DigiDOP: A framework for applying digital technology to the Differential Outcomes Procedure (DOP) for cognitive interventions in persons with neurocognitive disorders.

We present a framework -Digi-DOP- that includes a series of evidence-based recommendations to design and apply cognitive interventions for people with Neurocognitive Disorders (NCDs) using a relatively new approach, the Differential Outcomes Procedure (DOP). To do so, we critically review the substantial experimental research conducted with relevant clinical and non-clinical populations, and the theoretical underpinnings of this procedure. We further discuss how existing digital technologies that have been used for cognitive interventions could be applied to overcome some of the limitations of DOP-based interventions and further enhance DOP benefits. Specifically, we present three digital DOP developments that are currently being designed, investigated and/or tested. Finally, we discuss constraints, ethical and legal considerations that need to be taken into account to ensure that the use of technology in DOP-based interventions proposed here does not widen disparities and inequalities. We hope that this framework will inform and guide digital health leaders and developers, researchers and healthcare professionals to design and apply DOP-based interventions for people with NCDs.

On the role of the UMLS in supporting diagnosis generation proposed by Large Language Models.

OBJECTIVE: Traditional knowledge-based and machine learning diagnostic decision support systems have benefited from integrating the medical domain knowledge encoded in the Unified Medical Language System (UMLS). The emergence of Large Language Models (LLMs) to supplant traditional systems poses questions of the quality and extent of the medical knowledge in the models' internal knowledge representations and the need for external knowledge sources. The objective of this study is three-fold: to probe the diagnosis-related medical knowledge of popular LLMs, to examine the benefit of providing the UMLS knowledge to LLMs (grounding the diagnosis predictions), and to evaluate the correlations between human judgments and the UMLS-based metrics for generations by LLMs. METHODS: We evaluated diagnoses generated by LLMs from consumer health questions and daily care notes in the electronic health records using the ConsumerQA and Problem Summarization datasets. Probing LLMs for the UMLS knowledge was performed by prompting the LLM to complete the diagnosis-related UMLS knowledge paths. Grounding the predictions was examined in an approach that integrated the UMLS graph paths and clinical notes in prompting the LLMs. The results were compared to prompting without the UMLS paths. The final experiments examined the alignment of different evaluation metrics, UMLS-based and non-UMLS, with human expert evaluation. RESULTS: In probing the UMLS knowledge, GPT-3.5 significantly outperformed Llama2 and a simple baseline yielding an F1 score of 10.9% in completing one-hop UMLS paths for a given concept. Grounding diagnosis predictions with the UMLS paths improved the results for both models on both tasks, with the highest improvement (4%) in SapBERT score. There was a weak correlation between the widely used evaluation metrics (ROUGE and SapBERT) and human judgments. CONCLUSION: We found that while popular LLMs contain some medical knowledge in their internal representations, augmentation with the UMLS knowledge provides performance gains around diagnosis generation. The UMLS needs to be tailored for the task to improve the LLMs predictions. Finding evaluation metrics that are aligned with human judgments better than the traditional ROUGE and BERT-based scores remains an open research question.

Bundle-specific tractogram distribution estimation using higher-order streamline differential equation.

Streamline tractography locally traces peak directions extracted from fiber orientation distribution (FOD) functions, lacking global information about the trend of the whole fiber bundle. Therefore, it is prone to producing erroneous tracks while missing true positive connections. In this work, we propose a new bundle-specific tractography (BST) method based on a bundle-specific tractogram distribution (BTD) function, which directly reconstructs the fiber trajectory from the start region to the termination region by incorporating the global information in the fiber bundle mask. A unified framework for any higher-order streamline differential equation is presented to describe the fiber bundles with disjoint streamlines defined based on the diffusion vectorial field. At the global level, the tractography process is simplified as the estimation of BTD coefficients by minimizing the energy optimization model, and is used to characterize the relations between BTD and diffusion tensor vector under the prior guidance by introducing the tractogram bundle information to provide anatomic priors. Experiments are performed on simulated Hough, Sine, Circle data, ISMRM 2015 Tractography Challenge data, FiberCup data, and in vivo data from the Human Connectome Project (HCP) for qualitative and quantitative evaluation. Results demonstrate that our approach reconstructs complex fiber geometry more accurately. BTD reduces the error deviation and accumulation at the local level and shows better results in reconstructing long-range, twisting, and large fanning tracts.

