Genetic Risk Loci and Familial Associations in Migraine: A Genome-Wide Association Study in the Han Chinese Population of Taiwan.

BACKGROUND AND PURPOSE: Migraine is a condition that is often observed to run in families, but its complex genetic background remains unclear. This study aimed to identify the genetic factors influencing migraines and their potential association with the family medical history. METHODS: We performed a comprehensive genome-wide association study of a cohort of 1,561 outpatients with migraine and 473 individuals without migraine in Taiwan, including Han Chinese individuals with or without a family history of migraine. By analyzing the detailed headache history of the patients and their relatives we aimed to isolate potential genetic markers associated with migraine while considering factors such as sex, episodic vs. chronic migraine, and the presence of aura. RESULTS: We revealed novel genetic risk loci, including rs2287637 in DEAD-Box helicase 1 and long intergenic non-protein coding RNA 1804 and rs12055943 in engulfment and cell motility 1, that were correlated with the family history of migraine. We also found a genetic location downstream of mesoderm posterior BHLH transcription factor 2 associated with episodic migraine, whereas loci within the ubiquitin-specific peptidase 26 exonic region, dual specificity phosphatase 9 and pregnancy-upregulated non-ubiquitous CaM kinase intergenic regions, and poly (ADP-ribose) polymerase 1 and STUM were linked to chronic migraine. We additionally identified genetic regionsassociated with the presence or absence of aura. A locus between LINC02561 and urocortin 3 was predominantly observed in female patients. Moreover, three different single-nucleotide polymorphisms were associated with the family history of migraine in the control group. CONCLUSIONS: This study has identified new genetic locations associated with migraine and its family history in a Han Chinese population, reinforcing the genetic background of migraine. The findings point to potential candidate genes that should be investigated further.

Interaction between RNF4 and SART3 is associated with the risk of schizophrenia.

The pathogenesis of schizophrenia (SCZ) is heavily influenced by genetic factors. Ring finger protein 4 (RNF4) and squamous cell carcinoma antigen recognized by T cells 3 (SART3) are thought to be involved in nervous system growth and development via oxidative stress pathways. Moreover, they have previously been linked to SCZ. Yet the role of RNF4 and SART3 in SCZ remains unclear. Here, we investigated how these two genes are involved in SCZ by studying their variants observed in patients. We first observed significantly elevated mRNA levels of RNF4 and SART3 in the peripheral blood in both first-episode (n = 30) and chronic (n = 30) SCZ patients compared to controls (n = 60). Next, we targeted-sequenced three single nucleotide polymorphisms (SNPs) in SART3 and six SNPs in RNF4 for association with SCZ using the genomic DNA extracted from peripheral blood leukocytes from SCZ participants (n = 392) and controls (n = 572). We observed a combination of SNPs that included rs1203860, rs2282765 (both in RNF4), and rs2287550 (in SART3) was associated with increased risk of SCZ, suggesting common pathogenic mechanisms between these two genes. We then conducted experiments in HEK293T cells to better understand the interaction between RNF4 and SART3. We observed that SART3 lowered the expression of RNF4 through ubiquitination and downregulated the expression of nuclear factor E2-related factor 2 (NRF2), a downstream factor of RNF4, implicating the existence of a possible shared regulatory mechanism for RNF4 and SART3. In conclusion, our study provides evidence that the interaction between RNF4 and SART3 contributes to the risk of SCZ. The findings shed light on the underlying molecular mechanisms of SCZ and may lead to the development of new therapies and interventions for this disorder.

Causality Investigation between Gut Microbiome and Sleep-Related Traits: A Bidirectional Two-Sample Mendelian Randomization Study.

