The clinical correlation between adipokines levels in the blood and the incidence of senile osteoporosis (SOP) has not been clearly studied. We conducted this meta-analysis to elucidate the relationship between three common adipokines levels  and the incidence of SOP.We searched databases such as CNKI, CBM, VIP, Wanfang, PubMed, Web of Science, Embase, and the Cochrane Library to collect articles published since the establishment of the database until July 30, 2022.2 = I96%), chemerin levels were significantly higher , and adiponectin levels were not significantly different  in SOP patients compared with healthy older individuals with normal bone mineral density (BMD). In addition, correlation analysis showed that leptin levels were positively correlated with lumbar bone mineral density (LBMD) (r = 0.36) and femoral bone mineral density (FBMD) (r = 0.38), chemerin levels were negatively correlated with LBMD (r = -0.55) and FBMD (r = -0.48), and there were significant positive correlations between leptin and adiponectin levels and body mass index (BMI) (r = 0.91 and 0.97).In total, 11 studies met the selection criteria. Our meta-analysis showed that serum leptin levels were significantly lower (mean difference [MD], -2.53, 95% CI: -3.96 to -1.10, The likelihood of having SOP was higher in older individuals with low levels of leptin and higher levels of chemerin. In addition, BMI was somewhat lower with low levels of leptin and adiponectin.https://www.crd.york.ac.uk/prospero/, identifier CRD42022356469. Its most important pathogenesis is the inactivation of osteoblasts and the activation of osteoclasts, which ultimately leads to a disruption of bone metabolism and a reduction in bone tissue . OsteopoWith the accelerated aging of the global population, the incidence of senile osteoporosis (SOP) has increased significantly. Unlike postmenopausal osteoporosis (PMOP), SOP is caused by advancing age and therefore does not only affect women but is also closely associated with men . In recein vivo, but also have a connection with BMD  differentiate into more adipocytes than osteoblasts, which leads to reduced bone formation and contributes to SOP . Adipokiwith BMD , 7.Furthermore, in addition to adipokines, adipose tissue and body mass index (BMI) play an extremely important role in osteoporosis. It was found that adipose tissue and BMI influence both bone density and bone turnover. Some studies found that adipose tissue has a certain mechanical protective effect on BMD, which can effectively prevent the occurrence of fractures after osteoporosis, and a significant association between BMD and BMI has been reported (r = 0.3 to 0.6) –10.Several previous meta-analyses have examined the effects of adipokines levels on BMD and fracture risk in healthy men and women , the dif2We conducted a meta-analysis according to the Preferred Reporting Items for Systematic Reviews and Network Meta-Analyses (PRISMA) and Cochrane methods, and the completed PRISMA checklist is listed in 2.1“Osteoporosis”, “senile osteoporosis”, “senile bone losses”, “SOP”, “adipokines”, “leptin”, “adiponectin”, and “chemerin” were used as our search terms in databases such as China National Knowledge Infrastructure (CNKI), CBM, VIP information database, Wanfang Data, PubMed, Web of Science, EMBASE, and Cochrane Library to search the articles since their establishment until July 30, 2022. This category was limited to human studies. The detailed search strategies are documented in 2.2Two authors independently evaluated and selected relevant studies based on the following criteria: 1) study type: case-control studies and cohort studies; 2) study subjects: the elderly who meet the WHO diagnostic criteria for OP in the case group and healthy older individuals with normal BMD in the control group; 3) study index: data on leptin, adiponectin, or chemerin in SOP and control groups must be recorded completely; and 4) Newcastle Ottawa Scale (NOS) score ≥5. Exclusion criteria: 1) duplicate publications or overlapping study subjects; 2) the full text was not available; 3) relevant data were not provided in the original text or were incomplete and could not be obtained from the authors; 4) animal studies, case reports, editorials, reviews, expert opinion reports, and conference papers; and 5) study subjects suffered from diseases affecting bone metabolism or adipokines expression or had taken drugs affecting bone metabolism and lipid metabolism within 1 year.2.3From the included articles, two authors independently extracted the following information: 1) study characteristics, including first author, year of publication, and location; 2) basic characteristics of the individuals, including sample size, mean age, and mean BMI; and 3) details of biochemical examinations, including the source of the index and method of analysis. In addition, we extracted correlation coefficients  to furth2.4Studies were evaluated for quality using the NOS score , with tw2.5In this meta-analysis, we labeled the differences in leptin, adiponectin, and chemerin concentrations between individuals with SOP and healthy older individuals as the primary outcome and the relationships between the concentrations of adipokines and BMI, LBMD, and FBMD as secondary outcomes.2.6I2 statistic , standard deviation (SD), and corresponding 95% confidence interval (95% CI). Due to inevitable clinical heterogeneity between studies, the random-effects model was a more appropriate method to calculate summary effect measures. The statistical heterogeneity of individual outcome studies was assessed using the Cochrane Q test  and the tatistic . Subgrou33.1After the initial searching, we found 1829 records, used ENDNOTE X9 and manual weight-checking methods to remove 845, then excluded 925 ineligible studies after carefully screening titles and abstracts, read the full text to remove 48 studies that did not meet the inclusion criteria, and finally identified 11  studies 3.2Finally, a total of 11 studies were included in this meta-analysis, including 777 patients with SOP and 560 healthy older individuals with normal BMD. Of these studies, 10 studies –27 were 3.3P = 0.0005) ; therefore, we performed subgroup analyses based on leptin assay approaches to explore potential influencing factors. As shown in P = 0.005) and the RIA group  were statistically lower than in the control group, and there were no statistically significant differences between the two groups (P = 0.09), but statistical heterogeneity was not significantly reduced in the ELISA group (2 = I98%), while significantly reduced in the RIA group (2 = I0). And we performed a sensitivity analysis in which there was no significant change in the combined results after omitting one study at a time, indicating that it could confirm the robustness of the combined results  Figure 2P = 0.53). Although substantial statistical heterogeneity was detected , subgroup analysis was not performed because serum adiponectin levels were measured using ELISA in all studies. Sensitivity analysis has confirmed the robustness of the combined results . We detected significant statistical heterogeneity , but we did not perform subgroup analyses because serum chemerin levels were measured using ELISA in all studies. Moreover, sensitivity analysis has confirmed the robustness of the combined results , 0.12 , and -0.61 . Thus, the total r values for leptin, adiponectin, and chemerin were 0.36, 0.12, and -0.55, respectively. Analysis of the correlations showed that leptin levels were significantly and positively correlated with LBMD, and chemerin levels were significantly and negatively correlated with LBMD. Meanwhile, as shown in P <0.00001), 0.17 , and -0.52 . Thus, the total r values for leptin, adiponectin, and chemerin were 0.38, 0.17, and -0.48, respectively. Analysis of the correlations showed that leptin levels were significantly and positively correlated with FBMD, and chemerin levels were significantly and negatively correlated with FBMD. Sensitivity analysis confirmed the robustness of the combined leptin and chemerin results , 2.07  and 0.91 . Thus, the pooled r values for leptin, adiponectin, and chemerin were known to be 0.91, 0.97, and 0.72, respectively. The meta-analysis of correlation coefficients revealed significant positive correlations between leptin, adiponectin levels, and BMI, further confirmed by the sensitivity analysis results  states that publication bias studies with funnel plots are not required when there are fewer than 10 studies.We did not assess publication bias for leptin, adiponectin, and chemerin because the Cochrane Handbook of Interventional System Reviews  and AdipoR2 to positively regulate bone formation , on the It was found that chemerin can regulate lipogenesis and osteogenesis in BMSCs, thereby dynamically regulating bone metabolism in the body. One study found that chemerin-deficient mice showed reduced bone mass and bone formation, whereas chemerin-overexpressing mice showed increased osteogenic differentiation and increased bone mass and bone formation . HoweverIn conclusion, the results of our meta-analysis suggest that older individuals with lower levels of leptin and higher levels of chemerin are more likely to have SOP. Interestingly, many studies showed a positive correlation between BMI and BMD, and low BMI is an important risk factor for osteoporosis , 45. Our4.2Our study has unique advantages. First, this study is a cutting-edge analysis of studies designed to assess the clinical relevance of adipokines levels to SOP and explore other biomarkers that can predict and improve SOP. Second, our literature search and findings were comprehensive. Third, although some of the results showed heterogeneity, the quality of the included studies was at an intermediate to a high level of confidence, and we also performed a detailed subgroup analysis based on whether the assay approaches of different adipokines levels in blood influenced the result.Of course, there are several limitations to consider. First, despite our best efforts to conduct subgroup analysis, the heterogeneity of each subgroup remained high, suggesting that there are still several unknown factors that may have contributed to study heterogeneity, such as geography, mean BMI, ethnicity, actual age, and nutritional status of older individuals. Second, our study included only case-control studies and lacked validated longitudinal cohort studies to infer a causal relationship between adipokines levels and the occurrence of SOP. Third, the correlation between adiponectin and SOP is different from some other studies, although some studies indicate that adiponectin predicted the incidence of osteoporotic fractures, our meta-analysis did not detect a significant correlation. Given the limited number of trials and small sample sizes for some outcomes, these findings may not be sufficient to ensure significant differences. Despite these limitations, our meta-analysis adds statistical power by pooling the results of individual studies, which means that the total number of subjects is large enough to support stronger conclusions.4.3Currently, the prediction and diagnosis of osteoporosis and resulting fractures in the elderly are often based on imaging, but the use of imaging is limited to the analysis of only a single skeletal region, which is often not representative of the disease status of the entire skeleton in the elderly. Serum biomarkers may be more effective than imaging in predicting the status of the entire skeleton; moreover, BMD often does not reflect short-term changes in bone status during treatment, and complete reliance on BMD to predict treatment outcome and prognosis would be biased, whereas serum biomarkers can reflect bone metabolism in patients in the short term, which can improve the sensitivity and specificity of osteoporosis detection and monitoring in the continuous metabolism of bone tissue. In conclusion, the meta-analysis showed that the elderly with low leptin levels and high chemerin levels were more likely to have SOP, and BMD was positively correlated with leptin and negatively correlated with chemerin. However, the underlying mechanism of dysregulation of adipokines levels and whether correction of dysregulation of adipokines levels can be used to treat SOP and reduce the risk of osteoporotic fractures remains to be investigated.The original contributions presented in the study are included in the article/JW and SL designed the project. JW and YZ collected and sorted the data. SL and RD reviewed the data. JW completed the first draft. SN revised the draft. SL, YZ, and RD reviewed the full text. and SL provided fund support. All authors contributed to the article and approved the submitted version.