The deep latent space particle filter for real-time data assimilation with uncertainty quantification.

In data assimilation, observations are fused with simulations to obtain an accurate estimate of the state and parameters for a given physical system. Combining data with a model, however, while accurately estimating uncertainty, is computationally expensive and infeasible to run in real-time for complex systems. Here, we present a novel particle filter methodology, the Deep Latent Space Particle filter or D-LSPF, that uses neural network-based surrogate models to overcome this computational challenge. The D-LSPF enables filtering in the low-dimensional latent space obtained using Wasserstein AEs with modified vision transformer layers for dimensionality reduction and transformers for parameterized latent space time stepping. As we demonstrate on three test cases, including leak localization in multi-phase pipe flow and seabed identification for fully nonlinear water waves, the D-LSPF runs orders of magnitude faster than a high-fidelity particle filter and 3-5 times faster than alternative methods while being up to an order of magnitude more accurate. The D-LSPF thus enables real-time data assimilation with uncertainty quantification for the test cases demonstrated in this paper.

Assessing ChatGPT's Capability for Multiple Choice Questions Using RaschOnline: Observational Study.

BACKGROUND: ChatGPT (OpenAI), a state-of-the-art large language model, has exhibited remarkable performance in various specialized applications. Despite the growing popularity and efficacy of artificial intelligence, there is a scarcity of studies that assess ChatGPT's competence in addressing multiple-choice questions (MCQs) using KIDMAP of Rasch analysis-a website tool used to evaluate ChatGPT's performance in MCQ answering. OBJECTIVE: This study aims to (1) showcase the utility of the website (Rasch analysis, specifically RaschOnline), and (2) determine the grade achieved by ChatGPT when compared to a normal sample. METHODS: The capability of ChatGPT was evaluated using 10 items from the English tests conducted for Taiwan college entrance examinations in 2023. Under a Rasch model, 300 simulated students with normal distributions were simulated to compete with ChatGPT's responses. RaschOnline was used to generate 5 visual presentations, including item difficulties, differential item functioning, item characteristic curve, Wright map, and KIDMAP, to address the research objectives. RESULTS: The findings revealed the following: (1) the difficulty of the 10 items increased in a monotonous pattern from easier to harder, represented by logits (-2.43, -1.78, -1.48, -0.64, -0.1, 0.33, 0.59, 1.34, 1.7, and 2.47); (2) evidence of differential item functioning was observed between gender groups for item 5 (P=.04); (3) item 5 displayed a good fit to the Rasch model (P=.61); (4) all items demonstrated a satisfactory fit to the Rasch model, indicated by Infit mean square errors below the threshold of 1.5; (5) no significant difference was found in the measures obtained between gender groups (P=.83); (6) a significant difference was observed among ability grades (P<.001); and (7) ChatGPT's capability was graded as A, surpassing grades B to E. CONCLUSIONS: By using RaschOnline, this study provides evidence that ChatGPT possesses the ability to achieve a grade A when compared to a normal sample. It exhibits excellent proficiency in answering MCQs from the English tests conducted in 2023 for the Taiwan college entrance examinations.

Seasonal environmental factors drive microbial community succession and flavor quality during acetic acid fermentation of Zhenjiang aromatic vinegar.

This study investigated the impact of seasonal environmental factors on microorganisms and flavor compounds during acetic acid fermentation (AAF) of Zhenjiang aromatic vinegar (ZAV). Environmental factors were monitored throughout the fermentation process, which spanned multiple seasons. Methods such as headspace solid phase microextraction gas chromatography-mass spectrometry (HS-SPME-GC-MS), high performance liquid chromatography (HPLC), and high-throughput sequencing were employed to examine how these environmental factors influenced the flavor profile and microbial community of ZAV. The findings suggested that ZAV brewed in autumn had the strongest flavor and sweetness. The key microorganisms responsible for the flavor of ZAV included Lactobacillus acetotolerans, Lactobacillus plantarum, Lactobacillus reuteri, Lactobacillus fermentum, Acetobacter pasteurianus. Moreover, correlation analysis showed that room temperature had a significant impact on the composition of the microbial community, along with other key seasonal environmental factors like total acid, pH, reducing sugar, and humidity. These results provide a theoretical foundation for regulating core microorganisms and environmental factors during fermentation, enhancing ZAV quality.

