Falling is one of the leading causes of injury among elderly populations. As the population over 65 years old increases, medical costs due to falling will also increase. Urban and rural areas have different fall characteristics, and research into these differences is lacking.A survey was conducted on 2012 people over 60 years old between September 1, 2015, to October 12, 2015. Guro-gu (Seoul), Yeongdeungpo-gu (Seoul), and Jung-gu (Daegu) were classified as urban areas and included 1205 of the study participants. Dalseong-gun (Daegu) and Yangpyeong-gun (Gyeonggi-do) were categorized as rural areas and included 807 participants. The survey included questions about fall history, cause, season and time of recent falls, and external conditions associated with recent falls, like floor or ground materials and shoe types.P < .001) but did not differ significantly in gender proportion (P = .082). Fall history over the past year was not different between the 2 regions (P = .693), but lifetime fall history was greater among rural respondents (P < .001). Only 5.1% of all respondents had undergone fall-prevention education. A slippery floor was the most common cause of falls in both regions, but there was a significant difference in pattern of fall causes (P < .001). Falls were more frequent in the summer, spring, and the afternoon in urban areas, and in the summer, autumn, and the morning in rural areas. Cement and asphalt were the most common ground materials at the time of falls in both regions, but rural respondents had higher fall rates when walking on soil and when wearing slippers.Rural respondents were older than urban respondents (A fall-prevention program that reflects the characteristics and differences of falls in urban and rural areas should be developed and used to effectively prevent falling among elderly people. In addition, elderly people are at a greater risk of injury when they fall. A study of the 2014 Behavioral Risk Factor Surveillance System (BRFSS) survey found that 28.7% people 65 years or older in the United States (29.0 million people) experienced a fall during the past 12 months, and 37.5% of them (7.0 million people) had injuries that require medical treatment.A fall is defined by the World Health Organization (WHO) as an incident that leads to a person unintentionally coming to rest on the ground, floor, or other lower level.,5 Falls are responsible for 98% of hip fractures, and the 1-year mortality rate after hip fracture is 8.4%-36%, higher than nonhip fracture groups or community-living control populations. A survey of 409 community-dwelling elderly people aged 65 years or older in Montreal, conducted from May 1987 to October 1988, reported 197 falls, 91 fall-related injuries (46.2%), and 3 hip fractures out of a total of 5 fracture cases. Data based only on elderly people who live in assisted-living facilities indicate lower rates of fall-related injuries, but not fewer hip fractures. On the basis of fall data from 469 elderly people living in residential-care facilities in Sweden in 1997 over a single year, fall-related injuries occurred in 1 quarter of the 865 falls and 18 hip fractures among 24 total fractures were reported.Falls can cause nonlife-threatening injuries, such as dislocations, sprains, cuts, and abrasions, but hemorrhage from head injuries and fractures can be fatal. As the elderly population grows, medical expenses due to falls are becoming a great social burden. In the United States, a survey of seniors aged 65 years and older reported that the direct medical cost from falls was 616.5 million dollars in 2012  and 637.2 million dollars in 2015 . The direct medical cost from non-fatal falls was 30.3 billion dollars in 2012  and 31.3 billion dollars in 2015 . Total and per-fall costs are increasing; thus, efforts are needed to understand and reduce falls in older people.The number of elderly people and their proportion of the total population is increasing worldwide. The elderly population (≥65 years) in Europe is expected to increase from 129.6 million (17.4%) in 2015 to 196.8 million (27.8%), and from 54.6 million (15.1%) in 2015 to 94.6 million (21.4%) in 2050 in North America. The same trend is seen in other continents, including Asia and Africa.,12 A study by Park et al about rural region revealed that female gender and older age are risk factors for fall. In a study using data from 16,393 people over 65 in China, Zhang et al compared the fall characteristics of rural and urban areas. Fall incidence was higher in rural area, and hospitalization rate due to fall was higher in urban area in this study. In addition, more fall occurred in urban roads and rural yards. Yoo et al showed dizziness, walking discomfort, and fear of falling were influencing factors for fall in rural area, and stroke, visual impairment, and the fear of falling in urban area using data from 534 elderly people in Korea.To date, most research on falling has focused on urban residents, and studies on rural areas have only been published recently. Falling incidence and fall-related cost in rural elderly population has been studied but separately from urban populations.As there are insufficient data comparing the fall characteristics of rural and urban areas, research comparing 2 regions as an effort to understand and reduce falls in each region is still meaningful. This study compares fall characteristics between urban and rural areas using data from a large-scale survey carried out in Korea, and provides basic information that is not covered in other studies, such as frequency, cause, time, and shoes worn.22.1This study is based on survey results from people over 60 years old who visited a public health center in their district. The survey was completed by 2012 people living in 5 regions of Korea from September 1, 2015, to October 12, 2015. Among the 5 regions, Guro-gu , Yeongdeungpo-gu , and Jung-gu  were classified as urban areas, and Dalseong-gun  and Yangpyeong-gun  were classified as rural areas. Table 2.2The questionnaire, written in Korean, included several questions to characterize falls. It excluded questions about personally identifiable information, and only gender and age were recorded. Fall-experience questions included lifetime fall history, past 1-year fall history, experience with fall-prevention education, cause of fall, season and time of day, floor or ground materials, and shoe type.If there was more than one event that fitted the definition of fall as described above after becoming an adult, it was recorded in the lifetime fall history category. Slight fall that did not require medical treatment was also included. The past 1-year fall history item is to be answered only if fall has occurred in the past year. Those who have participated in fall-prevention education by government, public institutions, hospitals, or religious facilities were required to respond in fall-prevention education category.Participants in the study chose the cause of fall among slippery floor, ankle spraining caused by sudden inversion of ankle due to uneven floor when stepping on the foot, stumbling over door still, collision with people or objects unexpectedly, dizziness, steep slope, stumbling over stairs, dark lighting, and impaired eyesight. If participants could not select appropriate item, they were asked to respond to the “other” item. The season that fall occurred in the past year was selected among spring (March to May), summer (June to August), autumn (September to November), and winter (December to February). Floor or ground material at the time of the fall was chosen among cement or asphalt, soil, linoleum, tile, ice, and cannot recall. Shoe type was selected from running shoes, bare foot, slippers, dress shoes, and cannot recall.Only willing volunteers completed surveys, and informed consent was obtained after providing potential participants with sufficient study information. All participants were additionally informed that they could stop their participation at any time. This study and the questionnaire were approved by the Hanyang University Medical Center Institutional Review Board (No. HYI-15–008-2).2.3This study analyzed differences in fall characteristics between urban and rural areas using descriptive statistical methods. Handwritten survey responses were transcribed into a database by 6 researchers who followed consistent data-entry rules. The Chi-square test was used to compare demographic data such as age and gender between the urban and rural areas. To compare the characteristics of urban and rural falls, differences were estimated using the Chi-square test. Statistical analyses were carried out using SPSS software ver. 21.0 .33.1P < .001), and 63.4% and 67.0% of urban and rural area residents, respectively, were women . Lifetime fall history was significantly higher in rural areas (78.9%) than in urban areas . However, fall history during the 1 year before the survey was similar between areas (P = .693). Ninety-eight people in the urban areas (8.1%) and only 4 people in rural areas (0.5%) underwent fall-prevention education at any time in the past, which is a significant difference  and rural (42.1%) areas was a slippery surface, according to multiple-choice response. In urban areas, stumbling on a door sill (14.0%) and ankle sprain (13.6%) were the next common causes. In rural areas, ankle sprain (11.6%), dizziness (10.6%), and a steep slope (7.7%) were the next most common causes. Differences in cause of fall were significant between urban and rural areas in a Chi-square test (3.3P = .009). Regarding time of day, 55.2% of falls occurred in the afternoon (12:00 pm--18:00 pm) in urban areas, and 45.9% of falls occurred in the morning (06:00 am--12:00 pm) in rural areas. The fall rate according to time of day was significantly different between the 2 areas  and spring  in urban areas, and in the summer (27.8%) and autumn (26.3%) in rural areas. Seasonal fall rates were statistically different between the 2 regions (3.4P = .008). When asked about shoes worn at the time of the fall, 58.0% of urban and 45.2% of rural respondents reported running shoes. Other responses were barefoot (18.1%), slippers (11.2%), and dress shoes (4.6%) in urban areas, and slippers (22.6%), barefoot (16.3%), and dress shoes (3.3%) in rural areas, and the difference between urban and rural areas was significant  and rural (37.8%) areas. Soil (20.1%), tile 15.3%), linoleum (13.7%), and ice (5.1%) were the next common materials in urban areas, while soil (30.0%), linoleum (13.0%), tile (10.0%), and ice (4.1%) were the next common in rural areas; the differences between urban and rural areas were significant  and 19.8% of rural communities . However, we found that lifetime fall history was higher in rural areas, perhaps because the average age of our rural participants was higher than of urban participants. Also, it is thought that there are many people in rural areas who fall, including those younger than 60 years, due to environmental differences, and this is one of the causes for higher lifetime fall history in rural areas.We did not find a statistical difference in the fall rate over the previous year between urban and rural areas, which is in line with other studies. A 2014 study of Australians over 50 years found no difference in fall hospitalizations, fall mortality, or fall-related injuries between city and rural participants. found that fall patients were more likely to be treated by general practitioners and specialists in urban areas, while nonurban patients usually accessed community services and alternative health practitioners. Therefore, it is particularly important to provide good fall-prevention education in rural areas. However, as summarized in Table People in urban and rural areas typically use different medical services after falling because of disparities in medical accessibility. Byles et al Hill-Westmoreland et al showed that the fall rate was 4% lower among intervention groups compared with control groups in a meta-analysis of 12 studies. In addition, the effect was stronger when risk modification and comprehensive