BACKGROUND AND OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the causative agent of COVID-19 has significantly challenged the public health landscape in late 2019. After almost 3 years of the first ever SARS-CoV-2 case, the World Health Organization (WHO) declared the end of this global health emergency in May 2023. Although, despite the subsequent drop of COVID-19 cases, the SARS-CoV-2 infection still exhibited multiple waves of infection, primarily attributed to the appearance of new variants. Five of these variants have been classified as Variants of Concern (VOC): Alpha, Beta, Gamma, Delta, and the most recent, Omicron. Therefore, the development of methods for the timely and accurate detection of viral variants remains fundamental, ensuring an ongoing and effective response to the disease. This study aims to evaluate the feasibility of the application of an in-house approach in genomic surveillance for the detection of SARS-CoV-2 variants using in silico designed primers.

METHODS

The primers used for the study were particularly designed based on conserved regions of certain genes in the virus, targeting distinct mutations found in known variants of SARS-CoV-2. Viral RNA extracts from nasopharyngeal samples (n=14) were subjected to quantitative and qualitative tests (Nanodrop and AGE). Selected samples were then analyzed by RT-PCR and amplicons were submitted for sequencing. Sequence alignment analysis was carried out to identify the prevailing COVID-19 variant present in the sample population.

RESULTS

The study findings demonstrated that the in-house method was able to successfully amplify conserved sequences (spike, envelope, membrane, ORF1ab) and enabled identification of the circulating SARS-CoV-2 variant among the samples. Majority of the samples were identified as Omicron variant. Three out of four designed primers effectively bound into the conserved sequence of target genes present in the sample, revealing the specific SARSCoV-2 variant. The detected mutations characterized for Omicron found in the identified lineages included K417N, S477N, and P681H which were also identified as mutations of interest. Furthermore, identification of the B.1.448 lineage which was not classified in any known variant also provided the potential of the developed in-house method in detecting unknown variants of COVID-19.

CONCLUSION

Among the five VOCs, Omicron is the most prevalent and dominant variant. The in-house direct PCR product sequencing surveillance (DPPSS) method provided an alternative platform for SAR-CoV-2 variant analysis which is accessible and affordable than the conventional diagnostic surveillance methods and the whole genome sequencing. Further evaluation and improvements on the oligonucleotide primers may offer significant contribution to the development of a specific and direct PCRbased detection of new emerging COVID-19 variants.

Sars-cov-2 ; Polymerase Chain Reaction ; Dna Primers ; Oligonucleotide Primers ; Computer Simulation ; Conserved Sequence ; Coronavirus ; Covid-19 ; Disease ; Emergencies ; Evaluation Studies As Topic ; Genes ; Genome ; Global Health ; Health ; Identification (psychology) ; Infection ; Infections ; Membranes ; Methods ; Mutation ; Oligonucleotides ; Organizations ; Population ; Public Health ; Rna ; Rna, Viral ; Sars Virus ; Sequence Alignment ; Severe Acute Respiratory Syndrome ; Syndrome ; Viruses ; Whole Genome Sequencing ; World Health Organization

Objective: To understand the incidence risk and risk factors of tuberculosis (TB) among middle school students in Chongqing, so as to provide a basis for formulating TB prevention and control strategies. Methods: From September to December 2022, 32 181 middle school students were selected as the study cohort from 15 administrative districts in Chongqing by using the stratified cluster random sampling method. All cohort members were screened with the tuberculin skin test (TST), and relevant information was collected from January 1, 2023 to December 31, 2024. On the basis of active screening, the follow up data of the participants were compared with the National Tuberculosis Management Information System to obtain the incidence status of the study subjects. The Log rank test was used to compare the TB incidence rates among students with different characteristics, and a Cox proportional hazards model was established to analyze the incidence risk and risk factors of TB. Results: The TST screening rate of the cohort members was 93.0%. During the 2 year follow up period, a total of 36 TB cases occurred, with a cumulative incidence rate of 111.87/100 000 and an incidence density of 55.95/100 000. Among them, the cumulative incidence rate of students from public schools (170.44/ 100 000 ) was higher than that of students from private schools (41.16/100 000), the cumulative incidence rate of students in schools located in high epidemic areas (153.95/100 000) was higher than that in medium epidemic areas (69.00/100 000), and the difference was statistically significant ( χ 2=11.49, 4.73, both P <0.05). The Log-rank test for different TST results showed that the difference in TB comulative incidence rate between students with strongly positive TST results (216.55/ 100 000 ) and those with negative TST results (81.40/100 000) was statistically significant ( χ 2=5.85, P <0.05). Univariate analysis using the Cox proportional hazards model revealed that the risk of TB was lower in students from private schools ( HR=0.25, 95% CI = 0.10-0.59) and students in medium epidemic areas ( HR=0.46, 95%CI =0.23-0.94); whereas the risk of TB was increased in students with strongly positive TST results ( HR=1.39, 95%CI =1.05-1.84) (all P <0.05). Multivariate Cox regression analysis showed that the risk of TB in students from private schools was lower than that of students from public schools ( HR=0.23, 95%CI=0.08-0.62, P <0.05). Conclusions The annual average incidence rate of TB among middle school students in Chongqing is at a relatively high level. It is necessary to strengthen the management and intervention for student groups, including those in public schools, those in schools located in high epidemic areas, and those with strongly positive TST results, so as to reduce the incidence rate of TB.