Labeled sandwich-type electrochemical immunosensor based on Ti3C2Tx/AuNP and Ti3C2Tx/HKUST-1/TB composites for early liver cancer detection.

A novel sandwich-type electrochemical immunosensor for the detection of the liver cancer marker alpha-fetoprotein (AFP) in human serum is proposed. The two-dimensional MXene material Ti3C2Tx was first prepared using etching and ultrasonic stripping, and then Ti3C2Tx was used to reduce chloroauric acid to form Ti3C2Tx/AuNP composites which were modified on the surface of the glassy carbon electrodes to form probe-type sensors. The Ti3C2Tx/AuNPs provide a large number of binding sites for the AFP capture antibody (Ab1) and increase the electrochemical reaction active site. The Ti3C2Tx/copper metal-organic frameworks HKUST-1 composite was also prepared by solvothermal method and combined with toluidine blue (TB) and AFP detection antibody (Ab2) to form a labeled sandwich-type electrochemical immunosensor. The sensor achieved trace detection of AFP from 0.1 to 100 ng/mL with a detection limit of 0.073 pg/mL and possesses good selectivity, stability, and reproducibility. The sensor performs well in clinical samples and has good potential for clinical applications.

Efficient nitrite determination by electrochemical approach in liquid phase with ultrasonically prepared gold-nanoparticle-conjugated conducting polymer nanocomposites.

An electrochemical nitrite sensor probe is introduced herein using a modified flat glassy carbon electrode (GCE) and SrTiO3 material doped with spherical-shaped gold nanoparticles (Au-NPs) and polypyrrole carbon (PPyC) at a pH of 7.0 in a phosphate buffer solution. The nanocomposites (NCs) containing Au-NPs, PPyC, and SrTiO3 were synthesized by ultrasonication, and their properties were thoroughly characterized through structural, elemental, optical, and morphological analyses with various conventional spectroscopic methods, such as field-emission scanning electron microscopy, energy-dispersive X-ray spectroscopy, high-resolution transmission electron microscopy, powder X-ray diffraction, X-ray photoelectron spectroscopy, and Brunauer-Emmett-Teller method. The peak currents due to nitrite oxidation were characterized in detail and analyzed using conventional cyclic voltammetry (CV) as well as differential pulse voltammetry (DPV) under ambient conditions. The sensor response increased significantly from 0.15 to 1.5 mM of nitrite ions, and the sensor was fabricated by coating a conducting agent (PEDOT:PSS) on the GCE to obtain the Au-NPs/PPyC/SrTiO3 NCs/PEDOT:PSS/GCE probe. The sensor's sensitivity was determined as 0.5 µA/µM∙cm2 from the ratio of the slope of the linear detection range by considering the active surface area (0.0316 cm2) of the flat GCE. In addition, the limit of detection was determined as 20.00 ± 1.00 µM, which was found to be satisfactory. The sensor's stability, pH optimization, and reliability were also evaluated in these analyses. Overall, the sensor results were found to be satisfactory. Real environmental samples were then analyzed to evaluate the sensor's reliability through DPV, and the results showed that the proposed novel electrochemical sensor holds great promise for mitigating water contamination in the real samples with the lab-made Au-NPs/PPyC/SrTiO3 NC. Thus, this study provides valuable insights for improving sensors for broad environmental monitoring applications using the electrochemical approach.

Quantitative proteomics and immunohistochemistry uncover NT5DC2 as a diagnostic biomarker for papillary urothelial carcinoma.