risk assessment were performed simultaneously, rather than exercise alone. Therefore, fall-prevention efforts are a necessary part of an educational program, which should also address issues elucidated by our above-mentioned results, including causes, characteristics, and risk factors for urban and rural populations.The effectiveness of fall-prevention interventions has been demonstrated in a number of studies. A 27-month survey of people over 65 years old in Denmark, including 12,905 people who underwent fall-prevention education and 11,460 control participants showed that all types of fracture injuries were reduced in the intervention group, especially lower-extremity fractures, which decreased by 33%.,21 However, we also found that slipping rates were higher in rural areas than cities and stumbling on door sills was more common in urban areas. These findings should be taken into account when designing public buildings or facilities related to elderly citizens. Also, because falls caused by collisions are more common in urban areas, this should be emphasized in urban fall-prevention education.We found that slippery floors were the leading cause of falls in both urban and rural areas, which is consistent with findings from the United States and China. analyzed 10,992 fall-related fractures in Norwegian 65 years and older, and found that the risk of falling was greater during the colder months for people 65 to 79 years old and for people over 80 years. Furthermore, the fractures that occurred during the winter were mostly caused by ice and snow, and the rate of hip and arm fractures also increased. Caberlon and Bós studied people over 60 years old in Rio Grande do Sul of Brazil, a temperate region, and also found that the largest number of falls occurred during winter (26.8%), and 34% of all fractures occurred in winter as well. In this study, falls are concentrated in summer, when outdoor activity increased. However, almost 25% of falls still occur in winter, and winter falls usually carry the higher injury risk; thus, interventions to prevent winter falls are still very important.Both urban and rural participants in this study experienced the most falls during summer, a finding that differed from other studies. Bulajic-Kopjar most studies have been performed only in indoor environments, and further research is needed on the relationship between lighting and falls.This is the first study to compare the time of fall occurrence between urban and rural areas. As summarized in Table  In the United States, a study of people 65 years and older found the highest rate of falls occurred while working around the houses or yard (29%). This suggests that, for rural elderly people, particular attention should be paid to falls that occur during economic activities, such as farming, and to falls that occur on ordinary roads. Previously, there were few data about floor or ground materials with regard to elderly fall risk. One study of students on a playground found that the fall rate was highest on asphalt.We also evaluated whether there was a difference in the floor or ground materials during falls in urban and rural areas. Both had the highest number of falls on cement or asphalt. However, in rural areas, the rate of falls on soil was higher than in urban areas, possibly indicating that falls in rural areas frequently happen while performing farming activities, which is consistent with previous studies. found that, among 95 patients with hip-fracture patients, the largest number was wearing slippers when they fell. Therefore, special attention in rural areas should be paid to safety while wearing slippers. They also found that 75% of survey participants suffered falls when wearing shoes with suboptimal features such as no fixation or an excessively flexible heel or sole. Recently, shoes that sound an alarm when approaching an obstacle have been developed to reduce falls. The development of these technologies will also play an important role for the prevention of falls in the future, but now fall-prevention education, including how to wear shoes properly, should also be emphasized.Most urban and rural respondents in our study reported wearing running shoes at the time of their fall, but the proportion who fell in slippers was higher among rural people than urban people. Sherrington and MenzP = .001). In particular, the proportion of people falling on the tile wearing slippers in urban areas was relatively higher than rural areas. It seems that this is because elderly people usually wear slippers in tiled places in urban settings, while they live in more diverse environments wearing slippers in rural areas.When analyzing only the case of falling with slippers, the results showed that there seemed to be a region-dependent relationship between the floor or ground materials and the shoes. There were 44 cases of falling with slippers in urban areas , and 61 cases in rural areas , and there was a significant difference between 2 regions (This paper has several limitations. First, we only included elderly subjects who were able to visit public health centers; thus, we did not collect data from people who were severely injured by falling. This sampling error may cause study participants to lose their representativeness of the elderly group in the region. Second, because this is a retrospective and cross-sectional study with a survey design, a recall bias may have been present for many items about past falls. In particular, a person with recurrent fall experiences may have inaccurate memories about each fall. Future research should include further analyses of fall characteristics between urban and rural areas with larger patient sample for more robust results.5In this study, we compared fall characteristics between urban and rural areas. The rural participant group was older than the urban group. Although the previous-year fall rate was not different between the 2 groups, lifetime fall history was higher in the rural group. Falls most frequently occurred on slippery floors in both areas. Falls occurred most often in summer and spring in cities, and in summer and autumn in rural areas. Fall occurred most often in the afternoon in urban areas and in the morning in rural areas. Falls on soil and falls in slippers were more common in rural areas than urban areas. Because the proportion of elderly people who received fall-prevention education was low in both urban and rural areas, relevant policies to institute intervention programs that reflect the fall characteristics of urban and rural areas are immediately necessary.MK and MJK were involved in drafting and revising the manuscript. MC and EN contributed significantly to statistical analyses. Data collection and management were handled by SGK and SC in Seoul, DHR and SK in Daegu, and BYC in Yangpyeong-gun. All authors made substantial contributions to the conception, design, and data acquisition and analyses for this study. All authors read and approved the final manuscript.Conceptualization: Myeongkyu Kim, Mi Jung Kim.Data curation: Myeongkyu Kim, Mi Jung Kim.Formal analysis: Myeongkyu Kim, Misoo Chang, Eunwoo Nam, Mi Jung Kim.Funding acquisition: Mi Jung Kim.Investigation: Seul Gi Kim, Sung-il Cho, Dong Hee Ryu, Sin Kam, Bo Youl Choi.Methodology: Myeongkyu Kim, Mi Jung Kim.Project administration: Misoo Chang, Eunwoo Nam, Seul Gi Kim, Sung-il Cho, Dong Hee Ryu, Sin Kam, Bo Youl Choi.Resources: Seul Gi Kim, Sung-il Cho, Dong Hee Ryu, Sin Kam, Bo Youl Choi.Software: Myeongkyu Kim, Misoo Chang, Eunwoo Nam, Seul Gi Kim, Sung-il Cho, Dong Hee Ryu, Sin Kam, Bo Youl Choi.Supervision: Misoo Chang, Eunwoo Nam.Writing – original draft: Myeongkyu Kim.Writing – review & editing: Myeongkyu Kim, Mi Jung Kim.


The COVID-19 pandemic has become one of the most serious health crises in human history, spreading rapidly across the globe from January 2020 to the present. With prompt and drastic measures, Vietnam is one of the few countries that has largely succeeded in controlling the outbreak. This result is derived from a harmonious combination of many factors, with the policy system playing a key role. This study assessed the policy responses to the COVID-19 pandemic in Vietnam from the early days of the outbreak in January 2020 to 24 July 2020  by synthesizing and evaluating 959 relevant policy documents in different classifications. The findings show that the Vietnamese policy system responded promptly, proactively, and effectively at multiple authority levels , using a range of policy tools and measures. Parallel to the daily occurrence of 2.24 new cases, 5.13 new policy documents were issued on average per day over the study period. The pandemic policy response over the first six months in Vietnam were divided into four periods, I (23 January–5 March), II (6–19 March), III (20 March–21 April), and IV (22 April–24 July). This paper synthesizes eight solution groups for these four anti-pandemic phases, including outbreak announcements and steering documents, medical measures, blockade of the schools, emergency responses, border and entry control measures, social isolation and nationwide social isolation measures, financial supports, and other measures. By emphasizing diversification of the policy responses, from the agencies to the tools and measures, the case study reviews and shares lessons from the successful COVID-19 prevention and control in Vietnam that could be useful for other nations. The first confirmed case (CC) of the COVID-19 disease outside of China appeared in Thailand on 13 January 2020 ,5. COVIDWith CC number (#) 416 on 25 July ,12,13,142 land area [Therefore, a question arises: Why in so many nations and territories across the world, including the most wealthy countries such as the USA, Brazil, France, the UK, and Germany, did the pandemic spread and cause hundreds, even thousands of deaths daily ,20, wherand area  and overand area  that is and area ,23,24? Mand area  whereas and area . Anotherand area . This sthttps://ncov.moh.gov.vn/web/guest/trang-chu—the website of the Ministry of Health (MOH) in Vietnam on the COVID-19 pandemic status updates, as well as the related study by La et al. [https://thuvienphapluat.vn/ —the most powerful website on Vietnamese legal documents--and (2) https://ncov.moh.gov.vn/web/guest/chinh-sach-phong-chong-dich—a portal opened on the MOH’s website on the COVID-19 pandemic prevention and control policies.Data on CCs were compiled from a et al. . We furtOther relevant documents were also obtained from database analysis of recent policies, official press, articles, reports, briefs, and presentations from reliable data sources in Vietnam and globally. A total of 236 documents were obtained during the period of analysis.Within our research, PDs under the provincial level (districts and communes) were not collected for analysis. Although at the 13th National Assembly session, the district and commune authorities officially obtained the right to issue legal documents after 22 June 2015 , so far,After these documents were identified and collected, the analysis steps included (i) mapping a chronology of all CCs from the first day of outbreak in Vietnam (23 January) to the last day of the term (24 July) in each of four periods of the pandemic development in Vietnam, as declared by the Prime Minister (PM): I (23 January–5 March), II (6–19 March); III (20 March–21 April), and IV (22 April–24 July) . To specify, F0 people are confirmed as infected, F1 people are suspected as infected or contacted F0, and F2 people contacted F1 and similarly the next generations F3, F4, etc. ,32. The 24 July 2020 was the 184th day of fighting against the COVID-19 pandemic in Vietnam since the first two cases on 23 January. During these 184 days, there was a total of 413 CCs nationwide. Over more than six months of combat, an average of 2.24 NCs per day were detected. There were 365 RCs  out of 4The first period in the fight against COVID-19 in Vietnam consisted of 16 patients confirmed in 43 days , corresponding to 0.37 CC/day, who all had a history of movement from China—the country where the outbreak first