Recent research has highlighted associations between sleep and microbial taxa and pathways. However, the causal effect of these associations remains unknown. To investigate this, we performed a bidirectional two-sample Mendelian randomization (MR) analysis using summary statistics of genome-wide association studies (GWAS) from 412 gut microbiome traits (N = 7738) and GWAS studies from seven sleep-associated traits (N = 345,552 to 386,577). We employed multiple MR methods to assess causality, with Inverse Variance Weighted (IVW) as the primary method, alongside a Bonferroni correction ((p < 2.4 × 10-4) to determine significant causal associations. We further applied Cochran's Q statistical analysis, MR-Egger intercept, and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) for heterogeneity and pleiotropy assessment. IVW estimates revealed 79 potential causal effects of microbial taxa and pathways on sleep-related traits and 45 inverse causal relationships, with over half related to pathways, emphasizing their significance. The results revealed two significant causal associations: genetically determined relative abundance of pentose phosphate decreased sleep duration (p = 9.00 × 10-5), and genetically determined increase in fatty acid level increased the ease of getting up in the morning (p = 8.06 × 10-5). Sensitivity analyses, including heterogeneity and pleiotropy tests, as well as a leave-one-out analysis of single nucleotide polymorphisms, confirmed the robustness of these relationships. This study explores the potential causal relationships between sleep and microbial taxa and pathways, offering novel insights into their complex interplay.

Longitudinal assessment of plasma biomarkers for early detection of cognitive changes in subjective cognitive decline.

Background: Individuals experiencing subjective cognitive decline (SCD) are at an increased risk of developing mild cognitive impairment and dementia. Early identification of SCD and neurodegenerative diseases using biomarkers may help clinical decision-making and improve prognosis. However, few cross-sectional and longitudinal studies have explored plasma biomarkers in individuals with SCD using immunomagnetic reduction. Objective: To identify plasma biomarkers for SCD. Methods: Fifty-two participants [38 with SCD, 14 healthy controls (HCs)] underwent baseline assessments, including measurements of plasma Aß42, Aß40, t-tau, p-tau, and α-synuclein using immunomagnetic reduction (IMR) assays, cognitive tests and the Mini-Mental State Examination (MMSE). Following initial cross-sectional analysis, 39 individuals (29 with SCD, 10 HCs) entered a longitudinal phase for reassessment of these biomarkers and the MMSE. Biomarker outcomes across different individual categories were primarily assessed using the area under the receiver operating characteristic (ROC) curve. The SCD subgroup with an MMSE decline over one point was compared to those without such a decline. Results: Higher baseline plasma Aß1-42 levels significantly discriminated participants with SCD from HCs, with an acceptable area under the ROC curve (AUC) of 67.5% [95% confidence interval (CI), 52.7-80.0%]. However, follow-up and changes in MMSE and IMR data did not significantly differ between the SCD and HC groups (p > 0.05). Furthermore, lower baseline plasma Aß1-42 levels were able to discriminate SCD subgroups with and without cognitive decline with a satisfied performance (AUC, 75.0%; 95% CI, 55.6-89.1%). At last, the changes in t-tau and Aß42 × t-tau could differentiate between the two SCD subgroups (p < 0.05). Conclusion: Baseline plasma Aß42 may help identify people with SCD and predict SCD progression. The role of plasma Aß42 levels as well as their upward trends from baseline in cases of SCD that progress to mild cognitive impairment and Alzheimer's disease require further investigation.

Novel therapeutic activities of dragon blood from palm tree Daemonorops draco for the treatment of chronic diabetic wounds.