The accurate diagnosis of papillary urothelial carcinoma (PUC) is frequently challenging due to benign mimickers. Other than morphology-based diagnostic criteria, reliable biomarkers for differentiating benign and malignant papillary urothelial neoplasms remain elusive, so we sought to discover new markers to address this challenge. We first performed tandem mass spectrometry-based quantitative proteomics using diverse papillary urothelial lesions, including PUC, urothelial papilloma (UP), inverted urothelial papilloma, and cystitis cystica. We prioritized potential diagnostic biomarkers using machine learning, and subsequently validated through immunohistochemistry (IHC) in two independent cohorts. Metabolism, transport, cell cycle, development, and immune response functions were differentially enriched between malignant and benign papillary neoplasms. RhoB and NT5DC2 were shortlisted as optimal candidate markers for PUC diagnosis. In our pilot study using IHC, NT5DC2 was subsequently selected as its expression consistently differed in PUC (p = 0.007). Further validation of NT5DC2 using 49 low-grade (LG) urothelial lesions, including 15 LG-PUCs and 17 UPs, which are the most common mimickers, concordantly revealed lower IHC expression levels in LG-PUC (p = 0.0298). Independent external validation with eight LG-PUCs and eight UPs confirmed the significant downregulation of NT5DC2 in LG-PUC (p = 0.0104). We suggest that NT5DC2 is a potential IHC biomarker for differentiating LG-PUC from its benign mimickers, especially UP.

Investigating properties of sweet cherry (Prunus avium) flower buds that help promote freezing avoidance by supercooling.

Mechanisms involved in the supercooling of plant tissues as a means of low temperature survival are still not fully understood. We investigated properties that may promote supercooling in overwintering sweet cherry (Prunus avium) flower buds. We conducted experiments on sweet cherry flower buds using differential thermal analysis (DTA) and observed locations of ice formation in the bud structure. We also used anatomical development and water-soluble dye uptake throughout the overwintering period to identify changes that correlate with gain and loss of supercooling capacity. Our results revealed barriers to ice propagation are likely unique to each primordium, as inferred from exotherms produced from buds subjected to DTA, although multiple primordia may freeze simultaneously. Ice is accommodated between the bud scales and within the bud axis; however, full expression of supercooling was not dependent on the presence of scales. Anatomical and DTA studies revealed a correlation between vascular differentiation in primordia and loss of supercooling in the spring; these observations were at a higher temporal resolution than previously described for Prunus. Furthermore, disturbing tissues subtending the primordia interfered with typical patterns of supercooling, indicated more erratic and numerous exotherms produced during DTA. In summary, sweet cherry flower buds undergo extra-organ freezing. In winter, a barrier to ice propagation in the region directly subtending primordia protects the flower from freezing damage, but in the spring xylem differentiation in primordia provides a conduit for ice propagation that compromises supercooling.

Eco-evolutionary dynamics of structured populations in periodically fluctuating environments: a G function approach.

Understanding the ecological and evolutionary dynamics of populations is critical for both basic and applied purposes in a variety of biological contexts. Although several modeling frameworks have been developed to simulate eco-evolutionary dynamics, many fewer address how to model structured populations. In a prior paper, we put forth the first modeling approach to simulate eco-evolutionary dynamics in structured populations under the G function modeling framework. However, this approach does not allow for accurate simulation under fluctuating environmental conditions. To address this limitation, we draw on the study of periodic differential equations to propose a modified approach that uses a different definition of fitness more suitable for fluctuating environments. We illustrate this method with a simple toy model of life history trade-offs. The generality of this approach allows it to be used in a variety of biological contexts.

Facial Swelling and Neuritis After Internal Carotid Endarterectomy in an 81-Year-Old Woman With Type 2 Diabetes Mellitus: A Case Report.

OBJECTIVE: Patients with type 2 diabetes mellitus (DM2) may develop carotid artery stenosis (CAS), requiring surgical intervention. Nerve injury following carotid endarterectomy (CEA) is a rare and often unrecognized post-operative side effect. This case report describes the diagnostic process and rehabilitation course of a patient with greater auricular and trigeminal neuritis symptoms following internal CEA. METHODS: The patient is an 81-year-old woman with DM2 who underwent a left internal CEA. She subsequently developed swelling in her left neck and face, and pain along the greater auricular and trigeminal nerve pathways. Pertinent examination findings included incision placement across the path of the greater auricular nerve and cervical lymphatic vessels that drain the face, with overlying scar adhesion. A course of physical therapy was initiated 12 days after surgery, and included mobilization and manual lymphatic drainage, modalities, and application of kinesiotape. RESULTS: The Patient Specific Functional Scale (PSFS) improved from 10/30 at evaluation to 27/30 at discharge. Swelling and pain were significantly reduced, with patient reporting no difficulty with sleeping, chewing, or talking at discharge. The reported pain level consistently correlated with fluctuations in face swelling throughout treatment. CONCLUSION: Patients with DM2 may present with symptoms of cardiovascular disease, requiring invasive surgical procedures. DM2 can cause damage to neural and vascular structures, predisposing patients to nerve injuries or hypersensitivity following procedures. This case report demonstrates a likely connection between post-operative facial swelling and nerve irritation in the head and neck. Mobilization and manual lymphatic drainage, modalities, and kinesiotape were effective to reduce pain and swelling. IMPACT: Physical therapists are uniquely qualified to identify, evaluate, and treat post-operative swelling and nerve pain associated with CEA.