CFHR3/CFHR1 gene deletion who discontinued eculizumab maintenance therapy 24 weeks after achieving disease remission. We report the safety of discontinuing eculizumab treatment with the aim of minimizing the risk of adverse reactions, reducing the risk of meningitis, improving quality of life, and reducing the considerable treatment costs. Eculizumab has proven to be effective in patients with atypical hemolytic uremic syndrome (aHUS) in clinical trials and in the real world, but the optimal duration of therapy remains unknown. Standard maintenance treatment is often life-long, but the possibility of discontinuation has not yet been systematically tested. We describe a case of aHUS after ChAdOx1 nCoV-19 vaccination in a patient with homozygous  Atypical hemolytic uremic syndrome (aHUS) is a life-threatening disease characterized by microangiopathic hemolytic anemia, thrombocytopenia, and kidney injury that results from thrombotic microangiopathy (TMA) within the glomerular capillaries and arterioles, involving a genetic or acquired dysregulation of the complement alternative pathway. A second trigger is often necessary for disease manifestation. Eculizumab is a humanized monoclonal complement inhibitor that binds with high affinity to C5, preventing the generation of the terminal complement complex C5b-9, thus inhibiting complement-mediated TMA. Eculizumab has proven to be effective in patients with aHUS in clinical trials , howeverE. coli in stool culture (including O157 strain), normal levels of factors H, B, and I, a normal activity of the alternate complement pathway, and absence of anti-factor H antibodies. Molecular genetic study did not recognize any pathogenic variants in the analyzed genes, but it identified a polymorphism described as a possible risk factor for aHUS development: deletion in homozygosity of CFHR3/CFHR1. She accomplished 10 sessions of PEX, with normalization of platelet count and anemia improvement. Considering anuric AKI and clinical signs of volume overload, hemodialysis was started after the second session of PEX. Given the absence of a secondary cause for TMA, a diagnosis of aHUS was made, and therapy with eculizumab was started. After the first administration, the patient resumed diuresis, renal function partially recovered, and hemodialysis was suspended. The patient was discharged with a hemoglobin level of 11.6 g/dL, normal platelet count, and creatinine 4.4 mg/dL. She maintained eculizumab therapy, 900 mg/week for 4 weeks, followed by 1,200 mg/every 2 weeks for 20 weeks with no evidence of hemolysis, normal platelet count, and improvement of renal function (creatinine 2 mg/dL), after which therapy with eculizumab was discontinued. She was then closely monitored with no evidence of relapse and stable renal function 24 weeks after treatment cessation. A 54-year-old female was admitted to the emergency department with general malaise, vomiting, and low urine output 5 days after receiving a first dose of ChAdOx1 nCoV-19 vaccine  [CFHR3/CFHR1 deletion, previously associated with aHUS and the production of anti-factor H antibodies, in the presence of a trigger, was found [Therapeutic terminal complement blockade with eculizumab changed the natural history of aHUS, resulting in a decreased risk of chronic kidney disease stage 5 from 60% to less than 15% . At the as found . In factas found , all patas found , 8, 9. AInformed consent was obtained from the patient reported in this case. The participant has consented to the submission of the case report to the journal. No funding. The authors have no conflict of interest to declare that are relevant to the content of this article.


The objective of this study was to clarify the impact of adverse reactions on immune dynamics. We investigated the pattern of systemic adverse reactions after the second and third coronavirus disease 2019 (COVID-19) vaccinations and their relationship with immunoglobulin G against severe acute respiratory syndrome coronavirus 2 spike 1 protein titers, neutralizing antibody levels, peak cellular responses, and the rate of decrease after the third vaccination in a large-scale community-based cohort in Japan. Participants who received a third vaccination with BNT162b2 (Pfizer/BioNTech) or mRNA-1273 (Moderna), had two blood samples, had not had COVID-19, and had information on adverse reactions after the second and third vaccinations (n = 2198) were enrolled. We collected data on sex, age, adverse reactions, comorbidities, and daily medicine using a questionnaire survey. Participants with many systemic adverse reactions after the second and third vaccinations had significantly higher humoral and cellular immunity in the peak phase. Participants with multiple systemic adverse reactions after the third vaccination had small changes in the geometric values of humoral immunity and had the largest geometric mean of cellar immunity in the decay phase. Systemic adverse reactions after the third vaccination helped achieve high peak values and maintain humoral and cellular immunity. This information may help promote uptake of a third vaccination, even among those who hesitate due to adverse reactions. Vaccination is a key strategy for combating the COVID-19 pandemic. Messenger RNA (mRNA) vaccines mainly induce humoral and cellular immunity against COVID-19, preventing infection and severe disease2. Since July 2021, the third vaccination has been recommended due to waning vaccine effectiveness6. However, there are numerous challenges to address, including inequality in vaccine distribution7, waning effectiveness8, and adverse reactions after COVID-19 vaccination9. In addition, concerns about adverse reactions, including fever and fatigue, were essential factors in hesitation to receive the third vaccination12. Therefore, considering that the third vaccination is in progress worldwide, evaluating the short- and long-term effects of adverse reactions to COVID-19 vaccination is a crucial public health issue.Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which was first confirmed in China in December 2019, has resulted in 672 million infections and 6.7 million deaths as of January 19, 202313. Notably, it has been suggested that by-products of type I interferons, which guide and promote the adaptive immune response of T and B cells, can cause adverse reactions16. Furthermore, some studies have reported that systemic adverse reactions correlate with humoral immunity21. However, others insist that humoral immunity does not correlate with adverse reactions24, and few studies have investigated the long-term effects of adverse reactions25. To the best of our knowledge, no studies have investigated the relationship between systemic adverse reactions after third vaccinations and the long-term dynamics of cellular immunity.Several studies have been conducted on adverse reactions after COVID-19 vaccination. Adverse reactions can be influenced by age, sex, autoimmune disease, frequency of vaccine administration, injection technique, and use of immunosuppressants and non-steroidal anti-inflammatory drugs (NSAIDs)26. In Japan, a third vaccination was provided in December 2021 to healthcare workers and residents of geriatric facilities. Adults aged 65 years or older and more than 85 million people (67.9%) had received a third vaccination by January 19, 202327. In Fukushima Prefecture, the Fukushima Vaccination Community Survey (FVCS), which is a series of antibody tests against COVID-19, has been conducted since the early phase of the pandemic28. The FVCS conducts antibody testing every 3 months, surveys adverse reactions after COVID-19 vaccination with approximately 2500 people under multi-sectional collaboration between local governments, universities, and hospitals, and reports the findings to the local population36. Therefore, this area is suitable for evaluating adverse reactions and immune dynamics, after third vaccinations, at the community level.In Japan, more than 31 million infected patients and more than 63 thousand deaths have been reported as of January 19, 2023Although the third vaccination is in progress worldwide, information on the relationship between adverse reactions and humoral and cellular immunity after the third vaccination was limited. Therefore, this study aimed to clarify the impact of adverse reactions, particularly systemic adverse reactions, on immune dynamics. We investigated the pattern of systemic adverse reactions after the second and third vaccinations and their relationship with immunoglobulin G against spike 1 protein (IgG(S)) titers, neutralizing antibody (Nab) levels, peak cellular responses, and the rate of decrease after the third vaccination.Among the 2198 participants, the mean age, height, and weight were 53.5 ± 18.3 years, 161.4 ± 9.7 cm, and 61.8 ± 13.7 kg, respectively; and 1267 (57.6%) participants were female  and systemic reactions  were similar. The most reported systemic adverse reactions were fatigue , muscle/joint pain , fever , and headache . Of the 776 (35.3%) patients without systemic symptoms after the second vaccination, 539  had no systemic symptoms after the third vaccination, 138  had one, and 99  had two or more symptoms  Table . SignifiSignificantly, higher Nab at T1 was associated with BMI in Groups 3 and 5–9. Conversely, significantly lower Nab at T1 was associated with age, the interval between the second and third vaccinations, steroids, biologicals, NSAIDs, and immunosuppression. In addition, fold changes in Nab were larger in the group without systemic adverse reactions after the third vaccination . These groups had the lowest geometric mean of Nab at T2 . Fold changes in Nab were smaller in the groups with multiple systemic adverse reactions after the third vaccination .Peak ELISpot at T1 was significantly associated with age, Groups 5–9, and smoking habits. The geometric mean of ELISpot at T2 was the largest in the group with multiple systemic adverse reactions after the third vaccination .We investigated the pattern of systemic adverse reactions after the second and third COVID-19 vaccinations and their short- and long-term relationship with IgG(S), Nab, and ELISpot to clarify the impact of adverse reactions on immune dynamics.21. However, the results of Group 3 were consistent with reports that denied any association between adverse reactions and humoral immunity24. Immune dynamics after