A similar effectiveness was estimated for death in all age groups , whereas a higher effect was found for hospitalizations in subjects aged ≥75 years old. Vaccination was also effective both in males and females. Findings suggest a health benefit of the influenza vaccination in the elderly population. Efforts should be focused on strategies to increase the vaccination uptake as important instrument of prevention.Seasonal influenza is an important cause of morbidity and mortality, particularly among the elderly population. Determinants of vaccination uptake and its impact on health outcomes in the seasons 2014/2015–2016/2017 in elderly living in Treviso area  were evaluated. A retrospective cohort study was conducted combining information from several health administrative databases, and multiple Poisson regression models were applied to evaluate the influenza vaccine effectiveness, also adjusting for confounding factors. MF59-adjuvanted trivalent-inactivated vaccine was mainly administered. Data from more than 83,000 elderly people were analyzed by year. Vaccine coverage was about 50%; influenza vaccination uptake was independently associated with older age, male sex, increasing number of underlying chronic conditions, previous pneumococcal vaccination, annual expenses for specialist medical cares, and general practitioner to whom the elderly was in charge. After adjusting for previously described characteristics, vaccination was associated with lower mortality and influenza-related hospitalization rates. Specifically, during influenza season the adjusted incidence rate ratio of death and of influenza-related hospitalizations for vaccinated compared to unvaccinated persons was 0.63 [95% confidence interval (CI) 0.58–0.69,  Influenza virus results in annual epidemics, with 3–5 million cases of severe illness worldwide, and 290,000 to 650,000 respiratory deaths, most of them among elderly people, defined as those aged 65 years and older.4 In addition, evidence of the effectiveness of flu vaccine in preventing severe clinical outcomes was recently described as low or very low among elderly people.5 As a consequence, despite the Council of the European Union and the World Health Organization (WHO) recommended to annually vaccinate elderly people,7 influenza vaccine coverage in this group remained below the 75%, target in most European countries.8 In Italy, a decreasing trend in the vaccine coverage was observed since 2008/09, and in the last seasons, it was around 50% in elderly people,9 despite the recommendation of the Italian Ministry of Health through the National Vaccination Prevention Plan and the recommendations for influenza prevention published every season.10Vaccination is currently the most effective measure to prevent influenza or to reduce its morbidity. However, the need of evidence on vaccine effectiveness, its impact, and value across target groups and among seasons continue to generate a large discussion.11 Indeed, influenza viruses change rapidly due to antigenic drift so that vaccines are reformulated and delivered annually, commonly through seasonal campaigns. Licensed vaccines include inactivated or live-attenuated influenza A (B) strains, with three or four subtypes per vaccine.12 Since flu vaccines could be less effective in older than in younger adults, due to the weaker immune responses to vaccination for an age-related decline in immune function, strategies to enhance vaccine immunogenicity and overcome the limitations of immunosenescence include the use of high antigen doses and the formulation of vaccine with an adjuvant.13Another important aspect is that the vaccines developed to protect against seasonal influenza illness are updated every year in an effort to match the influenza A (B) vaccine strain with currently circulating viruses.18 or even no effectiveness.19Until now, only few studies were performed in Italy, aimed at assessing the effectiveness of influenza vaccination in reducing mortality and hospitalization due to pneumonia or influenza among elderly subjects, with heterogeneous results in terms of percentage of protection,In this context, we performed a retrospective cohort study to evaluate the vaccination coverage and its determinants, as well as to estimate the influenza vaccine effectiveness (IVE) in preventing deaths and hospital admissions in the elderly population in a North-Eastern Italian Province of the Veneto Region, during 2014/2015-2016/2017 seasons.The study refers to people living in the area of Treviso province , half of the entire province) in Veneto Region in the North-East of Italy. This is the area covered by the ex-Local Health Unit (LHU) 9 and now part of the LHU called ULSS  2 – Marca Trevigiana. LHU is the territorial unit into which Italian National Health Service is divided. In 2017, LHUs in Veneto Region were reorganized, and now the ULSS 2, which covers the entire Treviso province, includes LHU 9 – Treviso, LHU 7 – Pieve di Soligo, and LHU 8 – Asolo.rd and 28th in 2014, November 9th to December 4th in 2015, and November 14th to December 9th in 2016. A single 0.5 mL dose of MF59-adjuvanted trivalent-inactivated vaccine was usually administered by general practitioners (GP) to their elderly patients. This vaccine has been recommended for adults 65 years and older by local health authority since the early 2000s. GPs are required to return the unused vaccines to the LHU the following week the date of the end of the campaign ; therefore, all subjects were immunized within mid-december. Moreover, vaccinated subjects were identified by GPs who captured influenza vaccination data (vaccination date and type/brand of vaccine) through a process in which data were initially recorded on paper and subsequently manually entered into a database. However, GP did not always report the exact previous information on vaccination date and vaccine type/brand, therefore he may have used other non-adjuvanted trivalent/quadrivalent vaccines.Vaccination is offered free of charge to people aged 65 years or older, to other individuals aged 6-months–64 years with specific chronic conditions , to pregnant women, to people working in public services, and to healthcare workers. Vaccination campaigns were performed between November 321 In brief, for each individual registered in Veneto Health Care Population Registry , data on diagnoses, drugs, procedures and costs were retrieved from the administrative healthcare databases routinely available in the Veneto Region . With regard to drug expenditure and usage, only drugs reimbursed by the Regional Health Service were considered, as over-the-counter drug data were not available. Costs were calculated on the basis of a medications’ actual costs and of inpatient/outpatients. The underlying chronic conditions were established using the EDC (Expanded Diagnosis Clusters), which coincides with clinical diagnoses that the ACG System assigns to single patients by combining different information ﬂows as described above; (4) the Regional Hospital Discharge Records, including also hospital admissions in other Italian Regions, to assess dates of hospital admissions, main and secondary clinical diagnoses of discharges and (5) the Mortality database, to obtain date of death. For the latter, specific causes of death were not available; therefore, only death for all-causes was considered as outcome. Moreover, it was not possible to collect information on the laboratory-confirmed influenza cases.We performed a retrospective cohort study combining information from several health administrative databases from the LHU 9 of the Veneto Region. More specifically, different health electronic databases were linked at the individual level: (1) the influenza vaccination registry, to evaluate whether the individual had been vaccinated before the influenza season start; (2) the Veneto vaccination database, to evaluate if the individual had received pneumococcal vaccination. Pneumococcal vaccination is administered in dedicated LHU vaccination centers , and the information is recorded in the Regional Vaccination Registry; (3) the ACG database  containing information on comorbidities and healthcare resource utilization.th of each year (2014-2015-2016) were included in the analysis. Residents in long-term care facilities or assigned to a GP out of LHU 9 were excluded from the analysis because it was not possible to establish if they were or not vaccinated for influenza. Analyses were performed both stratifying by season-year and pooling calendar years. Influenza vaccination coverage was calculated as the ratio between vaccinated and exposed elderly in the LHU at December 15th of each year. Data were stratified by the following characteristics: sex, age groups , number of underlying chronic conditions , previous pneumococcal vaccination (yes/no), annual expenses for specialist medical care , and calendar year. Adjusted incidence rate ratios (IRR) of being vaccinated were estimated by a multi-level Poisson regression model,22 including all the above-mentioned variables. GP was included as a random effect first-level clustering variable; age and sex of the GP were initially included as fixed effect but, given that they were not significantly associated to the outcome, they were not included in the model here reported.All people aged ≥65 years old living in the municipalities of the LHU 9, alive at December 15th Revision, Clinical Modification) codes, recorded as primary or secondary diagnoses of discharge, were selected: 487 influenza; 480–486 pneumonia; 460–466, 490–496, 500–508, 510–516 respiratory diseases; 410, 422, 427, 428 cardiovascular diseases25 (a detailed description of the diagnoses codes has been reported in Supplementary Table S1).To evaluate the IVE, two outcome measures were considered: mortality rate  and hospital admission rate  that occurred between December 16, 2014, and December 14, 2017. IVE has been estimated as (1 – IRR) x 100, where IRR denotes the confounder-adjusted incidence rate ratio, comparing the death/hospitalization incidence rate among the vaccinated subjects to the death/hospitalization incidence rate among the unvaccinated subjects. IVE was evaluated in four different periods: December 15 to March 31 (Influenza season), April 1 to June 30 (post-influenza season), July 1 to September 30 (summer season), October 1 to December 14 (pre-influenza season). We considered this stratification in four periods to test the hypothesis that outside the influenza season no risk difference, in terms of deaths and influenza-related hospitalizations between vaccinated and unvaccinated individuals, should be detected. To define the influenza-related hospitalizations, the following ICD-9-CM , sex, and pneumococcal vaccine administration, a “zero inflated” Poisson model with the interaction terms between flu vaccination and sex, age group, and pneumococcal vaccine was also considered, always adjusted for chronic underlying conditions and expenses for medical cares. Same models were applied also to the outcome rate of hospitalization for any cause, and rate of influenza-related hospitalizations; for both outcomes a standard Poisson model, instead of a “zero-inflated” one, was used, because the latter did not add any improvement in the goodness of fit. Finally, to take into account among the potential confounding factors and effect modifiers the influenza vaccination in any of the previous two seasons, a subgroup analysis was performed considering only subjects who contributed to all three cohorts. All models considered as exposure time for each subject the number of days of each period or until the date of death if the subject died before the end of the period, or until the date of change of residence if the person moved to a municipality outside the LHU 9 territory before the end of the period.To estimate mortality and hospitalization IRR for vaccinated versus unvaccinated, univariable and multivariable Poisson regression models,All analyses were performed using the Stata software, version 13 .The study initially included three cohorts of elderly subjects of 86,724, 83,333, and 89,642 people at December 15 of 2014, 2015, and 2016, respectively. Residents in long-term facilities  or assigned to a GP out of the LHU 9  were excluded from the analysis. Therefore, the evaluated people were 83,265, 83,375, and 85,800 at December 15 of 2014, 2015, and 2016, respectively . Overall, study participants were 93,492, who contributed to at least one cohort year. Specifically, 75,148, 9,652 and 8,692 contributed to three, two and one cohort year, respectively. Person-years during the study period were 125,253 for vaccinated, and 123,752 for unvaccinated.MF59-adjuvanted trivalent inactivated flu vaccine was mainly administered in the elderly living in the LHU 9, during the immunization campaigns although no data were available by type or brand of vaccine; the coverage was around 50% for each year, near to the national value. Pneumococcal vaccination was previously administered in nearly 15% of individuals .th) of each calendar year for vaccinated/unvaccinated people with influenza vaccine are shown in Demographic and clinical characteristics at baseline , 38.2% did not receive any