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Abstract Gangliosides in the brain play a crucial role in modulating the integrity of vertebrate central nervous system in a region-specific manner. However, to date, a comprehensive structural elucidation of complex intact ganglioside isomers has not been achieved, resulting in the elusiveness into related molecular mechanism. Here, we present a glycolipidomic approach for isomer-specific and brain region-specific profiling of the mouse brain. Considerable region-specificity and commonality in specific group of regions are highlighted. Notably, we observe a similarity in the abundance of major isomers, GD1a and GD1b, within certain regions, which provides significant biological implications with interpretation through the lens of a theoretical retrosynthetic state-transition network. Furthermore, A glycocentric-omics approaches using gangliosides and N-glycans reveal a remarkable convergence in spatial dynamics, providing valuable insight into molecular interaction network. Collectively, this study uncovers the spatial dynamics of intact glyco-conjugates in the brain, which are relevant to regional function and accelerates the discovery of potential therapeutic targets for brain diseases.

Abstract Background Physiologic MRI-based tumor habitat analysis has the potential to predict patient outcomes by identifying the spatiotemporal habitats of glioblastoma. This study aims to prospectively validate the cut-off for tumor progression obtained from tumor habitat analysis based on physiologic MRI in ascertaining time-to-progression (TTP) and the site of progression in glioblastoma patients following concurrent chemoradiotherapy (CCRT). Methods In this prospective study (ClinicalTrials.gov ID: NCT02613988), we will recruit patients with IDH-wild type glioblastoma who underwent CCRT and obtained immediate post-operative and three serial post-CCRT MRI scans within a three-month interval, conducted using diffusion-weighted imaging and dynamic susceptibility contrast imaging. Voxels from cerebral blood volume and apparent diffusion coefficient maps will be grouped using k-means clustering into three spatial habitats (hypervascular cellular, hypovascular cellular, and nonviable tissue). The spatiotemporal habitats of the tumor will be evaluated by comparing changes in each habitat between the serial MRI scans (post-operative and post-CCRT #1, #2, and #3). Associations between spatiotemporal habitats and TTP will be analyzed using cox proportional hazard modeling. The site of progression will be matched with spatiotemporal habitats. Discussion The perfusion- and diffusion-derived tumor habitat in glioblastoma is expected to stratify TTP and may serve as an early predictor for tumor progression in patients with IDH wild-type glioblastoma. Trial registration ClinicalTrials.gov ID: NCT02613988.

Rhabdomyosarcoma (RMS) comprises of heterogeneous group of neoplasms that occasionally express epithelial markers on immunohistochemistry (IHC). We herein report the case of a patient who developed RMS of the skull with EWSR1 fusion and anaplastic lymphoma kinase (ALK) and cytokeratin expression as cytomorphologic features. A 40-year-old man presented with a mass in his forehead. Surgical resection was performed, during which intraoperative frozen specimens were obtained. Squash cytology showed scattered or clustered spindle and epithelioid cells. IHC revealed that the resected tumor cells were positive for desmin, MyoD1, cytokeratin AE1/AE3, and ALK. Although EWSR1 rearrangement was identified on fluorescence in situ hybridization, ALK, and TFCP2 rearrangement were not noted. Despite providing adjuvant chemoradiation therapy, the patient died of tumor progression 10 months after diagnosis. We emphasize that a subset of RMS can express cytokeratin and show characteristic histomorphology, implying the need for specific molecular examination.

Abstract Genomic alterations in tumors play a pivotal role in determining their clinical trajectory and responsiveness to treatment. Targeted panel sequencing (TPS) has served as a key clinical tool over the past decade, but advancements in sequencing costs and bioinformatics have now made whole-genome sequencing (WGS) a feasible single-assay approach for almost all cancer genomes in clinical settings. This paper reports on the findings of a prospective, single-center study exploring the real-world clinical utility of WGS (tumor and matched normal tissues) and has two primary objectives: (1) assessing actionability for therapeutic options and (2) providing clarity for clinical questions. Of the 120 patients with various solid cancers who were enrolled, 95 (79%) successfully received genomic reports within a median of 11 working days from sampling to reporting. Analysis of these 95 WGS reports revealed that 72% (68/95) yielded clinically relevant insights, with 69% (55/79) pertaining to therapeutic actionability and 81% (13/16) pertaining to clinical clarity. These benefits include the selection of informed therapeutics and/or active clinical trials based on the identification of driver mutations, tumor mutational burden (TMB) and mutational signatures, pathogenic germline variants that warrant genetic counseling, and information helpful for inferring cancer origin. Our findings highlight the potential of WGS as a comprehensive tool in precision oncology and suggests that it should be integrated into routine clinical practice to provide a complete image of the genomic landscape to enable tailored cancer management.

