Borderline personality disorder is a common, treatable condition that usually presents in late adolescence or early adulthood. Patients with borderline personality disorder are disproportionately represented in many clinical settings. Early identification and intervention of borderline personality disorder could help address the current mental health affecting young adults. College and university mental health settings have an opportunity to identify borderline personality disorder and to help guide students and families to appropriate treatment. College-based clinicians also have a role in educating campus administrators who may have little or no familiarity with standard borderline personality disorder symptoms or the trajectory of the disorder.

Lung cancer remains a global health concern, demanding the development of noninvasive, prompt, selective, and point-of-care diagnostic tools. Correspondingly, breath analysis using nanobiosensors has emerged as a promising noninvasive nose-on-chip technique for the early detection of lung cancer through monitoring diversified biomarkers such as volatile organic compounds/gases in exhaled breath. This comprehensive review summarizes the state-of-the-art breath-based lung cancer diagnosis employing chemiresistive-module nanobiosensors supported by theoretical findings. It unveils the fundamental mechanisms and biological basis of breath biomarker generation associated with lung cancer, technological advancements, and clinical implementation of nanobiosensor-based breath analysis. It explores the merits, challenges, and potential alternate solutions in implementing these nanobiosensors in clinical settings, including standardization, biocompatibility/toxicity analysis, green and sustainable technologies, life-cycle assessment, and scheming regulatory modalities. It highlights nanobiosensors\' role in facilitating precise, real-time, and on-site detection of lung cancer through breath analysis, leading to improved patient outcomes, enhanced clinical management, and remote personalized monitoring. Additionally, integrating these biosensors with artificial intelligence, machine learning, Internet-of-things, bioinformatics, and omics technologies is discussed, providing insights into the prospects of intelligent nose-on-chip lung cancer sniffing nanobiosensors. Overall, this review consolidates knowledge on breathomic biosensor-based lung cancer screening, shedding light on its significance and potential applications in advancing state-of-the-art medical diagnostics to reduce the burden on hospitals and save human lives.

BACKGROUND: Nasopharyngeal carcinoma (NPC) is diagnosed relatively late and has a poor prognosis, requiring early detection to reduce the disease burden. This diagnostic test accuracy meta-analysis evaluated the serological diagnostic value of nine EBV-related IgA antibody panels (EBNA1-IgA, VCA-IgA, EA-IgA, Zta-IgA, EBNA1-IgA + VCA-IgA, VCA-IgA + EA-IgA, VCA-IgA + Rta-IgG, EBNA1-IgA + VCA-IgA + Zta-IgA and VCA-IgA + EA-IgA + Rta-IgG), aiming to identify suitable serological detection biomarkers for NPC screening.
METHODS: PubMed, Embase, China National Knowledge Infrastructure and Chinese BioMedical Literature Database were searched from January 1st, 2000 to September 30th, 2023, with keywords nasopharyngeal carcinoma, IgA, screening, early detection, early diagnosis, sensitivity and specificity. Articles on the diagnostic value of serum EBV-related IgA antibody panels for NPC were included. Study selection, data extraction, and quality assessment were performed independently by two researchers, and a third researcher was consulted in the case of disagreement. Bivariate models were used for statistical analysis. The quality of included studies was evaluated through Quality Assessment of Diagnostic Accuracy Studies tool (QUADAS-2).
RESULTS: A total of 70 articles were included, involving 11 863 NPC cases and 34 995 controls. Among the nine EBV-related IgA antibody panels, EBNA1-IgA + VCA-IgA [0.928 (0.898, 0.950)], VCA-IgA + Rta-IgG [0.925 (0.890, 0.949)], EBNA1-IgA + VCA-IgA + Zta-IgA [0.962 (0.909, 0.985)] and VCA-IgA + EA-IgA + Rta-IgG [0.945 (0.918, 0.964)] demonstrated higher pooled sensitivity (95%CI). In terms of diagnostic odds ratio (DOR) (95%CI), EBNA1-IgA + VCA-IgA [107.647 (61.173, 189.430)], VCA-IgA + Rta-IgG [105.988 (60.118, 186.857)] and EBNA1-IgA + VCA-IgA + Zta-IgA [344.450 (136.351, 870.153)] showed superior performance. Additionally, the SROC curves for EBNA1-IgA + VCA-IgA and VCA-IgA + Rta-IgG were more favorable. However, publication bias was detected for VCA-IgA (P = 0.005) and EBNA1-IgA + VCA-IgA (P = 0.042).
CONCLUSIONS: In general, parallel detection of serum EBNA1-IgA, VCA-IgA and Zta-IgA antibodies using ELISA demonstrates better pooled sensitivity and DOR among the studied panels. In the cases where fewer indicators are used, serum VCA-IgA and EBNA1-IgA/Rta-IgG antibody panel exhibits a comparable performance.
BACKGROUND: The International Prospective Register of Systematic Reviews registration number: CRD42023426984, registered on May 28, 2023.