Patients with type 2 diabetes mellitus may develop carotid artery stenosis (CAS), requiring surgical intervention. Nerve injury following carotid endarterectomy (CEA) is a rare and often unrecognized postoperative side effect. Physical therapists diagnose and provide treatment to patients with greater auricular and trigeminal neuritis symptoms following internal CEA.

Cost-effective fully 3D-printed on-drop electrochemical sensor based on carbon black/polylactic acid: a comparative study with screen-printed sensors in food analysis.

3D-printing technology allows scientist to fabricate easily electrochemical sensors. Until now, these sensors were designed employing a large amount of material, which increases the cost and decreases manufacturing throughput. In this work, a low-cost 3D-printed on-drop electrochemical sensor (3D-PES) was fully manufactured by fused filament fabrication, minimizing the number of printing layers. Carbon black/polylactic acid filament was employed, and the design and several printing parameters were optimized to yield the maximum electroanalytical performance using the minimal amount of material. Print speed and extrusion width showed a critical influence on the electroanalytical performance of 3D-PES. Under optimized conditions, the fabrication procedure offered excellent reproducibility (RSD 1.3% in working electrode diameter), speed (< 3 min/unit), and costs (< 0.01 $ in material cost). The 3D-PES was successfully applied to the determination of phloridzin in apple juice. The analytical performance of 3D-PES was compared with an equivalent commercial on-drop screen-printed electrode, yielding similar precision and accuracy but lower sensitivity. However, 3D-PES provides interesting features such as recyclability, biodegradability, low-cost, and the possibility of being manufactured near the point of need, some of which meets several demands of Green Chemistry. This cost-effective printing approach is a green and promising alternative for manufacturing disposable and portable electroanalytical devices, opening new possibilities not only in on-site food analysis but also in point-of-care testing.

Electrochemical detection of anti-tissue transglutaminase antibody using quantum dots-doped polypyrrole-modified electrode.

A nanohybrid-modified glassy carbon electrode based on conducting polypyrrole doped with carbon quantum dots (QDs) was developed and used for the electrochemical detection of anti-tissue transglutaminase (anti-tTG) antibodies. To improve the polypyrrole conductivity, carrier mobility, and carrier concentration, four types of carbon nanoparticles were tested. Furthermore, a polypyrrole-modified electrode doped with QDs was functionalized with a PAMAM dendrimer and transglutaminase 2 protein by cross-linking with N-hydroxysuccinimide (NHS)/N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide hydrochloride (EDC). The steps of electrode surface modification were surveyed via electrochemical measurements (differential pulse voltammetry (DPV), impedance spectroscopy, and X-ray photoelectron spectroscopy (XPS)). The surface characteristics were observed by scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, and contact angle measurements. The obtained modified electrode exhibited good stability and repeatability. DPV between - 0.1 and 0.6 V (vs. Ag/AgCl 3 M KCl reference electrode) was used to evaluate the electrochemical alterations that occur after the antibody interacts with the antigen (transglutaminase 2 protein), for which the limit of detection was 0.79 U/mL. Without the use of a secondary label, (anti-tTG) antibodies may be detected at low concentrations because of these modified electrode features.

Development of MOF-derived Co3O4 microspheres composed of fiber stacks for simultaneous electrochemical detection of Pb2+ and Cu2.