Objective By combining biological detection and imaging evaluation, a clinical prediction model is constructed based on a large cohort to improve the accuracy of distinguishing between benign and malignant pulmonary nodules. Methods A retrospective analysis was conducted on the clinical data of the 32 627 patients with pulmonary nodules who underwent chest CT and testing for 7 types of lung cancer-related serum autoantibodies (7-AABs) at our hospital from January 2020 to April 2024. The univariate and multivariate logistic regression models were performed to screen independent risk factors for benign and malignant pulmonary nodules, based on which a nomogram model was established. The performance of the model was evaluated using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Results A total of 1 017 patients with pulmonary nodules were included in the study. The training set consisted of 712 patients, including 291 males and 421 females, with a mean age of (58±12) years. The validation set included 305 patients, comprising 129 males and 176 females, with a mean age of (58±13) years. Univariate ROC curve analysis indicated that the combination of CT and 7-AABs testing achieved the highest area under the curve (AUC) value (0.794), surpassing the diagnostic efficacy of CT alone (AUC=0.667) or 7-AABs alone (AUC=0.514). Multivariate logistic regression analysis showed that radiological nodule diameter, nodule nature, and CT combined with 7-AABs detection were independent predictors, which were used to construct a nomogram prediction model. The AUC values for this model were 0.826 and 0.862 in the training and validation sets, respectively, demonstrating excellent performance in DCA. Conclusion The combination of 7-AABs with CT significantly enhances the accuracy of distinguishing between benign and malignant pulmonary nodules. The developed predictive model provides strong support for clinical decision-making and contributes to achieving precise diagnosis and treatment of pulmonary nodules.

BACKGROUND AND OBJECTIVE

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as the causative agent of COVID-19 has significantly challenged the public health landscape in late 2019. After almost 3 years of the first ever SARS-CoV-2 case, the World Health Organization (WHO) declared the end of this global health emergency in May 2023. Although, despite the subsequent drop of COVID-19 cases, the SARS-CoV-2 infection still exhibited multiple waves of infection, primarily attributed to the appearance of new variants. Five of these variants have been classified as Variants of Concern (VOC): Alpha, Beta, Gamma, Delta, and the most recent, Omicron. Therefore, the development of methods for the timely and accurate detection of viral variants remains fundamental, ensuring an ongoing and effective response to the disease. This study aims to evaluate the feasibility of the application of an in-house approach in genomic surveillance for the detection of SARS-CoV-2 variants using in silico designed primers.

METHODS

The primers used for the study were particularly designed based on conserved regions of certain genes in the virus, targeting distinct mutations found in known variants of SARS-CoV-2. Viral RNA extracts from nasopharyngeal samples (n=14) were subjected to quantitative and qualitative tests (Nanodrop and AGE). Selected samples were then analyzed by RT-PCR and amplicons were submitted for sequencing. Sequence alignment analysis was carried out to identify the prevailing COVID-19 variant present in the sample population.

RESULTS

The study findings demonstrated that the in-house method was able to successfully amplify conserved sequences (spike, envelope, membrane, ORF1ab) and enabled identification of the circulating SARS-CoV-2 variant among the samples. Majority of the samples were identified as Omicron variant. Three out of four designed primers effectively bound into the conserved sequence of target genes present in the sample, revealing the specific SARSCoV-2 variant. The detected mutations characterized for Omicron found in the identified lineages included K417N, S477N, and P681H which were also identified as mutations of interest. Furthermore, identification of the B.1.448 lineage which was not classified in any known variant also provided the potential of the developed in-house method in detecting unknown variants of COVID-19.

CONCLUSION

Among the five VOCs, Omicron is the most prevalent and dominant variant. The in-house direct PCR product sequencing surveillance (DPPSS) method provided an alternative platform for SAR-CoV-2 variant analysis which is accessible and affordable than the conventional diagnostic surveillance methods and the whole genome sequencing. Further evaluation and improvements on the oligonucleotide primers may offer significant contribution to the development of a specific and direct PCRbased detection of new emerging COVID-19 variants.

