Comparison of Chandelier-Assisted versus Standard Scleral Buckling for the Treatment of Primary Rhegmatogenous Retinal Detachment: A Systematic Review and Meta-Analysis.

Introduction Compare the anatomical and functional outcomes, operation duration, and complication rates between standard scleral buckling (SSB) and chandelier-assisted scleral buckling (CSB) for phakic eyes with rhegmatogenous retinal detachment (RRD). Methods PubMed, Embase and Cochrane Library databases were searched from inception to June 2024. The primary endpoint will be set as a final success. The secondary endpoint will be primary success, operation time, and final BCVA. Results Our meta-analysis showed that there is no statistical difference between CSB and SSB for the final success rate (RR=1.00, 95%CI=0.97-1.03). For the primary success rate, there is no statistical difference between CSB and SSB (RR=1.00, 95%CI=0.94-1.06). For operation time, our meta-analysis showed that the CSB group is less than the SSB group (pooled MD= -15.8, 95%CI=-22.60- -9.00). For postoperative complications, our study shows that the CSB group presented with lower pooled risk than the SSB group (RR=0.59, 95%CI=0.41-0.89). There is a trend that the ERM formation risk is higher in the CSB group if there is no routine suture for the sclerotomy (p=0.08). Conclusion CSB showcases a significantly reduced operation duration and less postoperative complication in contrast to the SSB group, maintaining comparable primary and ultimate anatomical success rates as well as final BCVA.

Decoding human somatosensory sensitivity through resting EEG and behavioral analysis: a multimodal fusion approach.

In precision medicine and clinical pain management, the creation of quantitative, objective indicators to assess somatosensory sensitivity was essential. This study proposed a fusion approach for decoding human somatosensory sensitivity, which combined multimodal (quantitative sensory test and neurophysiology) features to classify the dataset on individual somatosensory sensitivity and reveal distinct types of brain activation patterns. Sixty healthy participants took part in the experiment on somatosensory sensitivity that implemented cold, heat, mechanical punctate, and pressure stimuli, and the resting-state electroencephalography (EEG) was collected using BrainVision. The quantitative sensory testing (QST) scores of the participants were clustered using the unsupervised k-means algorithm into four subgroups: generally hypersensitive (HS), generally non-sensitive (NS), predominantly thermally sensitive (TS), and predominantly mechanically sensitive (MS). Furthermore, two types of power spectral density (PSD), band-based PSD (BB-PSD) and frequency-based PSD (FB-PSD), and two types of inter-electrode connectivity (IEC), band-based connectivity (BBC) and frequency-based connectivity (FBC), derived from resting-state EEG were subjected to feature selection with a proposed prior-compared minimum-redundancy maximum-relevance (PCMRMR) protocol. Their effectiveness was then tested by the supervised classification tasks using support vector machine (SVM), k-nearest neighbor (kNN), random forest (RF), and Gaussian classifier (GC). Brain networks of four somatosensory types were revealed by decoding fused multimodal data, namely type-averaged connectivity. The data from sixty healthy individuals were divided into training (n = 59) and validation (n = 1) datasets according to leave-one-subject-out (LOSO) criteria. The FBC was identified, which can serve as better brain signatures than BB-PSD, FB-PSD, and BBC to classify subjects as HS, NS, TS, or MS groups. Using the SVM, kNN, RF, and GC models, the best accuracy of 87% was obtained when classifying participants into HS, NS, TS, or MS groups. Moreover, the brain networks were decoded from HS, NS, TS, and MS groups by decoding the type-averaged connectivity fused from somatosensory phenotypes and selected FBC. It indicated that quantified multi-parameter somatosensory sensitivity could be achieved with acceptable accuracy, leading to considerable possibilities for using objective pain perception evaluation in clinical practice.

Developing a Polygenic Risk Score with Age and Sex to Identify High-Risk Myopia in Taiwan.