appeared , with twCompared to periods I and II, period III (20 March–21 April) occurred over the highest number of 33 consecutive days with recorded NCs due to positive test results with the new strains of COVID-19 from both Vietnamese and foreign passengers from the flights from period II, still having flights to welcome repatriated Vietnamese, and especially outbreaks of infectious chains in the community. This phase recorded a total number of 183 CCs (5.55 CCs per day on average), of which there were up to 54 CCs related to infectious chains in the community (accounted for 29.51%). Notably, this period had three SCCs, #124 and #133 on 24 March and #243 on 6 April see . On 24 MPeriod IV existed for 94 days (22 April–24 July) and confirmed a total of 145 cases (1.54 CCs/day) and 280 PDs see . The mosIn brief, over the first six months of combatting COVID-19, Vietnam detected an average of 2.24 CCs and 1.98 RCs per day, which means that each day added 0.26 more NCs to treat. This indicator can be compared to other countries considered to have strong pandemic management in the first stages, such Taiwan, Hong Kong, Singapore, and South Korea ,49,50. DVietnam carefully prepared to respond to the pandemic before the appearance of the first two positive cases from Wuhan, China, on 23 January, 2020 ,45, whicBefore 23 January, there were seven COVID-19-related official legal documents. The terms “pneumonia” and “novel coronavirus” were first announced widely on 31 December, 2019 . The firImmediately, the People’s Committee (PC) of Lang Son province also directed the response to the pneumonia epidemic from China . Lang SoTo examine the levels and agencies in which policy actions were taken, four relevant policy levels were identified from the highest level  to the lowest  see . The claOne of the most distinctive features of the COVID-19 policy responses in Vietnam was the mobilization of the whole political system, accepting economic losses in exchange for the safety of people’s health and lives, minimizing deaths from the pandemic . As of 2The numbers of PDs were also identified by the five types of agencies  and by period. The multi-level policy approach came from 33 different agencies , classified in five groups , in all periods from the beginning days of the outbreak see .Not only did many government agencies promulgate PDs quickly, but each authority also issued different types of PDs. This study identified nine types of COVID-19 legal documents used in Vietnam as of 24 July 2020 see , each ofOf the 959 PDs created during the COVID-19 pandemic in Vietnam up to 24 July, dispatches were the most used policy communication type, with 577 dispatches , followed by 115 decisions (11.99%), 75 plans (7.82%), and 74 directives (7.72%). Other documents with lower rates included 51 announcements (5.32%), 32 telegrams (3.34%), 27 resolutions (2.82%), 5 circulars (0.52%), and 3 guidelines (0.31%) see .Analyzing the PD types used by various agencies, dispatches were again the most widely used; 24/33 agencies chose to make their policies known in this way, followed by decisions (14/33), telegrams (12/33), directives (10/33), and announcements (7/33). The lowest ranks belonged to circulars and guidelines, with each used only by two agencies see .Among the 33 agencies, PPCs  not only utilized the most diverse range of policy document types (8/9), but in each type (except circulars and announcements) they also accounted for the largest proportion of the communications generated in comparison with the other agencies, from 28.13% of telegrams to 96% of plans, as illustrated in The policy responses to the COVID-19 emergency have varied between countries and there have been different methodologies used to assess the nature and role of COVID-19 policies across the world. The Blavatnik School of Government at the University of Oxford, for example, has been using a “stringency index” based on seven policy measures to compare countries’ differing COVID-19 policies. They include school closings, workplace closings, cancellation of public events, shutting down of public transport, public information campaigns, restrictions on internal movement, and controls on international travel . The SchThe COVID-19 policy measures in Vietnam have also attracted the attention of many foreign scholars. In a study of the Exemplars in Global Health platform, Vietnam’s emerging COVID-19 success story as of 1 July, 2020, was identified as coming from six policy groups: rapid developed diagnostic test kits; contact tracing; infection prevention and control in healthcare settings; targeted lockdowns; mass gathering, travel, and mobility restrictions; and clear, consistent, creative public health messaging . Some ofIn this study, eight key overall policy measures were observed, as described in Box 11. Outbreak announcements and steering documents2. Medical measures3. School blockade4. Emergency responses5. Border and entry control6. Social isolation measures7. Financial supports8. Other solutionsThird, school blockade measures included the students of all 63 provinces nationwide having a significantly longer vacation, from early February to 4 May due to the pandemic . All schools of all 63 provinces nationwide were closed from 6 February ,93,94,95Fifth and sixth, Vietnam also conducted other strong measures, including border and entry control ,116,117 Seventh, in addition, financial supports from the national government  and theIn the current global context, it can be said that no pandemic has had such a profound impact on all aspects of socio-economic life for countries worldwide as COVID-19. Moreover, never in the past have so many unprecedented new policy documents (PDs) been issued widely and continuously in Vietnam to promptly respond to the pandemic . As of 2It should be noted that similar to Vietnam, other countries in the region such as Mongolia , Taiwan,Our analysis demonstrated the multidimensional approaches taken from different relevant government levels  in issuing various types of PDs  as well as a range of policy measures . As a result of these wide-ranging policy measures and communication tools, issued in a timely manner, the pandemic situation was controlled effectively in the first six months (23 January–24 July). Vietnam’s success in the prevention and fight against the pandemic offers significant lessons for other countries ,156,157.COVID-19 is at risk of becoming one of the toughest trials for humanity to confront in modern history ,159. AccThe strategy of preventing, detecting, tracing, isolating, localizing, and suppressing the pandemic has been thoroughly applied in Vietnam ,81,117. In terms of treatment, Vietnam has built and maintained connections from the front line in the concentrated isolation centers to hospitals through QRTs to share, exchange, and support each other in professional work and treatment regimens that are constantly updated for all infectious generations. The application of science and technology in the pandemic prevention and control was accelerated , making During the pandemic, special policies on education, health care, and social security have been put in place that did not previously exist. Second, besides “COVID-19,” “quarantine” and “isolation” are the most mentioned keywords in Vietnam, with a range of isolation measures put in place. Another highlight is that, despite the economy being seriously affected, the national government has constantly sought to assist all Vietnamese people with the difficulties they are experiencing, so that “no one is left behind” ,167,168.In summary, the system of COVID-19 policy responses up to 24 July 2020, were effective to defend against the pandemic in Vietnam. PDs were widely and regularly disseminated, which helped to effectively increase public awareness. Promptly prepared and released PDs provided knowledge and measures for all walks of life to prevent and control the pandemic and protect themselves and others in the community. Although not a rich country, Vietnam has become a model for other countries in the fight against the COVID-19 pandemic, with lessons learned in controlling the spread of the outbreak and for overall responses to a public health crisis ,156,168.


Background: Traditionally, fat mass is estimated using anthropometric models. Air-displacement plethysmography (ADP) is a relatively new technique for determining fat mass. There is limited information on the agreement between these methods in infants and young children. Therefore we aimed to longitudinally compare fat mass percentage values predicted from skinfold thicknesses (SFTs) and ADP in healthy infants and young children. Methods: Anthropometry and body composition were determined at the ages of 1, 4, and 6 months and 2 years. We quantified the agreement between the two methods using the Bland–Altman procedure, linear mixed-model analysis, and intra-class correlation coefficients (ICC). Results: During the first 6 months of life, fat mass% predicted with SFT was significantly different from that measured with ADP in healthy, term-born infants (n = 245). ICCs ranged from 0.33 (at 2 years of age) and 0.47 (at 4 months of age). Although the mean difference (bias) between the methods was low, the Bland–Altman plots showed proportional differences at all ages with wide limits of agreement. Conclusions: There is poor agreement between ADP and SFTs for estimating fat mass in infancy or early childhood. The amount of body fat was found to influence the agreement between the methods. Childhood overweight and obesity are major public health concerns. Childhood obesity is a risk factor for later obesity, type 2 diabetes mellitus, hypertension, dyslipidemia, and cardiovascular diseases ,4,5,6, wSeveral methods are available for the assessment of body composition in children up to 2 years of age. Traditionally, anthropometric prediction equations using skinfold thicknesses have been used to assess fat mass, but in recent years, various methods have been developed and have largely replaced anthropometric models. Among these methods is air-displacement plethysmography (ADP), which is now considered the most accurate and validated method of determining body composition in infants and young children ,15,16,17Both the PEA POD for infants and BOD POD for young children have been validated against reference values obtained from the gold-standard four-compartment model based on total body water, bone mineral content, and total body potassium ,17. HoweA nutritional intervention study was conducted as a double-blind, randomized controlled trial (ProtEUs study) at two centers: Amsterdam UMC, VU University Medical Center, Amsterdam, the Netherlands (study centre 1), and Dr. von Hauner Children’s Hospital, LMU–Ludwig-Maximilians-Universität, Munich, Germany (study centre 2). Healthy, term-born, formula-fed (n = 178) and breast-fed (n = 67) infants were enrolled between 22 October 2014, and 29 December 2016 [The intervention period was 6 months (from an average age of 1 month until the age of 6 months). During this period, infants visited the study site three times: at 1 month (baseline), 4 months, and 6 months of age, as well as a follow-up that was scheduled at the age of 2 years. During these visits, anthropometry and body composition measurements were obtained.Weight was measured without clothing or diaper to 0.5-g accuracy on a balance scale . Supine length was measured using a flexible measurement board  to the nearest 0.1 cm. At the age of 2 years, standing height was measured using a digital stadiometer  to the nearest 0.1 cm. The average of two measurements was used in the statistical analysis.The thicknesses of the triceps, biceps, suprailiac, and subscapular skinfolds were measured in duplicate on the left side of the body to the nearest 0.2 mm using Holtain skinfold callipers . The average of the measurements was used for the statistical analysis. The measurements were performed using a standardized protocol by well-trained observers, including research nurses and dieticians.2 (0.77) [We used the equations of Weststate and Deurenberg  to estim2 (0.77) .The predicted density (D) was converted to fat mass% using the sum of bicipital, tricipital, suprailiacal, and subscapular skinfold thicknesses (⅀SFT) .