Abstract Background The identification of viable tumors and radiation necrosis after stereotactic radiosurgery (SRS) is crucial for patient management. Tumor habitat analysis involving the grouping of similar voxels can identify subregions that share common biology and enable the depiction of areas of tumor recurrence and treatment-induced change. This study aims to validate an imaging biomarker for tumor recurrence after SRS for brain metastasis by conducting tumor habitat analysis using multi-parametric MRI. Methods In this prospective study (NCT05868928), patients with brain metastases will undergo multi-parametric MRI before SRS, and then follow-up MRIs will be conducted every 3 months until 24 months after SRS. The multi-parametric MRI protocol will include T2-weighted and contrast-enhanced T1-weighted imaging, diffusion-weighted imaging, and dynamic susceptibility contrast imaging. Using k-means voxel-wise clustering, this study will define three structural MRI habitats (enhancing, solid low-enhancing, and nonviable) on T1- and T2-weighted images and three physiologic MRI habitats (hypervascular cellular, hypovascular cellular, and nonviable) on apparent diffusion coefficient maps and cerebral blood volume maps. Using RANO-BM criteria as the reference standard, via Cox proportional hazards analysis, the study will prospectively evaluate associations between parameters of the tumor habitats and the time to recurrence. The DICE similarity coefficients between the recurrence site and tumor habitats will be calculated. Discussion The tumor habitat analysis will provide an objective and reliable measure for assessing tumor recurrence from brain metastasis following SRS. By identifying subregions for local recurrence, our study could guide the next therapeutic targets for patients after SRS. Trial registration This study is registered at ClinicalTrials.gov (NCT05868928).

BackgroundPulmonary tuberculosis (PTB) poses a global health challenge owing to the time-intensive nature of traditional diagnostic tests such as smear and culture tests, which can require hours to weeks to yield results. ObjectiveThis study aimed to use artificial intelligence (AI)–based chest radiography (CXR) to evaluate the infectivity of patients with PTB more quickly and accurately compared with traditional methods such as smear and culture tests. MethodsWe used DenseNet121 and visualization techniques such as gradient-weighted class activation mapping and local interpretable model-agnostic explanations to demonstrate the decision-making process of the model. We analyzed 36,142 CXR images of 4492 patients with PTB obtained from Severance Hospital, focusing specifically on the lung region through segmentation and cropping with TransUNet. We used data from 2004 to 2020 to train the model, data from 2021 for testing, and data from 2022 to 2023 for internal validation. In addition, we used 1978 CXR images of 299 patients with PTB obtained from Yongin Severance Hospital for external validation. ResultsIn the internal validation, the model achieved an accuracy of 73.27%, an area under the receiver operating characteristic curve of 0.79, and an area under the precision-recall curve of 0.77. In the external validation, it exhibited an accuracy of 70.29%, an area under the receiver operating characteristic curve of 0.77, and an area under the precision-recall curve of 0.8. In addition, gradient-weighted class activation mapping and local interpretable model-agnostic explanations provided insights into the decision-making process of the AI model. ConclusionsThis proposed AI tool offers a rapid and accurate alternative for evaluating PTB infectivity through CXR, with significant implications for enhancing screening efficiency by evaluating infectivity before sputum test results in clinical settings, compared with traditional smear and culture tests.