Myopia is the leading cause of impaired vision, and its prevalence is increasing among Asian populations. This study aimed to develop a polygenic risk score (PRS) followed by replication to predict myopia in the Taiwanese population. In total, 23,688 participants with cycloplegic autorefraction-measured mean spherical equivalent (SE), genetic, and demographic data were included. The myopia PRS was generated based on genome-wide association study (GWAS) outcomes in a Taiwanese population and previously published GWAS reports. The results demonstrated that the inclusion of age and sex in the PRS had an area under the curve (AUC) of 0.80, 0.78, and 0.73 (p < 0.001) for participants aged >18 years with high (SE < -6.0 diopters (D); n = 1089), moderate (-6.0 D < SE ≤ -3.0 D; n = 3929), and mild myopia (-3.0 D < SE ≤ -1.0 D; n = 2241), respectively. Participants in the top PRS quartile had a 1.30-fold greater risk of high myopia (95% confidence interval = 1.09-1.55, p = 0.003) compared with that in the remaining participants. Further, a higher PRS significantly increased the risk of high myopia (SE ≤ -2.0 D) in children ≤6 years of age (p = 0.027). In conclusion, including the PRS, age, and sex improved the prediction of high myopia risk in the Taiwanese population.

Sensitive Detection of Oral Leukoplakia: Analyzing P90 Biomarkers in Saliva and Tissue.

Oral cancer represents a significant global public health challenge, contributing substantially to the incidence and mortality of cancer. Despite established risk factors such as tobacco use and alcohol consumption, early detection remains crucial for effective treatment. This study introduces a novel approach using a transistor-based biosensor system for detecting the P90 (CIP2A) protein. We tested the presence of CIP2A in human leukoplakia samples, which can undergo malignant conversion into aggressive oral squamous cell carcinoma. The method used commercially available glucose test strips functionalized with P90 antibodies, providing high sensitivity and a low limit of detection which was five orders lower than that of commercial ELISA kits. A specially designed printed circuit board (PCB) facilitated accurate measurements, and the device's performance was optimized through characteristic tests. Human sample testing validated the biosensor's effectiveness in distinguishing samples after cell lysis. This study contributes to advancing accurate and cost-effective diagnostic approaches for oral pre-cancer and cancer tissues.

Deciphering the Prognostic Efficacy of MRI Radiomics in Nasopharyngeal Carcinoma: A Comprehensive Meta-Analysis.

This meta-analysis investigates the prognostic value of MRI-based radiomics in nasopharyngeal carcinoma treatment outcomes, specifically focusing on overall survival (OS) variability. The study protocol was registered with INPLASY (INPLASY202420101). Initially, a systematic review identified 15 relevant studies involving 6243 patients through a comprehensive search across PubMed, Embase, and Web of Science, adhering to PRISMA guidelines. The methodological quality was assessed using the Quality in Prognosis Studies (QUIPS) tool and the Radiomics Quality Score (RQS), highlighting a low risk of bias in most domains. Our analysis revealed a significant average concordance index (c-index) of 72% across studies, indicating the potential of radiomics in clinical prognostication. However, moderate heterogeneity was observed, particularly in OS predictions. Subgroup analyses and meta-regression identified validation methods and radiomics software as significant heterogeneity moderators. Notably, the number of features in the prognosis model correlated positively with its performance. These findings suggest radiomics' promising role in enhancing cancer treatment strategies, though the observed heterogeneity and potential biases call for cautious interpretation and standardization in future research.

Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation.

BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer segmentation. However, its efficacy across different clinical settings and tumor stages remains variable. METHODS: We conducted a comprehensive search of PubMed, Embase, and Web of Science until November 7, 2023. We assessed the quality of these studies by using the Checklist for Artificial Intelligence in Medical Imaging and the Quality Assessment of Diagnostic Accuracy Studies-2 tools. This analysis included data from various clinical settings and stages of lung cancer. Key performance metrics, such as the Dice similarity coefficient, were pooled, and factors affecting algorithm performance, such as clinical setting, algorithm type, and image processing techniques, were examined. RESULTS: Our analysis of 37 studies revealed a pooled Dice score of 79 % (95 % CI: 76 %-83 %), indicating moderate accuracy. Radiotherapy studies had a slightly lower score of 78 % (95 % CI: 74 %-82 %). A temporal increase was noted, with recent studies (post-2022) showing improvement from 75 % (95 % CI: 70 %-81 %). to 82 % (95 % CI: 81 %-84 %). Key factors affecting performance included algorithm type, resolution adjustment, and image cropping. QUADAS-2 assessments identified ambiguous risks in 78 % of studies due to data interval omissions and concerns about generalizability in 8 % due to nodule size exclusions, and CLAIM criteria highlighted areas for improvement, with an average score of 27.24 out of 42. CONCLUSION: This meta-analysis demonstrates DL algorithms' promising but varied efficacy in lung cancer segmentation, particularly higher efficacy noted in early stages. The results highlight the critical need for continued development of tailored DL models to improve segmentation accuracy across diverse clinical settings, especially in advanced cancer stages with greater challenges. As recent studies demonstrate, ongoing advancements in algorithmic approaches are crucial for future applications.