(1)FM%=We used inter-rater reliability tests to estimate the reliability between the different observers. For this purpose, we organized meetings at the start of the study and throughout. During these three meetings, the observers involved performed measurements of skinfold thickness sequentially on children aged 6 months to 2 years.Body composition was measured using a PEA POD  at the ages of 1, 4, and 6 months. At the age of 2 years, body composition was measured using a BOD POD . ADP is a densitometric technique that uses the inverse relationship between pressure and volume to calculate body density .With this technique, body mass and body volume are measured. Mass is measured on an electronic scale, and volume is measured in an enclosed chamber by applying gas laws that relate pressure changes to volumes of air in the chamber. The body mass and body volume measurements are used to calculate the whole-body density using a classic densitometric approach and age- and sex-specific fat-free mass density values   and constructing limits of agreement . The limThird, the intra-class correlation coefficient (ICC) was calculated as a reliability index . ICC values less than 0.5 are indicative of poor reliability, values between 0.5 and 0.75 indicate moderate reliability, values between 0.75 and 0.9 indicate good reliably, and values greater than 0.90 indicate excellent reliability. To estimate the ICC, we used a two-way mixed effects model, type single-measures, and absolute agreement. To estimate the ICC between the observers, we used a two-way mixed effects model, type average-measures, and absolute agreement. The statistical analyses were performed in SPSS (version 26).We investigated 245 infants at age 1 month, which declined to 173 children at age 2 years. The majority (72–73%) were formula-fed for the first 6 months of life . Figure Fat mass% predicted with SFTs was significantly different from that measured by ADP except at the age of 2 years . AnalyseThe plots showed proportional differences in that the overestimation or underestimation of fat mass% predicted by SFTs was affected by the amount of body fat . All BlaThe inter-rater reliability tests showed high ICCs between the observers (range 0.992–0.998), indicating excellent reliability.This study in infants and young children showed that fat mass% predicted by SFTs is significantly different from that measured by ADP. Many anthropometric methods have been developed to estimate fat mass%. Some of them have been used in infancy, such as the non-validated but frequently used Slaughter equation. One study involving 2-week-old infants showed that fat mass% estimated from the Slaughter equation  was stroAnother method for estimating fat% in the very early stages of life is the equation presented by Catalano et al. This method uses the flank and thigh skinfolds in addition to the triceps and subscapular skinfolds . The equDespite the limitations, the development of a suitable and validated anthropometric model to predict body fat in infants and young children is of great interest because it would have low cost and widespread availability. Recently, the adjustment of traditional anthropometric equations by using ADP as a reference has led to new equations to predict fat mass in infants and young children. However, a validation study showed that the outcomes were still significantly different from those of ADP .Another study involving neonates and young infants compared skinfold thicknesses to DXA-derived fat mass. The results showed that an exponential equation including the sum of four skinfold thicknesses and length best predicted fat mass . HoweverIt is frequently reported that the measurement of skinfold thicknesses in young children suffers from limited reproducibility and precision. Notably, the interobserver error is high . This asThe strength of our study is the inclusion of high numbers of paired observations in a healthy population from infancy to early childhood. Furthermore, this study was performed in two experienced clinical paediatric research centres in Europe, which adds external validity. Nevertheless, an important limitation of our study is the low number of objective methods used for estimating body composition.Among healthy infants and young children born at term, there is poor agreement between estimates of fat mass by SFTs and ADP. This hampers the comparability of studies using different methods for the assessment of body composition. Further research on the comparability of different body composition methods for longitudinal assessment of adiposity is warranted.


The presence of fluvastatin (FLU2) specifically promoted the growth of Escherichia/Shigella, Ruminococcaceae UCG 014, and Sutterella. However, the composition of the gut bacterial microbiota remained relatively static in samples treated with rosuvastatin (ROS), simvastatin (SIM), and atorvastatin (ATO). The PICRUSt program predicted moderate differences in the functional categories related to the biosynthesis of other secondary metabolites, cellular processes and signaling, and signal transduction in the FLU2 fermentation samples. Our study revealed substantial variation in the structure and function of microbiomes from the FLU2-treated samples. In addition, short-chain fatty acids (SCFAs) were also significantly decreased in FLU2-treated samples compared with the samples treated with other stains. Statins can be degraded by the human gut microbiota in vitro, and the degradation rate was approximately 7%–30% and 19%–48% after fermentation was allowed to proceed for 24 h and 48 h, respectively. Generally, FLU2 could largely shape the composition and function of human gut microbiota, which resulted in changes in the production of SCFAs. In turn, all statins could be degraded or modified by the gut microbiota. Our study paves the way for elucidating statin-gut microbiota interactions in vitro towards the improvement of the host health and personalized medicine.Statins, a class of drugs that can effectively remove cholesterol from serum, are used to regulate plasma total cholesterol and reduce the risk of cardiovascular diseases, but it is still unclear whether the drug are modulated by gut microbiota or the structures of gut microbiota are shaped by statins. We investigated the interactions between statins and the human gut microbiota during the  The gastrointestinal tract, the largest digestive and excretive organ in the human body, is colonized by a vast, complex, and dynamic consortium of microorganisms . It beco16S rRNA gene profiling revealed that Bacteroidetes and the Firmicutes constitute over 90% of the known phylogenetic categories of the human gut bacterial microbiota [To gain insight into the relationship among gut microbiota drugs and human health, it is necessary to disentangle the structure, diversity, and function of the gut microbiota. A previous study using  16S rRNAcrobiota . An incrcrobiota . Furthercrobiota . For exacrobiota  thought crobiota . Recent crobiota .16S rRNA gene high-throughput sequencing, GC, and HPLC analyses. Our aims were to discover if there are changes in the gut bacterial microbiota and KEGG pathways induced by different statins, statins are degraded or modified by gut microbiota, and whether the data would provide an explanation for the potential effects of statins on human health.Of the orally-administered lipid-lowering drugs developed to reduce the risk of cardiovascular disease, statins, which are 3-hydroxy-3-methylglutaryl-coenzyme A reductase inhibitors, are most commonly used worldwide. However, the long-term use of statins can cause a series of diseases, such as cytotoxicity, liver injury or necrosis, kidney damage, and myopathy . It has All procedures performed in studies involving human participants were in accordance with the ethical standards of the Hunan University of Science and Engineering research committee and the 1964 Helsinki declaration (and its latest amendments) or comparable ethical standards.FLU, ROS, SIM, and ATO  were purchased from Sigma-Aldrich Co.  and used as received, without further purification.Fourteen healthy volunteers aged 18–22 years and living in Yongzhou city, Hunan province, China, were recruited for this study. Detailed information on these volunteers is shown in Table A in 2, 10% CO2, and 80% N2). Statin was dissolved in 50% DMSO-50% water (v/v) and added to the medium to obtain a final statin concentration of 20 μM or colon concentration [in vitro, and we detected the concentrations at 0 h to establish a baseline . The filtered fresh fecal slurry (500 μL) was added to the previously prepared fermentation culture medium (9.5 mL), within an anaerobic chamber, and then incubated at 37°C. Two-milliliter samples collected at 24 h and 48 h for further analyses.Batch culture fermentation was carried out using the procedure described by Hu et al. , Rycroftntration ; controlntration , which iThe genomic DNA was extracted using the QIAamp DNA Stool Mini Kit according to the manufacturer’s instructions, and the lysis temperature was increased to 95°C to promote gram-positive bacterial lysis . The concentration of extracted DNA was quantified by a spectrophotometer .16S rRNA genes was amplified using the primers 338F (5’-ACT CCT ACG GGA GGC AGC AG-3’) and 806R (5’-GGA CTA CHV GGG TWT CTA AT-3’) [https://www.arb-silva.de/documentation/release-123/). Good’s coverage, α-diversity, Simpson and Shannon index, and richness (number of OTUs) were analyzed using Visual Genomics–AS software (http://amplicon.vgenomics.cn:9000/). The data have been deposited in the sequence read archive (SRA) of NCBI as GenBank Accession Number PRJNA562063. Basic data on 16S rRNA gene high-throughput sequencing are listed in Table B in The V3–V4 region of bacterial A AT-3’) . The ampA AT-3’) . The repA AT-3’) , 28 and 16S rRNA gene sequences. The distribution of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways of these samples predicted by PICRUSt software was further used for statistical analysis.PICRUSt  softwareSCFAs were determined by GC, as previously described , 31. BriStatins were analyzed by the Shimadzu Black 20 AT system  as previously described , 33 withP values and FDR less than 0.05 were considered statistically significant. Plot cladograms and significantly different bacterial taxa were analyzed using LEfSe Software (https://bitbucket.org/biobakery/biobakery/wiki/lefse) with default parameters and statistical methods.SPSS software  and the Tamhane’s T2 (M) test were used to perform statistical analyses. The p values were then used to compute the false discovery rate (FDR) to account for multiple hypothesis testing using Microsoft Excel. In summary, 245,914,669 and 2,011,409,834 valid reads were obtained from 14 original fecal samples and 126 fermented samples. Reads that could be classified as bacteria (97% sequence identity) were used for the subsequent analysis. The rarefaction curves, Shannon-Wiener curves, specaccum analysis, and rank-abundance distribution curve were used to evaluate the sequence quality , which iIn this study, a total of 1,212 OTUs from 140 samples were obtained based on 97% similarity. For the original fecal samples, 146–276 OTUs were identified, 69–511, and 98–372 OTUs were identified in the statins and DMSO fermented samples, respectively. The indexes of Ace, Chao, Shannon, and Simpson were calculated for fully illustrating the α-diversity of the bacterial communities. The α-diversity indexes of Ace, Chao, and Shannon were slightly different among the original fecal, DMSO, ROS, ATO, FLU and SIM samples  in FLU2 than in other statins, except for ATO2, as showed in the rectangular box C. For instance, the KEGG pathways of cellular processes and signaling, signal transduction, and neurodegenerative diseases, were significantly increased in FLU2; the pathways of biosynthesis of other secondary metabolites, endocrine system, nervous system, signaling molecules