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Perfluoroalkyl substances (PFASs) are a large class of persistent emerging pollutants, ubiquitous in different environmental compartments. In this study, twenty-one PFASs were determined in seventy-eight water samples collected from six different rivers in the Umbria region (central Italy) during a 13-month monitoring campaign. The sum of the twenty-one target analytes (Σ21PFASs) ranged from 2.0 to 68.5 ng L-1, with a mean value of 22.0 ng L-1. The highest concentrations of Σ21PFASs were recorded in the warmest months (from June to September) due to reduced river streamflow caused by low rainfall and high temperatures. PFASs with a number of carbon atoms between four and nine prevail over C10-C18 congeners due to their higher water solubility and to their increased use in industry. PFBA, followed by PFPeA, PFHxA, and PFOA, was the most abundant congeners detected in the analyzed river water samples. Finally, the calculation of risk quotients (∑RQs) has allowed to assess the risk for three aquatic organisms (fish, algae, and daphnid) deriving from the exposure to PFASs. The survey showed that the risk for the three aquatic organisms during the four seasons and throughout the year was always negligible. The only exception was a low risk for fish and daphnid in GEN river considering the annual exposure.

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Background: In South Africa, between 1966 and 2014, there were three kidney transplant eras defined by evolving access to certain immunosuppressive therapies defined as Pre-CYA (before availability of cyclosporine), CYA (when cyclosporine became available), and New-Gen (availability of tacrolimus and mycophenolic acid). As such, factors influencing kidney graft failure may vary across these eras. Therefore, evaluating the consistency and reproducibility of models developed to study these variations using machine learning (ML) algorithms could enhance our understanding of post-transplant graft survival dynamics across these three eras. Methods: This study explored the effectiveness of nine ML algorithms in predicting 10-year graft survival across the three eras. We developed and internally validated these algorithms using data spanning the specified eras. The predictive performance of these algorithms was assessed using the area under the curve (AUC) of the receiver operating characteristics curve (ROC), supported by other evaluation metrics. We employed local interpretable model-agnostic explanations to provide detailed interpretations of individual model predictions and used permutation importance to assess global feature importance across each era. Results: Overall, the proportion of graft failure decreased from 41.5% in the Pre-CYA era to 15.1% in the New-Gen era. Our best-performing model across the three eras demonstrated high predictive accuracy. Notably, the ensemble models, particularly the Extra Trees model, emerged as standout performers, consistently achieving high AUC scores of 0.95, 0.95, and 0.97 across the eras. This indicates that the models achieved high consistency and reproducibility in predicting graft survival outcomes. Among the features evaluated, recipient age and donor age were the only features consistently influencing graft failure throughout these eras, while features such as glomerular filtration rate and recipient ethnicity showed high importance in specific eras, resulting in relatively poor historical transportability of the best model. Conclusions: Our study emphasises the significance of analysing post-kidney transplant outcomes and identifying era-specific factors mitigating graft failure. The proposed framework can serve as a foundation for future research and assist physicians in identifying patients at risk of graft failure.

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We quantified the impact of Vietnam-era G.I Bill eligibility, which subsidized college education for eligible Veterans, on the later-life blood pressure distribution by exploiting the Vietnam draft lottery natural experiment. We restricted Health and Retirement Study data (2006-2018) to men born between 1947-1953 (N=1,970). We estimated intention-to-treat effects at the mean and 1st-99th quantiles of blood pressure using linear and quantile regressions. Our outcomes were systolic blood pressure (SBP), diastolic blood pressure (DBP), hypertension, and self-reported stroke. We proxied G.I. Bill eligibility using lottery-defined draft eligibility. We also conducted analyses stratified by childhood socioeconomic status (cSES) defined based on a previously validated measure. Draft eligibility reduced mean blood pressure outcomes (e.g., effect on SBP: -1.33 [95% confidence interval (CI) -2.85, 0.19]). Draft eligibility also had larger protective effects at higher quantiles of the SBP and DBP distributions relative to lower quantiles (effects on SBP at the 10th and 90th quantiles: -0.33mmHg [95% CI -2.35,1.68]; -3.00mmHg [95% CI -5.68,-0.32]). Draft eligibility had protective effects on blood pressure among low and medium cSES men but opposite effects among high cSES men. G.I. Bill eligibility reshaped the blood pressure distribution to one of lower morbidity risk, particularly among low and medium cSES men.