vaccination, 40.9% were vaccinated in all seasons, 11.2% were vaccinated during two seasons, and 9.7% were vaccinated only 1 year. In particular, 89.9% of vaccinated subjects in 2016 received influenza vaccination in at least one of previous two seasons (74.9% received vaccinations in both previous seasons), whereas 84.0% of unvaccinated subjects in 2016 were not previously immunized (data not shown in table).Both crude and adjusted IRR of being vaccinated were associated with demographic  and clinical characteristics  (Supplementary Table S2). Specifically, in the multivariable model, the adjusted IRR was higher for males compared to females and for those who received pneumococcal vaccination compared to unvaccinated ones; IRR increased with age, with the number of chronic diseases, and with annual individual health costs. Also GP, as a random effect, was significantly associated with vaccination, and the percentage of vaccinated patients largely varied among them . In the model including all three calendar years, an adjusted slightly higher IRR to be vaccinated was detected in 2016/17 as compared to 2014/15 (Supplementary Table S3).p < .001) during influenza season of 2014/15, 2015/16, and 2016/17, respectively, whereas during the post-influenza season reduction estimates ranged from 17% to 21% (p < .05). Only in the first year, a significant vaccination effect was observed also during summer. Regarding the other characteristics, adjusted IRR of death >1 were detected among males, older individuals, subjects with more chronic conditions and with greater annual expenses for medical cares, while a lower adjusted IRR  was found among pneumococcal vaccine recipients. In the pooled analysis made for all years, the adjusted IRR of death in vaccinated as compared to unvaccinated was lower during influenza period  and increased progressively in the other non-influenza periods [IRR ranged from 0.81 (p < .001) to 0.90 (p = .054)] (p = .028).There were 8,131 deaths during the study period, 4,855 and 3,276 in vaccinated and unvaccinated subjects, respectively (Supplementary Table S4). Among deaths, the median age was 84 years, and 92% of them had at least one chronic disease. In the unadjusted analysis, vaccinated subjects had significantly higher mortality rates, both during influenza and non-influenza seasons; when adjusting the model for the other confounding factors, vaccination was associated with significant reductions in death . Specifi= .054)] . Conside= .054)] . RegardiHospital admissions for all-causes were more frequent among vaccinated  as compared to the unvaccinated subjects , as well as for those influenza-related  (Table S4).p < .01) in the influenza-related hospital admission rates during the influenza season  rates were higher among vaccinated individuals . When ada season . Conversa season .10.1080/p < .001) and nearly 1 in the other periods (p = .971) for people aged ≤74 years, 0.85  for subjects aged 75–84 years, and 0.79  for elderly ≥85 years; differences were detected between who received  and not received  pneumococcal vaccination.Pooling all calendar years, the IRR of influenza-related hospitalization of vaccinated versus unvaccinated during influenza period was 0.86 , 22% for pneumonia , 14% for respiratory causes , and 12% for cardiovascular diseases   .10.1080/p < .001) in previously immunized subjects, whereas no effect was found in individuals who did not receive any vaccine in the previous 2 years . Similarly, vaccination was associated with 33% reduction in influenza-related hospitalization rates  in previously immunized subjects, while a not significant effect was observed in patients with no previously vaccination.Subgroup analysis, which considered only subjects who contributed to all three cohorts, highlighted that the influenza vaccination in any of the previous two seasons lowered both the mortality and influenza-related hospitalization rates in vaccinated as compared to unvaccinated subjects (Supplementary Table S5). Specifically, during influenza season 2016/17 the flu vaccination was associated with 66% reduction in mortality rates  and not really significant in terms of public health impact. There was also a significant and independently effect of the GP in the vaccination coverage, although no age and sex of the GP was associated with the vaccine uptake. These results are consistent with other studies conducted in other countries.33 that used similar outcomes as those reported in our study, and with a test negative design conducted in Valencia34 where the outcome was represented by confirmed influenza cases. In both studies,34 as well as in a recent meta-analysis,35 no negative effects of repeated seasonal vaccination were seen across more seasons, which strengthens the recommendation that persons belonging to this age group should be vaccinated yearly. Male sex, older age, more chronic conditions, and greater annual expenses for medical cares were significantly associated with an increased risk for both outcomes , while a lower risk was found among pneumococcal vaccine recipients. Of note, after adjusting for confounding factors, vaccination was associated with a reduction in the hospital admission rates only during the influenza season, whereas a lower, but still present, reduction in risk of all-cause mortality was found also in noninfluenza periods, suggesting that additional frailty indicators  could be necessary to eliminate possible unknown biasing factors; however, these are not usually included in standard administrative healthcare databases. In the absence of phase III trials using clinical endpoints, non-experimental designs to evaluate vaccine effects are often used, although methods to reduce potentially confounding factors should be considered.36 Indeed, according to the European Medicines Agency Guideline on influenza vaccine,37 secondary outcomes should address the ability of vaccines to prevent pneumonia and influenza-related hospitalization (associated with respiratory or cardiac disease) as well as all cause of deaths. Therefore, in order to control for differences in health status and health-seeking behavior in vaccinated compared with unvaccinated individuals, information on potentially confounding factors has been collected, including chronic diseases, pneumococcal vaccination, and healthcare utilization indicated by the expenses for specialist medical care. We were able to collect the most important known confounding factors except for the severity of chronic diseases, socioeconomic status, and antiviral drug use. Moreover, negative confounding may occur, as high-risk groups are more likely to be vaccinated and therefore reduce IVE. Conversely, positive confounding may occur as a result of a ‘healthy vaccine effect’.38 People with a healthy lifestyle are more likely to accept/request vaccination, thus leading to an increase of measured IVE. Vaccine coverage has been found to be low in frail elderly patients; consequently, there may be relatively fewer severely ill patients in the exposed.41 Therefore, adjusting for negative and positive confounding factors through multivariable analysis should minimize this potential bias, to make sure that the observed differences in the occurrence of selected outcomes in vaccinated and unvaccinated subjects were due to the effect of the vaccine and did not reflect baseline differences between the two groups.Regarding the effectiveness on reducing the risk of death (any cause) and the risk of influenza- related hospitalization, we found that, adjusting simultaneously for other individual characteristics, influenza vaccination was significantly associated with lower mortality and influenza-related hospitalization rates, and the benefit was found for all the discharge diagnoses including, influenza, pneumonia, respiratory, and cardiovascular diseases. Moreover, repeated influenza vaccination in the previous seasons influenced positively the vaccine effectiveness in the elderly. This is in line with recent published cohort studies in Stockholm County, Sweden43 In Veneto Region, among severe acute respiratory infections (SARI) cases, A/H3N2 was predominant.44 During the following 2015/16 season, the co-circulation of A and B viruses was observed with a lower IVE estimate for influenza A(H1N1)pdm09, with respect to previous season due to the emerging genetic variant 6B.1.46 In Veneto Region, most ILI and SARI cases were infected with A/H3N2 virus.47 Moreover, during this season, the antigenic match between circulating strains (Victoria lineage) and vaccine composition (Yamagata lineage) did not fit for B. However, the detection rate of influenza type B virus among elderly in Veneto Region was low, in line with previous studies.48 In 2016/17 period, A/H3N2 virus predominated, and the season was characterized by a very high incidence of ILI and ICU confirmed cases, due to differences in glycosylation between A/H3N2 egg-adapted vaccines and circulating strains.51During the study different viruses circulated. In the 2014/15 period, A/H1N1 and A/H3N2 co-circulated and the season were characterized by a very high incidence of influenza-like-illnesses (ILI) and Intensive Care Unit (ICU) confirmed cases, especially in ≥65 years old individuals, with a mismatch between the A/H3N2 circulating virus and that included in the seasonal vaccine formulation.23 with heterogeneous results. Recent observational studies in elderly adults have estimated that the adjuvanted vaccine provided significant protection against influenza-related hospitalization and that may further reduce influenza and pneumonia hospitalizations by an estimated 25% above those prevented by nonadjuvanted vaccine.52 In a study conducted in another north-eastern Italian area during 2016/17 season, it was found no significant effect of influenza vaccination in the elderly on the likelihood of hospitalization or death from pneumonia and influenza, indicating that some residual confounding might exist.19 However, another study conducted during 2014/15 season in the same Italian Region of the previous study showed that the overall risk of death was 18% lower among vaccinated compared to unvaccinated subjects (21% for pneumonia and influenza).18 A recent published test negative case–control study conducted in Italy during the 2016/17 season, to estimate vaccine effectiveness in preventing severe influenza in hospitalized elderly,53 showed a good adjusted IVE of the MF59-adjuvated vaccine against A(H1N1)pdm09 strains and a moderate adjusted IVE against B strains.Many previous studies examined IVE by analyzing a single or more seasons,23 In a study conducted in New Zealand from 2012 to 2015, influenza vaccination was associated with a 59% reduction in the risk of influenza-associated intensive care unit admissions among hospitalized influenza confirmed cases.54 Finally, a multicentre case–control study conducted in 11 European countries estimated that in the 2015–16 influenza season vaccination prevented approximately half of the hospitalized laboratory-confirmed influenza cases among the elderly population.55A study in US showed that vaccination in people with type 2 diabetes was associated with lower admission rates for pneumonia or influenza (IRR 0.85), and acute cardiovascular events .In interpreting our results, we need to consider some limitations. Relating to the representativeness of the study population and thus the generalizability of results, the study covered a part of a North-East Region of Italy and not the entire country. Vaccinated subjects were identified only by general practitioners’ medical records; indeed, currently does not exist a national influenza vaccination registry, and GP captured influenza vaccination data through a process in which data were initially recorded on paper and subsequently manually entered into a database; therefore, if some individuals were vaccinated outside GP practices these were considered unvaccinated. However, being the elderly individuals vaccinated free of charge, the proportion of those who paid out-of-pocket for influenza vaccine is likely to be negligible. All cases did not receive a laboratory-confirmed diagnosis of influenza; moreover, since specific causes of death were not available for all patients, only death for all-causes was considered as outcome. Since the exact vaccination date was not always available, it was not possible to define vaccinated as those subjects with an influenza vaccine administration >14 days before outcome onset. Finally, other possible factors associated with the evaluated outcomes, not considered in this analysis, could have affected the estimated IVE.Study results suggest that vaccination remains the most effective preventive measure against severe influenza, among elderly people. Our findings confirm that influenza vaccination is associated with a lower risk of influenza-related complications, indicating the benefit of flu vaccination in the elderly. Further strategies should be considered for improving influenza vaccine coverage, especially among subgroups with a higher risk of more serious events. Therefore, efforts should be focused on improvement of the immunization programs as important instrument of prevention.