Common buckwheat (Fagopyrum esculentum Moench.) is one of the most important orphan crops worldwide. Various research efforts have been done to improve cultivation methods to enhance important agronomic traits such as productivity and biotic/abiotic resistance. One important aspect is the seed trait, which has not been extensively studied due to the time-consuming and tedious nature of its examination. Despite this, understanding seed traits is crucial for meeting consumer needs and optimizing crop yields. Therefore, the aim of the study is to investigate the inheritance of common buckwheat seed traits—such as shape, size, and coat color—using an image-based approach. This method allows for the analysis of a large number of seeds with a level of accuracy and precision that was previously unattainable. The results indicate that seed coat color is inherited maternally. Notably, the parameters in size had substantial increases acting like overdominance. The number of seeds that were harvested from F1s of each cross differed a lot depending on the cross combinations and pin/thrum type. In addition, seed size had large reduction in F1s from the different seed-sized parents, especially in thrum type. These may show that there could be cross barriers. The results revealed trends of maternal inheritance for seed shape and coat color in buckwheat, an area that has not been extensively studied. These findings could support buckwheat breeding efforts, helping to address market needs and food demands in the face of significant climate change.

Abstract Background COPD is associated with the development of lung cancer. A protective effect of inhaled corticosteroids (ICS) on lung cancer is still controversial. Hence, this study investigated the development of lung cancer according to inhaler prescription and comorbidties in COPD. Methods A retrospective cohort study was conducted based on the Korean Health Insurance Review and Assessment Service database. The development of lung cancer was investigated from the index date to December 31, 2020. This cohort included COPD patients (≥ 40 years) with new prescription of inhalers. Patients with a previous history of any cancer during screening period or a switch of inhaler after the index date were excluded. Results Of the 63,442 eligible patients, 39,588 patients (62.4%) were in the long-acting muscarinic antagonist (LAMA) and long-acting β2-agonist (LABA) group, 22,718 (35.8%) in the ICS/LABA group, and 1,136 (1.8%) in the LABA group. Multivariate analysis showed no significant difference in the development of lung cancer according to inhaler prescription. Multivariate analysis, adjusted for age, sex, and significant factors in the univariate analysis, demonstrated that diffuse interstitial lung disease (DILD) (HR = 2.68; 95%CI = 1.86–3.85), a higher Charlson Comorbidity Index score (HR = 1.05; 95%CI = 1.01–1.08), and two or more hospitalizations during screening period (HR = 1.19; 95%CI = 1.01–1.39), along with older age and male sex, were independently associated with the development of lung cancer. Conclusion Our data suggest that the development of lung cancer is not independently associated with inhaler prescription, but with coexisting DILD, a higher Charlson Comorbidity Index score, and frequent hospitalization.

Abstract Differentiating clinical stages based solely on positive findings from amyloid PET is challenging. We aimed to investigate the neuroanatomical characteristics at the whole-brain level that differentiate prodromal Alzheimer’s disease (AD) from cognitively unimpaired amyloid-positive individuals (CU A+) in relation to amyloid deposition and regional atrophy. We included 45 CU A+ participants and 135 participants with amyloid-positive prodromal AD matched 1:3 by age, sex, and education. All participants underwent 18F-florbetaben positron emission tomography and 3D structural T1-weighted magnetic resonance imaging. We compared the standardized uptake value ratios (SUVRs) and volumes in 80 regions of interest (ROIs) between CU A+ and prodromal AD groups using independent t-tests, and employed the least absolute selection and shrinkage operator (LASSO) logistic regression model to identify ROIs associated with prodromal AD in relation to amyloid deposition, regional atrophy, and their interaction. After applying False Discovery Rate correction at

Abstract Objectives To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. Materials and methods In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers’ workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. Results In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1–92.2) and 88.2% (95% CI: 85.7–90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: −0.281, 95% CI: −2.888, 2.325) than with DLS (LoA: −0.163, 95% CI: −2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2–90.6) to 57.3 s (interquartile range: 33.6–81.0) (P