the third vaccination may differ depending on the experience and number of systemic adverse reactions.The pattern of systemic adverse reactions was associated with higher peak values of humoral and cellular immunity after the third vaccination. Groups with systemic adverse reactions in both the second and third vaccinations  and multiple systemic adverse reactions only after the third vaccination (Group 6) were associated with significantly higher IgG(S), Nab, and ELISpot in the peak phase (T1). The group with one systemic adverse reaction only after the third vaccination (Group 3) was associated only with peak values of Nab. The results for Groups 5–9 were consistent with previous reports that systemic adverse reactions were associated with higher antibody titers10. Studies examining the relationship between adverse reactions and the long-term dynamics of cellular immunity were limited. However, systemic adverse reactions were predominantly related to achieving and maintaining humoral and cellular immunity. Therefore, this information may help promote uptake of a third vaccination, even for those who hesitate to receive the vaccination because of concerns about adverse reactions.Those who experienced multiple systemic adverse reactions after the third vaccination achieved high levels of humoral and cellular immunity, which was maintained over 3 months. Overall, 930 (42.3%) participants who experienced multiple systemic adverse reactions after the third vaccination  had significantly higher peak values of humoral and cellular immunity, and the fold change in humoral immune levels between T1 and T2 was small. Notably, Groups 6, 8, and 9 had the largest geometric mean of cellar immunity at T2. Previous studies have reported an association between adverse reactions and the long-term kinetics of humoral immunity39. Previous studies have also reported that older patients had smaller antigen-specific memory B cell and antigen-specific memory T cell responses, and reduction of humoral immunity43. People who are less likely to have adverse reactions, including the elderly, may have less antibody and cytokine production by antigen-specific memory B and T cells. Therefore, they may need to continue infection control measures and discuss further vaccination. In addition, identifying those who are more likely to experience systemic adverse reactions and investigating their long-term effects may be crucial in preventing excessive adverse reactions.The group without systemic symptoms (Group 1), who were significantly older than all the groups, had the lowest peak values and largest reduction in humoral immunity and could not induce cellular immunity. Group 1 included 539 (24.5%) participants, who were significantly older than all the groups and significantly more likely to be male than Groups 2, 7, 8, and 9. Older age was significantly associated with lower peaks of Nab and ELISpot after the third vaccination. The result that older participants and men had fewer adverse reactions was consistent with previous reportsThis study had some limitations that should be considered when interpreting the results. First, we used the Wuhan strain of pseudo virus to measure humoral and cellular immunity. Therefore, we could not assess whether efficacy against the mutant strains differed among the groups, making it difficult to consider a fourth and subsequent vaccination. Further research is needed to determine the impact of adverse reactions and immune dynamics on the variant strains of SARS-CoV-2. Second, adverse reactions were self-reported and could not be evaluated based on the severity of each symptom or objective measures. Third, cellular immunity did not significantly change in any of the groups between T1 and T2, making it difficult to discuss the fold change. Despite these limitations, this study was the first to investigate systemic adverse reactions and their short- and long-term relationships to humoral and cellular immunity. They may need to continue infection control measures and discuss further vaccination.In conclusion, systemic adverse reactions after the third vaccination were beneficial in achieving high peak values and maintaining humoral and cellular immunity. This information may help promote uptake of a third vaccination, even for those who hesitate to receive the vaccine because of concerns about adverse reactions. In contrast, the population without systemic symptoms was associated with older age, males, and vulnerability in acquiring humoral and cellular immunity. They may need to continue infection control measures and discuss further vaccination.This was an observational historical cohort study. The study participants were recruited from healthcare workers, government office staff, residents, and nursing home residents in Ishikawa Country, Soma City, and Minamisoma City in Fukushima Prefecture. The recruitment of participants, blood sampling, and questionnaire surveys were conducted in cooperation with hospital groups and municipalities in the central and Soso areas of Fukushima Prefecture. This area has been continually testing for antibodies in healthcare workers and residents since 2020 to identify their infection status and control infections.The inclusion criteria were as follows: (i) completed the third COVID-19 mRNA vaccination (BNT162b2 or mRNA-1273); (ii) completed blood sampling at T1 and T2. Overall, 2368 individuals met the criteria for (i) and (ii). We excluded (iii) 114 patients without a record of adverse reactions after the second or third vaccination and (iv) 56 patients who self-reported COVID-19 by June 2022 (T2). Finally, 2198 participants were included in the study. The ethics committees of Hirata Central Hospital (number 2021-0611-1) and Fukushima Medical University approved this study (number 2021-116). This study conformed to the Code of Ethics of the World Medical Association (Declaration of Helsinki). Written informed consent was obtained from all participants. This study was supported by the Japan Agency for Medical Research and Development and was conducted as part of the FVCS.We collected information from participants on age, sex, weight, height, alcohol consumption, smoking habits, medications, comorbidities, second and third vaccination types, and adverse reactions after the second and third vaccinations from a questionnaire survey. Regarding adverse reactions, the patients were asked about local pain, fever, headache, muscle/joint pain, diarrhea, nausea, and dizziness. Of the 2198 individuals who met the study eligibility criteria, at T1, 2195, 2160, and 1047 completed measurements for IgG(S), Nab, and ELISpot, respectively; at both T1 and T2, 2041, 2039, and 979 completed measurements for IgG(S), Nab, and ELISpot, respectively . Blood samples were transferred from the hospital to LSI medicine within the blood sampling day; subsequently, all tests were performed in LSI medicine as per official guidelines. Effector T- cells generating interferon-gamma were counted as spots on the wells. The results were assessed by comparing the positive and negative control wells. The number of spots was counted up to 50; thus, more than 50 spots were shown as 50 and over. In addition, more than seven spots was evaluated as reactive, 5, 6, and 7 spots were evaluated as borderline, and fewer than five spots were evaluated as not reactive and complied following the official guidelines. The target antigen of ELISpot was the spike protein.45. The cutoff values were determined using the receiver operating characteristic curve method. For Nab, values > 800 AU/mL were not guaranteed to be accurate, according to the manufacturer’s instructions. Testing was performed according to the official guidelines. Quality evaluations were conducted daily before starting measurements.Serological assays for IgG(S) and Nab were performed using the chemiluminescence immunoassay with iFlash 3000  and iFlash-2019-nCoV series  as reagents at Tokyo University. A chemiluminescence immunoassay is used to quantitatively determine humoral immunity in human serum using an enzyme and chemiluminescence. The cutoff value of each assay  was 10 AU/mL, following the manufacturer’s official cutoff valuesWe classified the participants into nine groups according to the number of systemic adverse reactions , after the second and third vaccinations, to investigate the effect of adverse reactions on immune dynamics Table . SystemiWe compared the participants’ characteristics using descriptive statistics according to the nine patterns of systemic adverse reactions after the second and third vaccinations. Based on the BMI of the participants, < 18.5 was defined as thin, 18.5–25 as normal, and ≥ 25 as overweight. Categorical variables  were summarized as frequencies, and continuous variables  were summarized as means and standard deviations. First, a multinomial logistics regression analysis was performed to determine whether participants’ backgrounds differed among the nine groups based on the group without systemic adverse reactions (Group 1). Independent variables included sex, age, BMI, vaccine type, smoking habits, alcohol consumption, medications, and comorbidity in Groups 2–9. Second, a multiple regression analysis was performed to examine the relationship between the peak of immunity after the third vaccination and the pattern of systemic adverse reactions. The dependent variables were the values of IgG(S), Nab, and ELISpot at T1, and the independent variables included sex, age, BMI, type of third vaccination, Groups 1–9, the interval between the second and third vaccinations, smoking habits, alcohol consumption, medications, and comorbidities. Third, the geometric means of IgG(S), Nab, and ELISpot for each group were reported at T1 and T2. The fold change between the two-time points was calculated for the nine groups. Fourth, the participants’ ages were divided by 10 years, and the percentage of those who experienced one, or more than two, systemic adverse reactions after the second and third vaccinations was calculated for each weight and BMI. An IgG(S) antibody titer over 5000 arbitrary units per milliliter (AU/mL) was defined as 5000 AU/mL and Nab over 800 AU/mL was defined as 800 AU/mL for all analyses. Statistical significance was set at p < 0.05. All statistical analyses were performed using STATA/IC  and Python (version 3.7.12).Supplementary Information.