BACKGROUND: The clinical efficacy of Jinchuang Ointment, a traditional Chinese medicine (TCM), in treating chronic non-healing diabetic wounds has been demonstrated over the past decades. Both in vitro and in vivo angiogenic activities have been reported for its herbal ingredients, including dragon blood from the palm tree Daemonorops draco and catechu from Uncaria gambir Roxb. Additionally, crude extracts of dragon blood have exhibited hypoglycemic effects not only in animal studies but also in cell-based in vitro assays. RESULTS: Our findings indicate that crude dragon blood extract promotes the differentiation of myoblasts into myotubes. Partially purified fractions of dragon blood crude extract significantly enhance the expression of muscle cell differentiation-related genes such as myoG, myoD, and myoHC. Our results also demonstrate that crude extracts of dragon blood can inhibit platelet-derived growth factor-induced PAI-1 expression in primary rat vascular smooth muscle cells, thereby favoring changes in hemostasis towards fibrinolysis. Consistent with previous reports, reduced expression of plasminogen activator inhibitor 1 (PAI-1) accelerates wound healing. However, further separation resulted in a significant loss of both activities, indicating the involvement of more than one compound in these processes. Stem cells play a crucial role in muscle injury repair. Neither dragon blood nor catechu alone stimulated the proliferation of human telomerase reverse transcriptase (hTERT)-immortalized and umbilical cord mesenchymal stem cells. Interestingly, the proliferation of both types of stem cells was observed when crude extracts of dragon blood and catechu were present together in the stem cell growth medium. CONCLUSIONS: Dragon blood from D. draco offers multifaceted therapeutic benefits for treating chronic nonhealing diabetic wounds from various perspectives. Most drugs in Western medicine consist of small molecules with defined ingredients. However, this is not the case in TCM, as the activities of dragon blood reported in this study. Surprisingly, the activities documented here align with descriptions in ancient Chinese medical texts dating back to A.D. 1625.

Association Between Statin Use and the Incidence of Clinically Diagnosed Osteoarthritis: A Nationwide Retrospective Cohort Study in Taiwan.

OBJECTIVE: To investigate the effect of higher cumulative defined daily dose per year (cDDD/y) compared with lower cDDD/y of statin use in the incidence of any joint osteoarthritis (OA). DESIGN: In this population-based retrospective cohort study, patients who were aged ≥40 years were newly initiated on statin therapy between 2002 and 2011, and had a statin prescription for ≥90 days in the first year of treatment were identified from the 2000 Longitudinal Generation Tracking Database. All patients were separated into groups with higher cDDD/y (>120 cDDD/y) and lower cDDD/y (≤120 cDDD/y; as an active comparator) values. Propensity score matching was performed to balance potential confounders. All recruited patients were followed up for 8 years. Marginal Cox proportional hazard models were used to estimate time-to-event outcomes of OA. RESULTS: Compared with lower cDDD/y use, higher cDDD/y use did not reduce the risk of any joint OA (adjusted hazard ratio, 1.07; 95% confidence interval, 0.99-1.14). Dose-related analysis did not reveal any dose-dependent association. A series of sensitivity analyses showed similar results. Joint-specific analyses revealed that statin did not reduce the incidence of knee, hand, hip, and weight-bearing (knee or hip) OA. CONCLUSIONS: Higher cDDD/y statin use did not reduce the risk of OA in this Taiwanese nationwide cohort study. The complexity of OA pathogenesis might contribute to the ineffectiveness of statin. Repurposing statin with its anti-inflammation properties might be ineffective for OA development, and balancing the catabolism and anabolism of cartilage might be a major strategy for OA prevention.

Human lineage mutations regulate RNA-protein binding of conserved genes NTRK2 and ITPR1 involved in human evolution.

Background: The role of human lineage mutations (HLMs) in human evolution through post-transcriptional modification is unclear. Aims: To investigate the contribution of HLMs to human evolution through post-transcriptional modification. Methods: We applied a deep learning model Seqweaver to predict how HLMs impact RNA-binding protein affinity. Results: We found that only 0.27% of HLMs had significant impacts on RNA-binding proteins at the threshold of the top 1% of human common variations. These HLMs enriched in a set of conserved genes highly expressed in adult excitatory neurons and prenatal Purkinje neurons, and were involved in synapse organisation and the GTPase pathway. These genes also carried excess damaging coding mutations that caused neurodevelopmental disorders, ataxia and schizophrenia. Among these genes, NTRK2 and ITPR1 had the most aggregated evidence of functional importance, suggesting their essential roles in cognition and bipedalism. Conclusions: Our findings suggest that a small subset of human-specific mutations have contributed to human speciation through impacts on post-transcriptional modification of critical brain-related genes.