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BACKGROUND: Respiratory syncytial virus (RSV), a leading cause of lower respiratory tract infection (LRTI) among children, has resurged in the form of endemic or even pandemic in many countries and areas after the easing of COVID-19 containment measures. This study aimed to investigate the differences in epidemiological and clinical characteristics of children hospitalized for RSV infection during pre- and post-COVID-19 eras in Yunnan, China. METHODS: A total of 2553 pediatric RSV inpatients from eight hospitals in Yunnan were retrospectively enrolled in this study, including 1451 patients admitted in 2018-2019 (pre-COVID-19 group) and 1102 patients admitted in 2023 (post-COVID-19 group). According to the presence or absence of severe LRTI (SLRTI), patients in the pre- and post-COVID-19 groups were further divided into the respective severe or non-severe subgroups, thus analyzing the risk factors for RSV-associated SLRTI in the two eras. Demographic, epidemiological, clinical, and laboratory data of the patients were collected for the final analysis. RESULTS: A shift in the seasonal pattern of RSV activity was observed between the pre-and post-COVID-19 groups. The peak period of RSV hospitalizations in the pre-COVID-19 group was during January-April and October-December in both 2018 and 2019, whereas that in the post-COVID-19 group was from April to September in 2023. Older age, more frequent clinical manifestations (fever, acute otitis media, seizures), and elevated laboratory indicators [neutrophil-to-lymphocyte ratio (NLR), c-reactive protein (CRP), interleukin 6 (IL-6), co-infection rate] were identified in the post-COVID-19 group than those in the pre-COVID-19 group (all P < 0.05). Furthermore, compared to the pre-COVID-19 group, the post-COVID-19 group displayed higher rates of SLRTI and mechanical ventilation, with a longer length of hospital stay (all P < 0.05). Age, low birthweight, preterm birth, personal history of atopy, underlying condition, NLR, IL-6 were the shared independent risk factors for RSV-related SLRTI in both pre- and post-COVID-19 groups, whereas seizures and co-infection were independently associated with SLRTI only in the post-COVID-19 group. CONCLUSIONS: An off-season RSV endemic was observed in Yunnan during the post-COVID-19 era, with changed clinical features and increased severity. Age, low birthweight, preterm birth, personal history of atopy, underlying condition, NLR, IL-6, seizures, and co-infection were the risk factors for RSV-related SLRTI in the post-COVID-19 era.

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Our study helps to unravel the complexity of the Lomagundi-Jatuli event, the largest and longest positive carbon isotope excursion ever recorded on the Earth's surface, by providing a unique view of Paleoproterozoic graphitic rocks from the Borborema province of Northeastern Brazil. Through detailed mineralogical, textural, chemical and isotopic analyses, we bring a new perspective that provide support to elevated primary productivity and large-scale organic carbon burial during the Lomagundi-Jatuli event. Graphite crystals with distinctive textural features occur in association with silicate and oxidised manganese ores, manganese quartzites, garnetites, and gneisses. The graphites were crystallised at temperatures up to 634 °C, consistent with amphibolite facies metamorphism, according to Raman thermometry. An average total carbon content of 2.1 wt%, with δ13C values ranging from - 15.0 to - 21.5‰, is indicated by whole-rock geochemistry and carbon isotopic composition, respectively. Based on these results, our study proposes that these graphitic rocks may represent remnants of organic matter, possibly derived from bacterial biomass associated with manganese-rich sediments, preserved under reducing environmental conditions in a redox-stratified marine setting. Biological mediation on the origin of silicates is suggested by the close relationship between reduced manganese silicates and graphite. These constraints indicate that Paleoproterozoic graphite-rich rocks represent an important but overlooked reservoir of organic carbon that was partially degassed during the metamorphism of organic-rich sequences. Overall, this research provides new insights for the enigmatic emergence of the Lomagundi-Jatuli event, highlighting the intricate interplay among organic carbon, manganese-rich rocks and Earth's evolutionary processes during this period.