and interaction, were significantly decreased in FLU2. In box B, these pathways were altered more significantly (FDR<0.1) in FLU2 than others except for ATO2, the pathway of replication and repair was not significantly different between FLU2 and the control when considering FDR. In box A, it can be seen that some pathways were not affected by some statins or were affected but with an FDR>0.1 . The concentration of propionic and butyric acid was affected more in the ATO2 groups than in other groups .To evaluate whether the detected concentration of stains was different from the expected calculation concentration, the concentrations of ROS, SIM, FLU, and ATO at 0 h were used as a baseline for the calculation of the degradation ratio. After fermenting for 24 h and 48 h, the degradation or modification rates of ATO by the human gut microbiota were a little higher in the colon concentration groups than in the 20 μM groups . The degBacteroidetes decreased significantly in FLU2 fermentation samples, and numerous studies [Bacteroidetes, not only in mice but also in human. For instance, previous research with germ-free mice showed that obesity decreased the relative abundance of Bacteroidetes [Bacteroidetes occurs in Crohn’s disease/ulcerative colitis patients compared with controls. The meta-analysis also showed a significant lowering of LDL, and increases in height and weight with statin administration [The human body hosts a complex microbial ecosystem that facilitates host metabolism and adiposity by expanding nutrient sources, producing essential vitamins, and carrying out xenobiotic metabolism . Moreove studies , 39, 40 roidetes . Other sroidetes , 43 havestration . All of in vitro with higher than 80% similarity with the original fecal bacterial community [in vitro, the high throughput sequencing results showed that the FLU2 samples were enriched for the microbial groups of Escherichia/Shigella, Ruminococcaceae UCG 014, and Sutterella. Given the higher relative abundance of Escherichia/Shigella, the gut microbiota would be more susceptible to colorectal cancer [Escherichia [Sutterella is an opportunistic bacterium of the phylum Proteobacteria; human who have higher levels of the genera of Sutterella would have a lower concentration and percentage of butyric acid [Sutterella species is associated with autism, Down’s syndrome, and inflammatory disease [Ruminococcaceae. It is assumed that the effects of statins are largely individual-dependent. Specifically, the family Ruminococcaceae was enriched differently by statins. However, Scheperjans et al. [Ruminococcaceae population was increased significantly in Parkinson’s disease (PD) compared with healthy controls. Based on those analyses, our research could explain why the use of statin is associated with a higher risk of PD [Ruminococcaceae was also significantly increased in patients with constipation [Considering that the VI medium could simulate the human gut microbiota ommunity , VI medil cancer . Howeverherichia . Suttereric acid . In part disease , 49. Men disease  found ths et al.  found thsk of PD . Notablytipation .Bifidobacterium in the FLU2 fermentation samples compared with the other samples. These differences between FLU2 and other statins could be explained by the high doses and low lipid-lowering effect of FLU. Taken together, patients that need to reduce LDL-C levels more than 35% might choose ROS first, then ATO and SIM, and should not be given FLU. The patient’s statin-sensitive responses are linked to the gut microbiota, and the gut microbiota may guide the statin dosage adjustments [in vitro fermentation, the gut microbiota was not significantly affected by ROS, ATO, and SIM at a suitable dose. Hence, the differences between FLU2 and other samples might further support the findings that a high dose of statins is not adequate for Chinese people [After treating the gut microbiota with statins, there was a significant reduction of several clusters, including the genus ustments . There austments , 55–57. ustments , it seemustments . In anotustments . Some reustments . Thus, de people .Enterobacteriaceae. Given that clones can vary substantially in genome content and that evolutionary processes operate at the population level, the biological relevance of our results may require the demonstration of stability at lower taxonomic levels [We did not observe significant differences in bacterial diversity between two concentrations of the same statins (except for FLU), but differences were detected for different statins. This suggested that the gut microbiota was influenced differently by different statins and that a variation of the microbiota occurs much like the ecological successions associated with the genetics, social environment, diet, metabolic disorders, and antibiotic treatment . Howeverc levels .PICRUSt allowed us to know more about the overall microbiome metabolism and its functional specialization. We also performed a statistical analysis of differences in all samples. Interestingly, about 50.91% KEGG pathways were significantly changed in FLU2 compared with controls, and 10.67% KEGG pathways were significantly affected in ATO2, but there were no statistical differences in ROS and SIM when compared with controls. Consistently, the content of predictive metagenome functionalities was remarkably influenced by FLU2 when compared with other statins. FLU2 only slightly affected xenobiotics biodegradation and metabolism, but the effect was statistically different in some specific substances. For example, caprolactam degradation and styrene degradation were significantly overrepresented in the FLU2 metagenome. Those pathways may be related to FLU degradation, and further studies are required for elucidating the pharmacological or toxicological properties of the gut microbial metabolites and metabolites contribution during statin treatment.in vitro [From another perspective, very few studies have examined how the human gut microbiota degrades statins. We are aware of only two studies that have considered the degradation of statin by gut microbiota. One study, based on the fecal samples from nine healthy volunteers, found that β-hydroxy acid form lovastatin was degraded by the gut microbiota in vitro . Likewisin vitro . HoweverEscherichia/Shigella, Ruminococcaceae, and Sutterella. It is unclear whether the statistically significant alterations in gut microbiota have the potential for promoting key physiological adaptations and lipid-lowering effects. Further studies are needed to evaluate whether the gut microbiota alters the disposition, toxicity, efficacy of statins, and the analyses on interactions among drugs, gut microbiota, SCFA, and host should be extended to more populations and cover longer periods. In this myriad of untapped interactions, one may find strategies for personalized medicine to improve health and longevity.We observed that FLU2 significantly influenced the composition of the gut bacterial microbiota and the functional profiles, and that it changed SCFA production correspondingly. In turn, the statins were also degraded or modified by gut microbiota. The results may provide support for the microbial mediation of the therapeutic effects of FLU through short-chain fatty acid production, as well as for potential microbiota-mediated mechanisms behind the known intestinal adverse effects in the form of a relative increase in abundance of S1 TextTable A. Pharmacokinetic parameters of used statins. Table B. Basic statistical results for high-throughput 16S rRNA gene sequencing.(DOCX)Click here for additional data file.S1 Fig(PDF)Click here for additional data file.S2 Fig(PDF)Click here for additional data file.S3 Fig(PDF)Click here for additional data file.S4 Fig(PDF)Click here for additional data file.S5 Fig(PDF)Click here for additional data file.


C-range Ultraviolet (UVC) mercury (Hg)-vapor lamps have shown the successful decontamination of hydrocarbons and antimicrobial effects from titanium surfaces. This study focused on surface chemistry modifications of titanium dental implants by using two different light sources, Hg-vapor lamps and Light Emitting Diodes (LEDs), so as to compare the effectivity of both photofunctionalization technologies. Two different devices, a small Hg-vapor lamp (λ = 254 nm) and a pair of closely placed LEDs (λ = 278 nm), were used to irradiate the implants for 12 min. X-ray Photoelectron Spectroscopy (XPS) was employed to characterize the chemical composition of the surfaces, analysing the samples before and after the lighting treatment, performing a wide and narrow scan around the energy peaks of carbon, oxygen and titanium. XPS analysis showed a reduction in the concentration of surface hydrocarbons in both UVC technologies from around 26 to 23.4 C at.% (carbon atomic concentration). Besides, simultaneously, an increase in concentration of oxygen and titanium was observed. LED-based UVC photofunctionalization has been suggested to be as effective a method as Hg-vapor lamps to remove the hydrocarbons from the surface of titanium dental implants. Therefore, due to the increase in worldwide mercury limitations, LED-based technology could be a good alternative decontamination source. Titanium (Ti) dental implants have been widely used as prosthesis anchors since Brånemark and other colleagues discovered osseointegration ,2. HowevActinomyces israelii and Streptococcus sanguinis) are hydrophobic, and therefore, the hydrophilic surfaces attract highest numbers of hydrophilic bacteria [In particular, the microscopic characteristics of the surface such as topography, wettability and chemical composition have been proven to be the major matters affecting the bone–implant interactions and bacterial attachment ,18,19,20bacteria .2 layer surrounding the Ti implants gets contaminated by organic impurities, such as hydrocarbons present in the atmosphere, in the transit from its manufacture to the placement in the oral cavity bedding. This process is called biological ageing of titanium, and the presence of hydrocarbons contribute to the bacterial adhesion binding to the hydrophobic molecules on the bacterial surface [2 layer, to apply nonthermal atmospheric-pressure plasma (NTAPP) techniques, or to irradiate them by using ultraviolet light [Specifically, the chemical composition of the surface of Ti implants is one of the most crucial factors for successful osseointegration and anti-microbial effects. Despite being sterilized in the original packaging, the TiO surface ,23,24,25et light ,27,28.2 layer as well as by the hydrocarbon’s direct decomposition. In this regard, via titanium photocatalytic activity, reactive oxygen species  are generated, which are responsible for the decomposition of the outer membrane of microorganisms [In fact, recently, it has been reported that C-range ultraviolet light (UVC) irradiation emitted by mercury vapor lamps can reverse the biological ageing of titanium, in a process known as photofunctionalization ,30,31. Trganisms ,33. Furtrganisms ,37,38,39Nevertheless, in 2013 the worldwide treaty of the Minamata Convention on Mercury was negotiated in the United Nations (UN) Environment Program . The maiWe hypothesized that UVC light emitted by LED-based sources successfully eliminate contaminant hydrocarbons from the Ti oxide surfaces. The main purpose of the present in-vitro study was to compare the effectivity of two different UVC light photofunctionalization technologies, Hg-vapor and LED-based sources, in decontaminating the surface chemistry of commercially available titanium dental implants.The XPS measurements of all detected elements before UVC light treatment were shown in terms of relative atom concentrations (C at.%). Although the major elements presented on the implant surface were carbon (C), oxygen (O) and titanium (Ti), other elements such as aluminum (Al), silicon (Si), nitrogen (N), vanadium (V) and calcium (Ca) were also observed. In addition, a fluoride deposit appeared in the samples.In As far as carbon concentration is concerned, samples showed a higher carbon contamination around 26 C at.