In observational studies, the methods used to measure medication adherence may affect assessments of the clinical outcomes of drug therapy. This study estimated medication adherence to multidrug therapy in patients with hypertension using different measurement methods and compared their impacts on clinical outcomes.with≥1), PDC with a duration weighted mean (PDCwm), and the daily polypharmacy possession ratio (DPPR). The primary clinical outcome was a composite of cardiovascular and cerebrovascular disease-specific hospitalizations or all-cause mortality.This was a retrospective cohort study using the Korean National Health Insurance Service–National Sample Cohort database (2006-2015). Adults diagnosed with hypertension who initiated multidrug antihypertensive therapy in the index year 2007 were included. Adherence was defined as over 80% compliance. Adherence to multidrug antihypertensive therapy was measured in 3 ways using the proportion of days covered (PDC) with 2 approaches to the end-date of the study observations: PDC with at least one drug  primary outcomes varied from 1.38  to 1.44 .Non-adherence to multidrug antihypertensive therapy was significantly associated with an increased risk of a primary clinical outcome. Across the varying estimates based on different methods, medication adherence levels were similar. These findings may provide evidence to support decision-making when assessing medication adherence. Adherence to medications is defined as the extent to which a patient takes medications according to the prescribed dosage, frequency, and duration -4. Non-aHypertension, one of the most common chronic diseases worldwide, is particularly challenging because patients require long-term medications to maintain their blood pressure within the target range and to prevent clinical consequences such as cardio-cerebrovascular disease (CVD) . In Korewith≥1) and PDC with a duration-weighted mean (PDCwm) [To improve adherence to multidrug therapy, it is necessary to standardize the methods used to measure adherence. However, there are 2 barriers to measuring adherence. First, numerous studies have used different adherence measurements. Currently, the proportion of days covered (PDC) is suggested by the Pharmacy Quality Alliance as the preferred measurement for adherence to antihypertensive medications . The val (PDCwm) -19. Rece (PDCwm) ,20. Howe (PDCwm) ,21,22. Owith≥1 could be the preferred option. We also found that the end-date of the study’s observation period, as with prescription-based methodologies (PxMs) or fixed period-based methodologies (FxMs), may influence the measurements of adherence. However, these findings relied on samples that used both single-drug and multidrug regimens, and we did not study them separately. Therefore, the present study aimed to compare 3 measurements of adherence, including PDCwith≥1, PDCwm, and DPPR, across 2 approaches to defining the end-date of the study observations (PxM and FxM) in patients receiving multidrug antihypertensive therapy and investigate their clinical consequences.In a previous study we compared the 3 abovementioned measurements of adherence to antihypertensive medications and developed an algorithm of 8 different approaches incorporating 25 alternative assumptions . We suggThe Korean National Health Insurance Service (NHIS) provides coverage for over 97% of the Korean population . The KorIn this nationwide retrospective cohort study, we included patients aged > 20 years who had a diagnosis of hypertension and at least 2 prescriptions for antihypertensive medications  from 2 or more classes of drugs in the index year 2007. We included 5 classes of antihypertensive medications: angiotensin-converting enzyme inhibitors, angiotensin receptor blockers, beta-blockers, calcium channel blockers, and diuretics. The index date was the date of the patient’s first prescription for antihypertensive medications. We assessed adherence to the medications for 3 years from the index date . The folThe aim of this study was to measure the patients’ adherence to multidrug antihypertensive therapy. Multidrug antihypertensive therapy was defined as 2 or more medications from the 5 drug classes, prescribed from the index date for a period of 3 years to reflect chronic treatment. We categorized study participants into 2 groups with a cut-off value of 80%: adherent patients (≥ 80%) and non-adherent patients (< 80%) .with≥1, PDCwm, and DPPR, which had been analyzed in our previous study [with≥1 was calculated as the number of days covered by ≥ 1 prescription during the observation period, divided by the number of days from the first prescription to the end of the observation period [wm was calculated as the sum of values obtained by multiplying the PDC of each drug class by its duration weight, divided by the sum of the weights [To compare the measurements of adherence to multidrug antihypertensive therapy and their clinical consequences, we included PDCus study -20,22. Fn period ,19. Seco weights ,18. Thir weights ,20. DetaIn addition, we compared 2 approaches (PxM and FxM) to defining the end-date of the study, which had been shown to be a potentially significant factor when measuring adherence in previous studies ,26-28. TThe primary outcome was a composite of CVD-related hospitalizations and all-cause mortality. CVD was defined as a code for ischemic heart disease (ICD-10 codes I20-I25), cardiovascular diseases , stroke (ICD-10 codes I60-I64), and cerebrovascular diseases  . The secThe baseline characteristics included sex, age, disability, type of health insurance, socioeconomic status, type of medical institution, type of multidrug therapy, Charlson comorbidity index (CCI) score, and history of diabetes mellitus or dyslipidemia within 1 year prior to the index date . CategorIn the main analysis, we presented mean (standard deviation) and median  values for adherence levels and the numbers of adherent patients. We used Cox proportional hazards analysis to model the time to the primary outcome as a dependent variable, and the measurement of adherence as the independent variable. We adjusted the model for sex and age, and fully adjusted it for sex, age, disability, type of health insurance, socioeconomic status, type of medical institution, type of multidrug therapy, CCI score, and history of diabetes mellitus or dyslipidemia. To test the goodness-of-fit for the risk models in survival analyses, we compared the Akaike information criterion (AIC) and concordance indexes including Harrell’s and Uno’s C-index. The AIC was used to evaluate predictive performance. While Harrell’s C-index has been shown to have an upward bias if a large volume of patients is censored, Uno’s C-index has shown less bias in such situations. These C-indexes were used to assess differences between the unadjusted base model and the models adjusted for the 6 measurements of adherence. A C-index of > 0.7 was considered a good model and higher estimates when compared to the base model were considered better. A p-value of < 0.05 was considered to indicate statistical significance. All data analyses were conducted using SAS version 9.4 .This study protocol was approved by the Institutional Review Board (IRB) of Ajou University, Korea (201912-HB-EX-001). Informed consent was waived by the IRB.with≥1, PDCwm, and DPPR are shown in Among the study population of 4,226 patients who had a diagnosis of hypertension and initiated multidrug antihypertensive therapy in 2007, 47.4% were female, 37.2% were > 60 years old, 7.4% had a disability, 94.4% had National Health Insurance, and 37.9% had a high-income level. A total of 3,273 patients (77.4%) took 2 antihypertensive medications and 22.6% took ≥ 3 medications. Approximately 15.4% and 31.0% had a history of diabetes mellitus and dyslipidemia, respectively . A compawith≥1, PDCwm, and DPPR showed that, according to PxM and FxM, a total of 2,739 (64.8%) and 2,506 (59.3%), 2,440 (57.7%) and 2,240 (53.0%), and 2,720 (64.4%) and 2,493 (59.0%) patients were adherent, respectively. The mean values of adherence varied from 72.7% to 79.8%  of 1.38  to an HR of 1.44 . The estimates of adherence related to the risks of all-cause mortality were from  to . The risks of CVD-related mortality varied from  to . The risks of CVD-related hospitalization differed from  to  . In the with≥1, PDCwm, and DPPR) across 2 approaches to defining the end-date of observations (PxM and FxM), and their comparative clinical outcomes. This study found that (1) the adherence estimates of PDCwith≥1, PDCwm, and DPPR were similar, (2) these trends were consistently shown in both the PxM and FxM approaches, and (3) non-adherence was associated with a 40% increased risk of a primary outcome. To the best of our knowledge, this is the first study to compare the different approaches for measuring adherence to multidrug antihypertensive therapy over a 3-year period and to identify their clinical outcomes using a Korean nationwide claims database.This population-based cohort study compared the 3 measurements of adherence to multidrug antihypertensive therapy , type of multidrug therapy (≥ triple therapy), and the presence of diabetes or dyslipidemia, which were negatively related to non-adherence, and positively related to the study outcomes of mortality or hospitalization -7. These0 years, with≥1 showed similar results to the DPPR. Thus, the more traditional and simplest method, the PDCwith≥1, might be an alternative option to the more current and complex method, the DPPR, when measuring adherence to antihypertensive multidrug therapy.Although no gold standard for measuring adherence to multidrug therapy is currently available, DPPR is the most recent and theoretically the optimal measurement of adherence to multidrug therapy . This meThis study had several strengths. First, we demonstrated standardized ways of comparing and validating different measurements and approaches when assessing adherence to multidrug antihypertensive therapy in patients with hypertension. Second, this study affirmed the robustness of a higher risk for primary clinical outcomes by comparing non-adherence to adherence across the different adherence measurements.Our study also had limitations. First, the use of a claims database, which collects data for reimbursement and not for research purposes, limited our ability to validate whether patients actually took their medications as prescribed. However, our findings may help identify a reasonable range of medication adherence and the related clinical outcome risks according to how the adherence is measured. Second, as this study focused on measuring adherence to individual classes of antihypertensive medications, we were unable to compare fixed-dose combination pills to single-medication pills. Because adherence to medication is a complex phenomenon that requires comprehensive assessment, the current study needs to be expanded further. Third, due to the unique characteristics of the Korean NHIS, which is a unified nationwide single payer, we were able to conduct uninterrupted follow-up of the participants and measure their adherence to the chronic use of multidrug antihypertensive therapy. In addition, the Korean NHIS provides a relatively stable health system, which might influence medication adherence. Further studies in different health systems are necessary to validate our findings. Lastly, adherence might vary according to different types of medications and their regimens. Additional research is warranted to study adherence to other medications that are commonly used in chronic and multidrug therapy such as antidiabetic drugs.with≥1, PDCwm, and DPPR measurements across 2 approaches to defining the study’s end-date (FxM and PxM). This study proposes the potential utilization of PDCwith≥1 as a broad, practical, and reliable method, instead of other more complicated measurement options, in patients with hypertension.In conclusion, the associations between non-adherence to multidrug antihypertensive therapy and the higher risk of primary clinical outcomes were largely consistent when applying the PDC