Nitrospiraceae, and Gaiellaceae, and its community composition did not change during the incubation. In the less diverse soils, those taxa were absent but were replaced by copiotrophic bacteria, such as Caulobacteraceae and Beijerinckiaceae, until the end of the incubation period. SourceTracker analysis revealed that litter-associated bacteria, such as Beijerinckiaceae, only became part of the bacterial communities in the less diverse soils. This suggests a pivotal role of oligotrophic bacteria during the early phases of litter decomposition and the predominance of copiotrophic bacteria at low diversity.This study aimed to investigate the effects of different levels of soil- and plant-associated bacterial diversity on the rates of litter decomposition, and bacterial community dynamics during its early phases. We performed an incubation experiment where soil bacterial diversity (but not abundance) was manipulated by autoclaving and reinoculation. Natural or autoclaved maize leaves were applied to the soils and incubated for 6 weeks. Bacterial diversity was assessed before and during litter decomposition using 16S rRNA gene metabarcoding. We found a positive correlation between litter decomposition rates and soil bacterial diversity. The soil with the highest bacterial diversity was dominated by oligotrophic bacteria including Acidobacteria,  Plant litter decomposition is important for the recycling of carbon (C) and other nutrients in soil. It not only increases the amount of soil organic C ,2,3 but Litter decomposition is a continuous process, marked by significant changes in the chemical composition of litter materials ,15,16,17Although this general trend of bacterial succession during litter decomposition is well described, there is still a lack of knowledge regarding the impact of reduced soil and leaf bacterial diversity on the initial litter decomposition process. It is known that a large variety of soil bacterial species are able to produce diverse hydrolytic enzymes, catalyzing the early steps of litter decomposition. For example, beta-glucosidases are shared by multiple species belonging to different taxonomic groups, including the phyla Firmicutes and Proteobacteria ,29. ThisWe postulate that (i) soil bacterial diversity is positively correlated with litter decomposition rates, even during early phases of litter decomposition, and despite the high availability of labile C. Moreover, it has been demonstrated that low bacterial diversity in soils increases the chance of exogenous species to establish themselves in that environment ,33. HencThe impacts of microbial diversity and community shifts on ecosystem function have been studied in natural settings. For example, chronosequences of soil from the same parental material with different developmental stages  have been used as a model to assess correlations between microbial community composition and its function ,35. FurtIn this study, we performed a litter decomposition experiment under controlled conditions using natural and autoclaved maize leaves. We compared the litter decomposition rates in soils with different levels of bacterial diversity, prepared from one natural soil (NS) by manipulating its bacterial diversity. Autoclaving and reinoculation with an extract of microbes from NS eliminated parts of the “rare biosphere”. This approach substantially reduced bacterial diversity in autoclaved soil (AS) and inoculated soil (IS) compared to NS , while nZea mays L.) leaf samples were obtained from an agricultural field in Scheyern, Germany  in October 2015. The soil was characterized as Luvisol with an organic C content of 17.5 g C kg−1 dry soil, a total N content of 1.75 g N kg−1 dry soil, and a silt loam texture consisting of 18.9% sand, 58.6% silt, and 22.5% clay and a pH of 6.0. The soil was sieved through a 5 mm sieve. The natural and autoclaved maize leaves had an organic C content of 460 g C kg−1 dry matter and a total N content of 13.0 g N kg−1 dry matter.Soil and maize  were prepared from one source as depicted in g at 4 °C for 30 min to remove large soil particles. The supernatant was transferred to a new tube and centrifuged at 10,000× g for 2 min to obtain a cell pellet. The pellet was resuspended in 55 mL of 0.85% NaCl solution. In total, 2 mL of the resultant bacterial suspension, equivalent to 2.38 × 107 copies of the 16S rRNA gene, was added to 21 g of AS, resulting in “IS” inoculated soil. To control for volume, 2 mL of distilled water was added to 21 g of NS and AS instead of the bacterial suspension.The inoculum for IS was prepared by isolating microbial fractions from NS according to Bressan et al.  with a sTM 50 mL conical centrifuge tubes  with open headspace. The tubes were loosely covered by aluminum foil during the incubation period to avoid airborne contamination. Soil moisture in all treatments was adjusted to 60% of field capacity and maintained by adding distilled water throughout the experiment. Temperature was set to 18 °C and incubation was carried out in the dark. In order to allow the inoculated microbial community to adapt to the soils and reach a similar bacterial abundance as in NS based on 16S rRNA gene copies g−1 dry soil, all the soil samples were preincubated for 2 weeks (from week −2 to week 0). The length of the preincubation period was determined in a preliminary experiment for 3 weeks, where bacterial abundance based on 16S rRNA gene copies g−1 dry soil and diversity based on terminal restriction fragment length polymorphism (T-RFLP) profiles of 16S rRNA gene were tested every week . Accordingly, the preincubation period was set for 2 weeks , filled with natural or autoclaved maize litter , were placed into the soil at a depth of about 1 cm. For autoclaved maize litter, a part of natural maize litter was autoclaved for 20 min at 121 °C and stored at 4 °C until use. Sterility of the autoclaved maize litter was proven by putting pieces of the autoclaved maize litter on LB agar plates and subsequent incubation for 14 days at 20 and 37 °C. No microbial colonies were observed on the plates after the incubation, while the presence of slow-growing microbes might not be excluded completely.Soil samples were taken after the addition of the inoculum (week −2), after preincubation (week 0), and 6 weeks after incubation with the litter materials (week 6), and immediately frozen at −20 °C for molecular analysis. For each treatment and time point, three parallel tubes were prepared and treated as true replicates throughout the experiment. Litter materials were sampled on weeks 0 and 6. An aliquot of the litter material was dried at 60 °C for >48 h to obtain dry weight, and the remaining was stored at −20 °C. Litter decomposition rates were assessed by calculating the quotient of remaining and initial litter amounts (dry weight basis). Due to the very small amount of litter materials left in the litter bag on week 6, further molecular analysis was performed only on the maize samples collected on week 0.®Soil Kit . For cell lysis of soil and plant-associated microbes, buffer SL2 was used. As an extraction control (blank), an extraction without soil or maize litter was also processed. For the absolute quantification of bacteria and fungi in the soil and litter materials, 16S rRNA gene and internal transcribed spacer (ITS) regions were targeted as a proxy for bacterial and fungal abundance, respectively. SYBR Green-based quantitative real-time PCR (qPCR) assays were performed on a 7300 Real-time PCR System  with the primer pairs FP16S and RP16S primers for bacteria  × 100. Amplification efficiencies of qPCR runs in this study exceeded 80% and R2 value exceeded 0.99.Genomic DNA was extracted from 0.30 g of soil and 0.01 g of maize litter by using the NucleoSpinbacteria  and ITS1bacteria , respect® XT Index Kit v2 set A; Illumina, San Diego, CA, USA), 12.5 µL NEBNext High-Fidelity 2X PCR Master Mix, and 6.5 µL DEPC treated water. Afterward, all amplicons were purified using Agencourt AMPure beads (AMPure/PCR product ratio = 0.8) and quantified with the Quant-iT PicoGreen dsDNA Assay Kit. Their quality was checked using the Bioanalyzer 2100. Extraction and PCR controls did not give any amplification products at the end of the library preparation procedure. Each library was diluted to 4 nM and sequenced with the MiSeq Reagent kit v3 (600 cycles)  for paired-end sequencing. Sequences were deposited in the NCBI Sequence Read Archive and are available under the accession numbers SRP127524.For the assessment of bacterial communities and diversity, the “16S Metagenomic Sequencing Library Preparation” protocol  and quality guidelines recommended by Schöler et al.  were useRaw sequence data were separated from their adapters using AdapterRemoval v2.1.0, and reads were trimmed with a Phred score of 15 and a minimum read length of 50 bp . Datasetp-values [p-values. The effect of soil treatments on the relative abundance was determined by performing pairwise comparisons of the least-squares (LS) means among the soil treatments at each time point using the function lsmeans in the lsmeans package [Statistical analyses and data visualization were carried out using the statistical software R v3.5.3  and RStup-values  using thp-values . Variatip-values ) using tp-values . Bacteri package . To eval package . Maize aThe decomposition rates of natural maize litter were significantly higher in NS compared to IS and AS after 6 weeks of incubation, corresponding to 66.7%, 44.6%, and 37.8% of the litter mass loss in NS, IS, and AS, respectively . Differep < 0.05, two-way ANOVA). On week −2, bacterial abundance in IS and AS was significantly lower compared to NS . Bacterial abundance in IS and AS increased from week −2 to 0, while there were no changes in bacterial abundance in NS during the same period. As a result, no significant difference in bacterial abundance between NS and IS was observed on week 0. Bacterial abundance in AS was still lower than in NS at the same time point . In general, the application of maize litter introduced a significant increase in bacterial abundance for AS and IS, which resulted in no differences in bacterial abundance among NS, IS, and AS on week 6, irrespective of the maize treatments .The effect of soil treatments on bacterial abundance was dependent on the time points . On week −2, fungal abundance was significantly lower in IS and AS than NS . During the preincubation period, the fungal abundance of IS and AS increased while no changes were observed in NS. As a result, no significant differences in fungal abundance were observed among the soil treatments on week 0. The fungal abundance remained static until the end of the incubation experiment .Similarly, the effect of soil treatments on fungal abundance was dependent on time points . NS had higher values than IS and AS throughout the experiment , while no difference was observed