Sars-cov-2 ; Polymerase Chain Reaction ; Dna Primers ; Oligonucleotide Primers

Background: and Purpose The National Brain Biobank of Korea (NBBK) is a brain bank consortium supported by the Korea Disease Control and Prevention Agency and the Korea National Institute of Health, and was launched in 2015 to support research into neurodegenerative disease dementia (NDD). This study aimed to introduce the NBBK and describes clinicopathological correlations based on analyses of data collected from the NBBK. Methods: Four hospital-based brain banks have been established in South Korea: Samsung Medical Center Brain Bank (SMCBB), Seoul National University Hospital Brain Bank (SNUHBB), Pusan National University Hospital Brain Bank (PNUHBB), and Myongji Hospital Brain Bank (MJHBB). Clinical and pathological data were collected from these brain banks using standardized protocols. The prevalence rates of clinical and pathological diagnoses were analyzed in order to characterize the clinicopathological correlations. Results: Between August 2016 and December 2023, 185 brain specimens were collected and pathologically evaluated (SNUHBB: 117; PNUHBB: 27; SMCBB: 34; MJHBB: 7). The age at consent was 70.8±12.6 years, and the age at autopsy was 71.7±12.4 years. The four-most-common clinical diagnoses were Alzheimer’s disease (AD) dementia (20.0%), idiopathic Parkinson’s disease (15.1%), unspecified dementia (11.9%), and cognitively unimpaired (CU) (11.4%).Most cases of unspecified dementia had a pathological diagnosis of central nervous system (CNS) vasculopathy (31.8%) or AD (31.8%). Remarkably, only 14.2% of CU cases had normal pathological findings. The three-most-common pathological diagnoses were AD (26.5%), CNS vasculopathy (14.1%), and Lewy body disease (13.5%). Conclusions These clinical and neuropathological findings provide a deeper understanding of the mechanisms underlying NDD in South Korea.

Background: and Purpose: Amyloid-beta (Aβ) plaques are key in Alzheimer’s disease (AD), with Aβ positron emission tomography imaging enabling non-invasive quantification.To address regional Aβ deposition, we developed regional Centiloid scales (rdcCL) and commercialized them through the computed tomography (CT)-based BeauBrain Amylo platform, eliminating the need for three-dimensional T1 magnetic resonance imaging (MRI). Objective: We aimed to establish robust regional Aβ cutoffs using the commercialized BeauBrain Amylo platform and to explore the prevalence of subgroups defined by global, regional, and striatal Aβ cutoffs across cognitive stages. Methods: We included 2,428 individuals recruited from the Korea-Registries to Overcome Dementia and Accelerate Dementia Research project. We calculated regional Aβ cutoffs using Gaussian Mixture Modeling. Participants were classified into subgroups based on global, regional, and striatal Aβ positivity across cognitive stages (cognitively unimpaired [CU], mild cognitive impairment, and dementia of the Alzheimer’s type). Results: MRI-based and CT-based global Aβ cutoffs were highly comparable and consistent with previously reported Centiloid values. Regional cutoffs revealed both similarities and differences between MRI- and CT-based methods, reflecting modality-specific segmentation processes. Subgroups such as global(−)regional(+) were more frequent in non-dementia stages, while global(+)striatal(−) was primarily observed in CU individuals. Conclusions Our study established robust regional Aβ cutoffs using a CT-based rdcCL method and demonstrated its clinical utility in classifying amyloid subgroups across cognitive stages. These findings highlight the importance of regional Aβ quantification in understanding amyloid pathology and its implications for biomarker-guided diagnosis and treatment in AD.

Background: and Purpose: Plasma biomarkers, including phosphorylated tau (ptau217), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), are promising tools for detecting Alzheimer’s disease (AD) pathology. However, cross-laboratory reproducibility remains a challenge, even when using identical analytical platforms such as single-molecule array (Simoa). This study aimed to compare plasma biomarker measurements (ptau217, GFAP, and NfL) between 2 laboratories, the University of Gothenburg (UGOT) and DNAlink, and evaluate their associations with amyloid positron emission tomography (PET) imaging. Methods: Plasma biomarkers were measured using Simoa platforms at both laboratories:the UGOT and DNAlink Incorporation. Diagnostic performance for predicting amyloid PET positivity, cross-laboratory agreement, and the impact of normalization techniques were assessed. Bland-Altman plots and correlation analyses were employed to evaluate agreement and variability. Results: Plasma ptau217 concentrations exhibited strong correlations with amyloid PET global centiloid values, with comparable diagnostic performance between laboratories (area under the curve=0.94 for UGOT and 0.95 for DNAlink). Cross-laboratory agreement for ptau217 was excellent (r=0.96), improving further after natural log transformation. GFAP and NfL also demonstrated moderate to strong correlations (r=0.86 for GFAP and r=0.99 for NfL), with normalization reducing variability. Conclusions Plasma biomarker measurements were consistent across laboratories using identical Simoa platforms, with strong diagnostic performance and improved agreement after normalization. These findings support the scalability of plasma biomarkers for multicenter studies and underscore their potential for standardized applications in AD research and clinical practice.