Mesenchymal stromal/stem cells (MSCs) are multipotent cells that reside in multiple tissues are capable of self-renewal and differentiation into various cell types. These properties make them promising candidates for regenerative therapies. MSC identification is critical in yielding pure populations for successful therapeutic applications; however, the criteria for MSC identification proposed by the International Society for Cellular Therapy (ISCT) are inconsistent across different tissue sources. This study aimed to identify potential markers to be used together with the ISCT criteria to provide a more accurate means of MSC identification. Thus, we carried out a computational comparative analysis of the gene expression in human and mouse MSCs derived from multiple tissues to identify the differentially expressed genes that are shared between the two species. We show that six members of the proteasome degradation system are similarly expressed across MSCs derived from bone marrow, adipose tissue, amnion, and umbilical cord. Additionally, with the help of predictive models, we found that the expression profile of these genes correctly validated the identity of the MSCs across all the tissue sources tested. Moreover, using genetic interaction networks, we showed a possible link between these genes and antioxidant enzymes in the MSC antioxidant defense system, thereby pointing to their potential role in prolonging the life span of MSCs. According to our findings, members of the proteasome degradation system may serve as stemness-related markers. Unfortuin vitro . As a rein vitro . Proper in vivo  and one or two regulatory particles (19S). The 20S core comprises four rings, two of which are composed of seven alpha subunits, while the other two rings are composed of seven beta subunits. The 19S regulator is comprised of a base (containing six ATPase and two non-ATPase subunits) and a lid (containing up to 10 non-ATPase subunits) . The proIn this study, we carried out a comparative analysis of publicly available RNA-Seq data of MSCs derived from different tissues of origin  and various species  to yield a list of common differentially expressed genes (DEGs). We provide evidence that members of the proteasome show similar patterns of expression across all MSC samples. Furthermore, we offer a possible relationship between the proteasome and antioxidant enzymes in protecting MSCs from oxidative stress, highlighting their importance in MSC survival. Finally, we demonstrate that six proteasomal degradation systems can be used as supplementary stemness-related markers for MSC identification through predictive models.RNA-seq datasets were obtained from the Gene Expression Omnibus (GEO)  and ArraWe used Kallisto version 0.46.1 for pseudo-alignment and the quantification of abundances of transcripts from the RNA-Seq data . Human dSleuth loaded the Kallisto processed data, estimated the parameters for its response error measurement “full” model followed by the estimation of the parameters for its reduced model, and performed differential analysis using the likelihood ratio test. Sleuth normalizes the data by its ability to distinguish between technical and biological variance and performs shrinkage to the model only on the biological component of variance. Sleuth accounts for technical variability in the abundance estimates and models the true abundance using a general linear model, while including the technical variance as error in the response variable. Thereby, distinguishing between technical or biological sources of variance when determining differentially expressed transcripts.http://www.ncbi.nlm.nih.gov/homologene/) and the analysis repeated. Venn diagrams of the common DEGs were constructed using Venny version 2.0 (www.r-project.org)  with a q value less than 0.05.This step generated lists of significant DEGs for each tissue type and species. The lists included the gene symbols of significant DEGs and their corresponding TPMs . The gension 2.0 . Finallyect.org) .p-value threshold was set at 10e-3. Afterward, we visualized the enriched GO terms using ReViGO, and a scatter plot was produced showing the log10 p-value and log size of each GO term  to automatically find the best classification model for our provided dataset AutoWEKAClassifier performed 486 evaluations of its available classifiers and found random forest to be the best classifier with the best error rate for this dataset. The random forest tree classifier was used to train the model with 10-fold cross-validation method. Briefly, this method randomly divided the dataset into 10 parts; it used nine for training and reserved one for testing. This procedure is repeated multiple instances each time reserving a different part for testing. After training the Random Forest model, we created the testing dataset from the training dataset by hiding the type class in order to test its performance in predicting tissue type in both human and mouse samples. WEKA was also used for attribute selection.Finally, to assess the ability of the selected DEGs’ to identify MSCs, we built a predictive model using the Waikato Environment for Knowledge Analysis (WEKA) software version 3.8.4 . The genWe constructed clustering maps using the t-distributed stochastic neighbor embedding (t-SNE) statistical method to visualize the transcriptomic similarities and differences between the samples. The clustering maps showed that all human MSC samples clustering together in a distinct cluster apart from tissue-specific cell (TSCs) samples . LikewisSimilarly, for the mouse datasets, we compared the gene expression of mouse-derived MSCs with their tissue-specific counterparts and identified 14,843 DEGs for m_AT_MSCs and 12,783 DEGs for the m_BM_MSCs. We found that the common DEGs made up 78.8% of the examined DEGs, equivalent to 12,178 common DEGs . Finallyp-value of 10–3  and uniqueness. Following visualization, we identified a unique GO term (GO:2000736) to regulate stem cell differentiation with a significant p-value of 8.69E-4 and a q value of 4.64E-2. Moreover, this GO term had a frequency of 0.010% and a uniqueness score of 0.70  terms with a threshold  of 10–3 . ReViGO  of 0.70 . Other G of 0.70 .p-value of 6.27E-03 and a fold change of 0.136934557 in m_AT_MSCS, while in m_BM_MSCs, it had a p-value of 9.79E-03 and a fold change of –0.224675507. Since most genes were upregulated, we attempted to further explore the interplay between these genes and other systems that assist the proteasome in antioxidant defense, namely, antioxidant enzymes.Now that our attention was drawn to these 23 genes, we wanted to take a closer look at their behavior. First, we inspected their expression patterns in both species. We found that the 23 genes appeared to be upregulated in both the human and mouse MSC samples, except for PSMD4, which was downregulated in the mouse MSC samples compared to the TSC samples . PSMD4 hConsequently, members of the superoxide dismutase, glutathione peroxidase, peroxiredoxin, thioredoxin, and peroxidasin families were cross-referenced against the 1,583 common DEGs identified earlier. We found SOD3, GPX7, GPX8, PRDX2, PRDX4, TXN2, and PXDN were present in our list of common DEGs. The majority of the transcripts of these genes were upregulated but not all. Out of the seven genes, four  were upregulated in all analyzed MSC samples. Each of the other three  was upregulated in all the MSC samples except human AT-MSCs and BM-MSCs and mouse BM-MSCs, respectively .Next, we wanted to shed more light on the interaction between these antioxidant enzymes and the 23 members of the proteasomal degradation system identified earlier. We employed the help of Cytoscape and the Genemania database to understand the interplay between these antioxidant enzymes and the proteasomal genes. A gene interaction network was generated, and it showed all 23 genes of the proteasomal degradation pathway were co-expressed together and co-expressed with the antioxidant genes. Specifically, it showed PSMA7 to be co-expressed with GPX7, which in turn was co-expressed with GPX8, SOD3, and PXDN. Additionally, PRDX2 was co-expressed with PSMA7, PSMB3, PSMB6, PSMB7, PSMC4, PSMD3, and PSMD8. Furthermore, TXN2 was co-expressed with PSMA1, PSMA7, PSMB3, and PSMB6. Finally, PRDX4 was co-expressed with PSMA5, PSMB1, PSMB2, PSMB5, PSMB6, PSMC1, PSMC2, PSMC5, PSMD1, PSMD8, and PSMD14 .Finally, we wanted to test the genes’ efficiency in predicting the identity of MSCs across the different tissue sources in both human and mouse species. To test this hypothesis, AutoWEKAClassifier performed 486 evaluations of available classifiers and found random forest to be the best classifier with the best error rate. The random forest tree classifier was used to train the model with 10-fold cross-validation, and the trained model was finalized. The final model was loaded to test its performance in predicting stem cell type on the testing data. We used the upregulated proteasomal genes as attributes, and we removed PSMD4 from the list since it had inconsistent expression across both species. We proceeded with the other 22 genes and ran the random forest model. The model tested the data 40 times and showed that MSCs were correctly classified in all 40 instances. To test whether all 22 genes contributed equally to the classification process, we ran the gain ratio attribute selection evaluator in WEKA. We found six genes to be the top contributors in the classification: PSMB5, PSMB1, PSMD14, PSMC4, PSMA1, and PSMD8 . We repeEver since the discovery of MSCs by In this study, we integrated the RNA-seq data of MSCs derived from four different human tissues  and two different mouse tissues . Differential expression analysis presented us with a list of 1,583 DEGs common to MSCs and TSCs across all tissue types in humans and mice. Further gene ontology enrichment analysis categorized these genes into GO terms, one of which was the GO term for regulating stem cell differentiation.GO terms such as (GO:0055114) involved in oxidation-reduction process and GO term (GO:0006123) involved in mitochondrial electron transport, cytochrome c to oxygen were present in our results, however, they had higher frequencies than the GO term (GO:2000736) for stem cell regulation, which were 0.172%,0.044% and 0.010% respectively. Since a higher frequency denotes a more general term, we focused on the GOterm (GO:2000736) for stem cell regulation due to its uniqueness, high significance, and its specificity to stem cell processes. It included 23 members of the proteasome degradation system. Compelling evidence suggests a pivotal role for the proteasome in maintaining the pluripotency of mouse and human embryonic stem cells by supporting the clean-up of proteins oxidatively damaged during differentiation .The proteasome is an essential component of protein quality-control systems and plays a critical role in cellular homeostasis. It is involved in the degradation of abnormal, oxidized, or otherwise damaged proteins . The accFurthermore, we show that members of the 19S proteasome base  and lid  are also differentially expressed and upregulated in MSCs in comparison with TSCs. However, our results also showed that PSMD4 expression in mouse MSC samples was downregulated. PSMD4’s central role in the 19S lid is to recognize polyubiquitinated protein substrates and detach the ubiquitin molecules from them for their subsequent degradation through the 26S proteasomal pathway . PSMD4 iStudies have shown that during MSC proliferation, ROS are produced as byproducts of oxidative metabolism. However, increased ROS levels may lead to a decrease in cell survival and have also been implicated in cell senescence . To counFinally, to show the competence of these proteasome genes in validating the identity of MSCs, we employed the aid of predictive models. Predictive models have been used robustly to identify a general MSC phenotype that could distinguish MSCs from other cell types. A recent study showed that gene expression levels in prediction models increase the classification accuracy of the combined set of traditional MSC cell surface markers . Using tOur study carried out a comprehensive comparative analysis of MSCs RNA-seq data across two species and six different tissue types to ascertain potential identity markers. Our results showed that six members of the proteasomal machinery are promising candidates for validating the identity of MSCs. Moreover, we shed a light on their association with antioxidant enzymes in defending MSCs against high ROS levels, thereby maintaining their proliferation and self-renewal. These six genes can be used as additional stemness-related markers to refine and enhance the accuracy of MSC identification, which is a critical step in ensuring the yield of a pure population for consequent applications in regenerative therapies.Our prediction model is based only on MSCs and TSCs data for its training and testing sets; experimental validation of the expression levels of the six proposed stemness-related markers in MSCs from different sources, both on the RNA and protein levels, is crucial to confirm their efficiency in identifying MSCs. Experimental validation is beyond the scope of this study; however, it should be the focus of future confirmatory studies.