between IS and AS until the end of the incubation.Alpha-diversity based on Shannon–Weaver diversity index, Pielou’s evenness, and the number of ASVs, is summarized in p < 0.05) and positive correlation . NS communities clustered together independent of time points, while dynamic shifts in bacterial communities from week 0 to 6 were observed in IS and AS.Variations in beta-diversity of the bacterial communities are displayed in Xanthobacteraceae, unclassified Acidobacteria subgroup 6, Nocardioidaceae, Gaiellaceae, unclassified KD4-96, Bacillaceae, uncultured Gaiellales, and Gemmatimonadaceae, irrespective of time points. On week 0, the major bacterial families in IS were Streptomycetaceae, Micrococcaceae, Xanthomonadaceae, Microbacteriaceae, Xanthobacteraceae, and Bacillaceae, while AS mainly consisted of Micrococcaceae, Rhizobiaceae, Bacillaceae, Burkholderiaceae, Microbacteriaceae, Sphingobacteriaceae, Streptomycetaceae, and Propionibacteriaceae. On week 6, IS were dominated by Xanthomonadaceae, Burkholderiaceae, Chitinophagaceae, Sphingobacteriaceae, Rhizobiaceae, Streptomycetaceae, and Xanthobacteraceae. AS consisted of Streptomycetaceae, Xanthomonadaceae, Burkholderiaceae, Caulobacteraceae, Rhizobiaceae, Beijerinckiaceae, Spirosomaceae, and Sphingobacteriaceae at the same time point. Overall, on week 0, 106 bacterial families were shared among all the soils, which represented 88.5%, 97.6%, and 86.7% of the total bacterial communities in NS, IS, and AS, respectively. Meanwhile, the number of unique bacterial families, which were only detectable in one of the soil treatments, corresponded to 2.9%, 0.03%, and 0.9% of the total bacterial communities in the same soil treatments. In contrast, on week 6, only 42 bacterial families were detected in all soil treatments, accounting for 41.4%, 97.4%, and 99.3% of the total bacterial communities in NS, IS, and AS, respectively. This was a consequence of the large number of unique bacterial families, detected only in NS and accounted for 47.9% of the bacterial community in the same soil.The relative abundance of major bacterial families in NS, IS, and AS on weeks 0 and 6 is shown in Gaiellaceae, Gemmatimonadaceae, Ilumatobacteraceae, JG30-KF-CM45, Nitrospiraceae, SC-I-84, Solirubrobacteraceae, unclassified KD4-96, and uncultured Gaiellales was significantly higher in NS than in IS and AS, while the relative abundance of Acetobacteraceae and Rhizobiaceae were higher in AS than in NS . The relative abundance of Streptomycetaceae was higher in IS than in NS and AS on week 0 . Streptomycetaceae significantly decreased in IS and increased in AS on week 6 , resulting in no significant differences in the relative abundance of Streptomycetaceae among the soil treatments on week 6. In addition, the relative abundance of Nocardioidaceae, Sphingomonadaceae, Beijerinckiaceae, Devosiaceae, and Caulobacteraceae significantly increased in both IS and AS from week 0 to 6 whereas Spirosomaceae increased only in AS during the same period. As a result, Nocardioidaceae and Devosiaceae were more enriched in IS than in AS on week 6, while the relative abundance of Caulobacteraceae and Spirosomaceae were higher in AS than in IS at the same time point .Differences in the relative abundance of the dominant families among soil treatments and time points are shown in Sphingomonadaceae, Beijerinckiaceae, Microbacteriaceae, Burkholderiaceae, and Hymenobacteraceae (Acetobacteraceae (4 ASVs), Beijerinckiaceae (7 ASVs), Burkholderiaceae (3 ASVs), Rhizobiaceae (2 ASVs), Sphingomonadaceae (4 ASVs) Caulobacteraceae (1 ASV), Microbacteriaceae (1 ASV), Nocardiaceae (1 ASV), Pseudomonadaceae (1 ASV), and Spirosomaceae (1 ASV)  . The larIn this study, we aimed to examine the correlation between reduced bacterial diversity and maize litter decomposition rates, and to identify important bacterial taxa that gained a growth advantage 6 weeks after litter application. Furthermore, we investigated the extent to which litter-associated bacteria are capable of colonizing soil environments. In order to manipulate soil microbial diversity, a proportion of NS was autoclaved and reinoculated with microbes that were extracted from NS. In line with previous studies ,65, we iStreptomycetaceae, Micrococcaceae, and Microbacteriaceae). Some actinobacterial strains are known to form spores and cyst-like cells, which could help them survive under harsh environmental conditions [Xanthomonadaceae, Burkholderiaceae, and Sphingobacteriaceae, which also have been described as bacterial groups capable of quickly recovering after a disturbance [After preincubation, significant differences in bacterial community composition were evident between NS, IS, and AS, while bacterial abundance was similar between the three treatments. The presence of bacteria in AS could be explained by indigenous bacteria, which survived the autoclaving process and made use of nutrients derived from dead biomass and dissolved organic C (DOC) released by autoclaving . The majnditions ,67. In anditions ,69,70. Sturbance ,72.Several authors have postulated that a reduction in bacterial diversity is closely linked to reduced decomposition rates of plant residues. For example, Baumann et al. used γ-radiation for sterilization and reinoculation of sterilized soils with diluted soil suspensions to study the degradation of wheat-derived sugars . As expeIn our study, however, differences in fungal biomass might also be associated with different litter decomposition rates in the soil. Indeed, soil fungi became dominant in IS and AS, as indicated by the higher mean ratios of ITS copies to 16S rRNA gene copies  compared to the ratio in NS (0.84) on week 0. This could be explained by the improved persistence of spores against autoclaving and the absence of antagonistic microbes after soil autoclaving . HoweverGaiellaceae, and Nitrospiraceae, might have played an important role in litter decomposition. A high share of Acidobacteria has been observed in agricultural soils [Vicinamibacteraceae, known for a wide range of substrate preferences including recalcitrant C  and labile C [Gaiellaceae [Nitrospiraceae [Bacterial members, which were excluded from IS and AS, such as Acidobacteria , al soils ,78,79. Tal soils ,81 and aal soils ,83. The labile C ,84, thusellaceae  and Nitrpiraceae  were alspiraceae . Oligotrpiraceae . Thus, gSphingomonas within Sphingomonadaceae and Methylobacterium within Beijerinckiaceae, which have the capability of degrading cellulose and hemicellulose [Nocardioides within Nocardioidaceae can assimilate plant-derived C during litter decomposition processes [Brevundimonas within Caulobacteraceae [Sphingomonas within Sphingomonadaceae [Methylobacterium within Beijerinckiaceae [Caulobacteraceae and Sphingomonadaceae harbor alkaline phosphatases [Streptomycetaceae decreased in relative abundance in IS, which mainly contained the plant-degrading genus Streptomyces [Copiotrophic bacteria, which became more abundant during the early decomposition phases of the maize leaf litter-derived C, dominated in IS and AS. For example, the group included the genus ellulose ,90. The rocesses . Additioteraceae , Sphingonadaceae , and Metckiaceae . These bckiaceae . In contphatases , again uptomyces . The relSphingomonadaceae, Beijerinckiaceae, Microbacteriaceae, and Burkholderiaceae. This corresponded to previous studies showing the low diversity of maize leaf microbiomes, represented by Sphingomonadaceae and Microbacteriaceae [Methylobacterium within Beijerinckiaceae, successfully colonized only in AS during litter decomposition. However, the abundance of indigenous soil bacteria was also low in AS, which could increase the probability of a successful invasion from litter associated bacteria [Methylobacterium has also been detected in soils and different plant tissues [Methylobacterium did not result in higher decomposition rates in AS.The majority of bacteria derived from the natural maize litter consisted of eriaceae ,99. In abacteria ,33. The  tissues  and desc tissues , which c tissues . HoweverNitrospiraceae, and Gaiellaceae, remained consistent, whilst the litter decomposition rate in NS was higher than in other soil treatments. In IS and AS, oligotrophic bacteria were absent. Instead, copiotrophic bacteria and litter-associated bacteria benefited from low competition and increasing C substrates. Thus, the absence of oligotrophic bacteria might have created a gap in functional richness and caused delayed litter decomposition, which was compensated after a lag period by the fast-growth of copiotrophic bacteria.Our results demonstrate that litter decomposition rates of maize leaf litter were positively correlated with soil bacterial diversity, and that litter application caused a significant change in bacterial community composition when diversity was low. In this study, the bacterial community composition in NS, primarily composed of oligotrophic bacteria including Acidobacteria subgroup 6, 13C labeled litter material might help to identify microorganisms, which directly use the litter material, and those, which only indirectly benefit from plant-derived C .As we used autoclaved soil and microbial inoculation during this study, it was not necessarily comparable to natural conditions. For example, it has been reported that autoclaving increases DOC content of any material. Thus, we can assume that the autoclaved litter material might have contained larger amounts of DOC compared to the natural litter material. The high DOC content in the autoclaved maize compared to the natural maize could counteract the effect of plant-associated microbiomes on litter decomposition rates. Another limitation of this study is a missing strategy to exclude fungi from the experimental setup or to include them in our analysis. Indeed, our manipulation approach also influenced fungal diversity. However, this is a problem of all studies where similar strategies have been used to manipulate microbial diversity and can be considered as a drawback of the approach. Other methods  are also critical, and in many cases, only specific microbial groups are influenced by the treatment, or an additional effect on bacteria cannot be excluded. We also have reason to believe that fungi may not be strongly involved in the initial decomposition of litter material in our study. Future studies should also include the analysis of fungal communities during late phases of litter decomposition, where fungi are the main drivers for the decomposition of plant-derived polymers. Furthermore,