% before photofunctionalization, whereas after the UVC light treatment, a decrease in the carbon was observed in the Hg-vapor lamp device and the LED-based device, with 23.4 C at.% . Hence, This study was mainly focused on C, O and Ti elements detected on the surfaces of the implants. In this regard, The C1s spectra deconvolution of the treated samples consists of three components: the main peak at energy 284.6 eV concerning the hydrocarbons’ C atomic concentration in the Hg-vapor and LED-based devices, whereas the second and third components correspond to the C-O and C=O bonds in 2 bonds, whereas the second and third components could be ascribed to other kind of bonds such as Ti-OH, C=O…, which could not be dependably determined because of the non-significant peaks.The O1s spectral profile in 2 compounds. Thus, not only was the presence of the TiO2 determined by the main peak of Ti2p in all the measurements, but the metallic form of the titanium was also confirmed at 453.8–453.9 eV. Lastly, a double satellite peak could be observed at 471.5 eV.Finally, the analysis of the Ti doublet peak core lines, reported in In the present study, two varying devices based on different UVC sources were used to assess the successful decontamination of hydrocarbons from the surfaces of titanium dental implants, which is by far considered the most causal element related to the biological ageing and bacterial attachment. After 12-min UVC photofunctionalization with Hg-vapor and LED-based sources, both samples showed a clear reduction of about three percentage points of the atomic concentration of C, correlated with an enhancement of the atomic concentration of O and Ti. Consequently, taking into consideration the limitations of current mercury regulations, it is encouraging to see that LED-based technology produces equal results.By and large, implants resulted in mainly C, O and Ti major elements’ presence. Additionally, other elements were detected, such as Al and V, which are related to the most common titanium alloy (Ti-6Al-4V) used for dental implants . MoreoveThe XPS deconvolution spectral data of C1s showed the presence of hydrocarbon in all the implants, whose elimination from the surfaces was observed after the UVC lighting treatment. Indeed, it is known that there is a close relation between the reduction of hydrocarbon and an increase in Ti ratio, since the direct photolysis of hydrocarbon can improve the early healing bone–implant interactions by stimulating the osteoconductivity capacity and antimicrobial effects ,45. In aConcerning the characteristics of the surfaces, topography and alloys need to be taken into account. Therefore, machined surfaces, in comparison with rough surfaces, demonstrated more carbon content . AdditioOn the other hand, not only the osteoblasts conductivity was induced by ultraviolet treatment on Ti surfaces, but also the attachment, proliferation and viability of fibroblasts, which are responsible of a good soft-tissue sealing ,54.Although different wavelength ranges of UV irradiation are known to be effective to trigger chemical modifications, only the UVC wavelength  resulted in a remarkably better enhancement in the biological activity and greater reduction in bacterial attachment ,36,53,55As far as the duration of the irradiation is concerned, it was chosen to be 12 min, like several other published works ,63. ThesThe present study may provide a novel way of eliminating contaminant hydrocarbons from Ti oxide surfaces by using LED-based UVC sources. In this context, and bearing in mind the Minamata Convention on Mercury, the obtained results are encouraging to propose an alternative source of Hg-vapor lamps. Notwithstanding, although both devices show many similarities, the obtained results require careful interpretation, since only a single action protocol was used. Besides, future studies need to be conducted in order to determine the UVC effects with different exposure times.Furthermore, it can be considerably difficult to compare all the studies, due to the wide range of different irradiation treatment durations, types of samples and preparations, as well as different photofunctionalization devices in the market.Original screw-type commercially available Ti dental implants  were investigated. The length of all implants was 16 mm and the diameter 3.8 mm, according to the description of the manufacturers.One of the Ti dental implants was placed inside a Hg-vapor lamp sterilizer-device (λ = 254 nm) , whereas the other was placed inside a custom designed LED-based device (λ = 278 nm) . All the titanium specimens were treated by UVC light for 12 min, calculated by a digital timer and under ambient conditions. The distance from the light source was estimated to be 2 cm in both devices. The light power source used in both devices was 2 mW.After removing their original packages, both samples were placed on the metal deck of the XPS equipment, and introduced into it, in order to analyse the surface chemistry, prior to the UV treatment. For each implant sample, three evaluation areas were analysed by XPS to ensure homogeneity, between the 4th and 5th threads, 6th and 7th threads, and 19th and 20th threads of the implant.−5 mbar. The measured area was always 1 mm × 3 mm, and the exit angle was 90°. Then, a wide scan was first performed to determine the elements present on the surface ; and a detailed narrow scan was performed next, focusing on the major elements detected .The measurements were made using XPS equipment  with a Phoibos analyzer 150 1D-DLD and monochromatic Al Kα (1486.7 eV) X-ray source. The spectral data were analysed under vacuum pressure of 5 × 10The samples were then UVC-treated  as described in The XPS spectra were adjusted by using the CasaXPS 2.3.16 software , which models the Gauss–Lorentzian contributions, after a background subtraction (Shirley). Besides, a descriptive analysis and deconvolutions of the detected elements was made.The obtained results in this study suggest that LED-based UVC photofunctionalization (λ = 278 nm) is as effective a method as Hg-vapor lamps (λ = 254 nm) to remove the hydrocarbons from the surface of titanium dental implants. Moreover, the results of the present work make it possible to show the chemical modifications after UVC treatment associated with the reverse of the biological ageing of titanium and antimicrobial effects. As a result, this UVC-induced chemical change would involve positive biological and antibacterial effects reported in the scientific literature.


This study sought to assess the performance of the Fitbit Charge HR, a consumer-level multi-sensor activity tracker, to measure physical activity and sleep in children.59 healthy boys and girls aged 9–11 years old wore a Fitbit Charge HR, and accuracy of physical activity measures were evaluated relative to research-grade measures taken during a combination of 14 standardized laboratory- and field-based assessments of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills. Accuracy of sleep measures were evaluated relative to polysomnography (PSG) in 26 boys and girls during an at-home unattended PSG overnight recording. The primary analyses included assessment of the agreement (biases) between measures using the Bland-Altman method, and epoch-by-epoch (EBE) analyses on a minute-by-minute basis.Fitbit Charge HR underestimated steps (~11.8 steps per minute), heart rate (~3.58 bpm), and metabolic equivalents (~0.55 METs per minute) and overestimated energy expenditure  relative to research-grade measures (p< 0.05). The device showed an overall accuracy of 84.8% for classifying moderate and vigorous physical activity (MVPA) and sedentary and light physical activity (SLPA) . Mean estimates of bias for measuring total sleep time, wake after sleep onset, and heart rate during sleep were 14 min, 9 min, and 1.06 bpm, respectively, with 95.8% sensitivity in classifying sleep and 56.3% specificity in classifying wake epochs.Fitbit Charge HR had adequate sensitivity in classifying moderate and vigorous intensity physical activity and sleep, but had limitations in detecting wake, and was more accurate in detecting heart rate during sleep than during exercise, in healthy children. Further research is needed to understand potential challenges and limitations of these consumer devices. It is widely recognized that physical activity and sleep are important determinants of health. ,2 HoweveTools for the objective assessment of the frequency, intensity , and duration of physical activity and sleep in adults and children have largely been developed for short-term use  within a research and clinical environment . ,8 HoweveMost of the current commercial wearables have multi-sensor capabilities, which has led to refinements in data integration for outcome processing and new opportunities to obtaining data for multiple bio-domains within a single device. For example, the latest generation of consumer-level activity trackers typically use triaxial accelerometry to measure movement  and photoplethysmography to measure heart rate  and its variability. Other sensors are of increasingly used to measure a broad range of other bio-signals  as well as environmental data . Importantly, a combined sensing approach may theoretically address many of the limitations of using one channel of information . ,12 For eThe easy-to-use nature of these devices led to their proliferation, with a growing use of these commercial devices in health research. Despite their potential advantages, namely the capability of targeting and collecting objective multidimensional data from millions of individuals over time, several challenges and limitation should be addressed. For example, the proprietary nature of the algorithms presents some challenges in data assessment and interpretation. Thus, we believe that within the current unregulated space for consumer wearable technology, it is crucial to evaluate the capability and performance of these devices.Independent evaluation of the accuracy of the metrics from consumer-level activity monitors has increased recently.  However,In the present study, we assessed the Fitbit Charge HR’s accuracy to measure steps, heart rate, energy expenditure, physical activity intensity, total sleep time, and wake after sleep onset relative to research-grade measures in healthy boys and girls aged 9–11 years old through a combination of laboratory and field tests. We assessed the Fitbit Charge HR performance for measuring sleep in a subset of boys and girls during an unattended overnight at-home PSG assessment.Potential participants were recruited from the San Diego Unified School District and the greater San Diego area via in-class announcements and email listservs. Eligible participants were 9–11 years old; able to understand English or Spanish; do light to moderate physical activity for 60 minutes; and walk, jog, or run unassisted. Potential participants were excluded if they had any functional limitation or medical condition prohibiting their ability to be physically active or used medications to alter body weight or metabolism. All study procedures were approved by the University of California, San Diego Human Research Protections Program. All participants provided written assent, with written consent to participate from their parent or legal guardian. Participants were provided monetary compensation for their participation.Participants completed a 2- to 3-hour testing session at the Exercise and Physical Activity Resource Center at University of California, San Diego. Prior to the start of testing, participants were asked to self-report their age, sex, and handedness. Weight (to the nearest 0.1kg) and height (to the nearest 0.1cm) were measured using a calibrated digital scale and stadiometer . Participants were then fitted with a Fitbit Charge HR on their non-dominant wrist. The Fitbit Charge HR is not considered to be a medical device. It is made of a flexible, durable elastomer material similar to that used in many sports watches, with a surgical-grade stainless steel buckle. It is lightweight, 0.83 cm wide, and fits wrists