Tb has coordination chemistry similar to that of Lu; therefore, like 177Lu, it can stably radiolabel DOTATOC, one of the leading peptides used for the treatment of neuroendocrine neoplasms. However, 161Tb is a recently developed radionuclide that has not yet been specified for clinical use. Therefore, the aim of the current work was to characterize and specify 161Tb and to develop a protocol for the synthesis and quality control of 161Tb-DOTATOC with a fully automated process conforming to good-manufacturing-practice guidelines, in view of its clinical use. Methods:161Tb, produced by neutron irradiation of 160Gd in high-flux reactors followed by radiochemical separation from its target material, was characterized regarding its radionuclidic purity, chemical purity, endotoxin level, and radiochemical purity (RCP) in analogy to what is described in the European Pharmacopoeia for no-carrier-added 177Lu. In addition, 161Tb was introduced into a fully automated cassette-module synthesis to produce 161Tb-DOTATOC, as used for 177Lu-DOTATOC. The quality and stability of the produced radiopharmaceutical in terms of identity, RCP, and ethanol and endotoxin content were assessed by means of high-performance liquid chromatography, gas chromatography, and an endotoxin test, respectively. Results:161Tb produced under the described conditions showed, as the no-carrier-added 177Lu, a pH of 1–2, radionuclidic purity and RCP of more than 99.9%, and an endotoxin level below the permitted range (175 IU/mL), indicating its appropriate quality for clinical use. In addition, an efficient and robust procedure for the automated production and quality control of 161Tb-DOTATOC with clinically applicable specifications and activity levels, that is, 1.0–7.4 GBq in 20 mL, was developed. The radiopharmaceutical’s quality control was also developed using chromatographic methods, which confirmed the product’s stability (RCP ≥ 95%) over 24 h. Conclusion: The current study demonstrated that 161Tb has appropriate features for clinical use. The developed synthesis protocol guarantees high yields and safe preparation of injectable 161Tb-DOTATOC. The investigated approach could be translated to other DOTA-derivatized peptides; thus, 161Tb could be successfully applied in clinical practice for radionuclide therapy. It was approved by the European Medicines Agency in 2017 and the U.S. Food and Drug Administration in 2018  for the treatment of well-differentiated gastroenteropancreatic NENs     has emerged as an option in metastatic or nonresectable neuroendocrine neoplasms (NENs) expressing high levels of somatostatin receptor ,2. DOTATtic NENs ,6. Despiic NENs ,,7,8. The 154 keV 0%; half- 6.65 d)  because purposes  12,13).,13.,177Lurposes ,; therefoively) 9,,13, whicy) 9,,13,. 161Tb plimators ; a firstlimators . The nextudies 4,. One pur161Tb was produced by neutron irradiation of enriched 160Gd2O3 targets , via the 160Gd161Gd→161Tb nuclear reaction at the South African Fundamental Atomic Research Installation  or the Institut Laue-Langevin’s High Flux Reactor (∼1·1015 n·cm−1·s−1 neutron flux). The quartz ampoule, containing the irradiated target material, was processed at the Paul Scherrer Institute for radiochemical separation of the produced 161Tb from the target material, as described previously  monograph (eviously . An appronograph .161Tb were examined by γ-spectrometry using a high-purity germanium detector (Canberra), in combination with the InterWinner software package . The choice of γ-rays used for a reliable radionuclide identification accounted for possible interferences from other radionuclides that could create artifacts . The pH of the 161TbCl3 solution was assessed by means of pH paper.The identification and radionuclidic purity of 161TbCl3 solution was assessed via inductively coupled plasma–mass spectrometry . Four batches of 161Tb were analyzed after the radiochemical purification for contents of Cu, Fe, Pb, Zn, Gd, and Tb, as described in the Ph. Eur. monograph for 177Lu (161Tb productions performed during this study by means of apparent molar activity (AMA) determination through hand-operated labeling of the product to DOTATOC , as described elsewhere  (161Tb (maximum volume at the end of separation).Because terials) . In the 161Tb batches was assessed by thin-layer chromatography, as described in the Ph. Eur. monograph for 177Lu  (161Tb in ionic form (Rf = 0.4–0.7), over the total activity.The RCP of 3 terials) . Quantif161Tb-DOTATOC were performed using a Modular-Lab PharmTracer fully automated cassette synthesis system , which provided a graphical display of each step and the progress of the synthesis together with audit trail as required by GMP .,27.161Tb by GMP ,. Aliquot161Tb-DOTATOC, as well as the identification of the compound and possible impurities, by means of HPLC (Dionex P680 LGP Pump). First, the retention times of the product peaks in the radiochromatogram and UV chromatogram were assessed . Eventually, during the QC of 161Tb-DOTATOC, 20 μL of the synthesis product were analyzed by means of HPLC to estimate the RCP of 161Tb-DOTATOC from the HPLC radiodetector chromatogram and to identify the compound and possible impurities in the UV chromatogram.QC was performed to determine the RCP of Further evaluations of the method were performed to investigate the suitability and robustness of the QC. In particular, the linearity, limit of detection, and limit of quantification for both the radiodetector and the UV detector were assessed , together with the setup of a system suitability test .The ethanol content in the synthesis product was assessed by means of gas chromatography. The quantification of ethanol was based on the acetonitrile internal standard . The linearity of the gas chromatography method was assessed by linear regression analysis for 6 ethanol concentrations between 1% and 15% .161Tb-DOTATOC. Pyrogens were tested according to Ph. Eur. standards with a chromogenic Limulus amebocyte lysate test, as described earlier for the endotoxin test on the 161TbCl3 solution  . The bub161Tb-DOTATOC, was tested in both normal and stress conditions. To establish the stability of the radiolabeled peptide, all synthesized batches of 161Tb-DOTATOC were stored at room temperature and the RCP was assessed 3 and 24 h after synthesis. In addition, 1 batch of DOTATOC radiolabeled with high activity (8.77 GBq) was stored at room temperature for 96 h and the RCP was assessed by means of HPLC every 24 h with the method described earlier. After 96 h at room temperature, the product was incubated at 40 °C for an additional 72 h and HPLC analyses were conducted every 24 h.The stability of the synthesis product, 161Tb were analyzed, compared with the Ph. Eur. monograph for 177Lu, and found to comply with all requirements  (161Tb activity at end of bombardment (EOB), as reported previously by our group . Therefore, the specification for radionuclidic purity, which must apply until the end of shelf life, was prudently proposed for 160Tb at a level of 0.1%. With this level of impurity, the additional radiation dose to the patient was estimated to be about 0.4%  (The content of the long-lived impurity  72.3 d)  was lessur group . Three wterials) .161Tb samples was far below the limit for metal impurities reported for 177Lu . The Gd content was less than 0.4 ppm. Moreover, 6 of the 7 161Tb productions were tested for AMA and successfully labeled DOTATOC at 100 MBq/nmol. Even so, the 161Tb production that allowed labeling of only the lower AMA was sufficiently pure to be used for 161Tb-DOTATOC synthesis  over the total activity  was above 99.0%, thereby allowing the RCP of 161Tb to be established as greater than 99.0%.In all productions tested, the peak of 161Tb-DOTATOC were performed and form part of this work. For all syntheses, the 161Tb-labeled radioactivity at the shelf-life expiration date was in the range from 1 to 7.4 GBq ± 10%, which was the target to obtain a product comparable to a patient dose for 177Lu-DOTATOC . Unlike the bulk product and initial activity measurements, which were performed with appropriate calibration for each container , the restermined .The synthesis product complied with the established specifications .161Tb-DOTATOC was, on average, approximately 98% at the end of synthesis , which meets the 95% specification level normally applied to these types of products .,34.161TbThe UV chromatograms showed low levels or complete nonappearance of impurities. The peaks of the unlabeled peptide (DOTATOC), together with those of Tb-DOTATOC, Fe-DOTATOC, and Zn-DOTATOC, established during the development of the HPLC method, were identified during the QC of the synthesis product and considered product peaks .The ethanol content in the synthesis product was demonstrated to be less than 10%, which is the acceptable limit for radiopharmaceutical preparation for intravenous injection . In part,The synthesis resulted in a clear, colorless, particle-free product with a low level of bacterial endotoxins in the batch tested . This endotoxin content was compliant with the specification for radiopharmaceutical products defined by the Ph. Eur. monograph  ,33.Although the sterility of the final product requires further validation, the integrity of the sterilization filter used at the end of the synthesis was successfully proven with a bubble point test. In particular, the bubble point was less than 2.75 bar, as specified for the product matrix .161Tb-DOTATOC presented RCP of more than 95% up to 24 h for all batches tested . In addition, the RCP results of the batch selected for the stability test under stress conditions indicated that 161Tb-DOTATOC was highly stable at room temperature for up to 4 d  160Tb nuclear reaction, and the multistep reactions on the residual 159Tb, 158Gd, and 157Gd present as impurities in the target material. However, the content did not exceed 0.005% of the total 161Tb activity at EOB. Three weeks after EOB, the ingrowth of 160Tb was still no more than 0.03%. These results were observed in several productions after the irradiation of various amounts of target material with different neutron fluxes. According to previous studies by our group   was founur group , the quaed 177Lu . Becausearanteed . Howeverate test , resulti161Tb-labeled radiopharmaceuticals can easily be produced and made GMP-compliant for routine clinical use. The production, including synthesis and QC, of 161Tb-DOTATOC, the 161Tb equivalent of the somatostatin analogs currently used with 177Lu in clinical practice  and the NET Research Foundation Petersen Award. No other potential conflict of interest relevant to this article was reported.