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According to the current data, the endometrium acts as a "sensor" of embryo quality, which promotes the implantation of euploid embryos and prevents the implantation and/or subsequent development of genetically abnormal embryos. The present review addresses the nature of the "sensory function" of the endometrium and highlights the necessity for assessing its functional status. The first section examines the evolutionary origin of the "sensory" ability of the endometrium as a consequence of spontaneous decidualization that occurred in placental animals. The second section details the mechanisms for implementing this function at the cellular level. In particular, the recent findings of the appearance of different cell subpopulations during decidualization are described, and their role in implantation is discussed. The pathological consequences of an imbalance among these subpopulations are also discussed. Finally, the third section summarizes information on currently available clinical tools to assess endometrial functional status. The advantages and disadvantages of the approaches are emphasized, and possible options for developing more advanced technologies for assessing the "sensory" function of the endometrium are proposed.

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To evaluate the repeatability of a novel automated technique called Smart ERA (Smart Endometrial Receptivity Analysis) for the automated segmentation and volume calculation of the endometrium in patients with normal uteri,, and to compare the agreement of endometrial volume measurements between Smart ERA, the semi-automated Virtual Organ Computer-aided Analysis (VOCAL) technique and manual segmentation. This retrospective study evaluated endometrial volume measurement in infertile patients who underwent frozen-thawed embryo transfer (FET). Transvaginal three-dimensional ultrasound scans were performed using a Resona R9 ultrasound machine. Data was collected from patients between 2021 and 2022. Patients with normal uteri and optimal ultrasound images were included. Endometrial volumes were measured using Smart ERA, VOCAL at 15° rotation, and manual segmentation. Intra-observer repeatability and agreement between techniques were assessed using the intraclass correlation coefficient (ICC) and Bland-Altman analysis. A total of 407 female patients were evaluated (mean age 33.2 ± 4.7 years). The repeatability of Smart ERA showed an ICC of 0.983 (95% CI 0.984-0.991). The agreement between Smart ERA and the manual method, Smart ERA and VOCAL, and VOCAL and the manual method, as assessed by ICC, were 0.986 (95% CI 0.977-0.990), 0.943 (95% CI 0.934-0.963), and 0.951 (95% CI 0.918-0.969), respectively. The Smart ERA technique required approximately 3 s for endometrial volume calculation, while VOCAL took around 5 min and the manual segmentation method took approximately 50 min. The Smart-ERA software, which employs a novel three-dimensional segmentation algorithm, demonstrated excellent intra-observer repeatability and high agreement with both VOCAL and manual segmentation for endometrial volume measurement in women with normal uteri. However, these findings should be interpreted with caution, as the algorithm's performance may not be generalizable to populations with different uterine characteristic. Additionally, Smart ERA required significantly less time compared to VOCAL and manual segmentation.

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Background Despite the success of childhood vaccination in reducing vaccine-preventable diseases (VPDs), vaccine hesitancy remains a significant challenge in several countries, such as Saudi Arabia, both during and beyond the COVID-19 era. Furthermore, the pandemic may have impacted vaccine hesitancy trends, potentially affecting parents' intentions to adhere to scheduled childhood vaccination programs. Aim This article aims to assess the extent of parents' hesitancy toward childhood vaccination, determine if it increased or decreased due to the COVID-19 pandemic, highlight the factors and determinants that influenced this hesitancy, whether positively or negatively, during the COVID-19 era, and estimate the acceptance of COVID-19 vaccination in relation to the acceptance of scheduled childhood vaccination. Methods A cross-sectional study was conducted in Saudi Arabia through a snowball sampling technique. Data were collected between September 2022 and October 2022 using an online survey using Google Forms. The inclusion criteria were parents or guardians in Saudi Arabia with a child up to 18 years of age. Responses were analyzed using SPSS V25 (IBM Corp., Armonk, NY, US), with chi-square tests and logistic regression performed to compare hesitancy and vaccination status. Results Among the 1,209 parents and care providers who participated, the prevalence of parents' vaccine hesitancy was 374 (30.9%). The educational level of the parents was not significantly associated with hesitancy status (p 0.490). The most refused vaccine was Mpox (345; 28.5%), whereas the one that caused the most hesitancy was the COVID-19 vaccine (352; 29.1%). Regarding the parents' concerns, the main reason for their hesitancy was the influence of their negative perceptions from social media content, including false or misleading information and negative allegations about vaccines, reported by 449 (18.98%) of the participants. Logistic regression analysis indicated that negative social media perceptions significantly increased the likelihood of vaccine hesitancy (OR = 2.15, 95% CI = 1.78-2.60, p < 0.001). Conclusion Our study highlights the prevalence of parental vaccine hesitancy during the COVID-19 era; the most significant hesitancy was observed toward the COVID-19 vaccine, and the mpox vaccine was the most rejected. Negative social media was the main reason for parental hesitancy; public health efforts should focus on providing accurate and easily accessible information through educational campaigns on social media and other platforms.