Polygenic risk score predicting susceptibility and outcome of benign prostatic hyperplasia in the Han Chinese.

BACKGROUND: Given the high prevalence of BPH among elderly men, pinpointing those at elevated risk can aid in early intervention and effective management. This study aimed to explore that polygenic risk score (PRS) is effective in predicting benign prostatic hyperplasia (BPH) incidence, prognosis and risk of operation in Han Chinese. METHODS: A retrospective cohort study included 12,474 male participants (6,237 with BPH and 6,237 non-BPH controls) from the Taiwan Precision Medicine Initiative (TPMI). Genotyping was performed using the Affymetrix Genome-Wide TWB 2.0 SNP Array. PRS was calculated using PGS001865, comprising 1,712 single nucleotide polymorphisms. Logistic regression models assessed the association between PRS and BPH incidence, adjusting for age and prostate-specific antigen (PSA) levels. The study also examined the relationship between PSA, prostate volume, and response to 5-α-reductase inhibitor (5ARI) treatment, as well as the association between PRS and the risk of TURP. RESULTS: Individuals in the highest PRS quartile (Q4) had a significantly higher risk of BPH compared to the lowest quartile (Q1) (OR = 1.51, 95% CI = 1.274-1.783, p < 0.0001), after adjusting for PSA level. The Q4 group exhibited larger prostate volumes and a smaller volume reduction after 5ARI treatment. The Q1 group had a lower cumulative TURP probability at 3, 5, and 10 years compared to the Q4 group. PRS Q4 was an independent risk factor for TURP. CONCLUSIONS: In this Han Chinese cohort, higher PRS was associated with an increased susceptibility to BPH, larger prostate volumes, poorer response to 5ARI treatment, and a higher risk of TURP. Larger prospective studies with longer follow-up are warranted to further validate these findings.

Polymorphisms of HLA genes and hypersensitivity to penicillin among patients in a Taiwanese population.

Penicillin allergy is a potentially life-threatening condition that is common among patients. However, the genetic associations with penicillin allergy are not yet recognized for prevention or diagnosis, particularly in East Asian populations. We conducted a retrospective case-control study using data from the Taiwan Precision Medicine Initiative and analysing DNA samples to identify eight major MHC Class I and Class II loci. We employed imputation methods for accurate HLA typing and enrolled 17,827 individuals who received penicillin. Logistic regression analyses were utilized to explore associations between HLA genotypes, comorbidities and allergy risk, while simultaneously conducting a subgroup analysis to explore the association between HLA genotypes, comorbidities and the severity of allergic reactions. Our study assigned 496 cases to the penicillin allergy group and 4960 controls to a matched group. The risk of penicillin allergy was significantly higher with HLA-DPB1*05:01 (OR = 1.36, p = .004) and HLA-DQB1*05:01 (OR = 1.54, p = .03), with adjusted p-values of .032 and .24, respectively. Urticaria was identified as a separate risk factor (OR = 1.73, p < .001). However, neither the HLA alleles nor the comorbidities had a significant relationship with the risk of severe penicillin-induced allergy. HLA-DPB1*05:01 was found to be significantly associated with penicillin allergy reactions among the Taiwanese population.

Deep learning-based glomerulus detection and classification with generative morphology augmentation in renal pathology images.