Background/Aims Epidermal growth factor receptor (EGFR) mutation is important in determining the treatment strategy for advanced lung cancer patients with malignant pleural effusion (MPE). Contrary to serum carcinoembryonic antigen (S-CEA) levels, the associations between pleural fluid CEA (PF-CEA) levels and EGFR mutation status as well as between PF-CEA levels and treatment efficacy have rarely been investigated in lung adenocarcinoma patients with MPE. Methods This retrospective study enrolled lung adenocarcinoma patients with MPE and available PF-CEA levels and EGFR mutation results. The patients were categorized based on PF-CEA levels: < 10 ng/mL, 10–100 ng/mL, 100–500 ng/mL, and ≥ 500 ng/mL. The association between PF-CEA levels and EGFR mutation status as well as their therapeutic impact on overall survival was compared among the four groups. Results This study included 188 patients. PF-CEA level was found to be an independent predictor of EGFR mutation but not S-CEA level. The EGFR mutation rates were higher as the PF-CEA levels increased, regardless of cytology results or sample types. Among EGFR-mutant lung adenocarcinoma patients receiving EGFR-tyrosine kinase inhibitor (TKI) treatment, those with high PF-CEA levels had significantly better survival outcomes than those with low PF-CEA levels. Conclusion High PF-CEA levels were associated with high EGFR mutation rate and may lead to a favorable clinical outcome of EGFR-TKI treatment in EGFR-mutant lung adenocarcinoma patients with MPE. These findings highlight the importance of actively investigating EGFR mutation detection in patients with suspected MPE and elevated PF-CEA levels despite negative cytology results.

Abstract Background To evaluate the clinical significance of noninvasive prenatal testing (NIPT) for detecting fetal sex chromosome aneuploidies (SCAs) in Korean pregnant women. Methods We retrospectively analyzed NIPT data from 9,176 women with singleton pregnancies referred to the CHA Biotech genome diagnostics center. Cell-free fetal DNA (cffDNA) was extracted from maternal peripheral blood, and high-throughput massively parallel sequencing was conducted. Subsequently, the positive NIPT results for SCA were validated via karyotype and chromosomal microarray analyses. Results Overall, 46 cases were SCA positive after NIPT, including 20, 12, 8, and 6 for Turner, triple X, Klinefelter, and Jacob syndromes, respectively. Among 37 women with invasive prenatal diagnosis, 19 had true positive NIPT results. The overall positive predictive value (PPV) of NIPT for detecting SCAs was 51.35%. The PPV was 18.75% for Turner, 88.89% for triple X, 71.43% for Klinefelter, and 60.00% for Jacob’s syndromes. NIPT accuracy for detecting sex chromosome trisomies was higher than that for sex chromosome monosomy (P = 0.002). No significant correlation was observed between fetal SCA incidence and maternal age (P = 0.914), except for the borderline significance of Jacob’s syndrome (P = 0.048). No significant differences were observed when comparing NIPT and karyotyping validation for fetal SCA according to pregnancy characteristics. Conclusion Our data suggest that NIPT can reliably screen for SCAs, and it performed better in predicting sex chromosome trisomies compared with monosomy X. No correlation was observed between maternal age and fetal SCA incidence, and no association was observed between different pregnancy characteristics. The accuracy of these findings requires improvements; however, our study provides an important reference for clinical genetic counseling and further management. Larger scale studies, considering confounding factors, are required for accurate evaluation.

Rehabilitation training is essential for a successful recovery of upper extremity function after stroke. Training programs are typically conducted in hospitals or rehabilitation centers, supervised by specialized medical professionals. However, frequent visits to hospitals can be burdensome for stroke patients with limited mobility. We consider a self-administered rehabilitation system based on a mobile application in which patients can periodically upload videos of themselves performing reach-to-grasp tasks to receive recommendations for self-managed exercises or progress reports. Sensing equipment aside from cameras is typically unavailable in the home environment. A key contribution of our work is to propose a deep learning-based assessment model trained only with video data. As all patients carry out identical tasks, a fine-grained assessment of task execution is required. Our model addresses this difficulty by learning RGB and optical flow data in a complementary manner. The correlation between the RGB and optical flow data is captured by a novel module for modality fusion using cross-attention with Transformers. Experiments showed that our model achieved higher accuracy in movement assessment than existing methods for action recognition. Based on the assessment model, we developed a patient-centered, solution-based mobile application for upper extremity exercises for hemiplegia, which can recommend 57 exercises with three levels of difficulty. A prototype of our application was evaluated by potential end-users and achieved a good quality score on the Mobile Application Rating Scale (MARS).