Clinical identification of early neurodegenerative changes requires an accurate and accessible characterization of brain and cognition in healthy aging. We assessed whether a brief online cognitive assessment can provide insights into brain morphology comparable to a comprehensive neuropsychological battery. In 141 healthy mid-life and older adults, we compared Creyos, a relatively brief online cognitive battery, to a comprehensive in person cognitive assessment. We used a multivariate technique to study the ability of each test to inform brain morphology as indexed by cortical sulcal width extracted from structural magnetic resonance imaging (sMRI).We found that the online test demonstrated comparable strength of association with cortical sulcal width compared to the comprehensive in-person assessment.These findings suggest that in our at-risk sample online assessments are comparable to the in-person assay in their association with brain morphology. With their cost effectiveness, online cognitive testing could lead to more equitable early detection and intervention for neurodegenerative diseases.

BACKGROUND: Digital cognitive assessments, particularly those that can be done at home, present as low-burden biomarkers for participants and patients alike, but their effectiveness in the diagnosis of Alzheimer\'s disease (AD) or predicting its trajectory is still unclear. Here, we assessed what utility or added value these digital cognitive assessments provide for identifying those at high risk of cognitive decline.
METHODS: We analyzed >500 Alzheimer\'s Disease Neuroimaging Initiative participants who underwent a brief digital cognitive assessment and amyloid beta (Aβ)/tau positron emission tomography scans, examining their ability to distinguish cognitive status and predict cognitive decline.
RESULTS: Performance on the digital cognitive assessment was superior to both cortical Aβ and entorhinal tau in detecting mild cognitive impairment and future cognitive decline, with mnemonic discrimination deficits emerging as the most critical measure for predicting decline and future tau accumulation.
CONCLUSIONS: Digital assessments are effective at identifying at-risk individuals, supporting their utility as low-burden tools for early AD detection and monitoring.
CONCLUSIONS: Performance on digital cognitive assessments predicts progression to mild cognitive impairment at a higher proficiency compared to amyloid beta and tau. Deficits in mnemonic discrimination are indicative of future cognitive decline. Impaired mnemonic discrimination predicts future entorhinal and inferior temporal tau.

Gliomas are highly complex and metabolically active brain tumors associated with poor prognosis. Recent reports have found altered levels of blood metabolites during early tumor development, suggesting that tumor development could be detected several years before clinical manifestation. In this study, we performed metabolite analyses of blood samples collected from healthy controls and future glioma patients, up to eight years before glioma diagnosis, and on the day of glioma surgery. We discovered that metabolites related to early glioma development were associated with an increased energy turnover, as highlighted by elevated levels of TCA-related metabolites such as fumarate, malate, lactate and pyruvate in pre-diagnostic cases. We also found that metabolites related to glioma progression at surgery were primarily high levels of amino acids and metabolites of amino acid catabolism, with elevated levels of 11 amino acids and two branched-chain alpha-ketoacids, ketoleucine and ketoisoleucine. High amino acid turnover in glioma tumor tissue is currently utilized for PET imaging, diagnosis and delineation of tumor margins. By examining blood-based metabolic progression patterns towards disease onset, we demonstrate that this high amino acid turnover is also detectable in a simple blood sample. These findings provide additional insight of metabolic alterations during glioma development and progression.