Glomerulus morphology on renal pathology images provides valuable diagnosis and outcome prediction information. To provide better care, an efficient, standardized, and scalable method is urgently needed to optimize the time-consuming and labor-intensive interpretation process by renal pathologists. This paper proposes a deep convolutional neural network (CNN)-based approach to automatically detect and classify glomeruli with different stains in renal pathology images. In the glomerulus detection stage, this paper proposes a flattened Xception with a feature pyramid network (FX-FPN). The FX-FPN is employed as a backbone in the framework of faster region-based CNN to improve glomerulus detection performance. In the classification stage, this paper considers classifications of five glomerulus morphologies using a flattened Xception classifier. To endow the classifier with higher discriminability, this paper proposes a generative data augmentation approach for patch-based glomerulus morphology augmentation. New glomerulus patches of different morphologies are generated for data augmentation through the cycle-consistent generative adversarial network (CycleGAN). The single detection model shows the F1 score up to 0.9524 in H&E and PAS stains. The classification result shows that the average sensitivity and specificity are 0.7077 and 0.9316, respectively, by using the flattened Xception with the original training data. The sensitivity and specificity increase to 0.7623 and 0.9443, respectively, by using the generative data augmentation. Comparisons with different deep CNN models show the effectiveness and superiority of the proposed approach.

Design method for eliminating spectral line tilt in a multiple sub-pupil ultra-spectral imager (MSPUI).

A multiple sub-pupil ultra-spectral imaging system designed with a single spectrometer and detector can simultaneously detect multiple-channel spectra with ultra-high spectral resolution. However, due to using a prism in the system's front end, the nonlinear dispersion introduces spectral line tilt in the imaging spectra. This phenomenon can lead to bias in the final spectral data. To eliminate this issue, we propose a new design by introducing a second prism to correct this spectral tilt in the system. The angle of spectral line tilt generated by the nonlinear dispersion of the first prism is derived. It provides the theoretical basis for characterizing the second complementary prism. Finally, a UV multiple sub-pupil ultra-spectral imaging system is designed. The system employs two pupil separation prisms and one flat panel array to segment the pupil in three channels, each operating within spectral ranges of 180∼210 nm, 275∼305 nm, and 370∼400 nm, respectively. The spectral resolutions in all three channels are better than 0.1 nm. The corrected spectral line tilt is less than 1/3 of a pixel in the two channels with pupil separation prisms. At a Nyquist frequency of 30 lp/mm, the modulation transfer functions of all three channels are greater than 0.7, ensuring imaging quality. The design results indicate that the method proposed in this paper, utilizing complementary prisms, can effectively correct the spectral line tilt caused by the nonlinear dispersion of the pupil separation prisms. This design approach can be a reference for developing multiple sub-pupil ultra-spectral imaging systems.

Haplotype function score improves biological interpretation and cross-ancestry polygenic prediction of human complex traits.

We propose a new framework for human genetic association studies: at each locus, a deep learning model (in this study, Sei) is used to calculate the functional genomic activity score for two haplotypes per individual. This score, defined as the Haplotype Function Score (HFS), replaces the original genotype in association studies. Applying the HFS framework to 14 complex traits in the UK Biobank, we identified 3619 independent HFS-trait associations with a significance of p < 5 × 10-8. Fine-mapping revealed 2699 causal associations, corresponding to a median increase of 63 causal findings per trait compared with single-nucleotide polymorphism (SNP)-based analysis. HFS-based enrichment analysis uncovered 727 pathway-trait associations and 153 tissue-trait associations with strong biological interpretability, including 'circadian pathway-chronotype' and 'arachidonic acid-intelligence'. Lastly, we applied least absolute shrinkage and selection operator (LASSO) regression to integrate HFS prediction score with SNP-based polygenic risk scores, which showed an improvement of 16.1-39.8% in cross-ancestry polygenic prediction. We concluded that HFS is a promising strategy for understanding the genetic basis of human complex traits.