that are 5.4 to 8.7 inches in diameter. The device contains a triaxial accelerometer, optical heart rate monitor, altimeter, and vibration motor. Precise performance specifications of each sensor are not made available by the manufacturer. The devices used in this study were initialized using generic, study-specific Fitbit accounts and non-identifiable participant information . At the time of testing, Fitbit accounts could not be established with a user age less than 13 years old, so all accounts were established using the age of 13. Data from the generic, study-specific Fitbit accounts were downloaded via Fitabase , a third-party research platform designed to collect Fitbit data at granularity useful to researchers . The measures from the Fitbit Charge HR examined in the present study include heart rate, caloric energy expenditure, steps, physical activity intensity, and sleep.Participants were also fitted with a portable three-lead electrocardiogram (ECG) used to measure heart rate in beats per minute ; a portable indirect calorimeter used to measure caloric energy expenditure in kilocalories  and physical activity intensity in metabolic equivalents of task ; and a person-worn video camera mounted on a harness such that it consistently pointed at the participant’s feet and lower legs so that steps could be counted . All devices were fitted according to manufacturer recommendations, and recording start times were synchronized with the Fitbit Charge HR. The devices collected data throughout the testing session, which consisted of 14 structured tasks grouped into domains of sitting, stationary cycling, treadmill walking or jogging, stair walking, outdoor walking, and agility drills . The tasData from the aforementioned devices were extracted by trained research staff and metrics were aligned to the granularity of the corresponding Fitbit Charge HR metrics . BPM from the ECG and METs from the indirect calorimetry were aggregated to the minute-level after the removal of aphysiologic data . Physical activity intensity from indirect calorimetry was defined as sedentary <1.50 METs, light 1.50–2.99 METs, moderate 3.00 to 5.99 METs, and vigorous as ≥6.00 METs.  To deterUpon completion of the testing session, participants were sequentially asked if they would be willing to wear the Fitbit Charge HR at home during an unattended overnight polysomnographic (PSG) assessment of their sleep performed according to the American Academy of Sleep Medicine (AASM) guidelines.  Those whWake and sleep  from PSG were scored by a trained sleep laboratory technician in 30-second epochs according to AASM guidelines.  StandardPSG 30-second epochs were aggregated to the minute-level as follows: two consecutive PSG 30-second epochs scored as N1, N2, N3 or REM, were coded as sleep. If one or both of the consecutive PSG 30-second epochs were scored as wake, that minute was coded as wake. ,29 The cSample sizes in validity studies vary greatly. In the present study, the primary aim was to determine the agreement between measures. Thus, we aimed to include approximately 60 participants in the physical activity and exercise portion of the study, which would result in 95% confidence intervals around the limits of agreement equal to 0.44 standard deviations of the differences between measurements. Due to time and cost constraints, a subset of 26 participants from the physical activity sample completed the sleep portion of the study. This is a similar size to that used in other validity studies that included PSG. ,30,31The agreement between measures from the Fitbit Charge HR and corresponding research-grade measures were examined using the Bland-Altman method. The mean of differences for each of the measures being compared  were calculated along with 95% confidence intervals calculated as the mean difference ±1.96 times the standard error of the differences. Positive bias indicated that the Fitbit Charge HR underestimated the metric of interest compared to research-grade tools, whereas negative bias indicated that the Fitbit Charge HR overestimated these metrics. The limits of agreement were defined as the mean of the differences ±1.96 times the standard deviation of the differences. A Bland-Altman plot of the mean of the differences by the means of the measures, along with the limits of agreement, was used for visual judgment of how well the methods of measurement agree. Mean absolute percentage error (MAPE) was calculated as the average of absolute differences between the measures, divided by the relevant research-grade measure, multiplied by 100. Accuracy and corresponding Cohen’s kappa, sensitivity, and specificity were calculated for categorical values of physical activity intensity  and sleep states . Assumptions of the normality of underlying raw data were evaluated by inspection of Q-Q plots of residuals, and they were found to be normally distributed. Additionally, paired-sample t-tests were calculated to assess if differences in measures were statistically significant  and linear regression was used to explore the association between the amount of bias and the magnitude of measurement.A convenience sample of 60 participants was recruited for participation in the study. No participant assessed for eligibility was excluded. However, after 1 participant completed the testing, there was an error in syncing their Fitbit that resulted in minute-level data being unavailable. The exact source of the error is unknown, but it was likely due to incorrect initialization of the Fitbit. The participant was excluded from all analyses. Additional exclusions were task specific and reasons included participant’s legs being too short to comfortably reach the pedals of the cycle ergometer, ECG electrodes falling off, the facemask of indirect calorimeter becoming loose, or unusable video footage. Participant numbers by task, as well as descriptive statistics (means and standard deviations) for each of the measures of interest are provided in the supplemental results see . The meaβ = - 0.9, p<0.001).Excluding those tasks completed while sitting and on the cycle ergometer, the Fitbit Charge HR underestimated step count by a mean of 11.8 steps per minute  compared to direct observation, with 1 participant falling outside the agreement limits .Throughout all 14 tasks the Fitbit Charge HR underestimated heart rate by a mean of 3.58 beats per minute  compared to the ECG, with 4 participants falling outside the agreement limits .Throughout all 14 tasks the Fitbit Charge HR overestimated energy expenditure by a mean of 0.34 kcal per minute  compared to the indirect calorimeter, with 3 participants falling outside the agreement limits  compared to the indirect calorimeter, with 3 participants falling outside the agreement limits , a sensitivity of 85.4% in identifying moderate or vigorous activity , and a specificity of 83.1% in identifying sedentary or light activity .A total of 26 participants  were invited to participate in the at-home sleep portion of the study in the order in which they completed the physical activity portions of the study. Three participants were excluded from the sleep analysis and 4 from the HR analysis during sleep due to PSG device malfunction , short sleep time (3 standard deviations outside the group mean), or failure in the alignment of the Fitbit Charge HR to PSG, resulting in a final sample of 23 (sleep) and 22 (HR).From PSG, participants spent 553±59 (mean ± SD) min in bed, with a SOL of 22 ± 10 min, a WASO of 20 ± 11 min, and sleep efficiency of 93 ± 2%. They had 4 ± 1% of time in N1 sleep, 33 ± 10% in N2 sleep, 42 ± 11% in N3 sleep, and 21 ± 5% in REM sleep. The Fitbit Charge HR underestimated PSG TST  and overestimated PSG WASO . A maximum of 2 participants exceeded the Bland-Altman plot agreement limits for these measures see . There wMean participant HR from ECG was 72.60 ± 7.07 bpm, and 71.54 ± 7.04 bpm from the Fitbit Charge HR. The Fitbit Charge HR underestimated ECG HR , with one participant falling outside of falling outside the agreement limits  found in 12–21 y old healthy adolescents in whom the same device model was tested against standard ECG during sleep.  AlthoughIn our study, EBE analysis on a minute-by-minute basis indicated that the Fitbit Charge HR was able to identify sleep and wake epochs with 92% accuracy. The Fitbit Charge HR correctly identified 96% of sleep epochs. However, in concordance with research-grade actigraphy,  the FitbImportantly, when PSG is not accessible, clinical grade actigraphy is currently considered an accepted alternative for measuring sleep in non-laboratory settings. Actigraphy has been widely adopted in research, and it relies on openly validated standard algorithms. Due to a simple rationale , its performance in sleep/wake assessment is largely predictable and characterized by a well-known limitation in the assessment of motionless wake (poor specificity).  Despite However, despite the theoretical advantages in combining cardiac function data and motion for sleep/wake assessment (particularly in the classification of motionless wake), low specificity seems to still be a limitation of multi-sensor sleep trackers (see for review). ,42,43 ThWhile there is increasing study of physical activity and sleep in youth using consumer-level activity trackers, few studies to-date have used these objective measures to examine the links between physical activity, sleep, and other behavioral and neuropsychological indicators of well-being or dysfunction. The Adolescent Brain Cognitive Development (ABCD) study is a multi-site study following over 11,800 9- to 10- year-olds prospectively into young adulthood to increase our understanding of brain development, health, and psychosocial functioning in youth that is utilizing the device validated within the present study.  This stuThe present study has several important strengths and adds significantly to existing research on this topic. First, we used a relatively large sample of boys and girls who completed a variety of laboratory-, field-, and home-based tasks. Most validation studies of consumer-level activity monitors are restricted to laboratory-based assessments only. ,20–23 ThThe findings of the present study should be considered within its limitations. First, little to no consensus exists for the ideal combination of laboratory-, field-, and home-based tasks for assessing the validity of an activity tracker. Similarly, consensus is lacking regarding the optimal physical activity measures for criterion measures. Given the lack of gold standard measures for physical activity, it is not possible to truly know the source of bias . Further, the Fitbit Charge HR was not designed for use in children, and because the algorithms used to derive the various metrics are proprietary, we are unable to determine how differences in anthropometry and heart physiology influence accuracy. Regarding sleep, although unattended at-home PSG is feasible, technically adequate, and well-tolerated in school-aged children when performed under research conditions,  and may Cost-accessible, comfortable activity trackers that provide objective measurements among children are critically important to understanding the influence of physical activity and sleep on health. This is among the first studies to systematically evaluate the validity of a common multi-sensors consumer-level activity tracker to measure physical activity and sleep in children. The results of the present study provide researchers, clinicians, and consumers alike with estimates of accuracy and bias in multiple physical activity and sleep metrics across a variety of tasks. On balance, the ease of use, low cost, and palatability to youth make these multi-sensors consumer devices as promising tools for the study of youth physical activity, sleep, and health. More effort in evaluating challenges and limitation in using this technology is clearly needed.S1 File(DOCX)Click here for additional data file.S1 Data(CSV)Click here for additional data file.S2 Data(CSV)Click here for additional data file.S3 Data(CSV)Click here for additional data file.