A-LLTO Nanoparticles Embedded Composite Solid Polymer Electrolyte for Room Temperature Operational Li-metal Batteries.

Solid-state batteries (SSBs) have the potential to revolutionize the current energy storage sector. A significant portion of the current development of electric vehicles and the electrification of various appliances relies on Lithium (Li)-ion batteries. However, future energy demands will require the development of stronger and more reliable batteries. This report presents a novel solid state electrolyte (SSE) composed of a self-healing composite solid polymer electrolyte (CSPE) matrix and aluminum-doped (Li0.33La0.56)1.005Ti0.99Al0.01O3 (A-LLTO) nanofillers. The CSPE contains Jeffamine ED-2003 monomer, Benzene-1,3,5-tricarbaldehyde (BTC) crosslinker dissolved in a 1:1 ratio of Dimethylformamide (DMF) to LiPF6, and a certain amount (x) of A-LLTO nanofillers (x = 5, 7.5, 10, 12.5%). A CSPE containing x-amount of A-LLTO fillers (referred to as CAL-x%) demonstrates excellent ion-conducting properties and stable battery performance. The CAL-10% demonstrates 1.1 × 10-3 S cm-1 of ionic conductivity at room temperature (RT). A-LLTO nanofillers dispersed uniformly within the polymer matrix form a percolation network, which is believed to improve ionic conductivity and the diffusion of Li+ ions. The CR-2032 cell, consisting of LiFePO4 (LFP)║CAL-10%║Li, at RT offers an initial discharge capacity of ≈165 mAh g-1 at 0.1C rate for 120 cycles with 98.85% coulombic efficiency (C.E.).

Standalone deep learning versus experts for diagnosis lung cancer on chest computed tomography: a systematic review.

PURPOSE: To compare the diagnostic performance of standalone deep learning (DL) algorithms and human experts in lung cancer detection on chest computed tomography (CT) scans. MATERIALS AND METHODS: This study searched for studies on PubMed, Embase, and Web of Science from their inception until November 2023. We focused on adult lung cancer patients and compared the efficacy of DL algorithms and expert radiologists in disease diagnosis on CT scans. Quality assessment was performed using QUADAS-2, QUADAS-C, and CLAIM. Bivariate random-effects and subgroup analyses were performed for tasks (malignancy classification vs invasiveness classification), imaging modalities (CT vs low-dose CT [LDCT] vs high-resolution CT), study region, software used, and publication year. RESULTS: We included 20 studies on various aspects of lung cancer diagnosis on CT scans. Quantitatively, DL algorithms exhibited superior sensitivity (82%) and specificity (75%) compared to human experts (sensitivity 81%, specificity 69%). However, the difference in specificity was statistically significant, whereas the difference in sensitivity was not statistically significant. The DL algorithms' performance varied across different imaging modalities and tasks, demonstrating the need for tailored optimization of DL algorithms. Notably, DL algorithms matched experts in sensitivity on standard CT, surpassing them in specificity, but showed higher sensitivity with lower specificity on LDCT scans. CONCLUSION: DL algorithms demonstrated improved accuracy over human readers in malignancy and invasiveness classification on CT scans. However, their performance varies by imaging modality, underlining the importance of continued research to fully assess DL algorithms' diagnostic effectiveness in lung cancer. CLINICAL RELEVANCE STATEMENT: DL algorithms have the potential to refine lung cancer diagnosis on CT, matching human sensitivity and surpassing in specificity. These findings call for further DL optimization across imaging modalities, aiming to advance clinical diagnostics and patient outcomes. KEY POINTS: Lung cancer diagnosis by CT is challenging and can be improved with AI integration. DL shows higher accuracy in lung cancer detection on CT than human experts. Enhanced DL accuracy could lead to improved lung cancer diagnosis and outcomes.

Phonological processing in Chinese word repetition: Dementia effect and age effect.

This paper examined phonological processing as reflected in Mandarin word and non-word repetition. Issues related to dementia effects, age effects, and linguistic variables are discussed. Forty-five speakers of Mandarin Chinese in Taiwan took part in this study. Fourteen seniors with Alzheimer's disease (AD), 14 healthy adults, and 17 healthy seniors individually completed a task of word repetition. Scores of accurate repetition were calculated, and error types as well as error frequency were analysed. Linguistic variables, including lexicality, frequency, non-word, and syllable length, were discussed. Major findings were indicative of a significant dementia effect, in which participants with AD were significantly impaired in repeating words with more syllables, specifically 5-syllable non-words of low frequency. Numerous significant age effects were noted. Theoretical implications and directions for future study are discussed.

Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis.

Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice.

Supercharge end-to-side nerve transfer from anterior interosseous nerve to augment intrinsic recovery in high ulnar nerve injuries of varying magnitudes.

BACKGROUND: High ulnar nerve injuries result in intrinsic muscle weakness and are inconvenient for patients. Moreover, conventional surgical techniques often fail to achieve satisfactory motor recovery. A potential reconstructive solution in the form of the supercharge end-to-side (SETS) anterior interosseous nerve (AIN) transfer method has emerged. Therefore, this study aims to compare surgical outcomes of patients with transected and in-continuity high ulnar nerve lesions following SETS AIN transfer. METHODS: Between June 2015 and May 2023, patients with high ulnar palsy in the form of transection injuries or lesion-in-continuity were recruited. The assessment encompassed several objective results, including grip strength, key pinch strength, compound muscle action potential, sensory nerve action potential, and two-point discrimination tests. The muscle power of finger abduction and adduction was also recorded. Additionally, subjective questionnaires were utilized to collect data on patient-reported outcomes. Overall, the patients were followed up for up to 2 years. RESULTS: Patients with transected high ulnar nerve lesions exhibited worse baseline performance than those with lesion-in-continuity, including motor and sensory functions. However, they experienced greater motor improvement but less sensory recovery, resulting in comparable final motor outcomes in both groups. In contrast, the transection group showed worse sensory outcomes. CONCLUSIONS: Our findings suggest that SETS AIN transfer benefits patients with high ulnar nerve palsy, regardless of the lesion type. Nonetheless, improvements may be more pronounced in patients with transected lesions.

Gratification and its associations with problematic internet use: A systematic review and meta-analysis using Use and Gratification theory.

Problematic Internet Use (PIU) has been a growing issue for two decades, and many researchers have sought to explain its occurrence. Use and Gratification Theory (UGT) has guided empirical studies investigating the associations between gratification and PIU. However, their results appear equivocal. This study aimed to synthesize research findings applying UGT and pinpoint the associations between different gratifications and PIU. Effect sizes were pooled to obtain an overall effect size. Gratifications were then classified into four types (i.e., content, social, self-presentation, and process) according to the suggestions of previous studies and the measurement of included studies. Based on 216 effect sizes from 57 samples with 38,492 participants (mean age = 24.3; 55.1 % female), random effect model analyses yielded a medium effect size (r = 0.303, p <.001, 95 % CI [0.263, 0.343]), indicating a positive association between general gratification and PIU. Subgroup analyses revealed that all four gratifications were positively associated with PIU; self-presentation showed the largest effect size, followed by process gratification.This study enhances the conceptual relevance of UGT in understanding PIU and highlights the importance of process and self-presentation gratification as predictors for PIU in certain contexts, such as among university students and in Asian countries.

Identification of hyperthermophilic D-allulose 3-epimerase from Thermotoga sp. and its application as a high-performance biocatalyst for D-allulose synthesis.

D-Allulose 3-epimerase (DAE) is a vital biocatalyst for the industrial synthesis of D-allulose, an ultra-low calorie rare sugar. However, limited thermostability of DAEs hinders their use at high-temperature production. In this research, hyperthermophilic TI-DAE (Tm = 98.4 ± 0.7 ℃) from Thermotoga sp. was identified via in silico screening. A comparative study of the structure and function of site-directed saturation mutagenesis mutants pinpointed the residue I100 as pivotal in maintaining the high-temperature activity and thermostability of TI-DAE. Employing TI-DAE as a biocatalyst, D-allulose was produced from D-fructose with a conversion rate of 32.5%. Moreover, TI-DAE demonstrated excellent catalytic synergy with glucose isomerase CAGI, enabling the one-step conversion of D-glucose to D-allulose with a conversion rate of 21.6%. This study offers a promising resource for the enzyme engineering of DAEs and a high-performance biocatalyst for industrial D-allulose production.

Parental Depression and Self-Stigma Among Chinese Young People Living With Depression: A Qualitative Study.