Characterization and expression analyses of CaCA superfamily genes were performed in the tomato (Solanum lycopersicum) as a representative of dicotyledonous plants and fruit crops. Sixteen CaCA candidate genes were found and identified as tomato CaCA, SlCaCA, by a domain search. In a phylogenetic analysis of the SlCaCA superfamily, the 16 genes were classified into SlCAX, SlNCL, SlCCX, and SlMHX families. Among them, Solyc12g011070, belonging to the SlCAX family, had four splice variants, three of which were predicted to be nonfunctional because of a lack of important motifs. EF-hand domains were only found in SlNCL, in addition to consensus Na_Ca_ex domains, and the region containing EF-hand domains was characteristically long in some members of SlNCL. Furthermore, four genes of the SlCCX family were found to be intronless. As for intracellular localization, one SlCCX member was predicted to be localized to the plasma membrane, while other SlCCXs, SlCAXs, and SlMHXs were predicted to be localized to the vacuolar membrane. The expression patterns of SlCaCAs in various organs, including during several developmental stages of fruit, were classified into four groups. Genes involved in each of the SlCAX, SlNCL, and SlCCX gene families were categorized into three or four groups according to expression patterns, suggesting role sharing within each family. The main member in each subfamily and the members with characteristic fruit expression patterns included genes whose expression was regulated by sugar or auxin and that were highly expressed in a line having metabolite-rich fruit.The Ca The Ca2+/cation antiporter (CaCA) superfamily plays an important role in the regulation of Ca2+ and cation concentrations in plant cells [Arabidopsis and proposed that all CaCA proteins should be classified as Na+/Ca2+ exchangers (NCXs). However, Pittman and Hirschi [+/cation exchangers (CAXs), Na+/Ca2+ exchanger-like proteins (NCLs), cation/Ca2+ exchangers (CCXs), and Mg2+/H+ exchangers (MHXs). Taneja et al. [Cant cells ,4,5,6. Rnt cells  performe Hirschi  showed, a et al.  showed t2+ and tolerance for heavy metals in Brassica plants and diploid wheat relatives by using mutants and overexpressors. Ahmadi et al. [Of these four CaCA families, CAX and NCL may play roles in responses to abiotic stress ,10, and i et al.  showed tSolanum lycopersicum) are an important crop worldwide because of their high economic and nutritional value. They are also scientifically useful as an experimental model species of the Solanaceae family and of fleshy-fruited plants. Regarding tomato CaCA members, it has been reported that the tomato CAX, LeCAX2, transports Ca2+ and Mn2+ [4+7) [2+ is closely related to the occurrence of a serious physiological disorder called blossom-end rot in tomato plants, and the relationship between this physiological disorder and CaCA may be interesting [Arabidopsis and cereals such as rice and bread wheat [Tomatoes  . Ca2+ iseresting ,23. Howead wheat ,9, whileArabidopsis thaliana, it was estimated that 16 CaCA genes exist in the tomato plant. Phylogenetic analysis showed that the 16 putative CaCA proteins could be classified into four families: six into the CAX family , four into the NCL family , five into the CCX family , and one into the MHX family (Solyc06g009130). The Sol Genomics Network has published the nucleotide sequence and amino acid sequence of cv. Heinz 1706, although Saand et al. [Based on a BLAST search using the CaCA sequences of d et al.  reportedd et al. . TherefoArabidopsis [2+, Cd2+, and Mn2+ [2+ in Arabidopsis [Phylogenetic tree analysis was performed using the amino acid sequences of CaCA genes from the tomato, which were determined here, with those previously determined from rice and bidopsis ,26, as sbidopsis , plant Cbidopsis , and Edmbidopsis , Type1B and Mn2+ ,28, and bidopsis . Howeverbidopsis ,27,30.Solyc07g062700. As discussed later, this gene is of interest because of its expression pattern in fruit, the fact that it exhibits the highest expression among SlNCL genes, its response to sugar signals, and its high expression in fruit rich in metabolites. Functional analysis is necessary for both plant physiology and agricultural science.According to Emery et al. , the aveSlCaCAs of the tomato function as CaCAs, a domain search was performed on the obtained amino acid sequence using TMHMM (ver. 2.0) and InterPro of the EMBL-EBI. The four mRNA variants of Solyc12g011070 were referred to as Solyc12g011070a, b, c, and d. CaCA has transmembrane domains (TMs) because it performs the countertransport of cations through the membrane. According to previous reports, CaCA has an average of 10 TMs [Solyc12g011070b, c, and d, there were only five TMs : two in Solyc02g077270 and Solyc03g006260 and one in Solyc07g062700 and Solyc12g014110. According to Taneja et al. [Solyc12g011070b, c, and d did not have domains of importance to CaCA, so they were not considered to be functional, unlike CaCA, and were excluded from expression analysis. However, since expression regulation by selective splicing has been reported in the stress response [When the domain was searched with InterPro of the EMBL-EBI, the amino acid sequences translated from to CaCAs . That isresponse , it may Emery et al.  examinedhttps://www.ebi.ac.uk/ena) , the translated regions of Solyc12g011070b, c, and d were half the length of the functional sequence of Solyc12g011070a, and consequently contained only one Na_Ca_ex (Pfam ID: Pf01699) domain. Solyc09g005260 and Solyc02g069710 were different in sequence from cv. Heinz 1706. However, it was likely that there was no difference in their functions as CAXs because the difference in the sequence was not within the important motif. Solyc02g069710, Solyc07g006370, Solyc07g042000, and Solyc09g072690, belonging to CCX, had no introns. Three of five TaCCXs also lack introns in bread wheat [To examine the genomic structure of each CaCA, schematic diagrams of exons and introns were made, based on the genomic DNA sequences of the corresponding genes  . The secad wheat ,33. Thisad wheat , the intArabidopsis, with reference to Kamiya et al. [Arabidopsis are motifs characteristic of CaCA, and they are considered to be cation-binding regions [OsEFCAX1/NCL1, OsEFCAX2/NCL2, Solyc07g062700, Solyc12g01411, and AtNCL1/EFCAX1, which clustered together in the phylogenetic tree  and Solyc02g069710 (SlCCX) contain an α1-repeat in the region containing only TM3, whereas, according to Taneja et al. [Solyc06g009130 (SlMHX) contains an α2-repeat in the region containing TM9-10, whereas, according to Taneja et al. [Solyc01g098800, Solyc02g069710, and Solyc06g009130 were determined with reference to Taneja et al. [An α1-repeat was found in the region containing TM1-2, TM2-3, or TM3-4, and an α2-repeat was found in the region containing TM6-7, TM7-8, TM8-9, or TM9-10 . When thSolyc01g098800 was predicted to be localized to the plasma membrane, while other S1CCXs were predicted to be localized to the vacuolar membrane. It has been reported that CaCAs localize to vacuolar and plasma membranes [Solyc01g098800, has been experimentally confirmed to be localized to the plasma membrane [Identifying intracellular localization is important to understanding how CaCA regulates the cation concentration in cells. Therefore, the intracellular localization of CaCAs was predicted by ProtComp . It was embranes ,39; the embranes ,5,40,41,membrane .Solyc12g011070 and Solyc09g072690) for which expression was not detected at all or was lower than the median if detected. However, the mRNA levels of other genes were above the median in one or more of the organs and fruit developmental stages. Therefore, in order to clarify the characteristic expression patterns for each gene, expression levels in organs and fruit developmental stages were normalized, and hierarchical clustering was performed based on the expression patterns  and during breaker and ripe stages. The stages 10 DAF, 20 to 30 DAF, and breaker to ripe correspond to cell division, cell expansion, and ripening stages, respectively , and Patnd seeds , and tom reports ,45. As frecorded , and thel pectin .Solyc01g098800, belonging to SlCCX. CCX localizes to the vacuolar or plasma membrane [Arabidopsis, AtCCX1 and AtCCX3 play roles in Na+/K+ exchange and H+/K+ and Na+ exchange, respectively [+ uptake [Solyc01g098800 was closest to AtCCX5 in the phylogenetic tree  was induced by fructose, glucose, and mannitol (Solyc07g062700 (SlNCL) and Solyc07g006370 (SlCCX) expression was remarkably upregulated by fructose treatment, it was slightly downregulated or unchanged by glucose and mannitol. If gene expression is affected by mannitol, as well as fructose and glucose, as with Solyc09g005260, it is thought to be a response to osmotic stress; however, the expression of Solyc07g062700 and Solyc07g006370 appeared to occur in response to the sugar signal instead of osmotic stress. Interestingly, Solyc07g062700, an SlNCL, belonged to Pattern3  expression was not affected by auxin , whereas SlCAX (Solyc09g005260) expression was suppressed  was used for genomic DNA removal and the reverse transcription reaction to prepare cDNA.RNA was extracted using a Cica Geneus RNA Prep Kit . ReverTra Acehttp://www.ncbi.nlm.nih.gov/) and the Sol Genomics Network (http://solgenomics.net/) to search for putative SlCaCA proteins using Arabidopsis thaliana CaCA (AtCaCA) amino acid sequences identified by Emery et al. [Arabidopsis CaCAs, homologous sequences having a query cover of approximately 100 and the highest identity were selected. Furthermore, the BLAST search was performed again using the obtained sequence as a query to confirm the presence or absence of similar sequences. The presence or absence of similar sequences was also checked, taking into account the possible presence of tomato CaCA that does not correspond to each member of Arabidopsis CaCA. Specific primers were designed for each putative SlCaCA gene based on the sequence of S. lycopersicum cv. Heinz 1706 to amplify the cDNA by PCR with Q5 High-Fidelity DNA Polymerase . Primer sequences and annealing temperatures are shown in ® Gel and PCR Clean-up . Extracted DNA and primers were submitted to Macrogen, Japan, for general sequence analysis.A BLAST search was performed in the National Center for Biotechnology Information  and EMBL-EBI InterPro (https://www.ebi.nih.gov) on SlCaCA amino acid sequences to define CaCA proteins based on important conserved domains. Subcellular localization was examined using ProtComp . The protein and genomic structures of https://www.genetyx.co.jp/).Multiple sequence alignments using amino acid sequences of y et al.  and Pitty et al.  were mady et al. . A domaiFor real-time PCR, KOD SYBR qPCR Mix (TOYOBO) was used with primers at the annealing temperatures described in SlCaCA and SlUbiquitin of known concentrations. The cDNA fragments were extracted from the agarose gel mentioned in SlUbiquitin. To compare the expression levels among organs for each gene in To accurately compare the expression levels between genes in Solyc03g123790, Solyc12g055750, and Solyc07g062700) in M82 and its near-isogenic line IL8-3 in Solyc01g098800, another gene in Pattern3, was not shown in https://www.ssri.com/).For expression analysis of the sugar and auxin response in The structure and expression of CaCA superfamily genes were examined in the tomato, as a representative of dicotyledonous plants and fruit crops. Sixteen CaCA candidate genes were found, identified as CaCA, and classified into four subfamilies, and we identified their general and unique properties. The expression patterns of CaCAs were classified into four groups. The main members in each subfamily and the members with characteristic fruit expression patterns were revealed for further studies to elucidate the roles of CaCA family genes. Similar bioinformatics reports on other tomato transporters have had a significant impact on the many citations ,59, and