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Objectives: Hand-assisted laparoscopic surgery (HALS) combines the benefits of laparoscopic surgery with the tactile feedback from open surgery. In the current era of laparoscopic surgery, the significance of HALS as a technical transition has diminished. This study clarified the usefulness of HALS in restorative proctocolectomy (RPC) for ulcerative colitis (UC) in the era of laparoscopic surgery. Methods: The 212 patients who underwent RPC with ileal pouch-anal anastomosis between 2007 and 2023 were included in this study. The patients were divided into three groups, open surgery (OS), HALS, and conventional laparoscopic surgery (LAP), and their characteristics, surgical outcomes, surgical complications, and functional outcomes were compared. Results: The number of surgical techniques was OS in 21 cases, HALS in 184 cases, and LAP in 7 cases. The number of surgeons was two for OS and HALS, and four for LAP, with OS and HALS having fewer surgeons than LAP. The length of the skin incision was 13, 7, and 3 cm for OS, HALS, and LAP, respectively, and the operation times was 250, 286, and 576 minutes for OS, HALS, and LAP, respectively, with LAP having the longest operation time. The postoperative complications and function did not differ markedly among the three groups. Conclusions: In RPC for UC, HALS involved fewer surgeons and a shorter operative time than LAP. Even in the era of laparoscopic surgery, HALS remains a useful option, especially when a shorter operation time is required or when the number of available surgeons is insufficient.

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Background: The significant rebound of influenza A (H1N1) virus activity, particularly among children, with rapidly growing number of hospitalized cases is of major concern in the post-COVID-19 era. The present study was performed to establish a prediction model of severe case in pediatric patients hospitalized with H1N1 infection during the post-COVID-19 era. Methods: This is a multicenter retrospective study across nine public tertiary hospitals in Yunnan, China, recruiting pediatric H1N1 inpatients hospitalized at five of these centers between February 1 and July 1, 2023, into the development dataset. Screening of 40 variables including demographic information, clinical features, and laboratory parameters were performed utilizing Least Absolute Shrinkage and Selection Operator (LASSO) regression and logistic regression to determine independent risk factors of severe H1N1 infection, thus constructing a prediction nomogram. Receiver operating characteristic (ROC) curve, calibration curve, as well as decision curve analysis (DCA) were employed to evaluate the model's performance. Data from four independent cohorts comprised of pediatric H1N1 inpatients from another four hospitals between July 25 and October 31, 2023, were utilized to externally validate this nomogram. Results: The development dataset included 527 subjects, 122 (23.1 %) of whom developed severe H1N1 infection. The external validation dataset included 352 subjects, 72 (20.5 %) of whom were eventually confirmed as severe H1N1 infection. The LASSO regression identified 19 candidate predictors, with logistic regression further narrowing down to 11 independent risk factors, including underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, neutrophil-lymphocyte ratio (NLR), erythrocyte sedimentation rate (ESR), lactate dehydrogenase (LDH), interleukin-10 (IL-10), and tumor necrosis factor-α (TNF-α). By integrating these 11 factors, a predictive nomogram was established. In terms of prediction of severe H1N1 infection, excellent discriminative capacity, favorable accuracy, and satisfactory clinical usefulness of this model were internally and externally validated via ROC curve, calibration curve, and DCA, respectively. Conclusion: Our study successfully established and validated a novel nomogram model integrating underlying conditions, prematurity, fever duration, wheezing, poor appetite, leukocyte count, NLR, ESR, LDH, IL-10, and TNF-α. This nomogram can effectively predict the occurrence of serious case in pediatric H1N1 inpatients during the post-COVID-19 era, facilitating the early recognition and more efficient clinical management of such patients.

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The COVID-19 pandemic poses ongoing challenges to the sustainability of various socioeconomic sectors, including agriculture, the food supply chain, the food business, and environmental sustainability. This study employs data obtained from the World Health Organization (WHO), and Food and Agriculture Organization (FAO), as well as scientific and technical research publications, to evaluate the impacts of COVID-19 on agriculture and food security. This article seeks to highlight the profound influence of the COVID-19 pandemic on agriculture, the supply and demand of food, and the overall safety of food. The article also explores the several pathways by which COVID-19 can be transmitted in these areas and the various technologies employed for its detection. The ongoing and post-pandemic ramifications are substantial since they could decrease agricultural output due to limitations on migration, a downturn in international trade, less buying capacity, and disturbances in food production and processing. Therefore, based on this thorough investigation, recommendations are issued for mitigating and controlling the pandemic's effects.