As an ideal transition metal oxide, Co3O4 is a P-type semiconductor with excellent electrical conductivity, non-toxicity and low cost. This work reports the successful construction of Co3O4 materials derived from metal-organic frameworks (MOFs) using a surfactant micelle template-solvothermal method. The modified electrodes are investigated for their ability to electrochemically detect Pb2+ and Cu2+ in aqueous environments. By adjusting the mass ratios of alkaline modifiers, the morphological microstructures of Co3O4-X exhibit a transition from distinctive microspheres composed of fiber stacks to rods. The results indicate that Co3O4-1(NH4F/CO(NH2)2 = 1:0) has a distinctive microsphere structure composed of stacked fibers, unlike the other two materials. Co3O4-1/GCE is used as the active material of the modified electrode, it shows the largest peak response currents to Pb2+ and Cu2+, and efficiently detects Pb2+ and Cu2+ in the aqueous environment individually and simultaneously. The linear response range of Co3O4-1/GCE for the simultaneous detection of Pb2+ and Cu2+ is 0.5-1.5 µM, with the limits of detection (LOD, S/N = 3) are 9.77 nM and 14.97 nM, respectively. The material exhibits a favorable electrochemical response, via a distinctive Co3O4-1 microsphere structure composed of stacked fibers. This structure enhances the number of active adsorption sites on the material, thereby facilitating the adsorption of heavy metal ions (HMIs). The presence of oxygen vacancies (OV) can also facilitate the adsorption of ions. The Co3O4-1/GCE electrode also exhibits excellent anti-interference ability, stability, and repeatability. This is of great practical significance for detecting Pb2+ and Cu2+ in real water samples and provides a new approach for developing high-performance metal oxide electrochemical sensors derived from MOFs.

Fatal misdiagnosis of progressive disseminated histoplasmosis.

Disseminated histoplasmosis is the form of a mycosis caused by the fungus Histoplasma capsulatum that mainly occurs in immunosuppressed hosts, usually with non-specific symptoms. In non-endemic areas, where the disease is rarely involved in the differential diagnosis, a delay in treatment may lead to severe medical complications. Due to the rising prevalence of disseminated histoplasmosis in these areas, a thorough medical history is regarded as the decisive factor in prompt diagnosis of the disease. We, herein, report the case of an immunocompetent Greek farmer with disseminated histoplasmosis whose condition was initially misdiagnosed, and the consequential inadequate treatment led to his death.

Lipid degradation contributes to flavor formation during air-dried camel jerky processing.

Lipids play an important role in flavor formation in meat products. To determine the contribution of lipids to flavor formation during air-dried camel jerky processing, lipid changes were analyzed by UHPLC-Q-Exactive Orbitrap MS/MS in this study, and volatile compounds were identified by HS-SPME-GC-ToF-MS. Results showed that 606 lipid molecules belonging to 30 subclasses were identified and 206 differential lipid molecules were screened out (VIP > 1, P < 0.05); Cer/NS (d18:1/20:0), LPE (18:1), FA (18:0), GlcADG (12:0/24:1), and PE (18:2e/22:5) were identified as potential lipid biomarkers. A total of 96 volatile compounds were also identified, and 16 of these were identified as key aroma compounds in air-dried camel jerky. Meanwhile, 11 differential lipids significantly, negatively correlated with 7 key aroma compounds (P < 0.05) during processing, indicating that the precursors produced by the degradation of lipid molecules were important sources of volatile flavor substances in air-dried camel jerky.

Hollow-like three-dimensional structure of methyl orange-delaminated Ti3C2 MXene nanocomposite for high-performance electrochemical sensing of tryptophan.

Tryptophan(Trp) is being explored as a potential biomarker for various diseases associated with decreased tryptophan levels; however, metabolomic methods are expensive and time-consuming and require extensive sample analysis, making them urgently needed for trace detection. To exploit the properties of Ti3C2 MXenes a rational porous methyl orange (MO)-delaminated Ti3C2 MXene was prepared via a facile mixing process for the electrocatalytic oxidation of Trp. The hollow-like 3D structure with a more open structure and the synergistic effect of MO and conductive Ti3C2 MXene enhanced its electrochemical catalytic capability toward Trp biosensing. More importantly, MO can stabilize Ti3C2 MXene nanosheets through noncovalent π-π interactions and hydrogen bonding. Compared with covalent attachment, these non-covalent interactions preserve the electronic conductivity of the Ti3C2 MXene nanosheets. Finally, the addition of MO-derived nitrogen (N) and sulfur (S) atoms to Ti3C2 MXene enhanced the electronegativity and improved its affinity for specific molecules, resulting in high-performance electrocatalytic activity. The proposed biosensor exhibited a wide linear response in concentration ranges of 0.01-0.3 µM and 0.5-120 µM, with a low detection limit of 15 nM for tryptophan detection, and high anti-interference ability in complex media of human urine and egg white matrices. The exceptional abilities of the MO/Ti3C2 nanocatalyst make it a promising electrode material for the detection of important biomolecules.