Plants of the Asteraceae family have been cultivated worldwide for economic, medicinal, and ornamental purposes, including genera such as Aster, Helianthus, and Cosmos. Numerous studies examined their secondary metabolites; however, those of Aster × chusanensis, which is a natural hybrid species in South Korea, are unclear, and optimized propagation methods should be identified. We analyzed phenolic acid concentrations in each part of Aster × chusanensis through HPLC. Further, we investigated the growth characteristics and secondary metabolite concentrations under various growth temperatures using division propagation, followed by growing at 20, 25, and 30 °C in a growth chamber. Chlorogenic acid was the primary compound, which was particularly high in the leaves. The growth characteristics did not differ significantly between temperatures, and 30 °C was most efficient for phenolic acid biosynthesis. Our results provide valuable information on optimized propagation and secondary metabolite concentrations under different temperatures of Aster × chusanensis.

Although plant disease recognition has witnessed a significant improvement with deep learning in recent years, a common observation is that current deep learning methods with decent performance tend to suffer in real-world applications. We argue that this illusion essentially comes from the fact that current plant disease recognition datasets cater to deep learning methods and are far from real scenarios. Mitigating this illusion fundamentally requires an interdisciplinary perspective from both plant disease and deep learning, and a core question arises. What are the characteristics of a desired dataset? This paper aims to provide a perspective on this question. First, we present a taxonomy to describe potential plant disease datasets, which provides a bridge between the two research fields. We then give several directions for making future datasets, such as creating challenge-oriented datasets. We believe that our paper will contribute to creating datasets that can help achieve the ultimate objective of deploying deep learning in real-world plant disease recognition applications. To facilitate the community, our project is publicly available at https://github.com/xml94/PPDRD with the information of relevant public datasets. [ABSTRACT FROM AUTHOR]

Background: Somatic mutational signatures elucidate molecular vulnerabilities to therapy, and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Methods: Here, we develop a statistical model, Diffsig, for estimating the association of one or more continuous or categorical risk factors with DNA mutational signatures. Diffsig takes into account the uncertainty associated with assigning signatures to samples as well as multiple risk factors' simultaneous effect on observed DNA mutations. Results: We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. In simulation, our model was capable of accurately estimating expected associations in a variety of contexts. Conclusions: Diffsig allows researchers to quantify and perform inference on the associations of risk factors with mutational signatures. [ABSTRACT FROM AUTHOR]

Familial searching is a useful technique in a forensic investigation. Using genetic information, it is possible to identify individuals, determine familial relationships, and obtain racial/ethnic information. The total number of shared alleles (TNSA) and likelihood ratio (LR) methods have traditionally been used, and novel data-mining classification methods have recently been applied here as well. However, it is difficult to apply these methods to identify familial relationships above the third degree (e.g., uncle-nephew and first cousins). Therefore, we propose to apply a stacking ensemble machine learning algorithm to improve the accuracy of familial relationship identification. Using real data analysis, we obtain superior relationship identification results when applying metaclassifiers with a stacking algorithm rather than applying traditional TNSA or LR methods and data mining techniques. [ABSTRACT FROM AUTHOR]

Purpose Recently, we developed allele-discriminating priming system (ADPS) technology. This method increases the sensitivity of conventional quantitative polymerase chain reaction up to 100 folds, with limit of detection, 0.01%, with reinforced specificity. This prospective study aimed to develop and validate the accuracy of ADPS epidermal growth factor receptor (EGFR) Mutation Test Kit using clinical specimens. Materials and Methods In total 189 formalin-fixed paraffin-embedded tumor tissues resected from patients with non-small cell lung cancer were used to perform a comparative evaluation of the ADPS EGFR Mutation Test Kit versus the cobas EGFR Mutation Test v2, which is the current gold standard. When the two methods had inconsistent results, next-generation sequencing-based CancerSCAN was utilized as a referee. Results The overall agreement of the two methods was 97.4% (93.9%-99.1%); the positive percent agreement, 95.0% (88.7%-98.4%); and the negative percent agreement, 100.0% (95.9%-100.0%). EGFR mutations were detected at a frequency of 50.3% using the ADPS EGFR Mutation Test Kit and 52.9% using the cobas EGFR Mutation Test v2. There were 10 discrepant mutation calls between the two methods. CancerSCAN reproduced eight ADPS results. In two cases, mutant allele fraction was ultra-low at 0.02% and 0.06%, which are significantly below the limit of detection of the cobas assay and CancerSCAN. Based on the EGFR genotyping by ADPS, the treatment options could be switched in five patients. Conclusion The highly sensitive and specific ADPS EGFR Mutation Test Kit would be useful in detecting the patients who have lung cancer with EGFR mutation, and can benefit from the EGFR targeted therapy. [ABSTRACT FROM AUTHOR]