Source identification of heavy metals in agricultural soils using small sample sizes, simple experimental procedures, and convenient analysis is urgently required. This study employed a simple source identification model using a visual comparison via radar plots, cluster analysis, principal component analysis, and a multiple linear regression model to determine the source of heavy metal pollution in soil samples from the Chang-Zhu-Tan urban agglomeration area of China. The elemental compositions of major pollution sources  were compared with soil samples from 11 study locations and the model was used to determine the relative contribution of different pollution sources at each sample site. The results showed that the model successfully calculated the contribution of different pollution sources at each site based on the pollution characteristics and contaminant transport rules of the region. The proposed method overcomes the requirement for extensive data and complex experimental procedures. Furthermore, the model can determine the source of heavy metal contamination in single or small plots, which is important for the prevention and control of heavy metal soil pollution and remediation at the plot scale. Heavy metals are difficult to degrade. Therefore, heavy metal-contaminated soil is a serious environmental issue  that canNumerous methods for identifying these sources of pollution have been used with varying degrees of success. Inventory methods require counting of the number of inputs and outputs to establish a source inventory ,8,9,10. The isotope ratio method quantitatively distinguishes the contribution of pollution sources by comparing isotope ratios between samples and pollution sources. On account of their stable geochemistry, small sample size requirement, and high identification accuracy, the isotopes of Pb, Cd, Cu, and Zn have been used to identify the sources of heavy metals in soils ,19,20,21Integrated methods have previously been applied to take advantage of different approaches. For example, Zhang, et al.  successfThis study investigates the use of a relatively simple model for the source apportionment of heavy metal pollution based on a small sample size, simple experimental procedure, and convenient analysis method. First, the tendency of heavy metals to accumulate in surface soils was determined, after which the ratios of these for different heavy metals in the Chang-Zhu-Tan urban agglomeration were calculated and compared to pollution source data for the region, inspired by the isotope ratio method. Then, CA and PCA were used to investigate the relationships between the characteristics of the ratios of different sites and pollution sources. Finally, MLR was employed to analyze the contributions of the pollution sources at various sites.The study site was located in the east-central region of Hunan province, China , organic fertilizer (mg kg−1), tailings (mg kg−1), and irrigation water were collected from atmospheric particulates, livestock of pigs and chickens, mineral waste residue (-contaminated soil), and river water (μg L−1), respectively. In these studies, the samples were mostly digested with mixed acids. Although the determination method varied, each measurement was proceeded with quality control to make the data reliable. The average values and standard deviations are listed in Data on Cd, Cu, Pb, and Zn in atmospheric deposition, organic fertilizer, tailings, and irrigation water were collected from 13 previous investigations which studied Chang-Zhu-Tan and its surrounding areas. Data on heavy metal concentrations in the atmospheric deposition (ng mHM) was calculated as the difference between the heavy metal concentration in the surface soil  and in the bottom soil, which represented background values . Therefore, AHM is considered as the contribution from anthropogenic sources and the influence of soil parent materials is ignored in this study. The equation is given in Equation (1):In this study, the concentration of a certain heavy metal that accumulated in surface soil (Aa and HMb) in the surface soil (RHMa/HMb) were used to reflect the elemental characteristics of different sampling sites and evaluated according to Equation (2):The ratios of two accumulated heavy metals (denoted as HMa and HMb) from a particular pollution source (rHMa/HMb) were employed to determine the elemental characteristics of different pollution sources, as given in Equation (3):HM is the heavy metal concentration in pollution sources  within the regression equation if it could significantly increase the correlation of the result, and a significant default level of 0.01 was used. After normalization, the MLR equation can be described by Equation (4):PS-j is the standard deviation of the independent variable, and Cy is the standard deviation of the dependent variable. βPS-j represents the standardized coefficients and percentage contribution of different pollution sources. Although stepwise modeling can obtain different solutions, the one with the largest correlation coefficient is chosen.The standardized regression coefficient (β) can be derived from the coefficients above , except for Cu [−1) [−1, respectively, and were significantly lower than those in the surface soils. Although an overall decreasing trend was visible, the rate of the decrement varied for different heavy metals across the sampling sites  , indicatng sites . There wng sites a. Decreang sites b. Decreang sites c,d. Alth3) and Zn (288.7 ng m3) that were over 10 times greater than that of Cd (5.28 ng m3) (Pb/Cd = 23.0 and Zn/Cd = 43.3) (−1) and Zn (671.9 mg kg−1) were 280.4 and 1132.8 times higher, respectively, than that of Cd. The Pb/Cu and Zn/Pb ratios  were relatively low and high, respectively, due to a low Pb concentration (27.2 mg kg−1) in the fertilizer (−1), Zn (6195.7 mg kg−1), and Cd (46.6 mg kg−1) were high due to the widespread distribution of Pb-Zn mining and smelting in Hunan province [−1) was 9.4 to 15.9 times greater than the concentrations of the other heavy metals  a. The re = 43.3) a. The ra = 43.3) a. In orgrtilizer . Howeverrtilizer g. The rartilizer g. In taiprovince . This rey metals m. The ray metals m.Therefore, the sampling sites exhibiting similar shapes in radar plots as the pollution sources were grouped. Group 1 (atmospheric deposition) consisted of Site2, Site5, Site7, Site10, and Site11; Group 2 (organic fertilizer) comprised Site1 and Site3; Group 3 (tailings) consisted of Site4 and Site8; and Group 4 (irrigation water) contained Site6 and Site9. Although the heavy metal concentrations of the pollution sources were uncertain , a non-oThe ratios of heavy metals accumulated at different sampling sites and the ratios of heavy metals for different pollution sources were processed using the CA to create four clusters of indices with similar properties, as shown in In the PCA, two principal factors had a significant impact (77.71%) on the variance of the variables. Part of the variance in the first principal component  showed high positive factor loadings for Zn/Pb (0.856) and Zn/Cu (0.660), and high negative factor loadings for Pb/Cd (−0.861) and Pb/Cu −0.692) . Copper 2 . CoppeThe distance between the data points and the pollution sources in 2 = 0.948) and 97.0% (R2 = 0.929), respectively. For Site 10 and Site11, the contributions of atmospheric deposition were 99.3% (R2 = 0.983) and 99.6% (R2 = 0.991), respectively. For Site4 and Site8, the contributions of tailings were 99.4% (R2 = 0.987) and 98.6% (R2 = 0.966), respectively. For Site6, the contribution of irrigation water was 82.3% (R2 = 0.612). For Site2, the contributions of atmospheric deposition and organic fertilizer were 52.8% and 49.6% (R2 = 0.992), respectively. For Site5, the contributions of atmospheric deposition and tailings were 71.3% and 30.7% (R2 = 0.985), respectively. For Site7, the contributions of organic fertilizer and tailings were 83.9% and 23.4% (R2 = 0.992), respectively. Although the regression calculation for Site9 failed, the previous analysis suggests that the pollution source at this site may be associated with irrigation water.The contribution of different pollution sources to each site was obtained using stepwise linear regression . For SitThe application of organic fertilizers is the secondary source of heavy metal pollution in China . OrganicUntil recently, the most common simple multivariate statistical methods, such as PCA, CA, and FA, required large samples and could not determine how individual sources contributed to pollution. For example, recent developments allowed 131 samples of surface soil (0–20 cm), collected from farmlands in 31 provinces, municipalities, and autonomous regions in the seven regions of China, to be analyzed via a combination of interelement relationships and PCA. The results showed that soil organic matter, measured in terms of the application of organic fertilizer, sewage sludge, and other organic materials, had a strong relationship with heavy metals such as Pb, Cd, Cr, Zn, Cu, and Sb, which are of an anthropogenic origin, whereas elements such as Mn, Ca, and Mg mainly originated from lithogenic sources . GIS mapQuantitative methods, such as PCA-related methods, PMF, and the isotope method, are also used to conduct pollutant source identification in soils. PCA-related methods need large amounts of data . PMF hasOur source identification method was based on the behavior of elements in the environment; it assumed that heavy metals with anthropogenic origins accumulate in the surface soils. Different ratios of accumulated elements were used in place of the different proportions of isotopes measured in the isotope source analysis method, which was a source of inspiration. The complex experimental operations used in the isotope source identification method were replaced by a convenient and straightforward analysis of heavy metal concentrations. This study shows that using the CA and PCA, along with MLR, can yield superior results for an analysis of more than 10 sites. Even if there are fewer sampling sites, and the CA and PCA cannot be performed, the pollution sources can still be analyzed via visual radar plots and MLR. The method proposed in this study overcomes the extensive data requirement and can easily analyze the sources of heavy metals in single or small plots.The sources of heavy metal polluted soil at 11 sites in the Changsha–Zhuzhou–Xiangtan urban agglomeration were investigated using a relatively simple method for apportioning the pollution sources using a small sample size, simple experimental procedure, and convenient analysis technique. Four of the most common heavy metals  in the region were used as research objects to identify pollution sources. The concentrations of all heavy metals were higher in surface soils compared to deep soils. Therefore, the concentrations of heavy metals accumulated in the surface soils were calculated as the difference between their concentrations in the surface and bottom soils. To identify the sources of pollution, radar plots were used for visually comparing and dividing sites of pollution into four different groups based on the source of pollution . An MLR model successfully calculated the contributions of different pollution sources, and the results were consistent with those expected for the test sites compared with known pollution sources. This method does not require an extensive dataset or complex experimental procedures; moreover, it is advantageous in that it can identify the sources of heavy metals in single or small plots. This study can be considerably significant in controlling sources of pollution and remediating small-scale soil pollution.