BackgroundExcessive daytime sleepiness (EDS) can be measured by the Epworth Sleepiness Scale (ESS) and has been shown to be prevalent in the United States. This study aimed to survey the levels of EDS in US adults and explore factors related to sleep hygiene.MethodsAn anonymous web-based survey was conducted, polling the frequency of hypersomnia symptoms, sleep quality, and time spent in their occupation. Respondents were at least 21 years of age and United States residents. Participants reported information related to age, gender, education, income, and race in addition to their responses to the survey questions. All data were analyzed using JMP 16.0.ResultsIn our cohort of 200 adults, the median age was 40, with an interquartile range of 34-50 years. 48% were female. More than half of the cohort (53%) had severe or higher-normal EDS. Race (p=0.112), sex (p=0.426), age (p=0.063) shift work/shift timing (p=0.260), and screen time before bed (p=0.785) were not statistically significant for excessive daytime sleepiness. However, the length of participants’ workdays (p=0.001) and their income levels (p=0.008) were found to be significantly associated with EDS. In other words, longer workdays and lower income levels were associated with an increased likelihood of falling asleep during inactive periods of the day.ConclusionSleep deprivation and excessive daytime sleepiness are intertwined with sleep hygiene. This study highlights some of the possible issues that could lead to potential solutions. Sleep is beneficial in many ways to the human body and is necessary for survival. Although its exact function is still unconfirmed, sleep has been demonstrated to be essential for learning and memory consolidation, energy conservation, and the removal of toxins from the brain . Proper Excessive daytime sleepiness (EDS) can be the result of a sleep disorder or an inadequate amount of sleep. Examples of sleep disorders include obstructive sleep apnea, restless leg syndrome, narcolepsy, insomnia, and parasomnias . On the The development of EDS can be measured by the Epworth Sleepiness Scale (ESS). This scale is an eight-question survey that calculates how much daytime sleepiness someone has based on their answers to everyday scenarios. Participants can answer on a four-point scale per question, and scores are cumulatively tallied to give a score between 0 and 24, with different levels of daytime sleepiness indicated by the person’s score . There aThis study aimed to survey 200 participants about sleep quality to better understand the incidence of poor sleep, sleep disorders, and factors potentially affecting sleep.The study was conducted via an anonymous web-based survey in June 2022, using a survey research platform that uses organic sampling based on random device engagement. Through the platform’s integrated algorithms, single users on multiple accounts are excluded to ensure each response represents a unique individual participating in the study. The delivery of surveys to real consumers in targeted demographics occurs while they are organically engaged with the apps on their devices. While no personally identifiable information is collected, global information pertaining to age, gender, education, income, location (state), and race is obtained.To be considered eligible for the study, participants were required to be 18 years of age or older and live in the United States. No further screening criteria were used, and the survey was administered in English. The study aimed to poll the frequency of hypersomnia symptoms, sleep quality, and time spent in respondents' occupations. The first eight survey questions asked participants about their level of sleepiness at various parts of the day, including periods of inactivity. These questions were based on the ESS, which is a validated subjective scale to measure daytime sleepiness in typical life situations. It is graded on a scale from 0 to 42. The following two questions asked participants about their regular sleeping and waking times and time spent viewing a screen, including a smartphone, television, and computer. The remaining survey questions were related to sleep medication use and the timing of work. This survey study did not use a power analysis (ad hoc) for a specific minimum sample size. All data collected from the survey were analyzed using JMP 16.0. The significance threshold was alpha = 0.05, and all confidence intervals were 95%. The study was deemed exempt by our institutional review board, study # 2023-423.There were 200 survey respondents in the study, of whom 106 (53%) were male and 94 (47%) were female. Study participants had a median age of 40 years, an interquartile range of 33-50, and a range of 25-76 years. By background, 134 (67%) individuals identified as White/Caucasian, 27 (13.5%) as Black/African/African American, 16 (8%) as Hispanic/Latino, 4 (2%) as Asian, and 19 (9.5%) indicated other. Income levels in the cohort are represented as follows: 48 (24%) reporting earnings of under $25,000, 52 (26%) earning between $25,000 and $49,999, 27 (13.5%) between $50,000 and $74,999, 23 (11.5%) between $75,000 and $99,999, 20 (10%) between $100,000 and $124,999, 6 (3%) between $125,000 and $149,999, and 13 (6.5%) earning above $150,000.Epworth Sleepiness ScoreThe ESS is a score that ranges from 0 to 24. Scores are categorized as follows: 0-5: lower normal daytime sleepiness; 6-10: normal daytime sleepiness; 11-12: mild excessive daytime sleepiness; 13-15: moderate excessive daytime sleepiness; 16-24: severe excessive daytime sleepiness. The percentages of respondents' scores are depicted in  of respondents report never falling asleep during daytime periods of inactivity, 58 (29%) report a slight chance, 28 14%) report a moderate chance, and 12 (6%) report a high chance. These findings are visualized below . 61 participants, or 30.5%, work less than 8 hours, 38 (19%) work for 10 hours, and 7 or 3.5% work for 12 hours on average. These results are shown in Table Factors related to daytime sleepiness and coping mechanismsIn the survey, participants reported their propensity to fall asleep during the daytime. This urge reflects symptoms of untimely, unwanted sleepiness and is a proxy for the poor quality of sleep. Multivariate analysis was used to take into consideration demographic variables as well as information reported about participants’ occupations to understand sources of daytime sleepiness. Race (p=0.112), gender (p=0.426), age (p=0.063) were among the demographic factors that were not statistically significant in predicting unwanted sleepiness during the day. In addition, shift work/shift timing (p=0.260) and screen time before bed (p=0.785) were not statistically significant for daytime sleepiness. However, the length of participants’ workdays (p=0.001) and their income levels (p=0.008) were found to affect sleep quality. Lengthier workdays and lower income levels were associated with an increased likelihood of falling asleep during inactive periods of the day. While the length of one’s workday and its influence on sleep quality are intuitive, income is seemingly more indirect.Participants were also asked about their use of oral medications as sleeping aids. Melatonin was the most used drug by 57 (38.5%) participants, followed by cannabis , sleeping tablets , alcohol , antihistamines , benzodiazepines such as Xanax® , and other drugs . These findings are shown in Figure The findings of this study highlight multiple important correlates of sleep quality. Half of the participants reported earning less than $50,000, and lower income strongly correlated to an increased propensity to fall asleep during the day. Individuals with lower incomes are more likely to face financial stress. Although this relationship is seemingly less straightforward, financial stress has previously been correlated with shorter sleep durations in young adults and sleep disturbances in older adults . ConsistIt is notable that screentime before bed, shift work, and shift timing were not correlated to excessive daytime sleepiness. This disagrees with previous studies that demonstrated their impact on sleep quality or length. This could be the result of changes in technology that can filter out blue light, a smaller sample size of 200 individuals, or a relatively few individuals in the study who work night shifts. Additionally, the sample may not be perfectly representative of national data. Only 5% of individuals had insomnia, 6.5% of individuals had obstructive sleep apnea, and 25% of individuals reported sleep disorders, all data that are lower than national averages -18. A saLimitations to the current study are those inherent to most anonymous survey studies and stem from the lack of control the researchers have over the respondents and their answers. Any adult living in the United States could have answered our survey. Their motivations to participate in the survey may have skewed respondents' answers. In an anonymous online setting, the truthfulness of answers is also difficult to ascertain, although there is no incentive to purposefully be deceptive. A total of 200 responses were collected, which represents only 6 × 10-7% of the adult US population. Thus, the data may not be generalizable. Given this, these data should be interpreted as a snapshot in time of an audience that self-selects to answer a survey on sleep quality and possibly a starting point for informing the conversation on sleep habits.An overall assessment of the level of daytime sleepiness was obtained via an online survey. This study investigates multiple factors related to sleep and sleep hygiene, including the impact of shift work, screen time, income, and demographics. While online anonymous surveys can be a valuable tool for collecting data, they come with limitations inherent to survey methodology. These include self-selection bias, a lack of control over participants and their environment, and, due to the anonymous nature of the responses, a potential lack of accuracy or authenticity in responses. These factors may limit the generalizability of the current study's results.