Aspartame's designation by the International Agency for Research on Cancer as "possibly carcinogenic to humans" has raised questions about its potential association with pancreatic cancer. By requesting further investigations to ascertain the veracity of this relationship, this study seeks to contribute to this ongoing debate. Understanding the effect of aspartame on cancer risk is essential for public health, although the research remains ambiguous. A case-control study (doi: 10.1158/1055-9965. EPI- 09-0365) was conducted in 2009 by Bosetti et al. to determine whether low-calorie sweeteners such as aspartame increase the risk of developing gastric, pancreatic, and endometrial malignancies. Many participants were evaluated between 1991 and 2014. This significant pancreatic cancer trial involved 326 patients and 652 controls. The consumption of low-calorie sweeteners and the risk of several diseases, including pancreatic cancer, were not significantly correlated, according to the study. In 2017, one study investigated the effects of artificial sweeteners, such as aspartame and stevia, on pancreatic acinar cancer. A mouse model was used in this study. In a previous study, aspartame and stevia did not increase the incidence of pancreatic cancer in a mouse model.1 However, a study conducted in 2021 found that long-term aspartame exposure enhanced the number of cancer stem cells (CSC) and aggressiveness of tumour cells in pancreatic cancer cells.2 Additional research is required to fully comprehend the connection between artificial sweeteners and the risk of developing cancer. It is important to remember that a recent study from 2023 examined the health advantages of using Delonix regia extract as a natural substitute for artificial sweeteners. The antioxidant and antibacterial capabilities of the extract and its capacity to slow the growth of pancreatic cancer cells have been demonstrated in a previous study.3 The findings of this study show that it is not reasonable to conclude that all artificial sweeteners, and not just aspartame, may pose a risk for cancer. The link between aspartame and cancer, particularly pancreatic cancer, is convoluted. Existing research provides essential insights, but there is a dearth of investigations. Future research must take into account dose, exposure duration, and combinations of factors to provide clear evidence and recommend the use of artificial sweeteners. [ABSTRACT FROM AUTHOR]

Microbial fermentation has emerged as a pivotal process for sustainable production of essential goods and chemicals. Corynebacterium glutamicum is a proficient platform organism that contributes significantly to amino acid production through microbial fermentation. Despite its recognized safety, challenges persist in efficiently biosynthesizing natural products compared with other organisms. This study evaluated the safety of biomass products from bioengineered C. glutamicum through two different toxicological studies: a bacterial reverse mutation test (AMES test) and an acute oral toxicity test in rats. Three types of dried fermentation biomass products, each engineered for the enhanced production of specific amino acids (L-lysine, L-threonine, and L-tryptophan), were examined. The tests were conducted in compliance with Organization for Economic Co-operation and Development guidelines and revealed no mutagenicity or acute toxicity at the tested doses. These findings suggest the safety of biomass products from bioengineered C. glutamicum as potential feed materials, although further toxicity studies are recommended for comprehensive evaluation. This study underscores the importance of stringent safety assessments for advancing biotechnological applications and provides valuable insights into the potential utilization of microbial fermentation products in various industries. Moreover, this study highlights the significance of regulatory compliance and adherence to international standards to ensure the safety and efficacy of novel biotechnological products.

