSIIC‐USDA), the American Rambouillet Sheep Breeders Association, and the American Sheep Industry Association Let us Grow program. The State University of Southwest Bahia and the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior , Financial Code 001, provided the scholarship of the first author.The authors declare no conflict of interest.Appendix S1.Click here for additional data file.Appendix S2.Click here for additional data file.Appendix S3.Click here for additional data file.Appendix S4.Click here for additional data file.