Self-stigma is detrimental to psychosocial well-being and the recovery journey among people living with depression. However, there has been limited research exploring the experience of stigma internalization when depression runs in families. This study aims to address this gap by (1) characterizing the manifestations of self-stigma among individuals living with depression whose parent(s) also have depression and (2) exploring the potential mechanisms underlying the impact of parental depression on self-stigma. Essential principles of the constructivist grounded theory approach were adopted to collect data through in-depth interviews with 27 participants aged 15-30, living in Mainland China. Many participants perceived depression running in their family as an endless disaster and an incurable illness. These beliefs further led to stigmatizing emotions (such as suppression, anger, and guilt) and behaviors (such as concealment and social withdrawal). Participants also highlighted ambivalent intergenerational relationships, tense family atmospheres, lower parental emotional involvement and support, and a lack of family flexibility due to parental depression. Furthermore, parental depression impacted participants' self-stigma by interfering with family relationships, family functioning, and parenting styles. It also shaped their perceptions of family, illness attribution, and public stigma. Additionally, parental depression had an impact on participants' social functioning, self-esteem, and personality, making them more susceptible to self-stigma. This study emphasizes the crucial role that the family plays in the internalization of stigma among individuals living with depression. It suggests that family dynamics, rather than family structure or economic backgrounds alone, shape this process.

Identification of Genetic Variants for Risk Prediction and Early Diagnosis of Age-Related Macular Degeneration in the Taiwanese Population.

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly worldwide. The prevalence and phenotypes of AMD differ among populations, including between people in Taiwan and other regions. We performed a genome-wide association study to identify genetic variants and to develop genetic models to predict the risk of AMD development and progression in the Taiwanese population. In total, 4039 patients with AMD and 16,488 non-AMD controls (aged ≥ 65 years) were included. We identified 31 AMD-associated variants (p < 5 × 10-8) on chromosome 10q26, surrounding PLEKHA1-ARMS2-HTRA1. Two genetic models were constructed using the clump and threshold method. Model 1 included the single nucleotide polymorphism rs11200630 and showed a 1.31-fold increase in the risk of AMD per risk allele (95% confidence interval (CI) = 1.20-1.43, p < 0.001). In model 2, 1412 single-nucleotide polymorphisms were selected to construct a polygenic risk score (PRS). Individuals with the top 5% PRS had a 1.40-fold higher AMD risk compared with that of individuals with a PRS in the bottom quartile (95% CI = 1.04-1.89, p = 0.025). Moreover, the PRS in the upper quartile was related to a decreased age at AMD diagnosis by 0.62 years (95% CI = -1.15, -0.09, p = 0.023). Both genetic models provide useful predictive power for populations at high risk of AMD, affording a basis for identifying patients requiring close follow-up and early intervention.

Identification of anti-fibrotic and pro-apoptotic bioactive compounds from Ganoderma formosanum and their possible mechanisms in modulating TGF-ß1-induced lung fibrosis.

ETHNOPHARMACOLOGICAL RELEVANCE: The Compendium of Materia Medica and the Classic of Materia Medica, the two most prominent records of traditional Chinese medicine, documented the therapeutic benefits of Ganoderma sinense particularly in addressing pulmonary-related ailments. Ganoderma formosanum, an indigenous subspecies of G. sinense from Taiwan, has demonstrated the same therapeutic properties. AIM OF THE STUDY: The aim of this study is to identify bioactive compounds and evaluate the potential of G. formosanum extracts as a novel treatment to alleviate pulmonary fibrosis (PF). Using an in-house drug screening platform, two-stage screening was performed to determine their anti-fibrotic efficacy. METHODS AND MATERIALS: G. formosanum was fractionated into four partitions by solvents of different polarities. To determine their antifibrotic and pro-apoptotic properties, the fractions were analyzed using two TGF-ß1-induced pulmonary fibrosis cell models (NIH-3T3) and human pulmonary fibroblast cell lines, immunoblot, qRT-PCR, and annexin V assays. Subsequently, transcriptomic analysis was conducted to validate the findings and explore possible molecular pathways. The identification of potential bioactive compounds was achieved through UHPLC-MS/MS analysis, while molecular interaction study was investigated by multiple ligands docking and molecular dynamic simulations. RESULTS: The ethyl acetate fraction (EAF) extracted from G. formosanum demonstrated substantial anti-fibrotic and pro-apoptotic effects on TGF-ß1-induced fibrotic models. Moreover, the EAF exhibited no discernible cytotoxicity. Untargeted UHPLC-MS/MS analysis identified potential bioactive compounds in EAF, including stearic acid, palmitic acid, and pentadecanoic acid. Multiple ligands docking and molecular dynamic simulations further confirmed that those bioactive compounds possess the ability to inhibit TGF-ß receptor 1. CONCLUSION: Potential bioactive compounds in G. formosanum were successfully extracted and identified in the EAF, whose anti-fibrotic and pro-apoptotic properties could potentially modulate pulmonary fibrosis. This finding not only highlights the EAF's potential as a promising therapeutic candidate to treat pulmonary fibrosis, but it also elucidates how Ganoderma confers pulmonary health benefits as described in the ancient texts.