Scattered throughout the human genome are variations in the genetic code that make individuals more or less likely to develop certain traits. To identify these variants, scientists carry out Genome-wide association studies (GWAS) which compare the DNA variants of large groups of people with and without the trait of interest. This method has been able to find the underlying genes for many human diseases, but it has limitations. For instance, some variations are linked together due to where they are positioned within DNA, which can result in GWAS falsely reporting associations between genetic variants and traits. This phenomenon, known as linkage equilibrium, can be avoided by analyzing functional genomics which looks at the multiple ways a gene's activity can be influenced by a variation. For instance, how the gene is copied and decoded in to proteins and RNA molecules, and the rate at which these products are generated. Researchers can now use an artificial intelligence technique called deep learning to generate functional genomic data from a particular DNA sequence. Here, Song et al. used one of these deep learning models to calculate the functional genomics of haplotypes, groups of genetic variants inherited from one parent. The approach was applied to DNA samples from over 350 thousand individuals included in the UK BioBank. An activity score, defined as the haplotype function score (or HFS for short), was calculated for at least two haplotypes per individual, and then compared to various complex traits like height or bone density. Song et al. found that the HFS framework was better at finding links between genes and specific traits than existing methods. It also provided more information on the biology that may be underpinning these outcomes. Although more work is needed to reduce the computer processing times required to calculate the HFS, Song et al. believe that their new method has the potential to improve the way researchers identify links between genes and human traits.

BPR3P0128, a non-nucleoside RNA-dependent RNA polymerase inhibitor, inhibits SARS-CoV-2 variants of concern and exerts synergistic antiviral activity in combination with remdesivir.

Viral RNA-dependent RNA polymerase (RdRp), a highly conserved molecule in RNA viruses, has recently emerged as a promising drug target for broad-acting inhibitors. Through a Vero E6-based anti-cytopathic effect assay, we found that BPR3P0128, which incorporates a quinoline core similar to hydroxychloroquine, outperformed the adenosine analog remdesivir in inhibiting RdRp activity (EC50 = 0.66 µM and 3 µM, respectively). BPR3P0128 demonstrated broad-spectrum activity against various severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern. When introduced after viral adsorption, BPR3P0128 significantly decreased SARS-CoV-2 replication; however, it did not affect the early entry stage, as evidenced by a time-of-drug-addition assay. This suggests that BPR3P0128's primary action takes place during viral replication. We also found that BPR3P0128 effectively reduced the expression of proinflammatory cytokines in human lung epithelial Calu-3 cells infected with SARS-CoV-2. Molecular docking analysis showed that BPR3P0128 targets the RdRp channel, inhibiting substrate entry, which implies it operates differently-but complementary-with remdesivir. Utilizing an optimized cell-based minigenome RdRp reporter assay, we confirmed that BPR3P0128 exhibited potent inhibitory activity. However, an enzyme-based RdRp assay employing purified recombinant nsp12/nsp7/nsp8 failed to corroborate this inhibitory activity. This suggests that BPR3P0128 may inhibit activity by targeting host-related RdRp-associated factors. Moreover, we discovered that a combination of BPR3P0128 and remdesivir had a synergistic effect-a result likely due to both drugs interacting with separate domains of the RdRp. This novel synergy between the two drugs reinforces the potential clinical value of the BPR3P0128-remdesivir combination in combating various SARS-CoV-2 variants of concern.

An automated ICU agitation monitoring system for video streaming using deep learning classification.

OBJECTIVE: To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning. METHODS: We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as "Attention" and "Non-attention". After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances. RESULTS: The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods. CONCLUSION: Our study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application.

Dachshund Homolog 1: Unveiling Its Potential Role in Megakaryopoiesis and Bacillus anthracis Lethal Toxin-Induced Thrombocytopenia.