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Rapid urbanization and industrialization have intensified air pollution, posing severe health risks and necessitating accurate PM2.5 predictions for effective urban air quality management. This study distinguishes itself by utilizing high-resolution ERA5 reanalysis data for a grid-based spatial analysis of Istanbul, Türkiye, a densely populated city with diverse pollutant sources. It assesses the predictive accuracy of advanced machine learning (ML) models-Multiple Linear Regression (MLR), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting (LGB), Random Forest (RF), and Nonlinear Autoregressive with Exogenous Inputs (NARX). Notably, it introduces genetic algorithm optimization for the NARX model to enhance its performance. The models were trained on hourly PM2.5 concentrations from twenty monitoring stations across 2020-2021. Istanbul was divided into seven regions based on ERA5 grid distributions to examine PM2.5 spatial variability. Seventeen input variables from ERA5, including meteorological, land cover, and vegetation parameters, were analyzed using the Neighborhood Component Analysis (NCA) method to identify the most predictive variables. Comparative analysis showed that while all models provided valuable insights (RF > LGB > XGB > MLR), the NARX model outperformed them, particularly with the complex dataset used. The NARX model achieved a high R-value (0.89), low RMSE (5.24 µg/m³), and low MAE (2.94 µg/m³). It performed best in autumn and winter, with the highest accuracy in Region-1 (R-value 0.94) and the lowest in Region-5 (R-value 0.75). This study's success in a complex urban setting with limited monitoring underscores the robustness of the NARX model and the methodology's potential for global application in similar urban contexts. By addressing temporal and spatial variability in air quality predictions, this research sets a new benchmark and highlights the importance of advanced data analysis techniques for developing targeted pollution control strategies and public health policies.

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Background: Asymptomatic SARS-CoV-2 infection can significantly increase the spread of the COVID-19 pandemic. We aimed to investigate the epidemiological and clinical predictors of occult serologically confirmed SARS-CoV-2 cases among the general population during the fourth vaccination era in Israel. Methods: We conducted a cross-sectional study among individuals aged ≥18 years who had not been tested for COVID-19 in the preceding 5 months. Occult serologically confirmed cases were based on the presence of anti-N IgG antibodies. Potential risk factors were examined. Multivariable regression analysis identified independent predictors of subclinical SARS-CoV-2 infection. Results: This study included 504 participants. The prevalence of occult serologically confirmed SARS-CoV-2 was 12.5%. Chronic disease was found to be an independent predictor for the absence of occult disease (aOR) 0.4 [95% (CI): 0.18-0.87], p-value = 0.02). No significant differences were observed in age, sex, marital status, number of children, vaccination status, or exposure to COVID-19 infection between participants with and without SARS-CoV-2 sub-infection. Conclusions: We found a lower prevalence of occult serologically confirmed SARS-CoV-2 cases, compared to previous reports, and a negative correlation between chronic disease and occult SARS-CoV-2. Continued research, surveillance, and intervention strategies are needed to optimize long-term health outcomes and provide valuable insights for public health policymakers and clinicians.