Mentorship for young gay men in Hong Kong: A pilot mixed-methods randomized controlled trial.

This study examined whether mentorship could promote young gay men's identities and well-being, and whether a mentor's sexual orientation matters. A randomized control trial compared outcomes across three conditions: Arm A (a mentee matched with a sexual minority mentor), Arm B (a mentee matched with a heterosexual mentor), and a control arm receiving psychoeducation only. A community sample of 60 mentees aged 18-25 years was randomly allocated to the three arms and completed questionnaires at baseline, 3 months into the intervention, and at the end of the 6-month program. Fifteen mentees recounted their mentoring experiences through in-depth interviews. Linear mixed effects models showed that for both intervention and control conditions, internalized homonegativity declined while resilience, loneliness, and body acceptance improved over time. No time and group interactions were found. Meanwhile, a mentor's sexual orientation did not drive differential quality and outcomes of mentorship. Interviewees cited various benefits of mentorship, including providing companionship, enriching connection with lesbian, gay, bisexual (LGB) communities, and adding knowledge and perspectives of LGB lives and identities. Although quantitative data did not support any exclusive benefits of mentorship, most mentees recognized mentorship as a vital source of affirmation and companionship. Implications for research and mentoring programs are discussed.

High sensitivity saliva-based biosensor in detection of breast cancer biomarkers: HER2 and CA15-3.

The prevalence of breast cancer in women underscores the urgent need for innovative and efficient detection methods. This study addresses this imperative by harnessing salivary biomarkers, offering a noninvasive and accessible means of identifying breast cancer. In this study, commercially available disposable based strips similar to the commonly used glucose detection strips were utilized and functionalized to detect breast cancer with biomarkers of HER2 and CA15-3. The results demonstrated limits of detection for these two biomarkers reached as low as 1 fg/ml much lower than those of conventional enzyme-linked immunosorbent assay in the range of 1∼4 ng/ml. By employing a synchronized double-pulse method to apply 10 of 1.2 ms voltage pulses to the electrode of sensing strip and drain electrode of the transistor for amplifying the detected signal, and the detected signal was the average of 10 digital output readings corresponding to those 10 voltage pulses. The sensor sensitivities were achieved approximately 70/dec and 30/dec for HER2 and CA15-3, respectively. Moreover, the efficiency of this novel technique is underscored by its swift testing time of less than 15 ms and its minimal sample requirement of only 3 µl of saliva. The simplicity of operation and the potential for widespread public use in the future position this approach as a transformative tool in the early detection of breast cancer. This research not only provides a crucial advancement in diagnostic methodologies but also holds the promise of revolutionizing public health practices.

Progress in Serial Imaging for Prognostic Stratification of Lung Cancer Patients Receiving Immunotherapy: A Systematic Review and Meta-Analysis.

Immunotherapy, particularly with checkpoint inhibitors, has revolutionized non-small cell lung cancer treatment. Enhancing the selection of potential responders is crucial, and researchers are exploring predictive biomarkers. Delta radiomics, a derivative of radiomics, holds promise in this regard. For this study, a meta-analysis was conducted that adhered to PRISMA guidelines, searching PubMed, Embase, Web of Science, and the Cochrane Library for studies on the use of delta radiomics in stratifying lung cancer patients receiving immunotherapy. Out of 223 initially collected studies, 10 were included for qualitative synthesis. Stratifying patients using radiomic models, the pooled analysis reveals a predictive power with an area under the curve of 0.81 (95% CI 0.76-0.86, p < 0.001) for 6-month response, a pooled hazard ratio of 4.77 (95% CI 2.70-8.43, p < 0.001) for progression-free survival, and 2.15 (95% CI 1.73-2.66, p < 0.001) for overall survival at 6 months. Radiomics emerges as a potential prognostic predictor for lung cancer, but further research is needed to compare traditional radiomics and deep-learning radiomics.