Lethal toxin (LT) is the critical virulence factor of Bacillus anthracis, the causative agent of anthrax. One common symptom observed in patients with anthrax is thrombocytopenia, which has also been observed in mice injected with LT. Our previous study demonstrated that LT induces thrombocytopenia by suppressing megakaryopoiesis, but the precise molecular mechanisms behind this phenomenon remain unknown. In this study, we utilized 12-O-tetradecanoylphorbol-13-acetate (TPA)-induced megakaryocytic differentiation in human erythroleukemia (HEL) cells to identify genes involved in LT-induced megakaryocytic suppression. Through cDNA microarray analysis, we identified Dachshund homolog 1 (DACH1) as a gene that was upregulated upon TPA treatment but downregulated in the presence of TPA and LT, purified from the culture supernatants of B. anthracis. To investigate the function of DACH1 in megakaryocytic differentiation, we employed short hairpin RNA technology to knock down DACH1 expression in HEL cells and assessed its effect on differentiation. Our data revealed that the knockdown of DACH1 expression suppressed megakaryocytic differentiation, particularly in polyploidization. We demonstrated that one mechanism by which B. anthracis LT induces suppression of polyploidization in HEL cells is through the cleavage of MEK1/2. This cleavage results in the downregulation of the ERK signaling pathway, thereby suppressing DACH1 gene expression and inhibiting polyploidization. Additionally, we found that known megakaryopoiesis-related genes, such as FOSB, ZFP36L1, RUNX1, FLI1, AHR, and GFI1B genes may be positively regulated by DACH1. Furthermore, we observed an upregulation of DACH1 during in vitro differentiation of CD34-megakaryocytes and downregulation of DACH1 in patients with thrombocytopenia. In summary, our findings shed light on one of the molecular mechanisms behind LT-induced thrombocytopenia and unveil a previously unknown role for DACH1 in megakaryopoiesis.

Hypermethylation Loci of ZNF671, IRF8, and OTX1 as Potential Urine-Based Predictive Biomarkers for Bladder Cancer.

Bladder cancer (BCa) is a significant health issue and poses a healthcare burden on patients, highlighting the importance of an effective detection method. Here, we developed a urine DNA methylation diagnostic panel for distinguishing between BCa and non-BCa. In the discovery stage, an analysis of the TCGA database was conducted to identify BCa-specific DNA hypermethylation markers. In the validation phase, DNA methylation levels of urine samples were measured with real-time quantitative methylation-specific PCR (qMSP). Comparative analysis of the methylation levels between BCa and non-BCa, along with the receiver operating characteristic (ROC) analyses with machine learning algorithms (logistic regression and decision tree methods) were conducted to develop practical diagnostic panels. The performance evaluation of the panel shows that the individual biomarkers of ZNF671, OTX1, and IRF8 achieved AUCs of 0.86, 0.82, and 0.81, respectively, while the combined yielded an AUC of 0.91. The diagnostic panel using the decision tree algorithm attained an accuracy, sensitivity, and specificity of 82.6%, 75.0%, and 90.9%, respectively. Our results show that the urine-based DNA methylation diagnostic panel provides a sensitive and specific method for detecting and stratifying BCa, showing promise as a standard test that could enhance the diagnosis and prognosis of BCa in clinical settings.

Photochemical oxidation of VOCs and their source impact assessment on ozone under de-weather conditions in Western Taiwan.

The application of statistical models has excellent potential to provide crucial information for mitigating the challenging issue of ozone (O3) pollution by capturing its associations with explanatory variables, including reactive precursors (VOCs and NOX) and meteorology. Considering the large contribution of O3 in degrading the air quality of western Taiwan, three-year (2019-2021) hourly concentration data of VOC, NOX and O3 from 4 monitoring stations of western Taiwan: Tucheng (TC), Zhongming (ZM), Taixi (TX) and Xiaogang (XG), was evaluated to identify the effect of anthropogenic emissions on O3 formation. Owing to the high-ambient reactivity of VOCs on the underestimation of sources, photochemical oxidation was assessed to calculate the consumed VOC (VOCcons) which was followed by the source identification of their initial concentrations. VOCcons was observed to be highest in the summer season (16.7 and 22.7 ppbC) at north (TC and ZM) and in the autumn season (17.8 and 11.4 ppbC) in southward-located stations (TX and XG, respectively). Results showed that VOCs from solvents (25-27%) were the major source at northward stations whereas VOCs-industrial emissions (30%) dominated in south. Furthermore, machine learning (ML): eXtreme Gradient Boost (XGBoost) model based de-weather analysis identified that meteorological factors favor to reduce ambient O3 levels at TC, ZM and XG stations (-67%, -47% and -21%, respectively) but they have a major role in accumulating the O3 (+38%) at the TX station which is primarily transported from the upwind region of south-central Taiwan. Crucial insights using ML outputs showed that the finding of the study can be utilized for region-specific data-driven control of emission from VOCs-sources and prioritized to limit the O3-pollution at the study location-ns as well as their accumulation in distant regions.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Risk factor analysis and nomogram for predicting gastroparesis in patients with type 2 diabetes mellitus.