Gastric cardia adenocarcinoma (GCA) and esophageal squamous cell carcinoma (ESCC) present significant health challenges in China, often diagnosed at advanced stages with poor prognoses. However, effective biomarkers for early detection remain elusive. This study aimed to integrate methylome and transcriptome data to identify DNA methylation markers for the early detection of GCA and ESCC. In the discovery stage, we conducted Infinium MethylationEPIC array analysis on 36 paired GCA and non-tumor adjacent tissues (NAT), identifying differentially methylated CpG sites (DMCs) between GCA/ESCC and NAT through combined analyses of in-house and publicly available data. In the validation stage, targeted pyrosequencing and quantitative real-time RT-PCR were performed on paired tumor and NAT samples from 50 GCA and 50 ESCC patients. In the application stage, an independent set of 438 samples, including GCA, ESCC, high- and low-grade dysplasia (HGD/LGD), and normal controls, was tested for selected DMCs using pyrosequencing. Our analysis validated three GCA-specific, two ESCC-specific, and one tumor-shared DMCs, exhibiting significant hypermethylation and decreased expression of target genes in tumor samples compared with NAT. Leveraging these DMCs, we developed a GCA-specific 4-marker panel (cg27284428, cg11798358, cg07880787, and cg00585116) with an area under the receiver operating characteristic curve (AUC) of 0.917, effectively distinguishing between cardia HGD/GCA patients and cardia LGD/normal controls. Similarly, an ESCC-specific 3-marker panel (cg14633892, cg04415798, and cg00585116) achieved an AUC of 0.865 in distinguishing esophageal HGD/ESCC cases. Furthermore, integrating cg00585116, age, and alcohol consumption yielded a tumor-shared logistic model with good discrimination for two cancer/HGD (AUC, 0.767; 95% confidence interval, 0.720-0.813). The mean AUC of the model after 5-fold cross-validation was 0.764. In summary, our study identifies novel DNA methylation markers capable of accurately distinguishing GCA/ESCC and HGD from LGD and normal controls. These findings offer promising prospects for targeted DNA methylation assays in future minimally invasive cancer screening methods.

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95% CI: 0.915-0.981), with a sensitivity of 79.3% (95% CI: 71.5-87.0%) across all stages at a specificity of 96.0% (95% CI: 90.6-100.0%). Specifically, the sensitivity of the ESim-seq model was 76.5% (95% CI: 67.3-85.8%) in stage I patients, 100% (95% CI: 100.0-100.0%) in stage II patients, 100% (95% CI: 100.0-100.0%) in stage III patients and 87.5% (95% CI: 64.6%-100.0%) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95% CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.

Agriculture is considered the backbone of Tanzania\'s economy, with more than 60% of the residents depending on it for survival. Maize is the country\'s dominant and primary food crop, accounting for 45% of all farmland production. However, its productivity is challenged by the limitation to detect maize diseases early enough. Maize streak virus (MSV) and maize lethal necrosis virus (MLN) are common diseases often detected too late by farmers. This has led to the need to develop a method for the early detection of these diseases so that they can be treated on time. This study investigated the potential of developing deep-learning models for the early detection of maize diseases in Tanzania. The regions where data was collected are Arusha, Kilimanjaro, and Manyara. Data was collected through observation by a plant. The study proposed convolutional neural network (CNN) and vision transformer (ViT) models. Four classes of imagery data were used to train both models: MLN, Healthy, MSV, and WRONG. The results revealed that the ViT model surpassed the CNN model, with 93.1 and 90.96% accuracies, respectively. Further studies should focus on mobile app development and deployment of the model with greater precision for early detection of the diseases mentioned above in real life.

OBJECTIVE: Colonoscopy-based surveillance to prevent colorectal cancer (CRC) causes substantial burden for patients and healthcare. Stool tests may help to reduce surveillance colonoscopies, by limiting colonoscopies to individuals at increased risk of AN.
METHODS: This cross-sectional observational study included individuals aged 50-75 with surveillance indication. Before bowel preparation, participants collected samples for a multitarget stool DNA (mt-sDNA) test and two fecal immunochemical tests (FITs). Test accuracies were calculated for all surveillance indications. Only for the post-polypectomy indication, most common and associated with a relatively low CRC risk, long-term impact of stool-based surveillance was evaluated with the ASCCA model. Stool-based strategies were simulated to tune each tests\' positivity threshold to obtain strategies at least as effective as colonoscopy surveillance.
RESULTS: 3453 individuals had results for all stool tests and colonoscopy. 2226 had previous polypectomy, 1003 previous CRC and 224 familial risk. Areas under the receiver operating characteristic curve for AN were 0.72 (95% CI; 0.69-0.75) for the mt-sDNA test, 0.61 (95% CI; 0.58-0.64) for the FIT OC-Sensor and 0.59 (95% CI; 0.56-0.61) for the FIT FOB-Gold. Stool-based post-polypectomy surveillance strategies at least as effective as colonoscopy surveillance, reduced the number of colonoscopies by 15-41% and required 5.6-9.5 stool tests over the lifetime of a person. Mt-sDNA-based surveillance was more costly than colonoscopy surveillance, whereas FIT-based surveillance saved costs.
CONCLUSIONS: This study shows that stool-based post-polypectomy surveillance strategies can be safe and cost-effective, with potential to reduce the number of colonoscopies by up to 41%.