Healthcare workers (HCWs) are at a higher risk of contracting COVID-19 due to their close contact with infected patients. However, the true burden of COVID-19 among HCWs in Yemen is unknown due to the inadequate availability of healthcare and the subclinical nature of the disease. This study aims to estimate the seroprevalence of SARS-CoV-2 infection among HCWs in two Yemeni governorates and identify associated factors using a cross-sectional design.A total of 404 HCWs were surveyed from June 2022 to September 2022 in Lahj and AL-Dhalea hospitals. A self-administered questionnaire collected demographic data, COVID-19 infection history, and vaccination status. A total of 404 human sera were tested using a specific electrochemiluminescence immunoassay assay. Association analysis was conducted to identify associations between antibody prevalence and demographic and vaccine-related variables.P > 0.05).The median age of the HCWs was 31 (Range 20–64) years, with 65.0% being male and 35.0% female. Of all HCWs, 94% were SARS-CoV-2 seropositive and 77.0% had no confirmed test of COVID-19-related symptoms. There was no significant association between seropositivity and demographic factors such as age, gender, occupation, or COVID-19 vaccination  measures among HCWs in Yemen. The coronavirus diseases-19 (COVID-19) pandemic has rapidly spread across the world, with devastating effects on public health, economies, and societies. With its five consecutive waves of infection, it resulted in over 693 million confirmed cases and 6.9 million deaths as of August 14, 2023 [Based on the guidelines of the World Health Organization (WHO), several hospitals commenced their strict implementation of strategies to protect their HCWs. These included providing adequate personal protective equipment (PPE), implementing infection prevention and control (IPC) measures, and regular screening of staff , 4. DespOver the first three years of the pandemic variable seroprevalence among HCWs have been reported worldwide with different epidemiological models related to person-place and time. In early studies of the pandemic mainly in the year 2020, a COVID-19 seroprevalence of 14.8% was reported among HCWs in Saudi Arabia , 19% in Yemen reported its first COVID-19 case on April 28, 2020, with over 11,000 infections and 2,159 deaths reported to date .In mid-2020 COVID-19 cases in Yemen were estimated to have reached 1 million and this figure was predicted to reach 11 million with 85,000 deaths at the end of the year 2020 if people do not make serious behavioural changes, and if authorities do not introduce more mitigation to control the infection , 23. HowThe situation among frontline teams in Yemen has not been fully assessed as yet. Thus, this study aims to investigate the seroprevalence of SARS-CoV-2 infection among health care workers (HCWs) in hospitals of two Yemeni governorates and identify associated factors.This is a cross-sectional study conducted in the hospitals located of Lahj and AL-Dhalea governorates in the southern part of Yemen in the period from June 2022 to September 2022 (Map of Yemen identifying the study areas). Lahj governorate, with a population of nearly 900,000 people, is located northwest of major southern city of Aden and is divided into 15 administrative districts with the city of Al-Hawtah as its capital. Al-Dhalea governorate, with a population of almost 500,000 people, is located in the southern-central part of Yemen with a population of nearly 500,000 people and encompasses nine administrative districts, with Al-Dhalea as its capital,  as seen The study population included a range of HCWs, such as doctors, nurses, x-ray physicians, dentists, laboratory personnel, pharmacists, respiratory therapists, and nutritionists, in addition to auxiliary HCWs such as clerks, housekeeping staff, laundry personnel, and social workers. Those who didn't agree to participate in the study and did not fill in the questionnaire were excluded with around 12 out of the total 416 (2.9%). However, participants were gathered into four categories according to the importance of the participants and the level of contact with patients complaining of COVID-19 infection such as physicians, nurses, allied health workers who involved in giving healthcare services distinct from medicine or nursing , and finTo recruit the participants, the research team obtained permission from the hospital administration in each governorate to conduct the study , as wellThe prevalence of anti-SARS-CoV-2 antibodies among healthcare workers in Yemen is not well understood, so a large sample size was needed to estimate it accurately. Based on an assumed prevalence rate of 50%, a 95% confidence interval, and a 5% margin of error, a sample size of 384 was calculated using Daniel’s equation . To accoTo ensure a representative sample, we used the Probability Proportional to Size (PPS) sampling technique to enroll participants from both Lahj and Al-Dhalea governorates proportionally. Of the HCWs who presented in the hospitals during the sampling period, 267 (66%) from Lahj and 137 (34%) from Al-Dhalea were enrolled in the sample and a convenience sampling method was used to complete the required sample from each hospital and governorate.diabetes mellitus, and immune deficiency. The fifth domain encompassed data on IPC measures which included follow IPC standard precautions, following 5 recommended moments, use alcohol-based hand rub or soap, wear PPE with the COVID-19 patient, PPE available in sufficient quantity, and attended IPC training. In addition, this domain includes variables related to COVID-19 vaccination which included receiving of vaccine, acceptance, or hesitancy to receive the vaccine.The study used a self-reported questionnaire based on the WHO protocol for COVID-19 infection among HCWs in a healthcare setting . The HCWThe questionnaire was originally in English and was translated into Arabic for ease of understanding and back to English to secure the consistency of the questions.Reliability test was undertaken among 30 HCWs in analogy to the site of the study to ensured that the questionnaire was easy to use and acceptable by the interviewees. Reliability also was considered by reaching a Cronbach alpha of not less than 0.73 of the completed questionnaires which indicates that the overall response values for each participant across a set of questions are consistent.A volume of five ml venepuncture blood sample was drawn from each participant via the venipuncture technique with universal precautions conducted by the concerned technician. Blood samples were collected in EDTA tubes and stored at 4 °C and transferred on the same day to the hospital laboratory for analysis. The samples were labelled with the department name, name of the participant, date, and identification number , 33. TheP- of < 0.05 was considered statistically significant.The data was coded and entered into SPSS version 23 for analysis. Descriptive statistics, including mean and standard deviation, were used to summarize continuous variables, while absolute and relative frequencies were used for categorical variables. The Chi-square test was used to examine the association between the dependent and the independents variables. A P > 0.05) between; genders, age groups, work experience, different occupation categories (Table P > 0.05) between the seroprevalence and the reported risk factors such as smoking, comorbidities including, cardiovascular diseases, history of diabetes mellitus, or renal diseases with a reported history of COVID-19  between different governorates, districts, hospitals (Table P > 0.05) among HCW who reported or denied history of COVID-19, breathing difficulty, cough, anosmia or loss of taste or fever/ chills, (Table n = 112) only 22 i.e., 5.4% of total HCW reported that they were tested by PCR and were all positive. Among all HCWs, 12.0% received one vaccine dose, 11.5% received two doses’, 76.5% have not received any vaccine. Of all HCWs 95 (23.5%) were vaccinated, 66 (70%) with AstraZeneca vaccine and 29 (30%) with Jenson & Jenson/Osinovac. More than half of the HCWs (58.9%) reported lack of desire to receive COVID-19 vaccine, while 9.9% will think too carefully before they take it. Equally high seroprevalence (> 93.0%) was not significantly (P > 0.05) associated with vaccine uptake, vaccine type or number of vaccine doses  were male whereas 140 (34.7%) were female, the median age was 31 years (Range 20–64) years. Nurses constituted the highest proportion (51.2%) while physicians comprised the lowest proportion (11.6%). The overall seroprevalence of SARS-CoV-2 was 94.3%; 95%CI = (92.1–96.5%). Seroprevalence was similarly high and did not significantly differ  among HCWs in Yemen between June 2022 and the end of September 2022, after the peak of the COVID-19 epidemic in the country . This isDue to the political status in Yemen, epidemiological containment measures were inadequately implemented in the country. In fact, this study reported a lack of PPE in around 80.0% of HCWs despite reporting a high level of compliance with other IPC measures. The lack of PPE made compliance with other IPC measures ineffective, and this was translated into a seroprevalence that is as high as that among those reported non-compliance. This favours to some extent the inference of double exposure of HCW which occurred within and outside the health care setting in the community where poor containment measures were practiced. All these factors and others such as poor financial resources likely accelerated the spread of the virus among the HCWs and ultimately increased the pressure on the healthcare settings that lacked access to appropriate PPE and practiced limited compliance to IPC measures and ultimately intensely exposed HCWs to infection .The scenario of exposure outside the health care setting is more likely to have occurred as the seroprevalence among auxiliary HCW such as clerks, housekeeping staff, laundry personnel, and social workers who literally has less contact with patients was as high as 96%.The present study did not find a significant difference in seropositivity between HCWs who received the COVID-19 vaccine and those who did not. This is, realistically, due to the fact that the assay used in this study only detects anti-nucleocapsid antibodies , 33 whilTherefore, given the nature of the assay used in this study as it only detects anti-nucleocapsid antibodies , 33 thisThe high proportion of HCW who reported either unacceptance or hesitancy to take the COVI-19 vaccine highlights a concerning misconception that is likely to be prevalent among the general population. This will be a major barrier towards development of efficient COVID-19 vaccination program.P > 0.05) between seroprevalence and gender, age, residence, professional work experience, occupation categories and workplace. This suggests that all HCWs were equally at the same level of risk. These results are consistent with previous studies conducted elsewhere [Our study found no significant association . Finally, the study did not assess antibodies from vaccination, particularly anti-spike, which is an important limitation.The study findings highlight the urgent need for improving infection prevention and control measures among HCWs in Yemen, as well as increasing vaccination coverage. The high seroprevalence among HCWs may also suggest a high level of seroprevalence among the general population, emphasizing the importance of continued surveillance and monitoring of the epidemiological situation in Yemen.SARS-CoV-2 seroprevalence and its impact on the clinical outcomes of infection in term of severity and duration in HCW need to be regularly monitored. Vaccination campaigns should be enhanced according to the WHO current recommendation and assessment of the vaccine. In summary, this study highlights the high burden of SARS-CoV-2 infection among HCWs in Yemen and underscores the need for urgent action to improve infection prevention and control measures and increase vaccination coverage to protect HCWs and prevent the further spread of the virus.