Identifying Subgroups with Differential Responses to Amiodarone among Cardiac Arrest Patients with a Shockable Rhythm at Hospital Arrival using the Machine Learning Approach.

Background: There are few reports of studies on the differential effects of amiodarone among out-of-hospital cardiac arrest (OHCA) patients with a shockable rhythm at hospital arrival. The present study aimed to investigate the clinical heterogeneity of OHCA patients with a shockable rhythm upon hospital arrival and to identify subgroups with differential responses to amiodarone, using a machine learning approach. Methods: We used the Japanese nationwide OHCA registry of the Japanese Association for Acute Medicine for this study; data from OHCA patients with a shockable rhythm at hospital arrival were included in the analyses. The primary outcome was a favorable neurological outcome at 30 days. We developed a scoring system by the weighting method with logistic likelihood loss to identify patient subgroups showing differential effects of amiodarone from the point of view of the neurological outcome and survival at 30 days. Results: Among the 68,111 cases of OHCA in the registry, the data of 2333 OHCA patients with an initial shockable rhythm at hospital arrival were analyzed. The developed score identified higher age, longer interval between the call to the emergency medical service and hospital arrival, absence of a "witness", no defibrillation prior to hospital arrival, hypothermia at hospital arrival, and pre-hospital epinephrine administration as variables that were significantly associated with a beneficial effect of amiodarone. Based on the results of the developed scoring system, 47% (1107/2333) of the patients were considered to greatly benefit from amiodarone administration, whereas 53% (1226/2333) of patients were considered to not benefit from amiodarone administration. The effect of amiodarone on the neurological outcome at 30 days varied significantly among the subgroups identified by the developed score ( OR interaction : 1.07 [95% confidence interval (CI): 0.99-1.13], p = 0.005). Conclusions: We successfully developed a model that could discriminate between OHCA patients with an initial shockable rhythm at hospital arrival who would benefit or not benefit from the administration of amiodarone in terms of the neurological outcome at 30 days. There was clinical heterogeneity among OHCA patients with a shockable rhythm in terms of their response to amiodarone.

Identification of a Predictive Model for Schizophrenia Based on SNPs in a Chinese Population.

Background: Schizophrenia is a devastating mental disease with high heritability. A growing number of susceptibility genes associated with schizophrenia, as well as their corresponding SNPs loci, have been revealed by genome-wide association studies. However, using SNPs as predictors of disease and diagnosis remains difficult. Here, we aimed to uncover susceptibility SNPs in a Chinese population and to construct a prediction model for schizophrenia. Methods: A total of 210 participants, including 70 patients with schizophrenia, 70 patients with bipolar disorder, and 70 healthy controls, were enrolled in this study. We estimated 14 SNPs using published risk loci of schizophrenia, and used these SNPs to build a model for predicting schizophrenia via comparison of genotype frequencies and regression. We evaluated the efficacy of the diagnostic model in schizophrenia and control patients using ROC curves and then used the 70 patients with bipolar disorder to evaluate the model's differential diagnostic efficacy. Results: 5 SNPs were selected to construct the model: rs148415900, rs71428218, rs4666990, rs112222723 and rs1716180. Correlation analysis results suggested that, compared with the risk SNP of 0, the risk SNP of 3 was associated with an increased risk of schizophrenia (OR = 13.00, 95% CI: 2.35-71.84, p = 0.003). The ROC-AUC of this prediction model for schizophrenia was 0.719 (95% CI: 0.634-0.804), with the greatest sensitivity and specificity being 60% and 80%, respectively. The ROC-AUC of the model in distinguishing between schizophrenia and bipolar disorder was 0.591 (95% CI: 0.497-0.686), with the greatest sensitivity and specificity being 60% and 55.7%, respectively. Conclusion: The SNP risk score prediction model had good performance in predicting schizophrenia. To the best of our knowledge, previous studies have not applied SNP-based models to differentiate between cases of schizophrenia and other mental illnesses. It could have several potential clinical applications, including shaping disease diagnosis, treatment, and outcomes.