The pandemic of coronavirus disease 2019 (COVID-19) has affected the Italian community. The widespread use of quarantine had the desired impact of controlling the epidemic, although it caused many psychological consequences. To date, compliance of the Italian public with voluntary home quarantine has been very high, but little is known about the impact of psychological health on sociodemographic categories during the quarantine. The purpose of this study was to assess the prevalence of depressive symptoms in specific sociodemographic categories during the COVID-19 quarantine lockdown and the potential factors that contribute to, or mitigate, these effects. In the very early stage of the nationwide lockdown, 3,672 quarantined Italian adult residents  participated in a web-based cross-sectional survey, including measures of depressive symptoms, which were measured by the Teate depression inventory, and state anxiety levels. The overall prevalence was 27.8% for moderate and 9.3% for severe levels of depressive symptoms. A generalized logistic model was used to identify the factors associated with mental health problems. Among these factors, sociodemographic variables  and adherence to quarantine guidelines were analyzed. Females, younger people, students, singles, residents in northern Italy, people who were reluctant to adhere to quarantine guidelines, and people less worried about being infected with COVID-19 were at high risk of developing depressive symptoms during the COVID-19 epidemic, also after controlling for state anxiety. These findings showed that public levels of depressive symptoms did not increase the greater likelihood of being infected. Our study suggested that the monitoring of psychological outcomes for outbreaks could identify groups at higher risk of psychological morbidities due to the current pandemic in order to target future psychological interventions for implementation. The pandemic of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2  has affected the Italian community since late January. According to the Imperial College COVID-19 Response Team , cumulatTo contain the rapid spread of this pandemic, the Italian Government ordered nationwide lockdown by March 11: all public places were closed , all public events and any form of congregation were banned, and a distance of at least 1 m had to be maintained . All ItaYet, the pandemic created a breeding ground for direct psychological consequences, suddenly throwing many individuals into daily lives filled with health threats, existential depression, and generalized stress . A recenSocial distancing and isolation exacerbate the burden of stress, and often cause effects on immune, cardiovascular, and mental health because these measures frustrate the deep-seated human instinct to connect with others. On this point, social connection helps people to regulate negative emotions, remain resilient during difficult times, and cope with stress .Remarkably, the mental impact of quarantine can depend largely on the characteristics of participants and the quarantine variables selected. As documented by In Italy, in the early phase of quarantine (7–10 days after the decree of nationwide lockdown), deleterious consequences on the population’s psychological health were analyzed in a nationally representative survey of 3,452 participants . DiffereAccording to Further findings derived from an online survey  showed tThus, it is important that the potential advantages of home quarantine are weighed against the possible mental costs . QuarantResearch evidence aims of this study were to explore (1) the likely effects of quarantine on mental health (anxiety and depressive symptoms), immediately after the nationwide lockdown issued by the Italian Government, and (2) the factors that contribute to, or mitigate, these consequences.Among these factors, sociodemographic variables , worry about being affected by COVID-19, and adherence with quarantine guidelines were analyzed. Depression was the principal outcome, while anxiety was used as a covariate, given its close association with depression .Respondents were Italian quarantined adults aged 18 and older with access to a networked computer. An online cross-sectional study was conducted from March 21 to 26, immediately after the nationwide lockdown issued by the Italian Government on March 11 (#iorestoacasa). A virtual snowball sample via social media was used within a wider web-based study including other psychological measures .This study has been approved by the Department of Psychological Sciences, Health and Territory, University of Chieti, Italy Review Board. Written informed consent was obtained from all the participants included in the study.General information concerning sex, age, education, marital status, geographic area and region, employment status, yearly income, and health status including history of psychiatric illnesses and medical problems  were collected.never” to “always.” Cronbach’s α coefficient in our study was 0.90.The 21-item Teate Depression Inventory TDI; , 2018b, not at all” to 4 “very much so.” Cronbach’s α coefficient was 0.89.The 21-item state scale of the State-Trait Inventory for Cognitive and Somatic Anxiety (STICSA)  was adminever” to 4 “always.” Cronbach’s α coefficient was 0.70.Adherence to quarantine guidelines in response to the COVID-19 outbreak was measured by a global index composed of 11 items classified into preventive  and avoidant  disease behaviors . RespondNot worried at all”), moderate (“Slightly worried”), and quite a lot .Worry about being infected with COVID-19 was assessed by a single item drawn from a multidimensional questionnaire of risk perception for the COVID-19 infectious disease outbreak. Responses were classified according to three levels of worry severity: none  was applied to the explanatory model to analyze whether the severity of depression during the COVID-19 quarantine could be predicted by high levels of adherence to quarantine guidelines and worry about being infected with COVID-19, and by sociodemographic variables (Model 1), that resulted as significant in our previous study . Since dp-values), the predictors were compared (p < 0.05) were retained in the model.Predictors were selected according to a two-step process. Firstly, a potential set of considered variables were correlated with the outcome variable. All the potential variables that correlated significantly with the outcome were selected as predictors in the GLMs model. Hence, based on the test of model effects  based on robust estimates of the coefficients and covariance matrix were used to assess the models, and residual deviance as a goodness-of-fit statistic was applied to evaluate model overdispersion . The modp-values of less than 0.05.In addition, due to the high comorbidity between depression and anxiety symptomatology , the modN = 1660). Among these, 1,817 (49.5%) respondents held a high school diploma, while 1,535 (41.8%) held a higher education qualification (bachelor/master/doctorate). In terms of occupational status and income per year, 1,138 (31.20%) participants were students, 232 (6.3%) were healthcare workers, and 1,742 (47.4%) were employed. Concerning marital status, 2,249 (61.2%) participants were unmarried/single, 901 (24.5%) were married, 103 (2.8%) were divorced/separated, 366 (10%) were cohabiting, and 53 (1.4%) were widowed.The characteristics of the participants are shown in Most of the subjects had a high level of health: 2,616 (71.2%) were found to show no physical disease, while 24 (0.7%) were detected as “fragile,” having more than three diseases, and with a long history of chronic medical illness. A total of 967 (26.3%) participants had previously carried out psychotherapeutic treatment. Among these, 97.2% of respondents had carried out at least one psychotherapy treatment , 20.9% had undergone psycho-pharmacological treatment, and 0.6% had participated in other psychological treatments.Most (84.2%) of the participants spent their quarantine period with family members. A total of 1,787 (48.7%) were found to be highly adherent to quarantine guidelines, and 2,326 (63.34%) reported that they were worried about being infected with COVID-19.p < 0.05. The post-hoc analyses showed that the statistical power for this study was 0.859. Thus, there was an adequate power at the moderate to large effect size level. An N of approximately 4,182 would be needed to obtain statistical power at the recommended 0.90 level , p < 0.001] and anxiety  symptoms relative to the general public.The overall prevalence was 6.4% for severe, 24.2% for moderate, 39.7% for mild, and 29.7% for minimal depressive symptoms. The overall prevalence of anxiety symptoms was 13.6%, using the cut-off of >40. Taking together, the prevalence of depressive symptoms and state anxiety severity was significantly higher in female participants, and those younger than 30 years compared to participants aged 31 years or older (Spearman’s rho) were performed in order to select independent variables as predictors in the GLMs. As expected, all the sociodemographic variables, as well as worry were found to correlate negatively with the outcome variable (depressive symptom severity levels) ranging from rho = 0.298  to rho = −0.040 , except for sex and worry .Preliminarily, nonparametric correlations (2(df) = 3.19 (3), p = 0.372], and