Firmicutes and Proteobacteria were the main phyla detected in the milk samples, followed by Actinobacteria and Bacteroidetes. The proportion of Firmicutes to Proteobacteria changed considerably depending on the sampling week. A total of 411 genera or higher taxons were detected in the set of samples. Genus Streptococcus was detected during the 28-week sampling period, at relative abundances between 2.0% and 68.8%, and it was the most abundant group in 14 of the samples. Carnobacterium and Lactobacillus had low relative abundances. At the genus level, bacterial diversity changed considerably at certain weeks within the studied period. The weeks or periods with lowest relative abundance of Streptococcus had more diverse bacterial compositions including genera belonging to Proteobacteria that were poorly represented in the rest of the samples.Breast milk from a single mother was collected during a 28-week lactation period. Bacterial diversity was studied by amplicon sequencing analysis of the V3-V4 variable region of the 16S rRNA gene.  Lactobacillus fermentum, L. gasseri, L. rhamnosus, isolated from human breast milk, can be regarded as potential probiotic bacteria [Human milk is considered to be an important source of bacteria for the newborn. Many of these bacteria may be human commensals or have potential probiotic effects . Lactic bacteria ,3,4. Prebacteria . In addibacteria . Furtherbacteria . The benbacteria ,8,9.Weissella, Leuconostoc, Staphylococcus, Streptococcus, and Lactococcus were predominant in colostrum samples, whereas in 1- and 6-month milk samples, the typical inhabitants of the oral cavity  increased significantly. Khodayar-Pardo et al. [Bifidobacterium and Enterococcus spp. counts, increased throughout the lactation period. Most of these studies, however, do not report individual variations in the microbiota during the lactation period. Nevertheless, it is suspected that different changes may occur at the individual level due to different factors. The aim of the present study was to investigate the microbiota during the mid-to-late lactation period in breast milk from a single mother and to analyze possible changes in bacterial diversity during the period.In addition to classical studies based on isolation and identification of bacteria from human milk , cultureo et al.  applied Written informed consent was obtained, in accordance with the Declaration of Helsinki. Samples were taken from a single 38-year-old Latin American female donor, suffering from asthma and overweight, during a lactating period between weeks 21 and 48, inclusive, after cesarean delivery. Samples were taken three times in the day before baby lactation. The hands of the volunteer were cleaned with soap and covered with sterile gloves. Then, the nipples and surrounding areola were cleaned with cotton soaked with 70% ethanol. The first drops of milk were discarded, and then 5–7 mL of milk was extracted from each breast manually using a sterile manual pump . The milk was transferred to sterile falcon test tubes, stored at 4 °C, and transported on ice to the laboratory within the next 12 h, where it was stored at −20 °C until analysis.g for 7 min in a refrigerated centrifuge 5424 R . After removal of the supernatants, total DNA from the remaining pellets was extracted with a QIAamp Stool DNA Fast Mini Kit  following the manufacturer instructions. The quality and quantity of the extracted DNA was determined by QuantiFluor® ONE dsDNA system . The DNA was stored at −20 °C until analysis.Thawed milk samples from the same day were mixed thoroughly and centrifuged at 16,000× The 16S rDNA V3-V4 regions were amplified following the Illumina Metagenomics Sequencing Library Preparation protocol . Illumina adapter overhang nucleotide sequences were added to the gene-specific sequences. The following 16S rDNA gene amplicon PCR primer sequences were used: forward primer: 5′TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG; reverse primer: 5′GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGACTACHVGGGTATCTAATCC . MicrobiThe numbers of reads assigned to operational taxonomic units (OTUs) and the alpha diversity indicators are shown in Firmicutes and Proteobacteria were the main phyla detected in the milk samples , with relative abundances between 1.0% and 1.2%. Enterobacteriaceae were highly represented in most of the samples, ranking sometimes in first position in relative abundance. Members of Pseudomonadaceae, Moraxellaceae, and Xanthomonadaceae were also relevant groups among the Proteobacteria. Microccaceae and Weeksellaceae were the main representatives among Actinobacteria and Bacteroidetes, respectively.The 30 families that had relative abundances ≥ 2.5% are shown in Streptococcus was detected in all the samples (being the most abundant OTU in 14 samples), although there were large differences in its relative abundances between samples. For example, genus Streptococcus had relative abundances that were above 50% in samples from weeks 3, 14, 17, and 21–23. By contrast, the lowest relative abundances for this genus were detected in samples from weeks 5, 8–10, 12–13, 18, and 20. Box-plot representation of the relative abundances of the main genera across the samples  than the low-abundance samples. Apparently, groups G1B and G1C had higher relative abundances than group G1A (which would suggest an increase in the relative abundance of Streptococcus by the end of the sampling period). However, the differences between the three groups were not statistically significant (p > 0.05) for any of the univariate analyses carried out.The 38 genera with relative abundances ≥ 2.5% (representing between 81.7 and 97.5 of OTUs) are shown in  samples  indicatethe milk . Data onundances , insert Staphylococcus  were detected in many of the samples, in some cases with high relative abundances. Gemella was also detected in many samples, and Listeria was detected in three samples (reaching 6.8% in one sample).Another genus represented in all breast milk samples was lococcus c, with rEnterobacteriales belonged to Fam. Enterobacteriaceae (Others). Members of the genera Pantoea, Serratia, and Enterobacter were also relevant in some samples . Several samples showed high relative abundances of members of family Burkholderiaceae (Cupriavidus), with remarkably high values at week 5. All samples with a high relative abundance of Cupriavidus also had higher relative abundances of Vulcaniibacterium (Xanthomonadaceae). Among Actinobacteria, the main representative was Rothia (with relative abundances between 3% and 10% in many samples).Most of the  samples c. MemberStaphylococcus), with at least two minor clusters  characterized by a very low relative abundance of Streptococcus and high relative abundances of Pseudomonas/Acinetobacter/Bacillus, and samples W5, W9, W10, W18 .PCoA  revealedStaphylococcus, Streptococcus, Lactobacillus, Pseudomonas, Bifidobacterium, Corynebacterium, Enterococcus, Acinetobacter, Rothia, Cutibacterium, Veillonella, and Bacteroides. Another review paper reported that Streptococcus and Staphylococcus appear to be widely predominant in human milk without regard to differences in geographic location or analytic methods [Results from the present study provided information on the changes in bacterial diversity in human milk from a single individual during the late lactating period. By focusing on a single donor, the present study avoided the confounding effect of data from different subjects and allowed to follow this mother continuously. Although the data may be more difficult to analyze, the study revealed a sample variability that otherwise may be unnoticed in studies involving a compendium of samples. Many previous studies have addressed the microbial composition of human milk, but usually from a compendium of samples taken from different individuals and taken at early to mid lactating period. Zimmermann and Curtis  identifi methods .Streptococcus, Staphylococcus, Pseudomonas, Acinetobacter, Rothia, Cutibacterium, and Veillonella). Furthermore, the microbial composition of several samples from the present study resembled, in a certain way, that of the children salivary microbiome reported by other authors [Streptococcus, Veillonella, Rothia, Leptotrichia, Haemophilus, and Neisseria). These results would suggest colonization of mammary glands by bacteria from the baby’s mouth. As a matter of fact, human milk is considered to be colonized by bacteria from the mother’s gut and skin or the infant’s mouth [Results from the present study also identified main genera reported in previous studies  represented less than 2%. This could be related to the late lactation period. Carnobacterium was represented in two samples (2.1–3.8%). Carnobacterium has seldom been reported in human milk, although this bacterium could be a new, largely unexplored candidate for novel probiotic bacteria from human milk. One study reported that the presence of Carnobacterium in milk was associated with cesarean delivery [Contrary to other studies, delivery , which idelivery .Bacillus and Paenibacillus also had high relative abundances in several milk samples and contributed (together with Streptococcus) to the increase of Firmicutes during late lactation. Remarkably, genus Bacillus reached ca. 25% relative abundance in two samples (weeks 16 and 27). The presence of Bacillus in breast milk samples has also been reported in several previous studies [Bacillus and Paenibacillus in human milk, but Bacillus was associated with subacute or acute mastitis. However, during the sampling period of the present study, the mother did not report any signs of mastitis. Members of genus Bacillus are ubiquitous, sporulating, saprophytic microorganisms that can readily contaminate human milk during its collection or storage [B. cereus is a matter of concern in stored breast milk  since it can produce food poisoning toxins and may also cause severe illness in neonates [Bacillus species are commercialized as human probiotics [Bacillus coaulans and Bacillus clausii are being investigated for pediatric and infant formula applications [Aerobic endosporeformers of the genera  studies ,27,28. P storage . Among tneonates ,30,31. Oobiotics  and straications ,34,35.Results from the present study indicate large variations in the microbiota of the breast milk from a single woman during the sampling period. Tentatively, some of the changes could be explained by environmental contamination of the milk. The baby was already in the weaning period and crawling. Therefore, transfer of microbiota from the baby to the mother’s breast could account for some of the contaminants observed, including Enterobacteria. Previous work has suggested that the environment, utensils, and water could contribute considerably to the microbiota detected in breast milk . A seconStaphylococcus and an increase of other microbial groups belonging to phylum Proteobacteria. Factors such as environmental contamination and changes in the baby habits (weaning and crawling) could account for the observed differences during the sampling period.In conclusion, results from the present study reveal major changes in the microbiota of breast milk from a single mother during the late breastfeeding period. The main changes include a decrease in the relative abundance of