Objective To explore the nuanced relationship between sleep patterns, chronotype, quality and the influence of health and lifestyle factors on cognitive performance.Design, setting, participants This cross-sectional analysis used ordinary least squares regression within the UK Biobank database, assessing 26 820 participants aged 53–86 years, categorised into two cohorts: Cohort 1 (10 067 participants, 56% female; completed all four cognitive tests of Fluid Intelligence/reasoning, Pairs Matching, Reaction Time and Prospective Memory) and Cohort 2 (16 753 participants, 56% female; completed only two cognitive assessments of Pairs Matching and Reaction Time).Exposures Participant’s self-reported sleep duration, chronotype and quality. Cognitive function was assessed through standardised computerised tests. The analysis was adjusted for demographic and comorbidity covariates.Main outcomes and measures Cognitive performance scores were evaluated against sleep parameters and health and lifestyle factors including sex, age, vascular and cardiac conditions, diabetes, alcohol intake, smoking habits and body mass index.Results The regression highlighted a positive association between normal sleep duration (7–9 hours) and cognitive scores in Cohort 1 (β=0.0567, 95% CI 0.0284 to 0.0851), while extended sleep duration negatively impacted scores across both cohorts (Cohort 1: β=−0.188, 95% CI −0.2938 to −0.0822; Cohort 2: β=−0.2619, 95% CI −0.3755 to −0.1482). Chronotype distinctions, particularly intermediate and evening types, were linked to superior cognitive function. Gender, age, angina, high blood pressure, diabetes, alcohol intake and smoking emerged as significant cognitive influencers.Conclusions and relevance The study delineates a multifaceted and nuanced relationship between sleep variables, health and lifestyle factors in determining cognitive outcomes. These findings highlight the vital role of sleep quality on cognitive health.

Objectives This study used National Health Insurance claims data from Korea to report the prevalence of sleep disorders and treatment status, including traditional Korean medicine, in the last 10 years.Methods This is a retrospective cohort study in Korea. All diagnosis and prescription data, including herbal medicine claims, from the Health Insurance Review and Assessment Service from 2011 to 2020 were reviewed. Prevalence estimation, direct medical expenses and prescribed amounts for sleep disorders were recorded.Results The prevalence of sleep disorders increased from 3 867 975 (7.62%) in 2011 to 7 446 846 (14.41%) in 2020, nearly doubling over 10 years. Insomnia was observed in 91.44% (n=9 011 692) of the patients. The mean number of hospital visits per patient for sleep disorders was 11.5 (±26.62). Benzodiazepines are the most commonly prescribed medications for sleep disorders, and gamma-isoyosan is the most frequently prescribed herbal medicine.Conclusions Sleep disorders are continuously increasing, as is the use of medical services—personal and social medical expenses are also increasing accordingly. Sleep disorders should be recognised as a significant health problem that needs to be actively addressed to improve quality of life.

(1) Background: Non-invasive prenatal testing (NIPT) is a screening test for fetal aneuploidy using cell-free fetal DNA. The fetal fragments (FF) of cell-free DNA (cfDNA) are derived from apoptotic trophoblast of the placenta. The level of fetal cfDNA is known to be influenced by gestational age, multiple pregnancies, maternal weight, and height. (2) Methods: This study is a single-center retrospective observational study which examines the relationship between the fetal fraction (FF) of cell-free DNA in non-invasive prenatal testing (NIPT) and adverse pregnancy outcomes in singleton pregnancies. A total of 1393 samples were collected between 10 weeks and 6 days, and 25 weeks and 3 days of gestation. (3) Results: Hypertensive disease of pregnancy (HDP) occurred more frequently in the low FF group than the normal FF group (5.17% vs. 1.91%, p = 0.001). Although the rates of small for gestational age (SGA) and placental abruption did not significantly differ between groups, the composite outcome was significantly higher in the low FF group (7.76% vs. 3.64%, p = 0.002). Furthermore, women who later experienced complications such as HDP or gestational diabetes mellitus (GDM) had significantly lower plasma FF levels compared to those without complications (p < 0.001). After adjustments, the low FF group exhibited a significantly higher likelihood of placental compromise (adjusted odds ratio: 1.946). (4) Conclusions: Low FF in NIPT during the first and early second trimesters is associated with adverse pregnancy outcomes, particularly HDP, suggesting its potential as a predictive marker for such outcomes.

Competing Interests: Conflicts of interest: No.