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Anti-Spike IgG antibodies against SARS-CoV-2, which are elicited by vaccination and infection, are correlates of protection against infection with pre-Omicron variants. Whether this association can be generalized to infections with Omicron variants is unclear. We conducted a retrospective cohort study with 8457 blood donors in Tyrol, Austria, analyzing 15,340 anti-Spike IgG antibody measurements from March 2021 to December 2022 assessed by Abbott SARS-CoV-2 IgG II chemiluminescent microparticle immunoassay. Using a Bayesian joint model, we estimated antibody trajectories and adjusted hazard ratios for incident SARS-CoV-2 infection ascertained by self-report or seroconversion of anti-Nucleocapsid antibodies. At the time of their earliest available anti-Spike IgG antibody measurement (median November 23, 2021), participants had a median age of 46.0 years (IQR 32.8-55.2), with 45.3% being female, 41.3% having a prior SARS-CoV-2 infection, and 75.5% having received at least one dose of a COVID-19 vaccine. Among 6159 participants with endpoint data, 3700 incident SARS-CoV-2 infections with predominantly Omicron sublineages were recorded over a median of 8.8 months (IQR 5.7-12.4). The age- and sex-adjusted hazard ratio for SARS-CoV-2 associated with having twice the anti-Spike IgG antibody titer was 0.875 (95% credible interval 0.868-0.881) overall, 0.842 (0.827-0.856) during 2021, and 0.884 (0.877-0.891) during 2022 (all p < 0.001). The associations were similar in females and males (Pinteraction = 0.673) and across age (Pinteraction = 0.590). Higher anti-Spike IgG antibody titers were associated with reduced risk of incident SARS-CoV-2 infection across the entire observation period. While the magnitude of association was slightly weakened in the Omicron era, anti-Spike IgG antibody continues to be a suitable correlate of protection against newer SARS-CoV-2 variants.

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There are numerous studies dealing with olive oil management from ancient civilizations to the mid last century, but they are limited on the historical value of information. At the same time, much knowledge is widely available and accessible on the contemporaneous production of olive oil, the necessary inputs (water and energy) and outputs (by-products) of the production process. The present study aims to shed light on olive oil extraction management from antiquity to present and to bridge the gap between archaeological and modern agricultural, engineering, and environmental disciplines. For the purposes of this study, Crete, Greece, a well-known and traditional olive oil producing region is investigated. This study is dedicated to unveil practices concerning: (a) the processing of the olives, (b) the various energy aspects per era, (c) the role of water and energy at each stage of the extraction process, and (d) management of by-products per era. The main findings support that: (a) the evolution of the extraction processes was relatively slow and remained almost the same from Minoan times until the middle of the 20th century, (b) the importance of water has been demonstrated from the beginning in the efficient extraction of the maximum amount of olive oil, (c) wastewater was first reported during the Hellenistic-Roman period due to the increased quantities produced, (d) by-product management was only considered in the previous century for environmental purposes, (e) olive oil production has been a human-based process for centuries and was greatly increased by the introduction of animals, and (f) olive oil production was further increased with the utilization of mechanical and electrical energy. It can be therefore clearly concluded that past practices have both similarities and differences with the present ones, which in turn have been optimized in terms of energy sources, water uses, olive mill equipment, and environmental considerations, to result in maximum olive oil production with minimum environmental impacts. Based on this work, important lessons can be drawn that show the historical evolution of extraction and management practices.

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Although immunotherapy is rapidly changing the treatment approach to r/r diffuse large B cell lymphoma, there is still a place for autologous stem cell transplantation in some patients. The report by Stewart et al. focuses on induction therapy and the importance of the pretransplantation phase. Commentary on: Stewart et al. Canadian cancer trials group LY.17: A randomized phase II study evaluating novel salvage therapy pre-autologous stem cell transplant in relapsed/refractory diffuse large B-cell lymphoma-Outcome of rituximab-dose-intensive cyclophosphamide, etoposide, cisplatin (R-DICEP) versus R-GDP. Br J Haematol 2024 (Online ahead of print). doi: 10.1111/bjh.19555.

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BACKGROUND AND OBJECTIVES: In contemporary society, socially active women are increasingly planning their fertility for later in life. The fertility outcomes for advanced-age patients, even with egg donation, are often suboptimal due to endometrial aging. Recurrent implantation failure (RIF) is one of the core problems for assisted reproductive technology (ART), especially for advanced-age patients. High-quality, euploid embryos and synchronization between the embryonic stage and the uterine endometrial lining are crucial for positive outcomes. The study aims to improve ART outcomes with personalized embryo transfer (pET) according to endometrial receptivity analysis (ERA) in advanced-age patients with challenging reproductive histories, and RIF by utilizing, donor oocytes and preimplantation genetic testing for aneuploidy (PGT-A) for embryo testing. METHODS: A randomized, controlled observational follow-up study was conducted from 2020 to 2023. After obtaining informed consent, 320 patients with RIF were selected. Patients were allocated into the study group and control group 1 based on consistent application of randomization principles, while control group 2 was selected separately. The study group included patients undergoing PGT-A and ERA, aged 35-45 years, with a mean age of 40.5±3.7 years. Control group 1 comprised patients undergoing PGT-A, aged 35-45 years, with a mean age of 40±4.2 years. Control group 2 consisted of patients undergoing PGT-A and ERA, aged less than 35 years, with a mean age of 31.6±2.2 years. RESULTS: Results suggest that ERA may improve implantation and pregnancy outcomes in advanced-age patients, particularly those with RIFs. The pregnancy rate was significantly higher in the study group (77.9%), compared to control group 1 (57.6%) (p=0.0007), and no significant difference compared to control group 2 (77.3%) (p=0.94). The implantation rate was higher in the study group (54.1%) than in control group 1 (39.4%) (p=0.0009), and there was no significant difference between the study group and control group 2 (50%, p=0.87). The live birth rate was also higher in the study group (71.3%), compared to control group 1 (39.4%) (p<0.0001). There were no significant differences between the study group and control group 2 (65.9%, p=0.50). CONCLUSION: pET guided by ERA significantly improves pregnancy, implantation, and live birth rates in advanced-age patients with challenging reproductive histories. pET provides ART outcomes with no significant difference between advanced-age patients and younger patients with pET guided by ERA.