income per year  as predictors of depressive symptoms. To improve model fit, we discharged them and re-estimated the model. The Omnibus test , and residual deviance/df ratio (deviance/df = 1.038) of the re-estimated models suggested that the refined model fit significantly better than the proposed model (p < 0.001), with a greater risk of depressive symptoms . Participants aged from 30 years and above reported significantly lower levels of depression scores compared to the younger respondents , with a decreased risk of depressive symptoms . Likewise, widowed, cohabiting, and married participants were less likely to experience depressive symptoms than single/unmarried participants, with a decreased risk of depressive symptoms . Concerning geographic area, participants living in the south of Italy had significantly lower depression scores  compared to residents in the north-west, with a decreased risk of depressive symptoms . Students  and healthcare professionals  were slightly and/or not depressed compared to unemployed participants, respectively. The first group displayed a decreased risk of depressive symptom severity , while the latter showed a higher risk of depression symptoms  compared to the unemployed. Next, those who reported to adhere to the quarantine guidelines had significantly lower levels of depressive symptoms those who were not adherent , with a decreased risk of depressive symptoms . Lastly, participants less worried about being infected with COVID-19  had significantly lower level of depressive symptoms than those who reported to be quite worried, with a decreased risk of depressive symptoms .Parameter estimates of the generalized linear model and the exponentiated values of the coefficients [the “Exp(B)” column] are displayed in ed model . Female p < 0.001). Thus, state anxiety, as measured by the state STICSA, as the covariate was inserted in our model (Model 2).As expected, a preliminary analysis showed that depression and anxiety symptoms shared approximately 38% of the common variance, as derived by the Spearman rho coefficient . On the other hand, no statistical differences on depressive symptom severity was found in cohabiting participants compared to unmarried participants in the second model.Concerning geographic area, the participants living in central Italy were found less likely to experience severe depressive symptoms  compared to those living in north-west Italy, after removing anxiety effects. Far from the previous model, the healthcare professionals’ group were not found to differ from other occupational groups in predicting high levels of depressive symptoms compared to unemployed participants. Interestingly, participants “moderately” worried about being infected with COVID-19 were more prone to experience high levels of depressive symptoms  compared to participants who were “quite a lot” worried, after controlling for the state anxiety effect.We fitted both the GLMs models (without and with anxiety as the covariate) in the subsample of participants with a psychotherapeutic treatment history. No substantial differences were found in both the model’s goodness of fit (deviance/df = 1.048 vs. 0.911). However, differences in the estimates and standard errors of the two models were found for the model with anxiety as the covariate vs. the model without the covariate see .p < 0.05), with a greater risk of depressive symptoms . Participants aged from 30 to 39 and 50 to 59 years reported significantly lower levels of depressive symptoms scores compared to the younger participants , with a decreased risk of depressive symptoms .In line with models tested on the entire sample, in Model 1, female participants showed significantly higher depressive symptoms scores compared to male participants  and married  participants were less likely to have experienced depressive symptoms compared to single participants, with a decreased risk of depressive symptoms . Healthcare professionals  were less depressed compared to unemployed participants, with a decreased risk of depressive symptom severity . Next, those who reported to adhere to the quarantine guidelines had significantly lower levels of depressive symptoms compared to those who were not adherent , with a decreased risk of depressive symptoms .Likewise, cohabiting , with a decreased risk of depressive symptoms . Concerning geographic area, the participants living in south Italy were less likely to experience severe depressive symptoms  compared to those living in north-west Italy, after removing anxiety effects. As expected, participants “moderately” and “none” worried about being infected with COVID-19 were more prone to experience high levels of depressive symptoms  compared to participants who were “quite a lot” worried, after controlling for the state anxiety effect.Surprisingly, in Model 2, no statistically significant differences were maintained in depression symptoms for the sex, marital status, and occupational status groups, after controlling for anxiety. Participants aged from 30 to 39 years reported significantly lower levels of depression scores compared to the younger participants . With increasing age, depressive symptoms were less prevalent during the Italian lockdown due to the COVID-19 outbreak. Our results were similar to those from previous studies, such as a study during the SARS outbreak in Taiwan , a studyinfodemic”) through social media, which can easily trigger distress , compared to the north-east and north-west regions , where the disease spread first on a large scale. To explain this datum, it should be considered that the authorities introduced control measures in the northern regions (the “Red Zone”), before any other region and carried out extraordinary efforts to restrict the movement of people . In addiAs for occupational status, this study highlighted students as suffering from the highest level of psychological distress among all the other groups, including the unemployed group. Also, in this case, the public’s level of depressive symptoms did not increase with an increased probability of contracting the disease. Since the physical spaces of universities were closed, students’ mental well-being was affected by the sudden interruption of social interactions. However, the possibility of having online lessons and maintaining social contacts through social networks would not explain the onset of the depressive symptoms compared to other groups, for example employees who had been laid off or were retired.The reason for students’ greater risk of depressive symptoms, reported also by Compared with other professions and the general population, healthcare workers were associated with a lower risk of psychological outcomes compared to the unemployed in our sample. “Learned helplessness” could exp < 0.001)  . SimilarAs to adherence, people with low adherence were more likely to exhibited depressive symptoms relative to people with a great level of adherence to COVID-19 preventive measures. As expected, adherence has been found to be a protective factor against mental health problems . AdoptinAs for the whole sample, also in the subsample of participants with psychotherapeutic treatment history, the same sociodemographic factors and behaviors that contribute to, or mitigate mental effects of the quarantine in terms of depressive symptoms were reported. However, when anxiety symptoms or concomitant stressful events were present in comorbidity, these participants were found to experience higher levels of worry associated with increased depression symptomatology, compared to the whole sample. Current evidence showed similar results. A history of stressful situations and medical problems was associated with a greater degree of depression and anxiety during the COVID-19 quarantine in the Italian population . Again, During the COVID-19 quarantine outbreak in Italy, female participants, younger people, single people, students, people living in northern regions, and who were less compliant with quarantine guidelines and less worried about being infected with COVID-19 were at a high risk of displaying psychological issues. These findings suggest public levels of depressive symptoms did not increase with the greater likelihood of being infected. For example, although female and younger people reported a lower risk of COVID-19 infection, they experienced higher levels of depressive symptoms during the COVID-19 quarantine in Italy.Therefore, ongoing monitoring of the psychological strain associated with outbreaks of epidemic-potential, life-threatening diseases should become routine as part of preparedness efforts worldwide by establishing early targeted mental health interventions. In other words, more vulnerable groups, likes those cited above, should benefit from personalized “morale-boosting” interventions. Or, intervention research could be valuable to combat amplifications of gender inequalities, particularly to address the added challenges women are likely to face. This research can provide timely insights for government agencies toward improving and safeguarding the psychological well-being of women, younger people, and categories of subjects at a higher risk of suffering from psychological distress on the occasion of subsequent waves of the spread of COVID-19 or other epidemic diseases. This study has several limitations. Firstly, the analyses presented here were derived from a cross-sectional design, thus it is difficult to make causal inferences. Secondly, given that the research was conducted in close temporal proximity to the period of the COVID-19 quarantine, a web-based survey method was necessary to recruit a convenience sample by avoiding possible infections. This limited sampling in our study. As a consequence, a self-selection effect may have occurred and should be considered with those people who were experiencing the greatest or least levels of distress responding to the survey . In addiFinally, it could not evaluate whether the outcomes considered in this study will be long-lasting after the COVID-19 outbreak. However, follow-up with these participants will continue in order to facilitate our understanding about how long these outcomes will last. A deeper understanding of how the epidemic affects Italians’ psychological health by identifying which groups were at a high risk of psychological morbidities due to the current pandemic can help to guide and target future psychological intervention implementations.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation, to any qualified researcher.The studies involving human participants were reviewed and approved by the Department of Psychological Sciences, Health and Territory, University of Chieti, Italy, Review Board. The patients/participants provided their written informed consent to participate in this study.Both authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.