Purpose: The incidence of gastroparesis is higher in individuals diagnosed with type 2 diabetes mellitus (T2DM) compared to the healthy individuals. Our study aimed to explore the risk factors for gastroparesis in T2DM and to establish a clinical prediction model (nomogram). Methods: Our study enlisted 694 patients with T2DM from two medical centers over a period of time. From January 2020 to December 2022, 347 and 149 patients were recruited from the Beilun branch of Zhejiang University's First Affiliated Hospital in the training and internal validation cohorts, respectively. The external validation cohort consisted of 198 patients who were enrolled at Nanchang University's First Affiliated Hospital from October 2020 to September 2021. We conducted univariate and multivariate logistic regression analyses to select the risk factors for gastroparesis in patients with T2DM; subsequently,we developed a nomogram model. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), calibration curve, and decision curve analysis(DCA). Results: Four clinical variables, including age, regular exercise, glycated hemoglobin level(HbA1c), and Helicobacter pylori (H. pylori) infection, were identified and included in the model. The model demonstrated excellent discrimination, with an AUC of 0.951 (95% CI = 0.925-0.978) in the training group, and 0.910 (95% CI = 0.859-0.961) and 0.875 (95% CI = 0.813-0.937) in the internal and external validation groups, respectively. The calibration curve showed good consistency between prediction of the model and observed gastroparesis. The DCA also demonstrated good clinical efficacy. Conclusion: The nomogram model developed in this study showed good performance in predicting gastroparesis in patients with T2DM.

Challenges for Medical Students in Applying Ethical Principles to Allocate Life-Saving Medical Devices During the COVID-19 Pandemic: Content Analysis.

BACKGROUND: The emergence of the COVID-19 pandemic has posed a significant ethical dilemma in the allocation of scarce, life-saving medical equipment to critically ill patients. It remains uncertain whether medical students are equipped to navigate this complex ethical process. OBJECTIVE: This study aimed to assess the ability and confidence of medical students to apply principles of medical ethics in allocating critical medical devices through the scenario of virtual patients. METHODS: The study recruited third- and fourth-year medical students during clinical rotation. We facilitated interactions between medical students and virtual patients experiencing respiratory failure due to COVID-19 infection. We assessed the students' ability to ethically allocate life-saving resources. Subsequently, we analyzed their written reports using thematic analysis to identify the ethical principles guiding their decision-making. RESULTS: We enrolled a cohort of 67 out of 71 medical students with a mean age of 34 (SD 4.7) years, 60% (n=40) of whom were female students. The principle of justice was cited by 73% (n=49) of students while analyzing this scenario. A majority of them expressed hesitancy in determining which patient should receive life-saving resources, with 46% (n=31) citing the principle of nonmaleficence, 31% (n=21) advocating for a first-come-first-served approach, and 25% (n=17) emphasizing respect for patient autonomy as key influencers in their decisions. Notably, medical students exhibited a lack of confidence in making ethical decisions concerning the distribution of medical resources. A minority, comprising 12% (n=8), proposed the exploration of legal alternatives, while 4% (n=3) suggested medical guidelines and collective decision-making as potential substitutes for individual ethical choices to alleviate the stress associated with personal decision-making. CONCLUSIONS: This study highlights the importance of improving ethical reasoning under time constraints using virtual platforms. More than 70% of medical students identified justice as the predominant principle in allocating limited medical resources to critically ill patients. However, they exhibited a lack of confidence in making ethical determinations and leaned toward principles such as nonmaleficence, patient autonomy, adherence to legal and medical standards, and collective decision-making to mitigate the pressure associated with such decisions.