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Introduction: Mental health challenges have still been widely pervasive among Chinese university students after the outbreak of the COVID-19 pandemic. This article aims to explore mental health challenges encountered by Chinese university students in the post-pandemic era and explain causes to these challenges using a qualitative approach. Methods: We conducted six focus group interviews with university students in Zhuhai, China, and altogether 61 students including 43 female students, and 18 male students participated in the study. Results: Our results indicate that sleep difficulties, anxiety, and stress are the three primary mental health challenges experienced by students. Academic pressure, social influence including peer pressure and pursuit of social acceptance, and pandemic related policies and measures are the causes to the above mental health challenges. Discussion: The results of this study will inform the development of mental health promotion, intervention, and education activities for university students to bolster their resilience and cope with mental health problems in the post-pandemic era. Meanwhile, our results could illuminate the services stakeholders provide to university students in the future.

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Between March and October 2022, a peak of detection of Bordetella parapertussis by qPCR, real-time PCR was observed in France.Hypothesis/Gap Statement. Whether this peak was due to resurgence from previous circulating lineages or reintroduction into the country was unknown.Objective. The objective of this study is to understand B. parapertussis-transient increase observed in France in 2022 whereas it had virtually stopped being reported since the start of the COVID-19 pandemic in 2020.Methods. We analysed real-time PCR (qPCR) data from the two largest French outpatient laboratories performing whooping cough diagnosis and characterized all B. parapertussis isolates collected in the 2016-2022 period by the French National Reference Centre for Whooping Cough.Results. Microbiological analyses reveal that 13 of 18 bacterial isolates collected in 2022 produce the vaccine antigen pertactin, whereas none of the 22 isolates collected in the 2016-2021 period did.Conclusion. We hypothesize a re-introduction of B. parapertussis from regions of the world where whole-cell vaccines are still in use.

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This study investigates the performance of CMIP6 models in reproducing historical temperature and precipitation data for Iran and neighboring countries (Afghanistan, Pakistan, Turkmenistan, Azerbaijan, Armenia, Turkey, and Iraq) from 1980 to 2014. Reanalysis data from the ECMWF database (ERA5) for temperature and precipitation were utilized as a reference for the period 1980-2014. Additionally, ten Atmosphere-Ocean General Circulation Models (AOGCMs) from CMIP6 were employed to simulate temperature and precipitation data for the study region based on the IPCC Sixth Assessment Report databases. The Kling-Gupta Efficiency (KGE) index was used to evaluate the accuracy of CMIP6 models in replicating daily temperature and precipitation. The results indicate that different CMIP6 models exhibit varying degrees of accuracy in simulating historical temperatures and precipitation, depending on the month and the country. For instance, the IPSL-CM6A-LR model demonstrated the best annual performance in estimating temperature in Azerbaijan (KGE = 0.5), while the HadGEM3-GC31-LL model showed the lowest annual performance in Pakistan (KGE = -1.4). Interestingly, the models were found to be more accurate in simulating temperatures during warm months compared to cold ones. Furthermore, the accuracy of different models in estimating annual precipitation varied significantly, ranging from -0.64 (MRI-EMS2-0 model in Afghanistan) to 0.05 (CMCC-ESM2 model in Armenia). Similar to temperature, the study found that models were generally more accurate in simulating precipitation during cold months compared to warm ones.

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